Public Policy Research Funding Scheme 公共政策研究資助計劃

Public Policy Research Funding Scheme

公共政策研究資助計劃

Project Number : 項目編號： 2015.A8.029.15C

Project Title : A Sustainable Tourism and Mobility Framework for 項目名稱： Assessing the Effects of the Individual Visit Scheme on the Public Transportation System in Hong Kong 以可持續旅遊業及運輸業的框架評估個人遊計劃對香港公共交通系統的影響

Principal Investigator : Professor WONG Sze Chun 首席研究員：黃仕進教授

Institution/Think Tank : The University of Hong Kong 院校／智庫：香港大學

Project Duration (Month): 推行期 (月) ： 15

Funding (HK$) : 總金額 (HK$)： 690,000.00

This research report is uploaded onto the website of the Policy Innovation and Co- ordination Office (PICO) for public reference. The views expressed in this report are those of the Research Team of this project and do not represent the views of PICO and/or the Assessment Panel. PICO and/or the Assessment Panel do not guarantee the accuracy of the data included in this report.

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Public Policy Research Funding Scheme Central Policy Unit The Government of the Hong Kong Special Administrative Region

Project No: 2015.A8.029.15C

A Sustainable Tourism and Mobility Framework for Assessing the Effects of the Individual Visit Scheme on the Public Transportation System in Hong Kong 以可持續旅遊業及運輸業的框架評估個人遊計劃對香港公共交通系統的影響

Final Report (October 2017)

Submitted by: Professor S.C. Wong (Principal Investigator)

Department of Civil Engineering The University of Hong Kong

Research Team

Professor S.C. Wong Principal Investigator Dr. Y.C. Li Researcher Dr. S. Xie Researcher Dr. W. Yan Researcher

Acknowledgements

This research project (Project Number: 2015.A8.029.15C) was funded by the Public Policy Research Funding Scheme from the Central Policy Unit of the Hong Kong Special Administrative Region (HKSAR) Government.

The research team would like to express its sincere gratitude for the support from the many organizations, including the Transport Department and Planning Department of the HKSAR Government and individuals who contributed their time, knowledge and access to information to this research through various activities.

Finally, although the research team appreciates the support from all of the organizations and individuals, it remains responsible for the results of and any mistakes remaining in the study

Executive Summary

The Individual Visit Scheme (IVS) was first introduced in July 2003 as a measure to revive Hong Kong’s ailing economy in the wake of the SARS outbreak. Since its implementation, there has been a dramatic increase in the number of visitors from Mainland China every year, from 6.83 million in 2002 to 47.25 million in 2014. Although the scheme boosted the development of the tourism, retail, and catering industries in Hong Kong, the pressure placed on the public transportation system due to the influx of Mainland visitors was of vital concern to local residents.

The Hong Kong Special Administrative Region Government conducted various assessments to review future development. These assessments focused mainly on the capacity of boundary control points and tourism attractions, the supply of hotel rooms, and the livelihood of the community and the economy. However, the relationship between tourists and their mobility was rarely discussed. Considering that tourism travel accounts for a major and growing share of all travel in Hong Kong, a theoretical framework for achieving tourism sustainability and mobility is of paramount importance to policymakers in Hong Kong.

The ultimate objective of this study is to reveal potential public transportation policy measures that will enhance the sustainability and mobility of Mainland tourists. To achieve this objective, appropriate statistical modeling techniques were applied to meet the following specified goals:

- To evaluate the variation in tourists’ travel patterns before and after implementation of the IVS - To understand the needs for different public transportation modes - To evaluate the attitudinal and behavioral effects of tourists on their choice of public transportation mode/s - To assess the handling capacity of the public transportation system - To study the implications of tourism policy measures on the public transportation system.

The tourist interview survey data extracted from the Travel Characteristics Surveys of 2002 (before the IVS) and 2011 (after the IVS), and analytical models using data from 2016, were used to evaluate and reveal the travel patterns of Mainland tourists and local residents in Hong Kong over the past two decades. The surveys help us to uncover and visualize the tourists’ spatio-temporal travel characteristics. The survey findings provided a spatio-temporal review of trip-making patterns and an overview of the distribution of China tourist trips.

The top three modes of public transportation used by Mainland tourists were taxis, railways and tourist coaches. A remarkable trend in railway use was observed.

However, the overall travel demand from tourists using these modes of transportation remained relatively low compared with the demand from local residents.

In this study, a large-scale series of face-to-face interviews was conducted to further reveal the most recent travel characteristics of Mainland IVS tourists in Hong Kong. The demographic characteristics of the tourists, their modes of travel, levels of satisfaction with public transport services, and stated preferences for tourist attractions and shopping items were studied, based on transportation factors. Different statistical analytical models were developed to explore tourists’ travel characteristics and behaviours in 2016.

About 66% of the sample group consisted of repeat tourists, and more than one third of IVS tourists were day-trip visitors. Unlike the first-time tourists, the day-trip visitors mainly visited Hong Kong to go shopping (rather than sightseeing). The most visited locations reported by the respondents were shopping malls, the Peak, the Tower, and the Avenue of Stars, for both first-time and repeat visitors.

Assessments were conducted on the tourists’ level of satisfaction and the level of service they received when using public transportation. The interviewees were generally satisfied with the quality of service. Among a number of service aspects, safety and security gained the highest scores for service, followed by journey speed and accessibility.

In the last chapter of this report, the effectiveness of the current and suggested tourism policy measures for public transportation are evaluated using a scientific analytical approach. Making reference to the findings and observations of the travel trends and characteristics of Mainland tourists in Hong Kong, the following tourist transportation policy measures are discussed:

- Maintenance of existing visa arrangements - Monitoring of the list of IVS cities - Promotion of in-depth travel - Implementation of boundary shopping malls - Upgrading of railway service - Enhancement of franchised bus service - Improvement of taxi service - Introduction of multimodal travel tourist pass - Scrutinization of self-driving. -

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內容摘要

 為重振香港在嚴重急性呼吸系統綜合症（沙士）疫情爆發後疲弱的經濟，香港特區政府在 2003 年 7 月首次推出內地居民「個人遊」計劃（IVS）。自計劃實施以來，每年的內地訪港旅客人數持續大幅增加，由 2002 年的 683 萬人次增加到 2014 年的 4,725 萬人次。一方面，該計劃有效促進香港的旅遊業、零售業和餐飲業的發展。但另一方面，本地居民卻擔心大量內地旅客的湧入給公共交通系統帶來壓力。

 香港特區政府進行了一系列的評估，以檢討未來的旅遊業發展。這些評估主要針對邊境管制站、旅遊景點和酒店房間供應的接待能力，以及旅遊業發展對社會民生和經濟效應的影響，但較少涉及到訪港旅客的出行。考慮到訪港旅客的出行量將不斷增長，制定可持續旅遊業及運輸業的框架對香港的決策部門有重要作用。

 該項研究的最終目標是提出可行的公共交通政策措施，以持續吸引內地旅客并提升他們的出行體驗。為了實現這一點，我們將運用一系列統計模型分析以達到以下的目的：

- 評估內地旅客在「個人遊」計劃實施前及後出行模式的變化。 - 了解內地旅客對不同公共交通工具的需求。 - 評估內地遊客在選擇不同公共交通工具時的態度和行為。 - 評估公共交通系統的運載能力。 - 研究不同的旅遊政策措施對公共交通系統的影響。

 根據二零零二年交通習慣調查（實施「個人遊」計劃之前）和二零一一年交通習慣調查（實施「個人遊」計劃之後）的訪港旅客的調查數據，以及基於二零一六年最新調查數據的分析模型，我們評估并勾勒出過去二十年來內地旅客和本地居民的出行模式。這些調查有助於我們了解旅客的出行特徵及行為，并進一步歸納出內地旅客的出行決策模式及他們在香港各區的時空分佈。

 的士、鐵路和旅遊巴士是內地旅客最常使用的三種公共交通工具。雖然鐵路的使用率有顯著的上升趨勢，不過旅客總體的需求仍然比本地居民低。

 本研究進行了一項大規模的問卷調查，以進一步了解內地個人遊旅客訪港的最新概況。調查內容包括了旅客的個人背景、出行方式、對公共交通服

務的滿意程度、以及對觀光購物的取態。我們建立了不同的統計分析模型，以探究內地旅客於 2016 年的出行特徵和行為。

 其中，約佔 66%的受訪者為再次訪港旅客，當中超過三分之一的個人遊旅客以一日遊的模式訪港。有別於首次訪港的旅客，購物為他們主要的目的。大型的購物中心、山頂、鐘樓和星光大道為最受所有受訪者歡迎的目的地。

 受訪的內地旅客對公共交通服務質素總體感到滿意。在各項服務當中，安全和保安得到最高評分，其次是快捷程度和可達性。

 最後，我們運用科學的分析方法評估了現有和擬議的旅遊政策措施對公共交通系統的效用。我們根據對內地旅客的出遊趨勢，特徵和行為的調查結果，討論了以下各項旅遊交通政策措施：

- 維持現有的簽證安排 - 掌控個人遊城市之範圍 - 推廣深度旅遊 - 落實建設邊境購物城 - 提升鐵路的服務 - 加強專營巴士的服務 - 改善的士的服務 - 推出旅客公共交通通票 - 審視自駕旅遊的可行性

Contents

Page RESEARCH TEAM ...... I ACKNOWLEDGEMENTS ...... I EXECUTIVE SUMMARY ...... II CONTENTS ...... VI LIST OF FIGURES ...... VII LISTS OF TABLES ...... VIII 1. INTRODUCTION...... 1 1.1. BACKGROUND OF THIS STUDY ...... 1 1.2. PROJECT AIM AND OBJECTIVES ...... 3 1.3. OUTLINE OF RESEARCH METHODOLOGY ...... 4 1.4. STRUCTURE OF THIS REPORT ...... 6 2. LITERATURE REVIEW...... 7 2.1. SUSTAINABLE TOURISM AND MOBILITY...... 7 2.2. THE GROWTH OF MAINLAND TOURIST ARRIVALS ...... 9 2.3. TOURISTS’ USE OF PUBLIC TRANSPORTATION ...... 11 2.4. SATISFACTION ON PUBLIC TRANSPORTATION SERVICES ...... 12 2.5. PUBLIC TRANSPORTATION POLICY FOR THE TOURISM INDUSTRY ...... 14 3. TRAVEL CHARACTERISTICS OF TOURISTS IN HONG KONG ...... 16 3.1. HOTEL/GUESTHOUSE TOURIST SURVEY ...... 16 3.2. TRAVEL PATTERNS AND TOURIST BEHAVIOR ...... 17 3.3. USE OF PUBLIC TRANSPORTATION ...... 28 3.4. COMPARISON OF PUBLIC TRANSPORTATION USE OF LOCAL RESIDENTS AND MAINLAND TOURISTS ...... 30 3.5. STATISTICAL MODELS ON MAINLAND TOURISTS’ PUBLIC TRANSPORTATION CHOICES ...... 45 3.6. CONCLUDING REMARKS ...... 50 4. FACE-TO-FACE INTERVIEWS WITH MAINLAND TOURISTS WITH INDIVIDUAL VISIT SCHEME PERMITS ...... 52 4.1. FACE-TO-FACE INTERVIEWS ...... 52 4.2. TRIP CHARACTERISTICS ...... 54 4.3. USE OF PUBLIC TRANSPORT ...... 57 4.4. INTENTION TO MAKE A TRIP ...... 61 4.5. CONCLUDING REMARKS ...... 66 5. TOURIST SATISFACTION WITH PUBLIC TRANSPORT SERVICES ...... 68 5.1. LEVEL OF SATISFACTION ...... 68 5.2. LEVEL OF SERVICE ...... 72 5.3. CONCLUDING REMARKS ...... 80 6. POLICY IMPLICATIONS FOR TOURISM TRANSPORTATION POLICY MEASURES ...... 82 6.1. EFFECTIVENESS OF TOURISM POLICY MEASURES ...... 82 6.2. OBSERVATIONS AND INSIGHTS OF MAINLAND TOURISTS ...... 83 6.3. RECOMMENDED TOURIST TRANSPORTATION POLICY MEASURES ...... 89 REFERENCES ...... 96

Appendix A Face-to-face interview questionnaire script sample

List of Figures

Figure 1.1 Visitor arrivals in Hong Kong between 2002 and 2014 ...... 1 Figure 1.2 Schematic diagram of interactions between different research phases .. 4 Figure 2.1 Framework of sustainable tourism and mobility (Bieger, 2000) ...... 7 Figure 3.1 Hong Kong’s 38 traffic zones ...... 19 Figure 3.2 Tourist trip patterns (2002 and 2011) ...... 21 Figure 3.3 Tourist trip distribution (2002) ...... 23 Figure 3.4 Tourist trip distribution (2011) ...... 23 Figure 3.5 Tourist travel patterns for each group (2002) ...... 25 Figure 3.6 Tourist travel patterns for each cluster (2011) ...... 27 Figure 3.7 Transportation mode choices during different periods ...... 29 Figure 3.8 Patterns of taxi use ...... 32 Figure 3.9 Hourly distribution of taxi use ...... 33 Figure 3.10 Patterns of railway use ...... 36 Figure 3.11 Hourly distribution of railway use ...... 37 Figure 3.12 Effects of tourist trips on franchised bus services ...... 41 Figure 3.13 Hourly distribution of franchised bus use ...... 42 Figure 4.1 Distribution of the interviewed tourists by province (no. of respondents = 1,119) ...... 53 Figure 4.2 Distribution of the interviewed tourists by province (no. of respondents = 1,009) ...... 54 Figure 4.3 Trip-making patterns of tourists in 2016 ...... 55 Figure 5.1 Importance-satisfaction analysis and recommended priorities for service quality improvements ...... 71 Figure 5.2 Congestion levels on railways ...... 72 Figure 5.3 Congestion levels on franchised buses ...... 73 Figure 5.4 Observed handling capacity of the railway on Hong Kong Island ...... 76 Figure 5.5 Observed handling capacity of the railway in Kowloon ...... 77 Figure 5.6 Observed handling capacity of the railway in the New Territories ..... 78

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Lists of Tables

Table 2.1 Research on tourists’ satisfaction with public transportation ...... 14 Table 3.1 Respondents’ socio-demographic profiles ...... 17 Table 3.2 Hong Kong’s 38 traffic zones ...... 18 Table 3.3 Tourists’ daily trips by period (2002 and 2011) ...... 20 Table 3.4 Distribution of daily tourist trips to different zones...... 22 Table 3.5 Trips by period for each tourist group ...... 24 Table 3.6 Trip-making percentages for each group (2002) ...... 25 Table 3.7 Hourly trip characteristics for each cluster (2002) ...... 26 Table 3.8 Trips by period for each cluster of tourists (2011) ...... 26 Table 3.9 Hourly trip characteristics for each group (2011) ...... 27 Table 3.10 Modes of transportation used by tourists in 2002 and 2011 ...... 28 Table 3.11 Trips taken using various public transportation modes ...... 30 Table 3.12 Local resident and tourist taxi trips by period ...... 31 Table 3.13 Local resident and tourist railway trips by period ...... 35 Table 3.14 ATC 2015 daily variations in use of Hong Kong Territory roadways 39 Table 3.15 Local resident and tourist franchised bus trips by period ...... 40 Table 3.16 Summary of the luggage survey ...... 44 Table 3.17 Summary statistics for respondents’ travel characteristics ...... 45 Table 3.18 Likelihood ratio test results ...... 48 Table 3.19 Parameter estimates of the multinomial regression model for TCS 2002 and TCS 2011 ...... 49 Table 4.1 Face-to-face interview schedule ...... 53 Table 4.2 Socio-demographic characteristics (no. of respondents = 1,009) ...... 55 Table 4.3 Trip characteristics ...... 56 Table 4.4 The ten most popular tourism spots ...... 57 Table 4.5 Transport modes used by the tourist respondents ...... 57 Table 4.6 Summary statistics for personal characteristics (no. of respondents = 686) ...... 58 Table 4.7 Summary statistics for travel characteristics (sample size = 1,335) .... 58 Table 4.8 Parameter estimates of the multinomial regression model for 2016.... 59 Table 4.9 RMSE of the multinomial regression models ...... 60 Table 4.10 Factors and attributes in stated preference survey design ...... 61 Table 4.11 Parameter estimates of the logistic regression model for Condition 1 ...... 63 Table 4.12 Parameter estimates of the logistic regression model for Condition 2 ...... 65 Table 5.1 Respondents’ most important service aspects (sample size = 1,009) ...... 68 Table 5.2 Mean and standard deviation of scores for each service aspect ...... 69 Table 5.3 Coefficients and their t-statistics for the ordered probit model ...... 70 Table 5.4 Congestion levels on public transport ...... 73

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Table 5.5 Average train frequency on weekdays ...... 73 Table 5.6 Railway trip distribution ...... 74 Table 5.7 Railway handling capacity experienced by the interviewees (sample size = 1,101) ...... 75 Table 5.8 Franchised bus trip distributions ...... 78 Table 5.9 The handling capacity of franchised buses experienced by the interviewees (sample size = 247) ...... 79 Table 6.1 Views on the effectiveness of tourism policy measures ...... 83 Table 6.2 Trends in the travel characteristics of Mainland tourists ...... 83 Table 6.3 Summary of the use of public transport by Mainland tourists ...... 86 Table 6.4 Travel characteristics of first-time visitors against repeat visitors ...... 88

1. INTRODUCTION

1.1. Background of this study

The Individual Visit Scheme (IVS) was first introduced in July 2003 and was considered as a measure to revive the ailing economy of Hong Kong in the wake of the outbreak of severe acute respiratory syndrome. It was first implemented in four Guangdong cities and expanded several times between July 2003 and January 2007. Currently, residents of 49 major Chinese cities are allowed to visit Hong Kong as individual tourists instead of in tour groups or on business visas. Under the IVS, Mainland tourists are permitted to remain in Hong Kong for up to seven days upon each entry. Since the implementation of the IVS, there has been a dramatic increase in Mainland tourist arrivals every year, from 6.83 million (41.2% of all tourists) in 2002 to 47.25 million (77.7% of all tourists) in 2014 (Hong Kong Tourism Board, 2016). Among these visitors, 66.3% (31.34 million) came to Hong Kong under the IVS (see Figure 1.1), about 88% of whom came by land. Although the scheme boosted the development of the tourism, retail and catering industries in Hong Kong, it raised concerns from the local community about the pressure placed on the transportation network due to the influx of Mainland tourists.

Visitor Arrivals to Hong Kong 2002 - 2014 70

60.84 60 54.30 48.62 47.25 50 41.92 40.75 36.03 34.91 40 31.34 28.17 29.51 29.59 28.10 25.25 30 23.36 22.68 27.65 21.81 23.14 17.96 16.57 15.54 15.49 16.86 18.34 20 12.25 12.54 13.59 14.24 6.83 8.47 8.59 9.62 10.59 6.67 10 4.26 5.55 Visitor arrivals (millions) 0.67 0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Year Total visitors Mainland visitors Mainland visitors (under IVS)

Figure 1.1 Visitor arrivals in Hong Kong between 2002 and 2014

In particular, local residents have complained that Hong Kong has been overloaded with Mainland tourists and that the social-economic problems they raise, including the congestion of public transportation and shopping areas, are becoming more serious by the day, especially during long vacations such as the Golden Week (from October 1 to 7) that encourage more tourist arrivals. Indeed, the IVS has triggered a public outcry of concern that Hong Kong cannot deal with such a large influx of Mainland tourists. The

local community has strongly advocated tightening up the IVS in the past few years. A recent survey on Mainland tourist policy conducted in June 2014 by the Public Opinion Programme at the University of Hong Kong (POP-HKU, 2015) revealed that about 60% of Hong Kong people agreed that the number of Mainland tourists should be reduced by an average rate of 30%. Again, about 60% of the 1,023 respondents supported cancelling the IVS policy. Another recent survey conducted in February 2015 by the Hong Kong Institute of Asia-Pacific Studies at the Chinese University of Hong Kong (Hong Kong Institute of Asia-Pacific Studies-CUHK, 2015) also revealed that two thirds (66.7%) of local people wanted to reduce the scope of the IVS. Regarding the multiple-entry permit arrangement, 70.4% of the 742 respondents agreed that the current policy should be cancelled, and only 7.1% of them disagreed.

In fact, the HKSAR Government recognized the concerns of the local community, and the Commerce and Economic Development Bureau (CEDB) conducted an assessment to review the capacity of Hong Kong to receive tourists in 2013 (Commerce and Economic Development Bureau, 2013). The report covered various areas related to the tourism industry, such as the capacity of boundary control points and tourism attractions, the supply of hotel rooms and the effects on the livelihood of the community and economy. The assessment further projected that the number of tourist arrivals would exceed 70 million in 2017. Frankly speaking, although tourism has a positive external effect on our economy, its negative effects on the transportation system, such as traffic congestion caused by limited carrying capacity, present real challenges (Briassoulis, 2002; Albalate and Bel, 2010). The handling capacity of our public transportation system was briefly discussed in the aforementioned report, but a comprehensive quantitative analysis of the effects on the transportation system due to Mainland China tourists was not provided. Furthermore, the possible effects on public transportation due to the overconcentration of tourism activities in some tourist attraction areas, such as shopping centers in Mong Kok and Causeway Bay; tourism attractions like Ocean Park Hong Kong, Hong Kong Disneyland and the countryside; and the purchasing of daily products in individual districts were not explicitly mentioned. To facilitate the sustainable development of the tourism industry, a number of planned tourism developments at Kai Tak and Lantau Island will be implemented in the near future. The introduction of these new tourism attractions will indeed lead to more land dispersion and a higher number of accessibility requirements and hence induce an additional load on the transportation system. Did policymakers consider the transportation needs and effects when formulating these tourism policy measures? Did they think of any mitigations to and enhancements of the transportation system?

Tourism and transport planning policy

The vision of Hong Kong’s tourism strategy is to establish and promote Hong Kong as Asia’s premier international city, a world-class destination for leisure and business tourists (Tourism Commission, 2015). In response to the outcry of the local community, the HKSAR Government must create a friendly and better environment for

both tourists and local residents. In this regard, policymakers from the transportation and tourism sectors and the local community must collaborate closely to create sustainable tourism proposals to deal with public concerns about the carrying capacity of our transportation system, such that the inconvenience of the increased visitor arrivals to local residents can be minimized. As Bramwell and Lane (2011) stated, the development and implementation of sustainable mobility and tourism require strong coordination between different sectors to set up policies and frameworks that support sustainable mobility and tourism.

Hong Kong is expected to receive 70 million tourists annually within three years and 100 million per year by 2023. Gregory So Kam-leung, the Secretary for the CEBD, claimed that the city’s services could cope with more tourists. However, he admitted that problems such as congestion on the public transportation system could arise. To facilitate the sustainable development of the tourism industry, the HKSAR Government has made a great effort to enhance Hong Kong’s capacity to receive tourists. For example, the CEDB’s report suggested a three-pronged approach to promote sustainable tourism in Hong Kong, including the continuous enhancement of our capacity to receive tourists, attract high value-added visitor segments to visit Hong Kong and divert tourists away from popular tourist districts. However, finding a way to develop sustainable tourism together with mobility is the key challenge confronting policymakers in the transportation and tourism authorities.

1.2. Project aim and objectives

The purpose of this study was to reveal public transportation policy measures to enhance the mobility of Mainland tourists. It was necessary to first understand the travel characteristics, attitudes and needs of these tourists, and then to identify the improvement areas for the public transportation system. The following specific objectives were achieved completely in this study.

(i) To evaluate the variation in the travel patterns of tourists before and after implementation of the IVS; (ii) To understand the transportation needs for public transportation modes; (iii) To evaluate the attitudinal and behavioral effects of tourists on the choice of public transportation modes; (iv) To assess the handling capacity of the public transportation system; and (v) To study the implications of tourism policy measures on the public transportation system.

1.3. Outline of research methodology

The study was carried out in four phases to achieve the aforementioned objectives. Phase 1 involved the development of travel patterns of tourists before and after implementation of the IVS. Phase 2 involved the assessment of the transportation characteristics of tourists using a face-to-face interview survey. Phase 3 involved the assessment of tourists’ satisfaction with the current public transportation services of Hong Kong. Phase 4 involved recommendations on achieving sustainable tourism and mobility in Hong Kong. The details of each phase are described as follows.

Phase 1 Phase 2 Spatio-temporal travel Recent transportation

characteristics before and after characteristics of Mainland

the implementation of IVS IVS tourists

TCS 2002 and TCS 2011 Face-to-face interview survey

Phase 4 Phase 3

Transportation and tourism Handling capacity of the

policy evaluation and current public transportation comparison services

Figure 1.2 Schematic diagram of interactions between different research phases

Phase 1: Investigating the variation in the travel patterns of tourists

In this phase, the spatial layout of the current tourism attractions was formulated, and the ArcGIS technique was applied to depict the locations of these attractions in Hong Kong, such as shopping areas in Mong Kok, Causeway Bay and Tsim Sha Tsui, and scenic spots and theme parks such as Ocean Park Hong Kong and Disneyland. Based on this spatial layout, spatio-temporal models of the travel characteristics of tourists in Hong Kong were developed. These models are essential to creating sustainable tourism in our community.

Statistically, spatio-temporal models arise when data are collected across time and space. These models are useful for understanding the travel characteristics of travelers, and hence have been widely adopted in the field of transportation research in recent years (Li et al., 2013). In Hong Kong, the effects of the 2003 implementation of the IVS on the tourism industry were significant; therefore, it is meaningful to establish travel

models of the travel patterns of tourists before and after the implementation of the IVS. Information on the daily travel patterns of tourists surveyed in the Hotel/Guesthouse Tourists Survey of the Travel Characteristics Survey 2002 and 2011 (TCS 2002 and TCS 2011) was used to establish the zone-based travel distribution models in 2002 (before the IVS) and 2011 (after the IVS).

Phase 2: Understanding the transportation needs of tourists and local residents

In TCS 2011, 48% of the total surveyed tourists were from China, a level much lower than that recorded by the Hong Kong Tourism Board, which put Chinese visitor arrivals at 75% (Hong Kong Tourism Board, 2016). Thus, the transportation characteristics of Mainland tourists may be overlooked. In light of this, a separate face- to-face interview survey was carried out to reveal the tourists’ recent travel characteristics. As an extension of the TCS 2011 attitudinal survey, the interview survey collected the Mainland IVS tourists’ demographic characteristics, travel modes, level of satisfaction with the public transportation services and intentions to make trips. The interviewed tourists’ views on the effectiveness of different tourism and transportation policy measures, such as the development of a boundary shopping center, were then evaluated.

Phase 3: Reviewing the handling capacity of the current public transportation services

Based on the data collected from the face-to-face interviews, the tourists’ satisfaction with different public transportation services was revealed in Phase 3. The phase consisted of two assessments: (1) the level of satisfaction scored by the interviewed tourists and (2) the level of railway and franchised bus service experienced by the interviewed tourists. Possible transportation problems were identified, and their implications for the transportation tourism policy measures were discussed in the next phase.

Phase 4: Reviewing the policy implications of achieving sustainable mobility and tourism

The HKSAR Government has proactively reviewed its tourism strategy and taken on a wide range of initiatives to enhance the attractiveness of Hong Kong as a tourist destination. Nevertheless, how to maintain a balance between tourism travel and sustainable transportation remains an unanswered question. In an attempt to do so, possible effective public transportation and tourism policy measures were studied in this phase, based on the findings from the previous phases. Some recommendations were then discussed to achieve sustainable tourism and mobility for future development.

1.4. Structure of this report

Chapter 1 introduces the research background, aim and objectives. It also outlines the research methodology and the structure of the report.

Chapter 2 presents a thorough and critical account of the relevant literature.

Chapter 3 investigates the travel characteristics of the tourists in Hong Kong based on the tourism-related information in TCS 2002 (before the IVS) and TCS 2011 (after the IVS).

Chapter 4 discusses the findings on the recent travel characteristics of Mainland tourists under the IVS permit.

Chapter 5 further discusses the tourists’ satisfaction with the current public transportation system. The significant factors influencing tourists in making a trip are identified.

Chapter 6 provides the main conclusions of the study and recommendations for the way forward in relation to future research, practice and policymaking.

2. LITERATURE REVIEW

2.1. Sustainable tourism and mobility

Tourism travel is considerably understudied compared with the everyday and work travel of local residents. However, the relationship between tourism travel and sustainable transportation should be given greater emphasis, as tourism travel accounts for a major and growing share of all travel. Nevertheless, tourism travel is indeed necessary for maintaining mobility, which has become unsustainable in many developed countries (Holden, 2007). Until recent years, policymakers and scholars from around the world made great efforts to study the tourism phenomena in their own countries, aiming to formulate a tailor-made transportation policy for the sustainable development of the tourism industry. However, the theoretical framework in this area is not sufficiently well established to help us understand the relationships between tourists and their mobility.

Economy

Tourism Technology Society

Mobility

Nature and State and Environment Politics

Figure 2.1 Framework of sustainable tourism and mobility (Bieger, 2000)

Earlier tourism research and theory assumed that tourists were separated from their social networks and considered them independent units with little connection to local transportation. However, mobility and tourism do seem to be naturally connected, and tourism is considered a form of temporary mobility in the community (Dagmar and Frederic, 2013). Hoyer (2000) was among the first to introduce the concept of a link between sustainable tourism and mobility. This new concept drew the tourism and transportation disciplines closer together to minimize the negative effects of tourism on society, economy, politics, environment and technology (Bieger, 2000) (see Figure 2.1). In view of this, various approaches were suggested to act on and convert the mobility of

tourists into a sustainable pattern, such as by developing more efficient conventional transportation technologies, promoting an efficient and affordable public transportation system and encouraging environmental awareness. Although policymakers can take advantage of the ideas from these approaches, a detailed study of the mobility patterns and characteristics of tourists is still required to provide a tailor-made policy framework that suits Hong Kong’s special situation.

2.1.1. Mobility patterns of tourists

Mobility refers to a variety of phenomena at a range of spatial and temporal scales. Conventionally, the travel pattern of visitors is generally considered simple, but as Hall (1999) stated, the role of tourism mobility is indeed a critical issue of inequality and external effects on the local community. Previous discussions of sustainable tourism mainly addressed the mobility of tourists for leisure purposes, and the literature on transportation geography rarely evaluates the competition between tourists and local residents for transportation and transportation space. Recent tourism research has suggested that the travel mobility of tourists is difficult and complex. For example, Moscardo et al. (2013) recently found that different types of tourists had different patterns of mobility: slower, repeated and more extensive movement, accompanied by greater engagement. Furthermore, the transportation needs of tourists can vary significantly between first-time and repeat arrivals (Chang et al., 2013; Lau and McKercher, 2006). However, with the aid of geographic information system (GIS) technologies, scholars have recently adopted spatio-temporal modeling approaches to investigate tourist travel patterns. A survey conducted by McKercher and Lau (2008) identified 78 travel patterns and further categorized them into 11 broad movements or itinerary styles with respect to the trip characteristics of 250 tourists in Hong Kong. After that, Shoval et al. (2011) looked into the relationships between the locations of hotels and tourists activities and found that tourists spent considerable shares of time in the immediate vicinity of their hotels, indicating the existence of geomorphic barriers on tourist movement.

In spite of the preceding, the specific mobility characteristics of tourists contribute to the emerging conflicts between tourists and local residents to a certain extent (Moscardo et al., 2013). It is generally agreed that public transportation systems play an important role in relieving the problem of increasing travel demands in densely populated cities; hence, cities with effective and extensive public transportation networks are actually more attractive to tourists (Le-Klähn and Hall, 2015; Mandeno, 2011; Yang, 2010). As tourists are more willing to adopt public transportation systems, their effects on those systems may introduce potential conflicts with local residents in the use of public transportation. A systematic analysis of the mobility of tourists and local residents within public transportation systems is therefore needed to establish sustainable mobility for tourism over the long term.

2.1.2. Attitudes and behavioral effects of tourists

In the transportation research, it has generally been accepted that the attitude and behavioral effects of passengers are highly associated with their choice of transportation modes and hence their mobility in a transportation system (Kitamura et al., 1997; Mokhtarian and Salomon, 2001; Li et al., 2014). Tourism research has also revealed that the travel patterns of tourists correlate highly with their attitudes and behavior (Juvan and Dolnicar, 2014). For example, Hergesell and Dickinger (2013) found that travel time, travel convenience, environmental friendliness and particularly travel costs play important roles in stimulating a behavioral change in tourists’ choice of transportation mode. However, Dickinson and Peeters (2014) found that not all of these influencing factors were applicable to tourists, and further pointed out that the availability of time was the most pivotal factor. Travelers’ satisfaction is even increasingly being put forward as the key to the future development of public transportation in both theory and practice (Fellesson and Friman, 2008). Considered an essential element and an additional tourism product in tourism systems, public transportation plays a vital role in the tourist experience and is a direct factor in sustainable tourism development (Le- Klähn et al., 2014a, 2014b; Duval, 2007; Diana, 2012).

The attitudes and behavior of Mainland tourists have been widely discussed in the fields of transportation and tourism in recent years (Du et al., 2012). Several studies have investigated different aspects of the travel behavior of Mainland tourists, such as their food preferences (Chang et al., 2010, 2011) and shopping and tourist market expectations (Hsieh and Chang, 2006). A non-official survey conducted in December 2014 by an international investment group found that cost, safety, culture, vacation length and visa availability were the major factors influencing Mainland tourists’ choice of travel destination (CLSA, 2015). Nonetheless, the attitudinal and behavioral effects on the mobility of Mainland tourists have not been reported, even though they are considered critical factors in the development of sustainable tourism.

2.2. The growth of Mainland tourist arrivals

2.2.1. Outbound Chinese tourism

According to the World Tourism Organization, international tourist arrivals increased from 674 million globally in 2000 to 1.186 million in 2015 (United Nations World Tourism Organization, 2017). The People’s Republic of China continues to lead in global outbound travel, with a year-on-year increase of 18% (China Tourism Academy, 2016; United Nations World Tourism Organization, 2017). As reported by the China National Tourism Administration, Mainland tourists took 120 million trips overseas and spent a record US$104.5 billion in 2015, with China becoming the top global source of tourists in terms of both number of trips made and money spent on overseas trips. The figures were 12% and 16.7% higher than the 2014 totals, respectively, adding greatly to the growth in global spending on travel and tourism

(China Tourism Academy, 2016). Outbound Chinese tourism is expected to substantially increase as a result of more convenient visa policies, summer holiday travel peaks and the operation of more international flights.

Benefitting from the IVS, citizens of 47 Chinese cities took 45 million and 37 million trips to Hong Kong and Macau, respectively, contributing to more than 70% of all Mainland tourist outbound trips in 2015. From a geographical convenience perspective, Asian countries such as Japan, South Korea and Thailand also dominate China’s outbound tourism market. Among these Asia countries, the number of Mainland tourists to Japan was 4.99 million, a fivefold increase from the number in 2011 and 107% more than the 2.41 million arrivals in 2014 (China Tourism Academy, 2016). From 2004 to 2015, the number of Mainland tourists in the U.S. experienced double-digit growth, and the number of Chinese arrivals to the U.S. in 2015 totaled 2.59 million (National Travel and Tourism Office, 2016). Mainland tourists consider long- distance destinations such as Germany, France, Italy and Switzerland as famous European countries for sightseeing, shopping and leisure. In the first half of 2015, the number of Mainland tourists to Germany was 1.7 times that during the same period in the previous year.

2.2.2. Mainland tourist arrivals to Hong Kong

Tourism is one of the four pillar industries of Hong Kong’s economy, which has been the driving force of the city’s economic growth (Census and Statistics Department HKSAR, 2016). Due to the implementation of the IVS in July 2003, there has been a dramatic increase in Mainland tourist arrivals every year, from 6.83 million (41.2% of all tourists) in 2002 to 47.25 million (77.7% of all tourists) in 2014 (Hong Kong Tourism Board, 2016). Of these Chinese visitors, 66.3% (31.34 million) came to Hong Kong under the IVS permit, with about 88% coming by land. Indeed, the IVS was first implemented in four Guangdong cities and expanded several times between July 2003 and January 2007. Currently, permanent residents of 47 Chinese cities are allowed to visit Hong Kong as individual tourists instead of as part of a tour group or on business visas. Mainland tourists under the IVS are permitted to remain in Hong Kong for up to seven days upon each entry. In 2009, the Central Government of China implemented a measure to allow eligible permanent residents in Shenzhen, the Mainland city next to Hong Kong, to apply for one-year multiple-entry Individual Visit Endorsements (M- Permit) to visit Hong Kong. Benefitting from this measure, Shenzhen ranked at the top in the first half of 2013, contributing 48.8% (6.16 million) of the arrivals under the IVS (Commerce and Economic Development Bureau, 2013).

Unfortunately, the implementation of the IVS triggered a public outcry of concern that Hong Kong was incapable of accommodating such a large influx of Mainland tourists, and the local community had strongly advocated tightening up the IVS permit in the past few years. In light of this, the Central Government accepted a suggestion from the HKSAR Government and adjusted the IVS policy from “one-year multiple-

entry” to “one trip per week” Individual Visit Endorsements for Shenzhen permanent residents on April 13, 2015. Although the total number of Chinese visitor arrivals dropped slightly to 45.84 million (77.3% of all tourists) in 2015 (Hong Kong Tourism Board, 2016), the local community remains concerned about the pressure Mainland tourists place on the transportation network.

2.3. Tourists’ use of public transportation

Public transportation systems are designed to provide for the safe, rapid, comfortable, convenient, economical and environmentally compatible movement of people and goods. Large tourism cities are now enjoying the contributions of Chinese tourism to their economic and social development, while policymakers and scholars are thinking about the importance of maintaining sustainable mobility to accommodate increasing tourist demand. For urban areas with extensive public transportation networks, encouraging tourists to use public transportation modes for travelling is an effective approach to relieving road traffic congestion (Gronau, 2017; Le-Klähn and Hall, 2015). Public transportation systems generally consist of a variety of modes, such as trains, metros, buses, ferries and trams. It has been observed that tourists rely highly on metros, as in the case of Taipei in Asia (Change and Lai, 2009) and Paris and Munich in Europe (Simon, 2012; Le-Klähn et al., 2014b). It has also been reported that about 65% of international tourists use the subway in New York City (Permanent Citizens Advisory Committee to the MTA and Urban Land Institute New York, 2015). On the contrary, the major modes of transportation in developing countries are buses, private cars and taxis. In India, it was revealed that buses were the dominant transportation mode for tourists, constituting over 70% of all travel trips in 2002, with decreases to 67% and 57% of rural and urban trips, respectively, in 2009 (National Council of Applied Economic Research, 2003). However, the metro system in India accounted for only 7% and 27% of rural and urban trips, respectively, in 2009 (Ministry of Statistics and Programme Implementation, Government of India, 2010). Likewise, in South Africa, almost half of tourist trips were taken by cars (56% of day trips and 44% of overnight trips), and less than 40% were taken by taxis (34% of day trips and 38% of overnight trips) (Statistics South Africa, 2010).

In addition to the coverage and provision of public transportation systems, a survey conducted by Gutiérrez and Miravet (2016) found that the socioeconomic and demographic characteristics of tourists were associated with their likelihood of using public transportation. Some studies have recorded tourists’ personal reasons for using public transportation, including their experiences with different modes of transportation, their desire to avoid getting lost (Downward and Lumsdon, 2004), their enjoyment of the beauty of passing scenery when traveling (Lumsdon et al., 2006) and their desire to eliminate parking (Dickinson and Robbins, 2007; Guiver et al., 2007). To help policymakers understand tourists’ needs and concerns when using public transportation, Le-Klähn et al. (2014a) summarized the reasons why tourists use public transportation as follows: drive-free benefits, traffic reduction, advantages of local public

transportation and car unavailability. On the contrary, the reasons for not using public transportation include inconvenience and restriction, lack of information, disadvantages of public transportation and personal preferences. Nevertheless, Albalate and Bel (2010) raised the concern that the considerable travel demands of tourists would impose economic losses on local residents due to possible traffic delays and road congestion. They also pointed out that the authorities or planners did not respond properly to such additional demands, such as by strengthening the service supply, which caused serious supply constraints in some European cities.

Hong Kong has a world-class public transportation system, in which over 12 million passenger journeys are made by railway (40.9%), buses (32.0%), minibuses (15.0%), taxis (8.1%) and ferries (1.0%) (Transport Department, 2015). Likewise, most Mainland tourists use public transportation such as railways, franchised buses and taxis to travel around Hong Kong. The tourist coach is the traditional travel mode for tourists, but it was recorded that only 12% of Mainland tourists use a guided tour for their travel arrangements, where tourist coach services are normally provided (Hong Kong Tourism Board, 2013). According to the Travel Characteristics Survey 2011 (TCS 2011), the railway was the most popular transportation mode taken by tourists, a trend that increased from 26% in 2002 to 35% in 2011. Moreover, the average number of trips made per visitor was 2.3 trips/day, higher than the average trip rate of a local resident (1.83 trips/day). Most importantly, the survey showed that travel by tourists during the evening peak rush hour between 18:00 and 19:00 clearly coincided with that of the local residents. Thus, the travel demands tourists place on public transportation systems should not be overlooked.

According to the Assessment Report on Hong Kong’s Capacity to Receive Tourists, the MTR Cooperation Limited conducted market research in 2012 and found that the railway accounted for a 55% share of the various public transport modes used by tourists in Hong Kong. Although the overall loading during non-peak hours fell below 40%, over 1,200 train trips were added per week to increase the carrying capacity by 3 million passenger trips during 2012. The overall train service was further strengthened with additional and special runs of trains at busy interchange stations and bottleneck positions in 2013 (Commerce and Economic Development Bureau, 2013). In addition, the report found that the average occupancy rate of franchised buses during morning and afternoon peak hours was 70% in 2013.

2.4. Satisfaction on public transportation services

Many studies have drawn attention to the factors influencing the level of satisfaction with public transportation services. Attributes including travel costs, accessibility, convenience and flexibility have been suggested as the major factors influencing the choice of transportation modes (Johansson et al., 2006; Beirão and Cabral, 2007; Van et al., 2014; Hensher et al., 2003). In addition, environmental friendliness, information, safety, comfort and cleanness are important in stimulating

behavioral changes in the choice of transportation mode (Hensher et al., 2003; Beirão and Cabral, 2007). Fellesson and Friman (2008) applied their analysis based on 1,000 telephone interviews in nine European cities to investigate how the four factors of systems, comfort, staff and safety influenced travelers’ satisfaction with public transportation. Eboli and Mazzulla (2007) focused on 763 college student respondents’ satisfaction with public transportation based on 16 service attributes in Cosenza, Italy. Their results showed that service planning and reliability had a major effect on global customer satisfaction, while bus stop maintenance had a major effect on the comfort of bus services. Another wider-range study conducted in Italy by Diana (2012) examined multimodal travelers’ level of satisfaction with public transportation based on nine service aspects: frequency, punctuality, seats, speed, cleanliness, comfort, connection, convenience and cost. However, researchers have found that satisfaction and frequency of use of urban transit are uncorrelated among inhabitants. Tyrinopoulos and Antoniou (2008) investigated service quality, transfer quality, service production and information/courtesy as vital factors in users’ satisfaction with public transit.

2.4.1. Tourists’ expectations of public transportation systems

A small but growing number of studies have specifically focused on tourists’ satisfaction with public transportation. Some studies have supported the concept that public transportation performance can enhance the attraction and enjoyment of tourists (Hall, 1999; Law, 2002; Thompson and Schofield, 2007). For example, Thompson and Schofield (2007) found that public transportation performance used by overseas tourists had a slight influence on overall tourist satisfaction in Greater Manchester. On the other hand, some researchers have drawn attention to the effect of tourists’ level of satisfaction on public transportation services. Research has investigated the level of satisfaction with the use of public transportation across four different service dimensions, including traveling comfort, service quality, accessibility and other additional features, in Munich, Germany (Le-Klähn, 2013; Le-Klähn et al., 2014c). Thompson and Schofield (2007) further revealed that the influence of the ease of use of public transportation on destination satisfaction was greater than the influence of efficiency and safety. In terms of Asian countries, Budiono (2009) conducted a self- rated questionnaire survey in Jakarta and Jogjakarta, Indonesia. Both functional and soft factors were included in the study, which found that the interviewed tourists were not satisfied with the public transportation systems. In a Lebanon case study, Ladki et al. (2014) found that the performance of public transportation had an effect on the overall satisfaction with Lebanon as a destination. They found that tourists’ overall satisfaction was negatively affected by the safety and efficiency of the transport modes, ease of use of the transportation and ease of access to attractions, and positively affected by cleanliness and difficulty to reach. Some of these studies focusing on tourists’ satisfaction with public transportation are shown in Table 2.1.

Table 2.1 Research on tourists’ satisfaction with public transportation Level of City/ Sample Study Factors Satisfactio Country Size n Thompson and Manchester, Ease-of-use, efficiency Train 280 Schofield (2007) UK and safety, and parking 4.58/7 Jakarta, Budiono (2009) Jogjakarta, Functional and soft factors 265 2.5/5.0 Indonesia Munich, Le-Klähn (2013) 380 4.08/5.00 Germany Traveling comfort, service quality, accessibility, and Le-Klähn et al. Munich, additional features 466 4.08/5.00 (2014c) Germany Safety , efficiency, ease of Ladki et al. (2014) Lebanon use, cleanliness, price 250 N/A value, accessibility

To summarize, the factors measuring tourists’ satisfaction with local public transportation services have included safety and security, travel speed, accessibility, vehicle condition, customer support, information provision, fares and level of crowding. These factors have commonly been applied to examine the level of satisfaction with public transportation.

2.5. Public transportation policy for the tourism industry

Given that Mainland tourists continue to be the main impetus of growth for the tourism industry in Hong Kong, effective transportation policy measures should be considered to enhance tourism sustainability in the future. The policy implementations and experiences in other countries and cities should be considered to promote sustainable mobility and tourism in Hong Kong.

2.5.1. Provision of a tourist travel pass

Travel cost is one of the major factors influencing the attractiveness of public transportation usage (Redman et al., 2013; Budiono, 2009). It has been suggested that fare promotion and special ticket schemes may entice tourists to use public transportation (Le-Klähn, 2013; Le-Klähn et al., 2014c). In Australia, the “myki Explorer pack” was specifically created for international tourists to travel multiple times on Melbourne’s public transportation system within a single day at a capped price (Public Transport Victoria, 2017). In addition, a 30% fare discount is provided for travelers using trains during off-peak periods. Likewise, a discount for special transportation modes is allowed for travelers using “Oyster” and “Travelcards” in London (Visit London, 2017). The Singapore Tourist Pass also offers one- or seven-day

unlimited tourist passes on Singapore’s basic bus services and mass and light rapid transit to meet different tourists’ needs (Singapore Tourist Pass, 2017).

2.5.2. Provision of tourism information

Information is well recognized as an important factor for tourists when using public transportation systems (Redman et al., 2013) because tourists generally require more information than local residents (Thompson, 2004). In Australia, a free PTV mobile application helps tourists to access public transportation information in Melbourne and Sydney (Public Transport Victoria, 2017). These mobile applications allow tourists to view real-time information for metropolitan trains, trams and buses. In Hong Kong, several mobile applications such as Citymapper, MTR Mobile and HKeTransport are readily available for travelers to make public transportation plans. However, a comprehensive official real-time mobile application that integrates the preceding information has not yet been provided for tourists.

2.5.3. Restricting car use

Non-public transportation visitors may have a variety of reasons for using individual vehicles exclusively (Le-Klähn et al., 2014a). However, Lumsdon et al. (2010) conducted a survey in Greater Manchester and revealed that the enhancement of public transportation and provision of the right package of transportation and tourism remained probable ways of encouraging a modal shift in visitors. For example, Gronau and Kagerme (2007) suggested that restricting the use of private cars was a necessary condition on the supply side to ensure the success of leisure and tourism public transportation services. Much of this empirical research has shown that limited parking facilities as well as increases in parking cost are effective ways to divert tourists to use public transportation.

3. TRAVEL CHARACTERISTICS OF TOURISTS IN HONG KONG

This chapter presents the tourist interview survey data extracted from the Travel Characteristics Surveys of 2002 and 2011 (TCS 2002 and TCS 2011) and uses the findings to evaluate the travel patterns of tourists and local residents in Hong Kong over the past two decades. These surveys help us uncover and visualize tourists’ spatio- temporal travel characteristics. In particular, we examine the travel characteristics of tourists from Mainland China to reveal the results and possible implications of the Individual Visit Scheme (IVS) implemented in 2003.

3.1. Hotel/Guesthouse Tourist Survey

The 2003 implementation of the IVS clearly had significant effects on Hong Kong’s tourism industry. To understand this change, it is necessary to compare the characteristics of tourist travel before and after the IVS was implemented. Fortunately, we are able to use the hotel/guesthouse tourist interview survey data from TCS 2002 (before the IVS) and TCS 2011 (after the IVS) (Transport Department, 2014) to extract tourism-related information for evaluation. The 2002 and 2011 surveys were carried out with tourists who were staying in Hong Kong hotels or guesthouses to collect their travel characteristics and trip information. The data were first weighted according to the estimated number of tourists staying in the sampled hotels/guesthouses during the survey period, then further expanded to represent the territory-wide totals.

In the 2002 TCS, 1,378 visitors were randomly surveyed in the lobbies of selected hotels/guesthouses between September and December 2002. Likewise, 2,785 visitors were randomly surveyed between November 2011 and March 2012 for the 2011 TCS. Quality control measures were strictly applied in both periods of fieldwork to ensure that the data collected were of high quality. To represent the total numbers of visitor arrivals, the collected survey data were adjusted by expansion factors based on independent control data. Accordingly, expanded numbers of 45,369 and 107,894 respondents were calculated in 2002 and 2011, respectively 1 . To further evaluate variations in the travel patterns of Mainland tourists due to the IVS, tourist data from Mainland China and Macau were extracted in this study. The socio-demographic information of the respondents from Mainland China is summarized in Table 3.1.

1 The numbers of visitor arrivals were 45,387 in 2002 and 114,853 in 2011 (Hong Kong Tourism Board, 2003 and 2012).

Table 3.1 Respondents’ socio-demographic profiles Frequency (percentage) Personal particulars Overall TCS 2002 TCS 2011 [sample size = 1,054] [sample size = 141] [sample size = 913] Gender - Male 475 (45.1%) 74 (52.5%) 401 (43.9%) - Female 579 (54.9%) 67 (47.5%) 512 (56.1%) Age group - 18–25 165 (15.7%) 12 (8.5%) 153 (16.8%) - 26–35 424 (40.2%) 50 (35.5%) 374 (41.0%) - 36–45 254 (24.1%) 38 (27.0%) 216 (23.7%) - 46–55 138 (13.1%) 35 (24.8%) 103 (11.3%) - Over 55 73 (6.9%) 6 (4.3%) 67 (7.3%) Trip purpose - Sightseeing/shopping 747 (70.9%) 69 (48.9%) 678 (74.3%) (a) Sightseeing 253 (24.0%) 12 (8.5%) 241 (26.4%) (b) Shopping 494 (46.9%) 57 (40.4%) 437 (47.9%)

- Work/business 254 (24.1%) 57 (40.4%) 197 (21.6%) - Visiting relatives/friends 40 (3.8%) 9 (6.4%) 31 (3.4%) - Other 13 (1.2%) 6 (4.3%) 7 (0.8%)

After the implementation of the IVS in 2003, the number of tourists from the Mainland and Macau increased remarkably, going from 24.8% to 47.6% of the total inbound tourists in 2002 and 2011, respectively. Overall, 1,054 validated samples were collected from Mainland tourists, including 141 respondents in 2002 and 913 respondents in 2011. Gender distribution was fairly even, with slightly more than half of the 2011 respondents being female. In 2002, the highest proportion of tourists was aged between 26 and 35, with nearly 40% of the surveyed tourists in this age group. The average age of the tourists was lower in 2011 than in 2002, as the proportion of young adults (ages 18–25 and 26–35) rose to 57.8% of the surveyed tourists in 2011—an increase of 13.8%. Shopping was the most common purpose for visiting Hong Kong (40.4% in 2002 and 47.9% in 2011). However, a noticeable change of trip purpose from work/business (40.4% in 2002 and 21.6% in 2011) to sightseeing (8.5% in 2002 and 26.4% in 2011) was observed in the results.

3.2. Travel patterns and tourist behavior

To visualize the travel characteristics of tourists in Hong Kong, the spatio-temporal travel patterns and behaviors of tourists in 2002 and 2011 were evaluated. As illustrated in Figure 3.1 and Table 3.2, Hong Kong’s territory is divided into 38 traffic zones for the modeling analysis in this study (Planning Department, 2014).

Table 3.2 Hong Kong’s 38 traffic zones Zone Area Zone name no. Hong Kong Island 1 Western 2 Central/Mid-levels/Admiralty 3 Wan Chai/Causeway Bay 4 Eastern 5 Southern Kowloon 8 Tsim Sha Tsui 9 Jordan/Yau Ma Tei 10 Mong Kok/Prince Edward 11 Sham Shui Po/Cheung Sha Wan/Lai Chi Kok 12 Hung Hom/Ho Man Tin/To Kwa Wan 13 Kowloon Tong 14 Kowloon City/Wong Tai Sin/Tsz Wan Shan 15 Ngau Tai Kok/Kowloon Bay/Kwun Tong 16 Tai Kok Tsui/Nam Cheong 17 Former Kai Tak Airport Tsuen Wan/ Kwai 18 Kwai Chung/Tsuen Wan Tsing 19 Tsing Yi 33 Container Terminal Northwest New 20 Ting Kau/Sham Tseng/Tuen Mun Territories 21 Tin Shui Wai 22 Yuen Long 23 Kam Tin/Pat Heung 24 San Tin/Lok Ma Chau 34 Lung Kwu Tan/Tap Shek Kok 37 Ngau Tam Mei 38 Nan Sang Wai/Mai Po Northeast New 25 Fanling/Sheung Shui Territories 26 Sha Tau Kok 27 Tai Po/Tai Wo 28 Tai Wai/Shatin/Fotan 29 Ma On Shan/Wu Kai Sha 39 Pat Sin Leng/Tai Mei Tuk/Shuen Wan 40 Kwu Tung Southeast New Sai Kung/ Tsueng Kwan O 30 Territories Southwest New Discovery Bay/Sunny Bay/Ma Wan/Mui Wo/Tai O/Ngong 31 Territories Ping/Tung Chung 32 Peng Chau/Cheung Chau/Lamma Island/Po Toi Island 35 Hong Kong International Airport/Asia World-Expo 36 Disneyland

Legend Hong Kong Island Kowloon Tsuen Wan/ Kwai Tsing North West New Territories North East New Territories South East New Territories South West New Territories Tourist attraction

Figure 3.1 Hong Kong’s 38 traffic zones

Each zone has a certain degree of homogeneity in terms of land use. Five zones are on Hong Kong Island (shown in red), ten zones are in Kowloon (shown in orange), three zones are in Tsuen Wan/Kwai Tsing (shown in purple), eight zones are in the Northwest New Territories (shown in grey), seven zones are in the Northeast New Territories (shown in yellow), one zone is in the Southeast New Territories (shown in green) and the remaining four zones are in the Southwest New Territories (shown in blue).

In addition, the top ten tourist attractions in 2016 according to the Hong Kong Tourism Board (2016) are marked on Figure 3.1. The top ten tourist attractions are: (i) the Peak (Zone 2), (ii) the Avenue of Stars (Zone 8), (iii) Hong Kong Disneyland (Zone 36), (iv) Ladies’ Market (Zone 10), (v) Ocean Park Hong Kong (Zone 5), (vi) Temple Street Night Market (Zone 9), (vii) Clock Tower (Zone 8), (viii) Tsim Sha Tsui Promenade (Zone 8), (ix) Golden Bauhinia Square (and HKCEC) (Zone 3) and (x) Lan Kwai Fong (Zone 2). Most of these tourist attractions are located in zones along the shores of Victoria Harbour, where efficient and convenient transportation is provided.

3.2.1. A spatio-temporal review of tourists’ trip patterns

Information on the tourists’ daily trips in 2002 and 2011 was extracted from the TCS database to reveal the tourists’ basic trip-making patterns (see Table 3.3 and Figure 3.2). In general, the morning and evening peak periods of tourist activity were more dispersed than the peak traveling times of the local residents.

Table 3.3 Tourists’ daily trips by period (2002 and 2011) TCS 2002 TCS 2011 Variation Period Expanded no. of % Expanded no. of % (+/-) trips trips 0000–0100 9 0.0% 810 0.7% +0.7% 0100–0200 146 0.5% 413 0.4% -0.2% 0200–0300 0 0.0% 81 0.1% +0.1% 0300–0400 0 0.0% 155 0.1% +0.1% 0400–0500 0 0.0% 35 0.0% +0.0% 0500–0600 0 0.0% 0 0.0% +0.0% 0600–0700 60 0.2% 106 0.1% -0.1% 0700–0800 818 3.0% 1,747 1.6% -1.4% 0800–0900* 1,201 4.4% 3,952 3.6% -0.8% 0900–1000 1,686 6.2% 8,951 8.1% +2.0% 1000–1100 2,068 7.6% 8,473 7.7% +0.1% 1100–1200 2,038 7.5% 6,027 5.5% -2.0% 1200–1300 1,852 6.8% 5,497 5.0% -1.8% 1300–1400 1,823 6.7% 6,010 5.5% -1.2% 1400–1500 1,910 7.0% 6,134 5.6% -1.4% 1500–1600 2,077 7.6% 5,585 5.1% -2.5% 1600–1700 1,518 5.6% 5,889 5.3% -0.2%

1700–1800 1,661 6.1% 7,389 6.7% +0.6% 1800–1900* 2,214 8.1% 9,889 9.0% +0.9% 1900–2000 1,894 6.9% 7,816 7.1% +0.2% 2000–2100 1,474 5.4% 9,233 8.4% +3.0% 2100–2200 1,260 4.6% 7,646 6.9% +2.3% 2200–2300 1,037 3.8% 5,683 5.2% +1.4% 2300–0000 594 2.2% 2,740 2.5% +0.3% Total 27,341 110,261 * Morning and evening peak hours for Hong Kong residents.

The peak travel periods for local residents were found to be 0800–0900 in the morning and 1800–1900 in the evening, as identified in the 2002 and 2011 TCS data. However, the morning peak period for tourists was identified as 0900–1100. About 15% of the total daily tourist trips happened during this period (defined as the morning peak period and highlighted in pink in Figure 3.2). The evening peak period was identified as 1800–2100. About 25% of the total daily tourist trips occurred in this period (defined as the evening peak period and highlighted in purple in Figure 3.2). The remaining periods were further defined as the noon off-peak period (between 1100 and 1800) and the midnight off-peak period (between 2100 and 0900). Apparently, most tourists started their trips after 0900 to avoid traveling during peak congestion in the morning. The evening peak for both tourists and local residents started at 1800, but tourist travel in the evening peak period was more dispersed over several hours.

Mid-night Off-peak Morning Peak Noon Off-peak Evening Peak 10% 9%

7% 6% 5% 4%

3% Percentage ofTrips 2% 1%

1-2 5-6 0-1 2-3 3-4 4-5 6-7 7-8 8-9

9-10

13-14 17-18 21-22 10-11 11-12 12-13 14-15 15-16 16-17 18-19 19-20 20-21 22-23 23-24 Hourly Period of the Day

Tourist Trips (TCS 2002) Tourist Trips (TCS 2011)

Figure 3.2 Tourist trip patterns (2002 and 2011)

Table 3.4 presents data on the patterns in tourist trip distributions between 2002 and 2011, which are visualized in Figures 3.3 and 3.4. In 2002, about 44.1% of tourist visits were concentrated in zones along the Victoria Harbour shore (i.e., Zones 2, 3, 4

and 8). Excluding the boundary control points, i.e., Zone 35 (the Hong Kong International Airport) and Zones 24 and 25 (the land boundary control points), less than 1% of all Mainland tourists travelled to other parts of the New Territories. However, in 2011, tourist visits became more dispersed around different parts of Hong Kong. The majority of tourists still traveled along Victoria Harbour (Zones 2, 3 and 8), with visits to these zones accounting for 46.2% of the total trips. However, more tourists (about 12.9%) travelled to zones in the New Territories, especially to the northwest and southwest parts that aligned with stations on the East Rail Line (ERL).

Table 3.4 Distribution of daily tourist trips to different zones TCS 2002 TCS 2011 Zone Variation Area Expanded Expanded no. % % (+/-) no. of trips no. of trips 1 0 0.0% 1,345 1.2% +1.2% 2 2,719 10.0% 11,617 10.5% +0.6% Hong Kong Island 3 6,956 25.4% 19,635 17.8% -7.6% 4 4,145 15.2% 3,616 3.3% -11.9% 5 915 3.4% 7,555 6.9% +3.5% 8 2,369 8.7% 19,641 17.8% +9.1% 9 2,476 9.1% 3,236 2.9% -6.1% 10 1,368 5.0% 6,543 5.9% +0.9% 11 81 0.3% 318 0.3% 0.0% 12 1,320 4.8% 2,932 2.7% -2.2% Kowloon 13 76 0.3% 427 0.4% +0.1% 14 685 2.5% 1,578 1.4% -1.1% 15 240 0.9% 3,041 2.8% +1.9% 16 18 0.1% 1,621 1.5% +1.4% 17 0 0.0% 0 0.0% 0.0% 18 718 2.6% 1,791 1.6% -1.0% Tsuen Wan/Kwai 19 79 0.3% 97 0.1% -0.2% Tsing 33 0 0.00% 55 0.0% 0.0% 20 0 0.0% 1,321 1.2% +1.2% 21 0 0.0% 2,646 2.4% +2.4% 22 10 0.0% 26 0.0% 0.0% Northwest New 23 40 0.2% 198 0.2% 0.0% Territories 24 # 319 1.2% 2,086 1.9% +0.7% 34 0 0.0% 0 0.0% 0.0% 37 0 0.0% 0 0.0% 0.0% 38 58 0.2% 0 0.0% -0.2% 25 # 122 0.5% 2,995 2.7% +2.3% 26 0 0.0% 0 0.0% 0.0% 27 0 0.0% 186 0.2% +0.2% Northeast New 28 1,368 5.0% 1,324 1.2% -3.8% Territories 29 0 0.0% 72 0.1% +0.1% 39 0 0.0% 0 0.0% 0.0% 40 0 0.0% 31 0.0% 0.0%

Southeast New 281 0.3% +0.1% 30 31 0.1% Territories 31 0 0.0% 2,564 2.3% +2.3% Southwest New 32 0 0.0% 52 0.0% 0.0% Territories 35 # 1,227 4.5% 5,956 5.4% +0.9% 36 0 0.0% 5,477 5.0% +5.0% Total 27,341 110,261

Figure 3.3 Tourist trip distribution (2002)

Figure 3.4 Tourist trip distribution (2011)

Zones 9 and 10 (i.e., Jordan and Yau Ma Tei, and Mong Kok and Price Edward) are characterized by a mixture of old and new multi-story buildings. The open-air street markets in these zones include some of the most famous tourist attractions in Hong Kong, such as the Ladies Market and the Temple Street Night Market. Here, the atmosphere is less touristy, and the prices are usually lower. The surveys showed that 14.1% of all Chinese tourists travelled to these zones in 2002. However, the proportion of tourists visiting these zones fell by 5.3%, to only 8.8% in 2011. Seemingly, these open-air street markets are no longer most tourists’ favourite attractions.

As expected, Zone 35 (i.e., Hong Kong International Airport/AsiaWorld-Expo) had a high proportion of visitors—about 5% of the total daily tourist trips. This zone is a very popular destination for both local residents and tourists, with its reputation for outlet shopping and sightseeing attractions such as the Ngong Ping 360 cable car. Hence, only a minor variation of -0.5% in tourist visits was observed between 2002 and 2011. However, due to the opening of Hong Kong Disneyland (Zone 36) in 2005, the proportion of daily tourist trips to this zone increased by 5.0 percentage points by 2011. In 2011, tourist visits became more dispersed around Hong Kong. The majority of tourists still traveled along Victoria Harbour (Zones 2, 3 and 8), with visits to these zones accounting for 48% of the total trips. However, more tourists (about 13.4%) traveled to zones in the New Territories, especially to the northwest and southwest parts.

3.2.2. Tourists’ spatio-temporal travel characteristics by group

To gain a better understanding of the travel characteristics of Mainland tourists, we placed individual zones with similar travel patterns into three groups with the following characteristics. Zones with tourist trips of 5% or above in both 2002 and 2011 were defined as major tourist zones (Group 1), which covered about 45% of the total. Zones with tourist trips of 5% or above in either 2002 or 2011 were defined as minor tourist zones (Group 2), whereas zones with tourist trips less than 5% in both years were defined as non-tourist zones with non-major tourist attractions or spots (Group 3). The three groups can therefore be described as shown in Table 3.5.

Table 3.5 Trips by period for each tourist group Total trips Group Zone no. Description 2002 2011 Tourists visiting major tourist zones - Victoria Harbour waterfront - Luxury malls, e.g., IFC, Times Square and 1 2, 3, 8 44.1% 46.1% Pacific Place - Scenic spots, e.g., The Peak, Golden Bauhinia Square Tourists visiting minor tourist zones 4, 5, 9, - Street markets or shopping malls, e.g., 2 10, 28, 42.2% 30.6% Temple Street Night Market, Citygate 35, 36 Outlets shopping mall in Tung Chung,

Shatin New Town Plaza - Scenic spots, e.g., Disneyland, Ocean Park, Stanley Market Tourists visiting non-tourist zones 3 Others 13.7% 23.3% - No key tourist attractions or spots

(i) Trip-making patterns in 2002

We then calculated the tourist trip-making percentages for the four daily travel periods (as discussed in Section 3.2.1) for each group, as presented in Table 3.6 and Figure 3.5. Basically, the trip-making percentage profiles of Groups 1 and 2 (defined as tourist zone visitors) were comparable, but entirely different from that of Group 3 (defined as non-tourist zone visitors). The percentage variations between the midnight off-peak, noon off-peak and evening peak periods between Groups 1 and 2 were insignificant, exhibiting only about a ±2% variation. However, 16.4% of Group 1 tourists made trips during the morning peak period, indicating that the tourists in Group 1 were more likely than other tourist groups to make their trips during this period.

Table 3.6 Trip-making percentages for each group (2002) Midnight Morning Noon Evening Group Zone no. off-peak peak off-peak peak 1 2, 3, 8 20.8% 16.4% 40.7% 22.1% 4, 5, 9, 10, 28, 2 20.9% 12.2% 43.6% 23.4% 35, 36 3 Other 23.3% 3.5% 43.0% 30.2% Total 21.8% 7.0% 44.8% 26.4%

100% 22.1% 23.4% 30.2% 80%

60% 40.7% 43.6% 43.0% 40% 16.4% 12.2% 3.5%

Percentage ofTrips 20% 20.8% 20.9% 23.3% 0% Group 1 Group 2 Group 3

Midnight off-peak Morning peak Noon off-peak Evening peak

Figure 3.5 Tourist travel patterns for each group (2002)

The above information provides an overview of the travel patterns in Hong Kong’s tourist and non-tourist zones during the various periods. However, the durations of these periods had not yet been considered; therefore, the hourly characteristics of tourist trips for each group were calculated and tabulated as shown in Table 3.7. The overall

averages of hourly tourist trips during the morning peak period (average hourly tourist trips = 6,564; hourly distribution factor = 0.14) and the evening peak period (6,995; 0.15) were both higher than the averages for the off-peak periods. Furthermore, the hourly distribution factor for the noon off-peak period was similar (5,638; 0.12), meaning that the tourist trip distributions were fairly even, except during the midnight off-peak period. Again, in the first-tier tourist zones, the hourly distribution factors for all of the identified periods of travel by Group 1 tourists were generally higher than those for the other groups.

Table 3.7 Hourly trip characteristics for each cluster (2002) Average hourly tourist trips (1) (hourly distribution factor) (2) Group Zone no. Midnight Morning peak Noon off-peak Evening peak off-peak 767 3,581 2,678 3,286 1 2, 3, 8 (0.02) (0.08) (0.06) (0.07) 4, 5, 9, 10, 694 2,388 2,283 2,789 2 28, 35, 36 (0.02) (0.05) (0.05) (0.06) 234 596 677 920 3 Other (0.01) (0.01) (0.01) (0.02) 1,695 6,564 5,638 6,995 Total (0.04) (0.14) (0.12) (0.15) umber of tourist trips in the identified period Notes: (1) Average hourly tourist trips = umber of hours in the identified period Average hourly tourist trips (2) Hourly distribution factor = Expanded number of respondents

(ii) Trip-making patterns in 2011

Similarly, Table 3.8 and Figure 3.6 present the trip-making percentage profiles of Groups 1 and 2 (tourist zones) and Group 3 (non-tourist zone) during 2011. The overall percentages of trips made by the three groups were similar, with only slight variations during the midnight off-peak period (a variation of ±3.6%). However, clear differences could be observed in the percentage of trips made during the morning peak (a variation of ±4.8%), noon off-peak (a variation of ±14.6%) and evening peak (a variation of ±6.2%) periods among the three groups observed.

Table 3.8 Trips by period for each cluster of tourists (2011) Midnight off- Morning Noon Evening Group Zone no. peak peak off-peak peak 1 2, 3, 8 22.8% 12.3% 40.3% 24.6% 4, 5, 9, 10, 28, 23.6% 17.1% 34.2% 25.1% 2 35, 36 3 Other 20.0% 12.3% 48.8% 18.9% Total 20.8% 12.8% 45.4% 21.0%

100% 24.6% 25.1% 18.9% 80%

60% 40.3% 34.2% 48.8% 40% 17.1% 12.3% 12.3%

Percentage ofTrips 20% 22.8% 23.6% 20.0% 0% Group 1 Group 2 Group 3

Midnight off-peak Morning peak Noon off-peak Evening peak

Figure 3.6 Tourist travel patterns for each cluster (2011)

For Group 1, the figures concerning hourly tourist trip characteristics in 2011 were, understandably, similar to those from 2002 (see Table 3.9). However, the hourly trip characteristics of Groups 2 and 3 in 2011 were more comparable with those of Group 2 in 2002. It is interesting to see that the “non-tourist zone,” as defined in 2011, attracted a relatively substantial proportion of tourists.

Table 3.9 Hourly trip characteristics for each group (2011) Average hourly tourist trips (1) (hourly distribution factor) (2) Group Zone no. Midnight Morning Noon off-peak Evening peak off-peak peak 2,090 7,225 6,358 9,064 1 2, 3, 8 (0.02) (0.07) (0.06) (0.08) 4, 5, 9, 10, 1,496 5,976 4,057 6,422 2 28, 35, 36 (0.01) (0.06) (0.04) (0.06) 778 4,159 2,733 2,765 3 Other (0.01) (0.05) (0.03) (0.03) 4,365 17,361 13,148 18,250 Total (0.04) (0.16) (0.12) (0.17) umber of tourist trips in the identified period Notes: (1) Average hourly tourist trips = umber of hours in the identified period Average hourly tourist trips (2) Hourly distribution factor = Expanded number of respondents

3.3. Use of public transportation

3.3.1. Proportion of tourists using public transportation

Hong Kong has a world-class public transportation system, on which over 12 million passenger journeys are made each day by train (40.9%), bus (32.0%), minibus (15.0%), taxi (8.1%) and ferry (1.0%) (Transport Department, 2015). Although Mainland tourists commonly use public transport, it is generally agreed that their travel practices are different from those of local residents. To review the influence of tourists on the current public transportation system, we assessed the characteristics of public transportation used by tourists. Based on the TCS 2002 and 2011 data, the proportions of tourist trips made on various modes of transportation were calculated as shown in Table 3.10 and Figure 3.7.

Table 3.10 Modes of transportation used by tourists in 2002 and 2011 Mode Midnight Morning Noon Evening Total off-peak peak off-peak peak trips 2002 2011 2002 2011 2002 2011 2002 2011 2002 2011 12.4% 34.6% 12.5% 45.0% 20.5% 42.0% 15.8% 34.0% 16.9% 39.0% Railway (+22.2) (+32.5) (+21.5) (+18.1) (+22.0) 2.5% 3.7% 4.2% 6.0% 6.8% 7.9% 2.5% 8.2% 4.7% 6.8% Bus (+1.2) (+1.9) (+1.1) (+5.7) (+2.0) 22.8% 22.7% 7.9% 12.2% 19.3% 15.2% 22.2% 19.0% 19.0% 17.2% Taxi (-0.1) (+4.4) (-4.1) (-3.3) (-1.7) 48.5% 29.8% 67.7% 29.2% 44.9% 26.2% 48.7% 28.5% 49.5% 28.0% Coach (-18.7) (-38.5) (-18.7) (-20.3) (-21.5) 13.7% 9.2% 7.8% 7.5% 8.5% 8.7% 10.7% 10.4% 9.8% 9.0% Other (-4.5) (-0.3) (+0.2) (-0.3) (-0.8) Notes: (1) Values in parentheses represent the percentages of variation between 2002 and 2011. (2) Midnight off-peak: 2100–0900; morning peak: 0900–1100; noon off-peak: 1100–1800; evening peak: 1800–2100.

Unlike local residents, tourists used three major modes of transportation: railways, taxis and tourist coaches. In 2002, before the implementation of the IVS, tourist coaches were the most commonly used transport mode (49.5%), followed by taxis (19.0%) and railways (16.9%). This situation had changed by 2011, with railway usage increasing noticeably to 39.0% in 2011. In particular, during the morning peak periods, about 45.0% of Mainland tourists used railways. In contrast, tourists’ use of coaches declined by 21.5% over the decade. Although franchised buses in Hong Kong have an extensive service network that covers almost all of Hong Kong’s territory, less than 7% of tourists used franchised buses to make their trips in 2002 and 2011.

50% 50%

39% 40% 2002 2011 30% 28%

19% 20% 17% 17%

10% 9% 10% 7% 5%

0% Railway Franchised Bus Taxi Coach Other

Midnight off-peak Morning peak 49% 50% 80% 68% 35% 40% 60% 30% 45% 30% 23% 23% 40% 29% 20% 12% 14% 9% 20% 13% 12% 10% 3% 4% 4% 6% 8% 8% 8% 0% 0% Railway Bus Taxi Coach Other Railway Bus Taxi Coach Other

Noon off-peak Evening peak 49% 50% 42% 45% 50% 40% 40% 34% 26% 29% 30% 30% 22% 21% 19% 19% 20% 15% 20% 16% 11% 7% 8% 9% 9% 8% 10% 10% 10% 3% 0% 0% Railway Bus Taxi Coach Other Railway Bus Taxi Coach Other

Figure 3.7 Transportation mode choices during different periods

3.3.2. Public transportation distribution

Tourists visited more parts of Hong Kong in 2011; hence, the variations in tourist trip distributions across public transportation modes based on the groups identified in the previous section were calculated as presented in Table 3.11. Railway was the dominant transportation mode for Mainland tourists visiting the major and minor tourist zones (i.e., Groups 1 and 2, respectively) in 2011, replacing the traditional travel mode—the tourist coach. In addition to riding trains to key tourist zones along Victoria Harbour, tourists were more likely to travel to different parts of the New Territories using railways in 2011. For example, the Disneyland Resort Line and the Airport Express extension opened in 2005, and more tourists traveled to the Southwest New Territories as a result. Taking advantage of the IVS, more tourists travelled to

Hong Kong using land boundary points such as Lo Wu and Lok Ma Chu, where railways are considered a convenient and efficient mode of travel to the Hong Kong city center. Hence, a major dispersion of tourist trips to the Northeast New Territories was observed in 2011, especially to areas along the ERL. Due to a major reduction in business trips between 2002 and 2011 (from 39.9% to 26.5% of total trips), there was a remarkable reduction in the use of coaches by tourists in Group 1, from 49.4% in 2002 to 20.4% in 2011. Furthermore, the use of coaches also decreased by 19.0% and 21.5% for tourists in Group 2 and Group 3, respectively.

Table 3.11 Trips taken using various public transportation modes Mode Group 1 Group 2 Group 3 Total trips 2002 2011 2002 2011 2002 2011 2002 2011 16.1% 42.0% 18.5% 40.3% 15.0% 31.1% 16.9% 39.0% Railway (+25.9) (+21.8) (+16.1) (+22.0) 5.2% 4.7% 5.0% 11.4% 2.6% 4.7% 4.7% 6.8% Bus (-0.4) (+6.5) (+2.1) (+2.0) 21.0% 21.2% 17.5% 12.8% 17.1% 15.3% 19.0% 17.2% Taxi (+0.1) (-4.6) (-1.8) (-1.7) 49.4% 20.4% 51.1% 32.1% 44.8% 37.8% 49.5% 28.0% Coach (-29.1) (-19.0) (-7.0) (-21.5) 8.3% 3.5% 7.9% 3.3% 20.5 11.2% 9.8% 9.0% Other (-4.7) (-4.7) (-0.8) (-0.8) Note: Values in parentheses represent the percentages of variation between 2002 and 2011.

Hong Kong has five privately owned bus companies that provide franchised bus services across the city, operating some 5,800 buses on more than 700 routes. In 2011, there were over 18,138 taxis in Hong Kong, 15,250 of which were urban taxis, 2,838 New Territories taxis and 50 Lantau taxis. In general, there are no service boundaries on the transport networks of franchised buses or taxis. Since 2002, franchised bus companies have continued to introduce new super-bus models to enhance the experience of passengers, for example introducing fully air-conditioned buses. Despite these measures, the popularity of franchised buses among tourists was relatively low— less than 7% in 2002 and 2011—with a slight increase of 2.0% of the total. Likewise, between 2002 and 2011, the variations in the percentages of tourists using taxis were insignificant among the three groups (between -4.6% and 2.1%).

3.4. Comparison of public transportation use of local residents and Mainland tourists

Since the 2003 implementation of the IVS, local residents have raised concerns about the pressure the influx of Mainland tourists has placed on the transportation network. However, the actual effects of tourists on Hong Kong’s transportation system have not been sufficiently studied. Therefore, in this section, the relative travel demands of local residents and tourists on the three main public transportation modes used by local residents—taxis, trains and franchised bus systems—are examined.

3.4.1. Effects of tourist trips on the taxi system

For tourists, taxis provide point-to-point service, which is a convenient and efficient mode of transportation. Hence, in 2002, more than 27% of tourists reported using taxis to travel around Hong Kong. Although there was a 5.2% drop in taxi use between 2002 and 2011, some 22.4% of tourists still used taxis, which made taxis the second most popular mode of tourist transportation in 2011. As illustrated in Table 3.12, the proportions of tourists using taxis to travel around Hong Kong were much higher than the proportions of tourists using railways (less than 2% of the total of all travelers) or franchised buses (less than 1% of the total of all travelers). Overall, the percentage of tourists using taxis was about 14% for both 2002 and 2011. Indeed, as shown in Figure 3.8, the proportion of tourists using taxis was lower than the proportion of local residents, even during the peak hour periods.

Table 3.12 Local resident and tourist taxi trips by period Number of trips (percentage of trips) Time period TCS 2002 TCS 2011 Locals Tourists Total Locals Tourists Total 0000–0100 557 (0.32) 188 (0.1) 745 6,345 (1.8) 822 (0.2) 7,167 0100–0200 1,533 (0.9) 331 (0.2) 1,864 4,895 (1.4) 950 (0.3) 5,845 0200–0300 1,024 (0.6) 181 (0.1) 1,205 4,408 (1.2) 579 (0.2) 4,987 0300–0400 1,438 (0.8) 0 (0.0) 1,438 1,131 (0.3) 227 (<0.1) 1,358 0400–0500 998 (0.6) 0 (0.0) 998 983 (0.3) 62 (<0.1) 1,045 0500–0600 2,283 (1.3) 0 (0.0) 2,283 2,206 (0.6) 0 (0.00) 2,206 0600–0700 4,421 (2.5) 58 (<0.1) 4,479 5,747 (1.6) 0 (0.00) 5,747 0700–0800 16,787 (9.6) 188 (0.1) 16,975 24,543 (6.8) 495 (<0.1) 25,038 0800–0900 18,938 (10.8) 724 (0.4) 19,662 23,848 (6.6) 2,174 (0.6) 26,022 0900–1000 12,269 (7.0) 1,284 (0.7) 13,553 19,660 (5.4) 3,184 (0.9) 22,844 1000–1100 10,021 (5.7) 1,817 (1.0) 11,838 16,213 (4.5) 3,272 (0.9) 19,485 1100–1200 6,604 (3.8) 1,402 (0.8) 8,006 11,826 (3.3) 2,942 (0.8) 14,768 1200–1300 10,568 (6.0) 1,069 (0.6) 11,637 21,840 (6.0) 2,784 (0.8) 24,624 1300–1400 6,404 (3.7) 852 (0.5) 7,256 14,522 (4.0) 1,425 (0.4) 15,947 1400–1500 4,566 (2.6) 1,519 (0.9) 6,085 9,321 (2.6) 2,694 (0.7) 12,015 1500–1600 6,394 (3.7) 1,315 (0.8) 7,709 14,779 (4.1) 2,433 (0.7) 17,212 1600–1700 4,833 (2.8) 1,544 (0.9) 6,377 12,600 (3.5) 2,574 (<0.1) 15,174 1700–1800 5,635 (3.2) 1,229 (0.7) 6,864 13,793 (3.8) 4,025 (1.1) 17,818 1800–1900 8,960 (5.1) 1,719 (1.0) 10,679 21,728 (6.0) 4,283 (1.2) 26,011 1900–2000 5,639 (3.2) 2,818 (1.6) 8,457 21,232 (5.9) 3,389 (0.9) 24,621 2000–2100 4,233 (2.4) 2,121 (1.2) 6,354 14,991 (4.1) 4,416 (1.2) 19,407 2100–2200 4,286 (2.4) 2,499 (1.4) 6,785 15,908 (4.4) 3,898 (1.1) 19,806 2200–2300 4,003 (2.3) 1,525 (0.9) 5,528 13,404 (3.7) 3,414 (0.9) 16,818 2300–0000 7,024 (4.0) 1,564 (0.9) 8,588 14,341 (4.0) 2,297 (0.6) 16,638 Total 149,417 (85.2) 25,947 (14.8) 175,365 310,264 (85.6) 52,338 (14.4) 362,603 Note: The values in parentheses indicate the percentage of trips made by local residents or tourists, as compared with the total numbers of trips.

The demand for taxis from local residents was highest in the morning, between 0700 and 0900. At this time of day, about 21.4% of trips by local residents were made by taxi in 2002, but this percentage fell to 13.4% by 2011. However, as tourists typically liked to make their trips later in the day and wished to avoid the morning peak period, tourists’ demand for taxis was less than 1% of all demand during the peak morning hours. Therefore, tourist demand had a negligible effect on the availability of taxis to meet local demand. However, as tourists were more likely to travel by taxi at night, the taxi demands of local residents and tourists were more comparable at night, especially between 2000 and 2300.

Midnight off-peak Morning peak Noon off-peak Evening peak

100.00%

10.00%

1.00%

0.10%

0.01% Percentage ofTrips (log)

0.00%

1-2 0-1 2-3 3-4 4-5 5-6 6-7 7-8 8-9

9-10

10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 23-24 Hourly Period of the Day

Local Trips (TCS 2002) Tourist Trips (TCS 2002)

Figure 3.8 Patterns of taxi use

To better visualize the variations in the use of the three main public transportation modes over the 2002–2011 decade, the corresponding spatial hourly distributions during the four identified peak periods (midnight off-peak, morning peak, noon off-peak and evening peak) are presented in Figures 3.9a and 3.9b. The darkness of a zone represents its popularity or attractiveness (which was calculated as the proportion of trips to a specific zone during a specific time period over the total number of trips in a day). In addition, the trip-making percentages of Mainland tourists and local residents that are greater than or equal to 1% are further illustrated in a bar chart for the corresponding zone area.

Figure 3.9a Hourly distribution of taxi use (2002)

Figure 3.9b Hourly distribution of taxi use (2011)

Overall, Mainland tourists were more likely to travel by taxi at night, as demonstrated in Table 3.12 and Figure 3.8. However, the level of popularity, i.e. the demand for taxis, was higher during the morning peak periods in Zones 2, 3 and 18 in 2002, as illustrated in Figure 3.9a. Fortunately, the shared taxi usage proportions between Mainland tourists and local residents were relatively low (4% in Zone 3, and less than 1% in Zones 2 and 18). However, a remarkable percentage of corresponding taxi demand was observed during the evening peak period in Zones 9 and 10—40% and 26%, respectively—which possibly caused problems for local residents. The situation worsened in 2011 such that the proportion of taxi usage between tourists and local residents generally increased for the four identified periods in the Hong Kong territories, particularly in the major tourist zone (i.e. Zones 2, 3 and 8). During the morning peak periods in Zone 8, about 77% of taxi users were Mainland tourists. Relevant authorities should be advised of this.

3.4.2. Effects of tourist trips on the railway system

The 2011 surveys indicated that trains were the most popular mode of public transportation for tourists, replacing tourist coaches from 2002. In 2011, some 39.0% of Hong Kong tourists used the railways to travel around (see Table 3.11). As these railways are also the most important mode of public transportation for local residents, the local community is considerably concerned about the burden that tourist travel imposes on the railways.

Table 3.13 Local resident and tourist railway trips by period Number of trips (percentage of trips) Time period TCS 2002 TCS 2011 Locals Tourists Total Locals Tourists Total 0000–0100 934 (0.5) 0 (0.0) 934 13,630 (0.3) 730 (<0.1) 14,360 0100–0200 52 (<0.1) 40 (<0.1) 92 3,302 (0.1) 81 (<0.1) 3,383 0200–0300 0300–0400 No service 0400–0500 0500–0600 9,270 (0.5) 0 (0.0) 9,270 16,138 (0.3) 26 (<0.1) 16,164 0600–0700 77,936 (4.3) 0 (0.0) 77,936 161,802 (2.9) 32 (<0.1) 161,834 0700–0800 313,852 (17.4) 26 (<0.1) 313,878 696,522 (12.5) 365 (<0.1) 696,887 0800–0900 403,371 (22.4) 617 (<0.1) 403,988 843,286 (15.2) 2,340 (<0.1) 845,626 0900–1000 122,314 (6.8) 1,610 (<0.1) 123,924 313,376 (5.6) 6,405 (0.1) 319,781 1000–1100 69,290 (3.8) 2,218 (0.1) 71,508 199,573 (3.6) 8,046 (0.1) 207,619 1100–1200 47,769 (2.7) 1,101 (<0.1) 48,870 133,544 (2.4) 4,820 (<0.1) 138,364 1200–1300 50,515 (2.8) 1,601 (<0.1) 52,116 129,761 (2.3) 5,016 (<0.1) 134,777 1300–1400 42,790 (2.4) 1,194 (<0.1) 43,984 122,871 (2.2) 4,851 (<0.1) 127,722 1400–1500 44,013 (2.4) 1,509 (<0.1) 45,522 123,918 (2.2) 5,103 (<0.1) 129,021 1500–1600 65,804 (3.7) 1,772 (0.1) 67,576 197,779 (3.6) 5,480 (0.1) 203,259 1600–1700 57,669 (3.2) 976 (<0.1) 58,645 282,256 (5.1) 6,061 (0.1) 288,317 1700–1800 110,061 (6.1) 2,233 (0.1) 112,294 397,632 (7.2) 5,374 (0.1) 403,006 1800–1900 166,299 (9.2) 1,913 (0.1) 168,212 768,246 (13.8) 7,219 (0.1) 775,465 1900–2000 78,281 (4.3) 1,879 (0.1) 80,160 414,143 (7.5) 5,714 (0.1) 419,857

2000–2100 47,840 (2.7) 1,154 (<0.1) 48,994 233,286 (4.2) 7,031 (0.1) 240,317 2100–2200 30,056 (1.7) 1,647 (<0.1) 31,703 201,537 (3.6) 7,311 (0.1) 208,848 2200–2300 24,887 (1.4) 1,032 (<0.1) 25,919 130,856 (2.4) 5,423 (0.1) 136,279 2300–0000 18,273 (1.0) 1,240 (<0.1) 19,513 84,409 (1.5) 2,231 (<0.1) 86,640 Total 1,781,276 (98.7) 23,763 (1.3) 1,805,209 5,467,869 (98.4) 89,660 (1.6) 5,557,529 Note: The values in parentheses indicate the percentages of trips made by local residents or tourists, as compared with the total number of trips.

As shown in Table 3.13, tourists’ railway trips make up less than 2% of all railway trips (1.3% in 2002 and 1.6% in 2011). Accordingly, the trip profiles of local residents and tourists are plotted in Figure 3.10. Obviously, the largest demands on the railways by local residents occur during the morning peak hour (22.4% of the total railway trips in 2002, and 15.2% in 2011) and evening peak hour (9.2% in 2002, and 13.8% in 2011). However, the hourly demands of tourists on the railways during these peak periods are negligible, comprising only 0.1% of the total daily trips.

Midnight off-peak Morning peak Noon off-peak Evening peak

100.00%

10.00%

1.00%

0.10%

0.01%

Percentage ofTrips (log) 0.00%

0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9

9-10

10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 23-24 Hourly Period of the Day

Local Trips (TCS 2002) Tourist Trips (TCS 2002)

Figure 3.10 Patterns of railway use

The corresponding spatial hourly distributions during the four identified peak periods of railway usage in 2002 and 2011 are presented in Figures 3.11a and 3.11b, respectively. Unlike the hourly distribution of taxi use, the proportion of railway use by Mainland tourists was remarkably lower than for local residents. In 2002, excluding the Airport Express in Zone 35, the highest railway sharing proportion was 6% in Zone 12 during the evening peak period, which was not a critical area for local residents. Other areas, such as Zones 2, 3, 4 and 18, had more popular railways, but the influences of Mainland tourists in these areas were generally minimal. Apparently, the situation in 2002 was satisfactory, and Hong Kong’s transportation system was good enough to handle tourists’ travel demand.

Figure 3.11a Hourly distribution of railway use (2002)

Figure 3.11b Hourly distribution of railway use (2011) 38

In 2011, it was observed that Zone 30 (i.e. Sai Kung and Tsueng Kwan O) was relatively popular for railway trips during the morning peak period, probably due to the MTR Tseung Kwan O Extension commissioned in August 2002. However, the levels of popularity of the major and minor tourist zones were reduced, implying that people were less likely to travel to these areas during the morning. Besides the changes in the hourly distribution patterns, there was a general increase in the proportion of railway use by Mainland tourists in different parts of Hong Kong. For example, during the noon off- peak periods, the proportion of railway sharing reached 67% and 71% in Zones 5 and 36, respectively. Although we estimated percentage shares of Mainland tourists of 28% in the morning peak in Zone 8 and 17% in the evening peak in Zone 1, the popularity levels were relatively low and therefore the situations were controllable in the critical zones (i.e. the darker zones). Furthermore, upon the opening of a new east–west railway corridor, the Shatin to Central Link (SCL—Tai Wai to Hung Hom Section) in 2021, it is expected that about 23% of southbound passengers on the ERL to Kowloon will be changed to the new corridor of the SCL, enhancing the railway capacity in the minor tourist zones (Zones 8, 9 and 10).

To further demonstrate the influence of Mainland tourists on the railway system, statistics from the Annual Traffic Census 2015 (ATC 2015) were used to calculate the daily variation in traffic flow on the Hong Kong road network (see Table 3.14). Although this information may not perfectly reflect the actual daily variations in railway patronage, it provides a reference that helps us to understand the general daily travel demand in Hong Kong.

Table 3.14 ATC 2015 daily variations in use of Hong Kong Territory roadways Weekdays Weekends (Monday to Friday) (Saturday & Sunday) Hong Kong Island Urban 1 3.85% 9.64% Hong Kong Island Urban 2 (Major Road Network) 2.29% 6.64% Hong Kong Island Urban 2 (Minor Road Network) 5.71% 14.39% Hong Kong Island Remote and Recreational 3.77% 6.80% Kowloon Urban 1 3.13% 10.37% Kowloon Urban 2 1.67% 6.60% Kowloon Urban 3 6.64% 17.59% New Territories 1 3.97% 13.73% New Territories 2 3.06% 9.17% New Territories 3 2.45% 8.59% Overall 3.65% 10.35%

Note: ∑| | Percentage of daily variation of each region =

According to the ATC (2015), the Hong Kong road network is divided into 10 regions. The group scaling factor of each region2 (extracted from Appendix D of ATC 2015) was used to estimate the average weekday and weekend daily variations in 2015. For weekdays, a variation in road usage of 1.67%–6.64% was observed, with an average of 3.65%. This road usage was even higher during the weekends, with an average of 10.35%. As the background variations in daily usage were already higher than the total tourist demand, i.e., 1.3% in 2002 and 1.6% in 2011, we expect that the influence of tourist travel on the public transportation system is minimal. However, particular concern should be paid to critical urban areas with relatively high trip-marking proportions between Mainland tourists and local residents, such as Zone 3 (9% in the morning peak), Zone 8 (28% in the morning peak), Zone 9 (8% in the morning peak) and Zone 10 (6% in the morning peak).

3.4.3. The effects of tourist trips on the franchised bus system

As with the other modes of transportation, the daily patterns of franchised bus trips by local residents and tourists are tabulated in Table 3.15 and Figure 3.12. Unlike the local residents, only 7% of the surveyed tourists reported using franchised buses. It is therefore reasonable to assume that the possible burden of tourists on this transportation mode is insignificant. Indeed, as shown in Table 3.15, tourists constituted only 0.3% of the franchised bus users in 2002 and in 2011, which is much lower than the demand they placed on the taxi and railway systems.

Table 3.15 Local resident and tourist franchised bus trips by period Number of trips (percentage of trips) Time period TCS 2002 TCS 2011 Locals Tourists Total Locals Tourists Total 0000–0100 1,874 (<0.1) 30 (<0.1) 1,904 17,415 (0.4) 98 (<0.1) 17,513 0100–0200 2,338 (<0.1) 0 (0.0) 2,338 14,411 (0.3) 26 (<0.1) 14,437 0200–0300 1,459 (<0.1) 0 (0.0) 1,459 2,278 (<0.1) 0 (0.0) 2,278 0300–0400 1,959 (<0.1) 0 (0.0) 1,959 3,499 (<0.1) 0 (0.0) 3,499 0400–0500 3,649 (0.1) 0 (0.0) 3,649 4,220 (<0.1) 0 (0.0) 4,220 0500–0600 39,156 (1.5) 0 (0.0) 39,156 48,781 (1.0) 0 (0.0) 48,781 0600–0700 185,688 (7.0) 0 (0.0) 185,688 194,203 (4.1) 62 (<0.1) 194,265 0700–0800 551,995 (20.7) 21 (<0.1) 552,016 694,893 (14.7) 151 (<0.1) 695,044 0800–0900 436,387 (16.4) 95 (<0.1) 436,482 592,981 (12.5) 189 (<0.1) 593,170 0900–1000 154,553 (5.8) 250 (<0.1) 154,803 258,689 (5.5) 765 (<0.1) 259,454 1000–1100 103,392 (3.9) 479 (<0.1) 103,871 176,755 (3.7) 1,292 (<0.1) 178,047

2 The group scaling factor is the reciprocal of the mean of the ratios of a 24-hour count recorded for any day of the week and month of the year to the annual average daily traffic at the same station for all of the core stations in the same group.

1100–1200 78,769 (3.0) 266 (<0.1) 79,035 127,640 (2.7) 716 (<0.1) 128,356 1200–1300 84,923 (3.2) 395 (<0.1) 85,318 112,539 (2.4) 763 (<0.1) 113,302 1300–1400 65,233 (2.4) 300 (<0.1) 65,533 108,105 (2.3) 622 (<0.1) 108,727 1400–1500 59,238 (2.2) 468 (<0.1) 59,706 124,513 (2.6) 1,064 (<0.1) 125,577 1500–1600 105,041 (3.9) 702 (<0.1) 105,743 210,898 (4.5) 907 (<0.1) 211,805 1600–1700 97,261 (3.6) 728 (<0.1) 97,989 251,316 (5.3) 1,599 (<0.1) 252,915 1700–1800 155,414 (5.8) 604 (<0.1) 156,018 362,443 (7.7) 1,830 (<0.1) 364,273 1800–1900 223,531 (8.4) 909 (<0.1) 224,440 606,885 (12.8) 1,680 (<0.1) 608,565 1900–2000 116,715 (4.4) 462 (<0.1) 117,177 317,177 (6.7) 1,580 (<0.1) 318,757 2000–2100 70,664 (2.7) 350 (<0.1) 71,014 178,260 (3.8) 1,019 (<0.1) 179,279 2100–2200 49,809 (1.9) 314 (<0.1) 50,123 144,716 (3.1) 1,037 (<0.1) 145,753 2200–2300 43,042 (1.6) 297 (<0.1) 43,339 105,109 (2.2) 639 (<0.1) 105,748 2300–0000 29,616 (1.1) 41 (<0.1) 29,657 66,842 (1.4) 144 (<0.1) 66,986 Total 2,661,707 (99.7) 6,712 (0.3) 2,668,417 4,724,568 (99.7) 16,185 (0.3) 4,740,751 Note: The values in parentheses indicate the percentage of trips made by local residents or tourists, as compared with the total numbers of trips.

Midnight off-peak Morning peak Noon off-peak Evening peak

100.00%

10.00%

1.00%

0.10%

0.01% Percentage ofTrips (log)

0.00%

0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9

9-10

17-18 18-19 10-11 11-12 12-13 13-14 14-15 15-16 16-17 19-20 20-21 21-22 22-23 23-24 Hourly Period of the Day

Local Trips (TCS 2002) Tourist Trips (TCS 2002) Local Trips (TCS 2011) Tourist Trips (TCS 2011) Figure 3.12 Effects of tourist trips on franchised bus services

The corresponding spatial hourly distributions of franchised buses in 2002 and 2011 are presented in Figures 3.13a and 3.13b, respectively. Due to the low usage of franchised buses by Mainland tourists, their influence on this transportation mode was insignificant.

Figure 3.13a Hourly distribution of franchised bus use (2002) 42

Figure 3.13b Hourly distribution of franchised bus use (2011) 43

3.4.4. Handling capacity of the public transportation system

As considerable numbers of Mainland tourists carry heavy suitcases packed with purchases after bulk buying, additional surveys were carried out to examine the luggage carrying patterns of local residents and Mainland tourists at two major tourist zones, including Causeway Bay and Tsim Sha Tsui. Passengers queuing at taxi stands and bus stops were interviewed during the survey periods. Railway passengers were randomly chosen at one-minute intervals at the MTR exit, to undertake interviews. In this survey, a total of 6,892 respondents were interviewed, constituting 5,160 local residents (74.9%), 1,185 Mainland tourists (17.2%) and 547 foreign visitors (7.9%). The numbers of luggage items carried by the respondents were recorded and are tabulated in Table 3.16. It was generally observed that almost 50% of the Mainland tourists carried luggage while using public transportation, while 27.8% of the interviewed foreign visitors carried luggage. Only 6.9% of local residents carried luggage on public transportation. Across different public transportation modes, 56.4% of the Mainland tourists were found to bring luggage on the railways, followed by 51.8% on franchised buses and 26.0% on taxis.

Table 3.16 Summary of the luggage survey Frequency (percentage) Local residents Mainland tourists Foreign visitors Without With Without With Without With Mode luggage luggage luggage luggage luggage luggage 632 30 154 54 130 33 Taxi (95.5%) (4.5%) (74.0%) (26.0%) (79.8%) (20.2%) 2,784 241 339 439 217 106 Railway (92.0%) (8.0%) (43.6%) (56.4%) (67.2%) (32.8%) Franchised 1,390 83 96 103 48 13 bus (94.4%) (5.6%) (48.2%) (51.8%) (78.7%) (21.3%) 4,806 354 589 596 395 152 Total (93.1%) (6.9%) (49.7%) (50.3%) (72.2%) (27.8%)

In this overview of the characteristics of tourist travel, the overall travel demands of tourists on the three major public transportation modes, particularly the railways (less than 2%) and franchised buses (less than 1%), are relatively low compared with the demands from local residents. Furthermore, as tourists usually make their trips later in the day to avoid the morning commuting peak for local residents, it is reasonable to conclude that the travel demands from Mainland tourists are generally insignificant for the current public transportation system. However, local residents in some critical areas, including Zones, 2, 3 and 8 (the major tourist zones) and Zones 9 and 10 (the minor tourist zones in Kowloon) might suffer from the influx of Mainland tourists, particularly during the morning peak hour periods. In addition, the high percentage of Mainland tourists carrying luggage would reduce the handling capacity of the transportation system, particularly the railway and franchised bus system. Targeted policy measures

may be needed to tackle these problems and hence to minimize conflict between local residents and Mainland tourists in the future.

3.5. Statistical models on Mainland tourists’ public transportation choices

The 2002 and 2011 TCS databases provide comprehensive information on the travel characteristics of tourists over the previous decade. For example, this set of surveys identifies the morning and evening peak periods, the proportion of tourists using the public transport systems, and the possible implications of tourists’ demands on those systems. To reveal the variation in travel patterns due to the 2003 implementation of IVS, statistical prediction models were developed to evaluate the factors that influence tourists’ choices of public transport modes, including taxis, railways and franchised buses.

3.5.1. Model framework

Based on the characteristics of the natural clusters in the TCS 2011 findings, predictive models were established to identify the factors contributing to Mainland tourists’ choices regarding modes of public transportation. Unlike the general review of the tourists’ travel characteristics, the predictive models focused on the travel patterns of the Mainland tourists only. Again, the computer software package STATA 14.1 was used to formulate the multinomial logistic regression models.

Table 3.17 Summary statistics for respondents’ travel characteristics Frequency (percentage)/mean (S.D.) Total TCS 2002 TCS 2011 Factor [sample size = [sample size = [sample size = 2,384] 333] 2,051] Mode - Taxi 677 (28.4%) 137 (41.1%) 540 (26.4%) - Railway 1,458 (61.1%) 158 (47.4%) 1,299 (63.3%) - Franchised bus 249 (10.5%) 38 (11.5%) 212 (10.3%) Travel period - 0900–1100 357 (15.0%) 30 (9.0%) 327 (16.0%) - 1100–1800 983 (41.2%) 168 (50.5%) 815 (39.7%) - 1800–2100 540 (22.7%) 70 (21.0%) 470 (22.9%) - 2100–0900 504 (21.2%) 65 (19.5%) 439 (21.4%) Origin - Group 1 1,110 (46.6%) 140 (42.0%) 970 (47.3%) - Group 2 849 (35.6%) 146 (43.8%) 703 (34.3%) - Group 3 425 (17.8%) 47 (14.1%) 378(18.4%) Destination - Group 1 1,162 (48.7%) 140 (42.0%) 1,022 (49.8%) - Group 2 838 (35.2%) 147 (44.1%) 691 (33.7%) - Group 3 384 (16.1%) 46 (13.8%) 338 (16.5%)

The socio-demographic characteristics of the respondents are summarized in Table 3.1. Table 3.17 presents the local modes of travel used by the Chinese respondents. In the multinomial regression model, the public transportation mode was the dependent

variable. In 2002, the proportions of travelers using taxis and railways were more or less equal, with taxis constituting 41.1% and railways constituting 47.4% of public transport use. In 2011, however, about 63.3% of the respondents used railways as their major mode of transportation. Franchised buses ranked third among the public transportation modes used by the Mainland tourists; as such, buses were used for only about 10% of all trips.

In terms of travel periods, 41.2% of the Mainland tourists made their trips during the noon off-peak period, i.e., from 1100 to 1800. The proportions of their trips made during the evening peak period (22.7%) and the midnight off-peak period (21.2%) were similar. Over 80% of the Mainland tourists started their trips from the main tourist zones (46.67% from Group 1 and 35.6% from Group 2). The distribution of group members at their destinations was similar to their distribution at their points of origin: 48.7% were from Group 1 and 35.2% were from Group 2. The length of each individual trip was also included as one of the travel characteristics in the predictive model.

3.5.2. Multinomial (logistic) regression model

In this analysis, STATA 14.1 statistical software was used to formulate the multinomial (logistic) regression models. These models predicted the interaction between the probability of making a trip using a given mode of public transportation and the aforementioned explanatory variables. The nominal dependent variable was the public transportation mode, with its three categories of taxis, railways and franchised buses. The taxi mode was selected as the reference for comparison with the other transportation modes. The multiple linear regression functions for railway and franchised bus use were therefore defined as follows:

1 Pm  , (3.1) 1 exp(U m ) where Pm is the probability that a tourist will decide to use a given mode of public m transportation m and U is the deterministic utility. captures the factors influencing the travel decisions of individual Mainland tourists, which are calculated as follows:

m m m Ux ii  , (3.2) where  m is the model constant associated with a mode of public transportation m, which equals zero for taxis as the model control.  m is the corresponding model i coefficient of the independent variable x of a specified transportation mode m. The i independent variable includes the travel characteristics (i.e., travel period, origin and

destination for the overall model) along with the tourists’ individual characteristics (i.e., gender, age group and trip purpose).

According to Equation (3.1), a higher utility implies a higher probability that a tourist will choose a certain mode of public transportation. If a variable-associated coefficient is positive, it has a positive effect on the probability of making a trip. This probability increases with the value of this variable. Conversely, if a variable has a negative coefficient, it adversely influences the probability of making a trip.

Goodness of fit

In developing the multinomial logistic regression models, two individual models (the TCS 2002 and TCS 2011 models) were developed. However, a combined model was also developed to reveal the overall travel characteristics of the Mainland tourists. To check the goodness of fit of the models, the likelihood ratio (LR) test was conducted in this analysis. The LR test provided evidence in support of one model over a competing model.

The LR test statistic is defined as follows:

( ) ( ) , (3.3)

where ( ) is the log likelihood for a combined model, and ( ) is the sum of the log likelihoods of the two individual models developed for TCS 2002 and TCS 2011. The null hypothesis that there was no intervention in 2002 or 2011 was rejected, as the test statistic exceeded the threshold value specified for the distribution at the chosen level of significance. The degree of freedom was calculated as the difference between the number of variables in the combined models and the sum of the number of variables in the individual models.

3.5.3. Model results

(i) Individual models vs. combined model

Table 3.18 shows the log likelihood values of the sum of the individual models and the combined model, which were used to calculate the log likelihood ratios. Given that the degree of freedom was 14, the critical value at the 1% level of significance was significantly lower than each of the log likelihood ratios. Therefore, the null hypothesis that there was no intervention in 2002 or 2011 was rejected. Hence, the individual models were likely to vary from the combined model, and individual models were provided for 2002 and 2011 accordingly.

Table 3.18 Likelihood ratio test results Measures Results TCS 2002 model TCS 2011 model Combined model Log likelihood - Individual -287.11 -1,685.73 -2,016.73 - Sum --- -1,972.84 --- Degree of freedom 14 statistic 87.78 critical value (1) 29.14 Conclusion of hypothesis test Reject Note: (1) critical value, with a degree of freedom of 14 and a level of significance of 0.01.

As it was determined that individual models should be provided to evaluate the travel characteristics of Mainland tourists in 2002 and 2011, the model coefficients and their associated t-statistics for the individual logistic regression models were determined as presented in Table 3.19. The likelihood ratio test statistics of 69.69 and 218.68 were obtained for the TCS 2002 and TCS 2011, respectively, which demonstrated that the individual models fit well at a 1% significance level.

(ii) TCS 2002 vs. TCS 2011

To simplify this pattern of findings, age, gender and trip purpose should have had no significant contribution to choice of public transportation in 2002. In fact, only the trip characteristics of the respondents (such as travel period, origin and destination) made a contribution to the 2002 model formulation. For example, Mainland tourists preferred to use taxis over railways and franchised buses at night (travel period/railway: 2100–0900: -0.741; 1100–1800: control; travel period/franchised bus: 1800–2100: -1.346; 2100–0900: -1.194; 1100–1800: control). Mainland tourists traveling to/from the major tourist zones were more likely to use taxis than railways (origin/railway: Group 2: 0.624; Group 1: control) (destination/railway: Group 2: 0.723; Group 3: 0.852; Group 1: control). For the TCS 2011 model, age group, trip purpose, travel period, origin and destination all contributed significantly to the transportation mode choices made by Mainland tourists. All of these factors were significant at the 5% significant level.

(iii) Taxis vs. railways (TCS 2011)

Younger tourists were less likely to travel by taxi than train if other variables were the same (age group: 36–45: -0.753; 46–55: -1.098; over 55: -0.501; 18–25: control). Not surprisingly, trip purpose also affected transport mode choice; hence, we found that Mainland tourists preferred to use taxis for business trips more than for sightseeing/shopping trips (trip purpose: work/business: -0.661; shopping/leisure: control).

Table 3.19 Parameter estimates of the multinomial regression model for TCS 2002 and TCS 2011 TCS 2002 TCS 2011 Mode Railway Franchised buses Railway Franchised buses (control = taxi) Coefficient (t-stat.) Coefficient (t-stat.) Coefficient (t-stat.) Coefficient (t-stat.) Constant -0.969 (-1.81) -0.696 (-1.02) 1.477 (8.33)** -0.729 (-2.67)** Gender - Male -0.009 (-0.03) -0.720 (-1.68) -0.024 (-0.22) -0.077 (-0.44) - Female (Control) (Control) (Control) (Control) Age group - 18–25 (Control) (Control) (Control) (Control) - 26–35 0.785 (1.64) -0.820 (-1.17) -0.261 (-1.57) -0.088 (-0.36) - 36–45 0.804 (1.54) 0.923 (1.39) -0.753 (-4.27)** -0.693 (-2.53)* - 46–55 0.881 (1.69) 0.580 (0.84) -1.098 (-5.30)** -0.870 (-2.59)** - Over 55 -1.003 (-1.04) -14.811 (-0.01) -0.501 (-2.05)* -0.814 (-1.93) Trip purpose - Sightseeing/shopping (Control) (Control) (Control) (Control) - Work/business -0.225 (-0.79) -0.441 (-1.00) -0.661 (-4.76)** -0.687 (-3.02)** - Visiting relatives/friends 0.496 (0.81) -14.970 (-0.01) -0.278 (-0.89) 0.245 (0.55) - Other -0.307 (-0.48) -15.371 (-0.01) -1.041 (-1.60) -13.908 (-0.02) Travel period - 0900–1100 0.442 (0.92) 0.286 (0.44) 0.176 (1.03) -0.211 (-0.79) - 1100–1800 (Control) (Control) (Control) (Control) - 1800–2100 -0.349 (-1.11) -1.346 (-2.24)* -0.407 (-2.87)** -0.055 (-0.27) - 2100–0900 -0.741 (-2.19)* -1.194 (-1.96)* -0.695 (-4.95)** -1.036 (-4.13)** Origin - Group 1 (Control) (Control) (Control) (Control) - Group 2 0.624 (2.25)* 0.706 (1.64) 0.511 (4.02)** 1.290 (6.67)** - Group 3 -0.070 (-0.17) 0.596 (0.90) 0.254 (0.18) 0.426 (1.75) Destination - Group 1 (Control) (Control) (Control) (Control) - Group 2 0.723 (2.57)** -0.167 (-0.38) 0.419 (3.35)** 0.305 (1.59) - Group 3 0.852 (2.03)* -0.463 (-0.61) -0.134 (-0.91) -0.322 (-1.32) Number of observations 333 2,051 Unrestricted log likelihood -421.96 -1,795.07 Restricted log likelihood -387.11 -1,685.73 Likelihood ratio test statistic 69.69** 218.68** ** 1% significance level; * 5% significance level.

As discussed earlier, the railway was the dominant transport mode for Mainland tourists in Hong Kong. However, negative coefficients for travel period were observed at the 5% significant level, implying that Mainland tourists were more likely to use taxis than railways during the night, particularly during the midnight off-peak period when railway services might be unavailable (travel period: 1800–2100: -0.407; 2100–0900: 0.695; 1100–1800: control). It was also found that Mainland tourists traveling to/from the major tourist zones were more likely to use taxis, which are a convenience-oriented transport mode (origin: Group 2: 0.511; Group 1: control) (destination: Group 2: 0.419; Group 1: control).

(iv) Franchised buses vs. taxis (TCS 2011)

The factors contributing to the choice between franchised buses and taxis were similar to those for railways vs. taxis. The analyses found that younger tourists were more likely to travel using franchised buses than taxis (age group: 36–45: -0.693; 46– 55: -0.870; 18–25: control). These findings were consistent with previous tourism research on tourists’ travel behavior by gender and age (Beerli & Martin; 2004; Le- Klaehn et al., 2014). Furthermore, it is interesting that older adults (over 55) had no significant difference between their use of taxis and franchised buses. For work/business travel, taxis were preferred to franchised buses (trip purpose: work/business: -0.687; sightseeing/shopping: control). Understandably, as franchised bus services are limited during midnight periods, taxis were the preferred transport mode for Mainland tourists during this time (travel period: 2100–0900: -1.036; 1100–1800: control). All of these relationships were significant at the 5% level.

3.6. Concluding remarks

After the implementation of the IVS in 2003, the number of tourists from the Mainland increased dramatically. The tourist interview survey data extracted from TCS 2002 (before the IVS) and TCS 2011 (after the IVS) were used to evaluate and reveal the travel characteristics of tourists visiting Hong Kong in 38 identified traffic zones. The findings from these surveys provided a spatio-temporal review of trip patterns and an overview of the distribution of tourist trips in China. The observations on the socio- demographic characteristics and travel patterns of Mainland tourists are summarized as follows:

- The gender distribution and the main purpose for visiting Hong Kong (i.e. shopping/leisure travel) were similar over the past two decades. However, the average age of Mainland tourists was younger in 2011 than in 2002. In addition, a noticeable change of trip purpose from work/business to sightseeing was observed. - The morning peak period for tourists was identified as 0900–1100, constituting about 15% of the total daily tourist trips. - The evening peak period was identified as 1800–2100. About 25% of the total daily tourist trips occurred during this period.

- Zones 2, 3 and 8 were identified as the major tourist zones, which attracted about 45% of the tourists in both 2002 and 2011. - In 2011, tourist visits became more dispersed around Hong Kong. More tourists travelled to zones in the New Territories, especially to the northwest and southwest parts.

Unlike for local residents, the top three modes of public transportation used by tourists were taxis, railways and tourist coaches. A remarkable trend in railway use was observed, as the preference for this mode of transportation rose from 16.9% in 2002 to 39.0% in 2011 (see Table 3.10). Discussions on the influences of Mainland tourists on the major public transportation systems used by local residents, including taxis, railways and franchised buses, were provided. The key findings in this section are as follows:

- The overall travel demand from tourists using these three modes of transportation remained relatively low compared with the demand from local residents. - Among the three modes of travel, the proportion of tourists to residents using taxis to travel around Hong Kong was much higher than the proportion of tourists to residents using railways or franchised buses. - Mainland tourists were responsible for a remarkable percentage of taxi usager40% and 26% during the evening peak period in Zones 9 and 10, respectively—which possibly caused travel problems for residents in 2002. - In 2011, the railway became the dominant transportation mode for Mainland tourists visiting the major tourist zones and non-tourist zones. - Over the decades, there was a general increase in the railway use proportion by Mainland tourists in different parts of Hong Kong. - Due to the low usage of franchised buses by Mainland tourists, their influence on this transportation mode was insignificant. - Particular attention should be paid to the high proportion of Mainland tourists carrying luggage, which reduces the handling capacity of the public transportation systems.

To further reveal the travel characteristics of Mainland tourists in Hong Kong over the past two decades, multinomial regression models were developed based on the TCS 2002 and 2011 databases. We generally found that the travel characteristics of Mainland tourists have changed over recent decades. The individual characteristics of the respondents had a greater effect on their choice of public transportation mode in 2002. In 2011, however, it was their trip characteristics, such as purpose, travel period and the origin and destination of their trips that had a greater influence on their choice of transportation mode.

4. FACE-TO-FACE INTERVIEWS WITH MAINLAND TOURISTS WITH INDIVIDUAL VISIT SCHEME PERMITS

To reveal the recent travel characteristics of Mainland tourists in Hong Kong, a large-scale series of face-to-face interviews was conducted between July 14, 2016 and October 2, 2016. The findings from these interviews are presented in this chapter. The demographic characteristics of the tourists and their modes of travel, levels of satisfaction with public transport services, and stated preferences for tourist sites and shopping items, based on transportation factors, were studied. Based on the findings, suggestions to improve public transport services for Mainland tourists are proposed.

4.1. Face-to-Face Interviews

The questionnaire comprised four parts: (i) basic information about the respondent, (ii) the transport characteristics and patterns of their trip, (iii) their intentions for the trip, and (iv) their views on public transport and tourist mobility. Part i collected information on gender, age, country, personal income, and visa type. Only respondents from Mainland China using individual visit scheme (IVS) visas were interviewed in this study. Part ii of the questionnaire collected information on their trip purpose, traveling partners, tourist spots visited, and the spatial and temporal trip characteristics of a given day. Part iii used stated preference (SP) games to ascertain the respondents’ likelihood of making a trip under different specified conditions. In Part iv, the respondents’ views on current public transport and tourism transport policy measures were collected (see Appendix A).

4.1.1. Data collection

Face-to-face interviews were conducted between July 14, 2016 and October 2, 2016. Overall, 1,119 self-reported IVS tourists from the Mainland were interviewed at different locations in Hong Kong, including the Peak, Causeway Bay, Tsim Sha Tsui, Mong Kok, Tsuen Wan, Sha Tin, Yuen Long, and Tung Chung, on weekdays and weekends. The interview schedule is given in Table 4.1.

The respondents came from almost all of the provinces of Mainland China. The province distributions are presented in Figure 4.1. Most were from Guangdong Province (the 21 IVS cities), making up 55.9% of the total, with others mainly from southern coastal provinces of Mainland China, with 5.9% from Shanghai, and 5.4% from Fujian Province. Other IVS tourists came from Beijing (4.42%), Hunan Province (3.84%), Zhejiang Province (3.34%), Hubei Province (2.75%), Sichuan Province (2.59%), and Guangxi Province (2.50%).

Table 4.1 Face-to-face interview schedule Zone Frequency (percentage) Area Location no. Weekdays Weekends Total Hong Kong 2 The Peak 123 (10.3%) 90 (7.5%) 213 (17.8%) Island 3 Causeway Bay 80 (6.7%) 79 (6.6%) 159 (13.3%) Kowloon 8 Tsim Sha Tsui 93 (7.7%) 152 (12.7%) 245 (20.4%) 10 Mong Kok 34 (2.8%) 74 (6.2%) 108 (9.0%) Tsuen Wan/ 18 Tsuen Wan --- 130 (10.8%) 130 (10.8%) Kwai Tsing New 22 Yuen Long --- 48 (4.0%) 48 (4.0%) Territories 28 Sha Tin 47 (3.9%) 80 (6.7%) 127 (10.6%) 31 Tung Chung 84 (7.0%) 85 (7.1%) 169 (14.1%) Total 466 (38.3%) 750 (61.7%) 1,199 (100.0%)

Figure 4.1 Distribution of the interviewed tourists by province (no. of respondents = 1,119)

Of the 1,119 respondents, 190 were not from IVS cities; therefore, 1,009 questionnaires completed by respondents from cities that permit IVS tourists were extracted for evaluation. The province distribution of the specific IVS tourists is

presented in Figure 4.2. Of those interviewed, 66.4% were from Guangdong Province, followed by Shanghai (7.0%), Beijing (5.3%), Fujian (Fuzhou, Quanzhou, and Xiamen; 3.8% overall), Zhejiang (Hangzhou, Ningbo, and Taizhou; 2.9% overall), Hunan (Changsha, 2.9%), Hubei (Wuhan, 2.7%), and Sichuan (Chengdu, 2.4%). These 32 cities from 8 provinces, from the total 49 IVS cities from 22 provinces, contributed 93.4% of the interviewed tourists.

Figure 4.2 Distribution of the interviewed tourists by province (no. of respondents = 1,009)

4.2. Trip Characteristics

Table 4.2 summarizes the respondents’ socio-demographic characteristics. The gender distribution was fairly even, with 1.9% more female than male IVS tourists. Relatively young adults (age groups of 18–25 and 26–35) were the most numerous, and constituted 68.1% of the total respondents. Middle-aged adults (age groups of 36–45 and 46–55) constituted 26.6%, while relatively older visitors (56 and over) only constituted 4.0% of the total. The monthly income of almost 60% of the respondents was below RMB10,000 (around HK$11,000–12,000). The income of 20.9% was RMB10,000–19,999.

Table 4.2 Socio-demographic characteristics (no. of respondents = 1,009) Personal particulars Frequency (percentage) Gender - Male 488 (48.4%) - Female 508 (50.3%) - Declined to respond 13 (1.3%) Age group - 18–25 262 (26.0%) - 26–35 425 (42.1%) - 36–45 212 (21.0%) - 46–55 56 (5.6%) - Above 55 40 (4.0%) - Declined to respond 13 (1.3%) Monthly Income (RMB) - Below 5,000 250 (24.8%) - 5,000–9,999 333 (33.0%) - 10,000–19,999 211 (20.9%) - 20,000–49,999 70 (6.9%) - 50,000 or above 16 (1.6%) - Declined to respond 129 (12.8%)

Making reference to Table 3.3 and Figure 3.1 in Section 3.2.1, the temporal trip- making patterns of the Mainland respondents were plotted as shown in Figure 4.3. Interestingly, the morning peak period shifted to 1000–1200. In addition, more Mainland tourists wished to make trips during this period, with a range of 8.8% (between 1100 and 1200) to 9.7% (between 1000 and 1100). Similar to in 2002 and 2011, the evening peak started from 1800, with an estimated 7.9% of trips made at this time. However, this value was comparable with the noon off-peak period identified between 1200 and 1800.

Mid-night Off-peak Morning Peak Noon Off-peak Evening Peak 10% 9.7% 9% 8.8% 7.8% 7.8% 7.9%

8% 7.6%

7% 6.4% 6.4% 7.1% 6.9% 6.0% 6% 5.2% 6.2% 5% 4% 3.0%

3% Percentage ofTrips 1.4% 2% 1.1% 1% 0.2% 0.4% 0.1%

0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9

9-10

10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 23-24 Hourly Period of the Day

Figure 4.3 Trip-making patterns of tourists in 2016

4.2.1. First-time visitors vs. repeat visitors

To reveal the trip characteristics of the Mainland IVS tourists in Hong Kong, the overall analysis compared first-time visitors (343 respondents, 34.0% of the total), repeat visitors (666 respondents, 66.0% of the total), and overall first-time visitors (first- time visitors and repeat tourists who reported first-time visit information). Table 4.3 summarizes the trip characteristics of the interviewees. Of the repeat visitors, 31.4% reported their main trip purpose as shopping, compared to 17.8% of first-time visitors. Sightseeing was the main purpose of first-time visitors (71.1%), while repeat visitors also came to Hong Kong to shop, or were in transit, visiting for training, or for other reasons. Day-trip visitors made up 30.8% of the tourists, mainly from Guangdong Province, and 35.4% of these were repeat visitors, while 21.9% were visiting for the first time. Most of the interviewees visited Hong Kong with either family or friends, with 7.1% of the repeat visitors choosing to travel alone.

Table 4.3 Trip characteristics Frequency (percentage) Trip characteristics First-time Repeat visitors Overall visitors Trip purpose - Sightseeing/shopping 305 (88.9%) 547 (82.2%) 852 (84.5%) (a) Sightseeing 244 (71.1%) 338 (50.8%) 582 (57.7%) (b) Shopping 61 (17.8%) 209 (31.4%) 270 (26.8%)

- Work/business 9 (2.6%) 24 (3.6%) 33 (3.3%) - Visiting relatives/friends 21 (6.1%) 46 (6.9%) 67 (6.6%) - Other 6 (1.7%) 25 (3.7%) 30 (3.0%) - Declined to respond 2 (0.6%) 25 (3.8%) 27 (2.7%) Number of nights in Hong Kong - 0 (day trip visitor) 75 (21.9%) 236 (35.4%) 311 (30.8%) - 1 48 (14.0%) 102 (15.3%) 150 (14.9%) - 2 40 (11.7%) 89 (13.4%) 129 (12.8%) - 3 70 (20.4%) 76 (11.4%) 146 (14.5%) - 4 41 (12.0%) 45 (6.8%) 86 (8.5%) - 5 or more 67 (19.4%) 99 (14.9%) 166 (16.5%) - Declined to respond 2 (0.6%) 19 (2.9%) 21 (2.1%) Traveling partner - Family 168 (49.0%) 365 (54.8%) 533 (52.8%) - Friends 125 (36.4%) 200 (30.0%) 325 (32.2%) - Colleagues 20 (5.8%) 34 (5.1%) 54 (5.4%) - Alone 26 (7.6%) 47 (7.1%) 73 (7.2%) - Other 3 (0.9%) 1 (0.2%) 4 (0.4%) - Declined to respond 1 (0.3%) 19 (2.9%) 20 (2.0%)

Table 4.4 The ten most popular tourism spots First-time visitors Repeat visitors Sightseeing spots Yes No Rank Yes No Rank Shopping malls (Zones 2, 3, & 8) 74.1% 25.9% 1 78.8% 21.2% 1 The Peak (Zone 2) 55.0% 45.0% 2 33.6% 66.4% 3 Avenue of Stars (Zone 8) 52.9% 47.1% 3 33.5% 66.5% 4 Street markets (Zones 9 & 10) 50.6% 49.4% 4 38.2% 61.8% 2 Golden Bauhinia Square (Zone 3) 30.9% 69.1% 5 20.4% 79.6% 6 Ocean Park Hong Kong (Zone 5) 31.8% 68.2% 6 17.1% 82.9% 7 Hong Kong Disneyland (Zone 36) 30.0% 70.0% 7 22.9% 77.1% 5 Ngong Ping and the Big Buddha (Zone 36) 20.9% 79.1% 8 14.8% 85.2% 8 Lan Kwai Fong (Zone 2) 15.6% 84.4% 9 10.9% 89.1% 9 Other 2.9% 97.1% 10 1.7% 98.3% 10

Table 4.4 summarizes the ten most popular tourism spots reported by the interviewed IVS tourists. Of the repeat visitors, 78.8% had visited the main shopping malls in the tourist zones, for example IFC, Times Square, and Grand Century Plaza. In addition to the main shopping malls in Central, Causeway Bay, and Tsim Sha Tsui, the top three most visited sightseeing spots were the Peak, The Avenue of Stars, and the street markets at Mong Kok, for both first-time and repeat visitors. This finding is similar to the information from the Hong Kong Tourism Board (2016), as discussed in Section 3.2 of this study. Mong Kok was preferred by the repeat tourists, and ranked second for sightseeing spots, compared to fourth for first-time visitors.

4.3. Use of Public Transport

4.3.1. Transport mode choices

The modes of transport that the interviewees used to travel around Hong Kong are presented in Table 4.5. Unsurprisingly, the railway was the main transport mode for most respondents (89.0%), followed by franchised buses (53.8%), taxis (24.6%), and ferries (18.4%). Tourist coaches are traditional modes of transport for tourists, but only 6.9% of the interviewees used these. The railway, taxis, and franchised buses were the three main public transportation modes used by the Mainland tourists, which is consistent with the 2002 and 2011 TCS databases, as discussed in Chapter 3. Nonetheless, franchised bus usage was much higher than expected, with 53.8% of the interviewed tourists having used franchised buses.

Table 4.5 Transport modes used by the tourist respondents Transport mode Yes No Railway 89.0% 11.0% Franchised bus 53.8% 46.2% Taxi 24.6% 75.4% Ferry 18.4% 81.6% Minibus 8.2% 91.8% Tourist coach 7.0% 93.0% Tram 6.3% 93.7% Other 2.6% 97.4%

4.3.2. Statistical model of the public transportation mode choices in 2016

To reveal the factors contributing to the Mainland tourists’ public transportation mode choices in 2016, the modeling approach discussed in Section 3.5 was adopted. Likewise, the travel information for the three modes of transportation was extracted from the 2016 interview database, resulting in 686 respondents and 1,335 trips being used for the model formulation. The personal and trip characteristics of the respondents are summarized in Tables 4.6 and 4.7, respectively.

Table 4.6 Summary statistics for personal characteristics (no. of respondents = 686) Factor Attribute Frequency (percentage) Gender - Male 335 (48.8%) - Female 351 (51.2%) Age group - 18–25 176 (25.7%) - 26–35 301 (43.9%) - 36–45 136 (19.8%) - 46–55 39 (5.7%) - Over 55 34 (5.0%) Trip purpose - Sightseeing/shopping 575 (83.8%) - Work/business 30 (4.4%) - Visiting relatives/friends 43 (6.3%) - Other 38 (5.5%)

Table 4.7 Summary statistics for travel characteristics (sample size = 1,335) Factor Frequency (percentage) Mode - Taxi 60 (4.5%) - Railway 1,046 (78.4%) - Franchised bus 229 (17.2%) Travel period - 0900–1100 123 (9.2%) - 1100–1800 663 (49.7%) - 1800–2100 352 (26.4%) - 2100–0900 197 (14.8%) Origin - Group 1 631 (47.3%) - Group 2 422 (30.7%) - Group 3 282 (22.0%) Destination - Group 1 597 (44.7%) - Group 2 308 (23.1%) - Group 3 430 (32.2%)

The socio-demographic characteristics of the Mainland tourists were generally similar to those of the 2002 and 2011 respondents in the TCS database. Most of the interviewed Mainland tourists were young adults between 26 and 35 years old (about 43.9% of the total). Sightseeing and shopping were the main trip purposes for over 80% of the interviewed tourists. However, the proportions of trips for work/business and to visit relatives/friends were much lower than in 2002 and 2011, representing 4.4% and

6.3% respectively. A higher proportion of railway usage was observed in 2016, representing 78.4% of the total. The interviewed IVS tourists appeared to be more likely to use franchised buses (17.2%) than taxis (4.5%), which was different from the 2002 and 2011 data.

As discussed in Section 3.5.3, individual models for 2002 TCS and 2011 TCS could be used to evaluate the travel characteristics of Mainland tourists. Hence, an alternative 2016 model was established to reveal the most recent travel characteristics of tourists using a multinomial logistic regression model. The model result is presented in Table 4.8. A likelihood ratio test statistic of 75.78 was obtained for the 2016 regression model, indicating that the model fit well at a 1% significance level.

Table 4.8 Parameter estimates of the multinomial regression model for 2016 Mode Railway Franchised bus (control = taxi) Coefficient (t-stat.) Coefficient (t-stat.) Constant 2.396 (6.64)** 0.663 (1.66) Gender - Male -0.145 (-0.52) -0.346 (-1.12) - Female (Control) (Control) Age group - 18–25 (Control) (Control) - 26–35 0.010 (0.03) 0.121 (0.31) - 36–45 -0.655 (-1.75) -0.271 (-0.65) - 46–55 0.054 (0.07) 0.224 (0.27) - Over 55 -1.001 (-1.62) -0.342 (-0.52) Trip purpose - Shopping/leisure (Control) (Control) - Work/business 0.2174 (0.35) 0.360 (0.53) - Visiting relatives/friends 13.689 (0.03) 13.607 (0.03) - Other -1.221 (-2.06)* -0.296 (-0.45) Travel period - 0900–1100 -0.166 (-0.35) -0.322 (-0.61) - 1100–1800 (Control) (Control) - 1800–2100 -0.077 (-0.22) 0.233 (0.61) - 2100–0900 -0.333 (-0.87) -0.640 (-1.47) Origin - Group 1 (Control) (Control) - Group 2 0.615 (1.88) 0.495 (1.39) - Group 3 0.837 (1.83) 1.060 (2.20)* Destination - Group 1 (Control) (Control) - Group 2 1.024 (2.77)** 0.634 (1.54) - Group 3 1.539 (3.35)** 1.893 (3.95)** Number of observations 1,335 Unrestricted log likelihood -845.04 Restricted log likelihood -802.59 Likelihood ration test statistic 84.89** ** 1% significance level; * 5% significance level.

As railways and franchised buses were the dominant transportation modes of the interviewed Mainland tourists in 2016, they were more likely to use either railways or buses to travel to minor tourist zones and non-tourist areas (destination/railway: Group 2: 1.024; Group 3: 1.539; Group 1: control; destination/franchised bus: Group 3:

1.893; Group 1: control). These tourists preferred to use taxis between the major tourist zones. Again, as railway service may be unavailable in some rural and remote areas of Hong Kong, for example Sai Kung, Mainland tourists from these minor tourist areas had a higher tendency to use franchised buses than taxis (origin/franchised bus: Group 3: 1.893; Group 1: control).

Model validation

Various measures have been proposed for verifying the predictive accuracy of models. Among these measures, the root mean squared error (RMSE), which is based on the residuals from the estimates, is commonly adopted.

∑( ̂ ) √ , (4.1)

where and ̂ are the predicted and observed probability of the ith sample, and n is the number of observations. In addition, the relative root mean squared error (rRMSE) of each model is calculated using Equation (4.2).

√ ∑( ̂ ) , (4.2) ̂̅

Table 4.9 summarizes the RMSE and the rRMSE of the developed prediction models. The 2002 and 2011 models produced low RMSE values of 0.156 and 0.155, respectively. The lowest rRMSE (%) was obtained for the 2016 model. All three models are good enough to represent their observations in the corresponding year.

Table 4.9 RMSE of the multinomial regression models 2002 model 2011 model 2016 model

(before the IVS) (after the IVS) (after the IVS) (i) Observation - RMSE 0.156 0.155 0.258 - rRMSE (%) 4.56% 4.53% 1.26% (ii) Validation (by 2016 data) - RMSE 0.983 0.427 N.A. - rRMSE (%) 4.78% 2.08% N.A.

Subsequently, the 2016 data was used to validate the accuracy and reliability of the 2002 and 2011 prediction models. Obviously, the 2002 traffic model could not represent the traffic characteristics of Mainland tourists after the implementation of the IVS, so an RMSE of 0.983 was obtained. However, for the 2011 prediction model, the RMSE and rRMSE (%) increased marginally, implying that the 2011 prediction model may still be a useful tool for predicting the public transportation mode choices of Mainland IVS tourists, and forecasting the possible influences on public transportation systems. However, considering that the contributory factors on mode choice might vary

significantly over time, frequent updates should be performed on the developed model, for example every 3–5 years, to enhance the reliability and accuracy of the model.

4.4. Intention to Make a Trip

To understand the travel characteristics and patterns of Mainland tourists in Hong Kong, it is essential to explore the reasons and contributory factors for their trips. Thus, the respondents’ intentions for their trips were studied in this section.

4.4.1. Stated preference experiment design

The SP method is widely used to study behavioral responses to situations under different combinations of attributes that are not revealed in the market (Hensher, 1994). In this study, respondents were exposed to two different conditions: Condition 1, using public transport to reach a given destination from an urban area, and Condition 2, using public transport to reach a remote shopping mall. Each respondent was required to participate in eight SP games, in which four games with different combinations of destination, travel time, and travel cost were designed for Condition 1, and four games with different combinations of product category, discount provided, travel time, and travel cost were designed for Condition 2. The attribute levels of these factors are presented in Table 4.10.

Table 4.10 Factors and attributes in stated preference survey design Factor Attribute Condition 1 Destination Shopping area, theme park, scenic spot, - Go/do not go restaurant Travel time (minutes) 15, 30, 60, 90 Travel cost (HK$) 10, 20, 30, 50 Condition 2 Category Daily necessities, jewelry, electrical - Go/do not go appliances, cosmetics, luxury fashion to the remote Discount 10%, 20%, 30% shopping mall Travel time (minutes) 30, 60, 90 Travel cost (HK$) 10, 30, 50

Fractional factorial design was used to reduce the experiment sizes and make effective use of resources. Sixteen combinations of the explanatory variables were generated for each condition. These combinations were then randomly segregated into four groups, each of which was assigned to one of four sets of questionnaire scripts.

4.4.2. Model formulation

The statistical software SPSS 24.0 was used for the statistical analysis in this study. For the SP survey, a binary logistic regression model was developed for each condition, to predict the interaction between the probability of making a trip and the aforementioned SP explanatory variables. The binary logistic regression model is used

when the outcome yi is binary; y = 1 refers to someone who would make the trip, and y

= 0 refers to someone who would not. The dependent variable is the probability, Pi . In this study, i represents each respondent, with i 1,2, ,1,009 in the face-to-face P interview survey. The logit, defined as LN i , is a linear function of these 1 Pi independent variables, and in developing the logistic regression equation, the LN of the odds represents a logit transformation, where the logit is a function of covariates, so:

Pi Yilog it ( P i )  LN 0   1 X 1, i   2 X 2, i    K X K , i (4.3) 1 Pi

where 0 is the model constant, and 1, K are the unknown parameters corresponding to the explanatory variables ( Xk , k 1, , K ; the set of independent variables). The probability function on the respondents’ propensity in making a trip by individual i is therefore given by:

EXP0  1 X 1,i   2 X 2, i    K X K , i Pi  (4.4) 1EXP0   1 X 1,i   2 X 2, i    K X K , i

P i EXPˆ  ˆ X   EXP  ˆ  EXP  ˆ X  (4.5)  00i i     i i  1 Pi which shows that when the value of an explanatory variable increases by one unit, all other variables are held constant (Washington et al., 2010).

The logits of the decision to make a trip are explored using the explanatory variables of location, travel time, trip cost, and other socio-demographic parameters. A higher logit implies a higher probability of the tourist making a trip to the designated location. If a variable associated coefficient is positive, it has a positive effect on the probability of making a trip. This probability increases with the value of this variable. Conversely, if a variable has a negative coefficient, it has a negative effect on the probability of making a trip.

The influence of an attribute on propensity is given by the odds ratio (OR), specifically as:

OR EXP()i (4.6)

with 95% confidence intervals of (  1.96s ) and (  1.96s ), and s as the i i i i i standard error of the coefficient β. An odds ratio greater than 1 indicates that the focal attribute leads to a higher propensity to make a trip and vice versa.

4.4.3. Intention to make a trip

Table 4.11 presents the model coefficients and the corresponding t-statistic tests of the explanatory variables in the binary logistic regression model for Condition 1. The prediction model was established to associate the behavioral response to making a trip to a given destination from an urban area.

Table 4.11 Parameter estimates of the logistic regression model for Condition 1 Coefficient t-statistic Odds ratio Trip characteristics Destination - Sightseeing spot 1.172 (10.85)** 3.228 - Theme park 1.303 (11.95)** 3.680 - Shopping 0.841 (7.44)** 2.318 - Restaurant (Control)

Travel time (minutes) -0.028 (-28.00)** 0.972 Travel cost (HK$) -0.018 (-6.00)** 0.982 Demographic characteristics Gender - Male 0.001 (0.01) 1.001 - Female (Control) Age group - 18–25 1.230 (5.59)** 3.421 - 26–35 1.457 (2.67)** 4.293 - 36–45 1.309 (8.39)** 3.701 - 46–55 1.336 (7.91)** 3.805 - Over 55 (Control)

Monthly income (HK$) - 9,999 and below -0.193 (-1.34) 0.825 - 10,000–19,999 -0.212 ((-1.37) 0.809 - 20,000 or above (Control) First visit to Hong Kong 0.113 (1.33)** 1.120 Length of stay -0.013 (-2.60)** 0.987 ** 1% significance level; * 5% significance level.

Mainland tourists were more likely to go sightseeing, to theme parks, and shopping (destination: sightseeing spot: 1.172; theme park: 1.303; shopping: 0.841; restaurant: control). The OR of making a trip to a theme park was the highest, at 3.68 times that of the control group, at the 1% significant level. The tourists’ willingness to go to theme parks was thus the highest, which may be due to the convenient public transport links between urban areas and theme parks, such as the railway to Hong Kong Disneyland. Negative coefficients for travel time (-0.028) and travel cost (-0.018) were observed at a 1% significant level, implying that the Mainland tourists were more likely to travel

around Hong Kong if travel time and cost were decreased, and trip duration was shortened. The OR of the decision to go decreased by 2.8% for every one minute increase in travel time, and by 1.8% for every HK$1 increase in travel cost. The demographic characteristics of the respondents were input as the confounding variables for model formulation. The age of the tourists contributed significantly to their intention to make a trip (age group: 18–25: 1.230; 26–35: 1.457; 36–45: 1.309; 46–55: 1.336; 56 or above: control). Those aged 26–35 had the highest tendency (OR = 4.293) to travel. The middle-aged tourists had higher motivation to explore Hong Kong, possibly because of their relatively high potential consuming capability, relatively high mobility, and good health. The model result also indicated that the tourists were less likely to travel around if they stayed in Hong Kong longer (-0.013), as the odds of deciding to travel decreased by 1.3% for each extra day they extended their stay in Hong Kong. The tendency for first-time Mainland visitors to make a trip (0.113) was 12% higher than for repeat visitors.

4.4.4. Intention to visit a remote shopping mall

The model coefficients and the corresponding t-statistic tests of the explanatory variables in the binary logistic regression model of the individuals’ behavioral responses to visiting a remote shopping mall are presented in Table 4.12. The model used different explanatory variables, including product category, travel time, trip cost, and shopping discount provided.

There is no evidence that product categories in the remote shopping mall affected the Mainland tourists’ decisions to make a trip, except that jewelry was observed at a 5% significant level, indicating that the tourists were 28.6% more likely to make a trip to shop for jewelry than for luxury clothes. The discount, travel time, and travel cost to the remote shopping mall did, however, significantly contribute to their decision to make the trip. A positive coefficient of 0.017 resulted for a discount, as the visitors had a 1.7% higher tendency to make a trip for every 1% increase in the discount offered. Negative coefficients for travel time (-0.019) and trip cost (-0.009) were observed at a 1% significant level, implying that the tourists were more likely to go shopping if the travel time and trip cost decreased. The odds of them deciding to go decreased by 1.8% for every one-minute increase in travel time, and decreased by 0.9% for every HK$1 increase in trip cost. In summary, lower trip costs, shorter travel times, and more discounts could stimulate consumer spending.

The findings of the demographic characteristics of Condition 2 were similar to those of Condition 1, except their time spent in Hong Kong did not contribute to their decision to make a trip to the remote shopping mall. However, the coefficient of Mainland tourists visiting Hong Kong for the first time was 0.246, at a 1% significant level, indicating that first-time tourists had a 27.9% higher tendency to make a remote trip than repeat tourists. The repeat tourists were more familiar with the shopping malls, and had relatively fixed shopping habits that did not include traveling to more remote

malls. Again, middle-aged tourists were more likely to make a remote trip (age group: 18–25: 0.597; 26–35: 0.797; 36–45: 0.885; 46–55: 0.730; 56 or above: control), as the odds for tourists aged 36–45 were the highest (OR = 2.422). This group of tourists thus had a higher tendency to travel to a remote shopping mall, and the odds of those aged 36–45 making a shopping trip were 1.422 times higher than for those aged 56 and above, at a 1% significant level.

Table 4.12 Parameter estimates of the logistic regression model for Condition 2 Coefficient t-statistic Odds ratio Trip characteristics Product category - Cosmetics 0.117 (1.079) 1.124 - Electric appliances 0.091 (0.105) 1.095 - Jewelry 0.252 (4.207)* 1.286 - Daily articles 0.028 (0.058) 1.028 - Luxury clothes (Control) Discount (% off) 0.017 (14.755)** 1.017 Travel time (minutes) -0.019 (169.473)** 0.982 Travel cost (HK$) -0.009 (15.707)** 0.991 Demographic characteristics Gender - Male -0.115 (2.450) 0.891 - Female (Control) Age group - 18–25 0.597 (7.722)** 1.817 - 26–35 0.797 (22.607)** 2.218 - 36–45 0.885 (31.595)** 2.422 - 46–55 0.730 (19.143)** 2.075 - Over 55 (Control) Monthly income (HK$) - 9,999 and below -0.006 (0.002) 0.994 - 10,000–19,999 -0.096 (0.639) 0.908 - 20,000 or above (Control) First visit to Hong Kong 0.246 (10.243)** 1.279 Length of stay -0.006 (1.727) 0.994 ** 1% significance level; * 5% significance level.

4.5. Concluding remarks

To identify the most recent travel characteristics of Mainland IVS tourists, supplementary face-to-face interviews were conducted at different locations in Hong Kong on weekdays and weekends. Their trip characteristics, use of public transportation and perceptions of making a trip were investigated. Descriptive summaries of the respondents’ demographic characteristics were provided. The multinomial regression model developed in Section 3 was updated using the 2016 survey information to identify the current characteristics, and two binary logistic regression models were developed to explore the respondents’ perceptions of making a trip under different specified conditions.

4.5.1. Recent travel characteristics and patterns of Mainland tourists

A total of 1,119 Mainland tourists were interviewed in this study. Among these respondents, 1,009 were from IVS cities, mainly from Guangdong Province (66.4%). The trip characteristics and travel patterns of the interviewed tourists are concluded as follows:

- The majority (68.1%) of the respondents were young adults (under 36 years old), and more than half (57.8%) reported a monthly income below RMB10,000. - Of the respondents, 66% were repeat tourists and nearly one-third were day-trip visitors. The purposes of their visits were different from those of the first-time tourists, and they mainly visited to go shopping, rather than sightseeing. - The most visited locations in Hong Kong reported by the respondents were shopping malls, the Peak, the Tower, and the Avenue of Stars, for both first-time and repeat visitors. - More Mainland tourists would like to make their trips during the morning peak periods between 1000 and 1200, constituting 8.8% (between 1100 and 1200) and 9.7% (between 1000 and 1100) respectively. Of the respondents, 7.9% made their trips during the evening peak periods (between 1800 and 2100). - The railway was the main public transportation mode for all of the interviewees. Railways, taxis and franchised buses were the three main public transportation modes used by the Mainland tourists. - Unlike in 2002 and 2011, Mainland tourists decided on their mode of transportation based mainly on their origin and destination. Their personal characteristics did not significantly contribute to their choices in 2016.

Subsequently, to reveal the interviewed respondents’ perceptions of making trips, an SP experiment was designed to record the respondents’ behavioral responses under two different conditions: Condition 1, the use of public transport to reach a given destination from an urban area; and Condition 2, the use of public transport to visit a remote shopping mall. Separate binary logistic regression models were developed for each condition. The findings are listed as follows:

- Mainland tourists had greater intention to make a trip to visit theme parks, followed by sightseeing, then shopping. - Shorter travel times and costs could increase the travel intention of Mainland tourists. - Tourists aged 26–35 had the highest tendency to travel around, probably due to their relatively high potential consuming capability, relatively high mobility, and good health. - The longer the tourists stayed in Hong Kong, the lower their intention to make a trip became. - The tendency for first-time Mainland visitors to make a travel trip was higher than that of repeat visitors. - Discounts offered, travel time, and travel cost significantly contribute to tourists’ decisions to travel to a remote area.

5. TOURIST SATISFACTION WITH PUBLIC TRANSPORT SERVICES

The travel characteristics of Mainland tourists using IVS are discussed in previous chapters. Most tourists were happy to use public transportation, but their satisfaction in terms of service aspects such as accessibility, travel speed, and travel cost were not studied. In this chapter, separate assessments of tourists’ satisfaction with (1) the existing public transportation system, and (2) the level of service (LOS) of railways and franchised buses are conducted, to identify possible areas for improvement in each service aspect.

5.1. Level of Satisfaction

5.1.1. Important service aspects ranked by the respondents

In the section of the questionnaire relating to the service level of Hong Kong public transportation, all respondents were asked to rank the three most important public transportation service aspects from eight choices. A summary of the frequency of their choices is presented in Table 5.1. Accessibility (19.8%), safety and security (18.7%), and fare (18.4%) are the three most important service aspects of public transportation as ranked by the Mainland tourists. Over half (56.9%) of the tourists considered these the most important factors. Among these, accessibility was considered the most important service aspect by 32.0% of the respondents.

Table 5.1 Respondents’ most important service aspects (sample size = 1,009) Frequency (percentage) Service aspect First choice only Top 3 choices Accessibility 323 (32.0%) 599 (19.8%) Safety and security 205 (20.3%) 566 (18.7%) Fare 204 (20.2%) 556 (18.4%) Journey speed 110 (10.9%) 445 (14.7%) Level of crowding 95 (9.4%) 415 (13.7%) Vehicle condition 15 (1.5%) 165 (5.5%) Information provision 28 (2.8%) 146 (4.8%) Customer support 20 (2.0%) 111 (3.7%) Declined to respond 9 (0.9%) 24 (0.8%)

5.1.2. Tourists’ satisfaction with existing public transportation

To assess the tourists’ level of satisfaction for eight different service aspects, a 5- point scale was used, in which 5 indicated “very satisfied”, 4 indicated “satisfied”, 3 indicated “neutral”, 2 indicated “dissatisfied”, and 1 indicated “very dissatisfied.” Table 5.2 presents the respondents’ satisfaction levels with the public transportation services.

Table 5.2 Mean and standard deviation of scores for each service aspect Service aspect Min. Max. Mean S.D. Safety and security 1 5 4.03 0.595 Journey speed 1 5 3.93 0.611 Accessibility 2 5 3.91 0.623 Vehicle condition 1 5 3.91 0.656 Customer support 1 5 3.72 0.716 Information provision 1 5 3.68 0.720 Fare 1 5 3.45 0.804 Level of crowding 1 5 3.40 0.788 Overall performance 1 5 3.87 0.530 S.D. = standard deviation

The average score for overall service performance was 3.87 (higher than the average score for all aspects of 3), which indicates that the interviewed tourists were generally satisfied with the quality of the current public transportation service. Safety and security produced the highest scores (mean = 4.03), followed by journey speed (3.93), accessibility (3.91), vehicle condition (3.91), customer support (3.72), and information provision (3.68). Although public transport fares in Hong Kong are relatively low, the satisfaction level regarding fares was the second-lowest (3.45). Interestingly, the interviewees were least satisfied with the level of crowding, which had a mean score of 3.40. Accessibility did not receive the highest score, but it was the only service aspect with no response score lower than 2.

(i) Contribution of individual service aspects to overall service performance

In this section, the ordered probit model is adopted to calculate the coefficients associated with individual service aspects, along with the threshold values (cut-off points) between each pair of adjacent levels using the statistical software STATA 14.1. As the rating scale representing the degree of satisfaction has five levels, there are four threshold values separating the choices. In theory, the estimated coefficient associated with each service aspect should be non-negative, as individual service quality should have positive or no repercussions on overall service performance. Furthermore, as each of the variables has the same potential range, all of the coefficients are unit-less and can be directly compared.

Assuming that yi represents the reported overall satisfaction level of respondent i, * a latent (unobserved) variable yi is then introduced as: * kk yXii  , (5.1) k k where k is the index of the individual service aspect, X i is the score of the service aspect k as reported by respondent i and  k is the corresponding coefficient. is equal to jJ1,..., under the following conditions:

* 1 , if yi  1 ;  * yi  jy, if j1  i  j ; (5.2)  * Jy, if i  J 1 , where J is the number of satisfaction levels (in this case, J  5 ) and  j is the threshold value (cut-off point) to be estimated for each pair of adjacent levels, where

11... J  . From Equation (2), the probabilities of yi taking on each of the values of jJ1,..., are determined as: * Py(i  1)  1  yi  ; ** P(); yij j   yyi    j1  i  (5.3) * P( yi  J )  1  J 1  yi  , where P() yi  j is the probability that response variable yi of individual i will take a * specific level, j. 1 yi  is the cumulative standard normal distribution function of

* k 1  yi . Both  and  j are unknown parameters to be calibrated jointly based on the maximum likelihood estimation method.

(ii) Ordered probit model results

Table 5.3 summarizes the results of the ordered probit model. The results demonstrate that all of the individual service aspects contribute significantly to overall performance at a 1% significance level. Furthermore, all four threshold values are significant at the 1% level, and all of the estimated coefficients are positive.

Table 5.3 Coefficients and their t-statistics for the ordered probit model Explanatory variables Coefficients* t-statistics 1. Customer support 0.86 8.78 2. Vehicle condition 0.55 5.83 3. Fare 0.53 7.22 4. Accessibility 0.46 4.53 5. Journey speed 0.42 4.14 6. Safety and security 0.31 3.09 7. Level of crowding 0.31 4.15 8. Information provision 0.24 2.65

1 (cut-off point of levels 1 and 2) 7.65 9.96

2 (cut-off point of levels 2 and 3) 8.93 14.01

3 (cut-off point of levels 3 and 4) 12.24 17.54

4 (cut-off point of levels 4 and 5) 16.48 19.78 * All variables are significant at the 1% level.

Customer support (coefficient = 0.86), vehicle condition (0.55), fare (0.53), accessibility (0.46) and journey speed (0.42) are more likely to contribute to the overall evaluation of public transportation services. Unexpectedly, customer support has the highest contributing coefficient in the model, implying that most of the interviewed

Mainland tourists were more likely to be concerned about traveler care and support. However, safety and security (0.31), level of crowding (0.31) and information provision (0.24) contributed to the IVS tourists’ overall evaluation, but with smaller coefficients. The interviewees wanted to receive good customer service, including regular service provision and valuable feedback, instead of simply being provided with information.

5.1.3. Priorities for service quality improvement

To identify the priorities for action to improve the public transportation service quality, an importance-satisfaction analysis (IPA) was conducted to provide a quick visual representation of the service satisfaction scores (collected from the questionnaire) and the importance scores (calibrated by the ordered probit model).

Highest priority Appropriate for improving performance Customer support service quality zones

Vehicle condition Fares Accessibility Journey speed

Level of Safety and crowding security Information provision Monitor service quality

Figure 5.1 Importance-satisfaction analysis and recommended priorities for service quality improvements

The importance-satisfaction matrix, as shown in Figure 5.1, consists of nine cells with each axis divided into three sections. The two vertical lines (3.50 and 4.01) are determined based on the mean value of the service performance rating of 3.75, plus or minus one standard deviation of 0.25. Similarly, the two horizontal lines (0.34 and 0.58) are calculated by adding or subtracting one standard deviation of 0.12 from the average importance rating of 0.46. The service aspects in the three cells in the top-left corner (highlighted in red) are those most in need of improvement according to the respondents, due to their relatively high importance combined with low satisfaction. These variables, including travel fares and customer support, require immediate attention and are top priorities for enhancement. In contrast, information provision, safety and security are located in the three cells in the bottom-right corner (highlighted in green), indicating their relatively low importance and significant customer satisfaction. Subsequent

recommendations are to keep monitoring their quality and make no improvements at this stage. The remaining four service aspects, including vehicle condition, accessibility, journey speed and level of crowding, fall into the middle cells, reflecting average scores for both satisfaction and importance. These service aspects should be maintained to prevent deterioration, and can be improved if resources allow.

5.2. Level of service

In recent years, it was claimed that the pressure placed on the public transportation system due to the influx of Mainland tourists decreased the LOS of the public transportation system in Hong Kong. Hence, to understand the effects on the public transportation system’s LOS because of the Mainland tourists, we interviewed tourists about the congestion levels and waiting times they experienced, and adopted their responses to indicate the LOS of the public transportation system. Among the three major public transportation modes discussed in Section 3 of this report, there is no congestion with taxis; therefore, only tourists’ experiences on railways and/or franchised buses were recorded.

To quantify the congestion level, four congestion levels (see Table 5.4) were adopted to represent the utilization of the railways or franchised buses. Then, in the face-to-face interviews, the respondents were asked to indicate the congestion levels of railways or franchised buses using reference photos (see Figures 5.2 and 5.3).

Congestion level 2 Congestionlevel

Congestion level 1 Congestionlevel

Congestion level 4 Congestionlevel Congestion level 3 Congestionlevel Figure 5.2 Congestion levels on railways

Congestion level 2 Congestionlevel Congestion level 1 Congestionlevel

Congestion level 4 Congestionlevel Congestion level 3 Congestionlevel Figure 5.3 Congestion levels on franchised buses

Table 5.4 Congestion levels on public transport Congestion Density Description level Empty/almost 1 - Most seats are available, and no passengers have to stand. empty 2 Low - A few seats are available, and no passengers have to stand. 3 Medium - All seats are occupied, and a few passengers have to stand. 4 High - All seats are occupied, with standing passengers packed in.

5.2.1. Railway service schedule

Train frequency is a key criterion for improving the handling capacity of the railway services. Service information for the main railway lines on weekdays is presented in Table 5.5 (MTR, 2016). The average train frequency during the morning and evening peak hours ranges from 1.9 to 4.0 minutes, and is between 3 and 8 minutes during non-peak hours.

Table 5.5 Average train frequency on weekdays Area Morning Evening Non-peak Railway line HKI KLN NT peak (min) peak (min) (min) Island Line  1.9 2.1 3.6-6.0 Tsuen Wan Line    2.0 2.0 3.1-5.5 Kwun Tong Line  2.1 2.3 2.8-5.0 Tseung Kwan O Line    2.2 2.2 4.0-5.8 East Rail Line   2.6 4.0 3.5-8.0 West Rail Line   2.9 3.5 5.0- 7.0 Ma On Shan Line  3.0 4.0 5.0-8.0 Tung Chung Line    4.0 4.0 6.0-12.0 Note: The commuting peaks of local residents are adopted in this LOS assessment, i.e. morning peak: 0800–0900 and evening peak: 1800–1900.

5.2.2. Handling capacity of railway services

The reported congestion levels for 1,101 railway trips were extracted from the interviews. To obtain an overview of the service levels in different parts of Hong Kong, the railway trips are categorized into three geographical groups, as shown in Table 5.6.

Table 5.6 Railway trip distribution Group Area Traffic Zones (1) Number of trips (percentage) 1 Hong Kong Island 1–4 303 (27.5%) 2 Kowloon 8–16 499 (45.3%) 3 New Territories 18–31, 35 & 36 299 (27.2%) Note: Zones 5, 17, 32–34, and 37–40 were not covered by the railway system during the interview survey period.

The average congestion level and waiting time for the interviewed respondents were used to represent the handling capacity of the railway services. Table 5.7 and Figures 5.3 to 5.5 present the spatial-temporal findings on the railway service capacities accordingly. The congestion level is found to vary between 1 and 4, with average congestion levels of 2.11, 2.72, and 2.27 for Hong Kong Island, Kowloon, and the New Territories respectively. The congestion level of Kowloon was the highest, at over 2.8 on average during the morning peak period between 0800 and 0900, meaning that most of the train seats were occupied and many passengers had to stand. A low level of congestion was observed for Hong Kong Island, indicating that the railway service was capable of accommodating travel demands, so that passengers could usually find seats, although some had to stand when they used the railway during peak hours. Although the average congestion level was lower than 3, the highest congestion level (4) was reported by some respondents throughout the three zones. Some passengers therefore had to endure crowded cars during the morning and evening peak periods, particularly at critical interchange stations such as Admiralty and Mong Kok.

The average waiting times were 3.12, 2.83, and 3.70 minutes for Hong Kong Island, Kowloon, and the New Territories, respectively, which was generally consistent with the train service frequency during the non-peak periods shown in Table 5.5. However, the average waiting times experienced by the interviewees during the morning and evening peak periods were slightly higher than the service frequency of the railway system, implying that tourists could need to wait for two or three trains at the platform during those periods.

Table 5.7 Railway handling capacity experienced by the interviewees (sample size = 1,101) Congestion level Waiting time (minutes) Group Time period Mean S.D. Mean S.D. Morning peak 1.88 0.83 2.88 1.55 Hong Noon off-peak 2.08 0.82 3.15 3.54 Kong Evening peak 2.78 1.20 1.94 0.88 Island Midnight off-peak 2.25 0.87 3.50 2.54 Average 2.11 3.12

Morning peak 2.83 1.03 2.83 2.72 Noon off-peak 2.76 0.92 2.78 2.44 Kowloon Evening peak 2.64 1.01 3.50 2.59 Midnight off-peak 2.19 0.92 2.79 4.59 Average 2.72 2.83

Morning peak 2.33 0.82 5.33 3.27 Noon off-peak 2.35 0.94 3.78 3.21 New Evening peak 1.60 0.70 3.80 2.57 Territories Midnight off-peak 1.77 0.76 3.81 1.55 Average 2.27 3.70 Notes: (1) S.D. = standard deviation (2) The commuting peaks of local residents are adopted in this LOS assessment, i.e. morning peak: 0800–0900 and evening peak: 1800–1900.

(i) Hong Kong Island

As shown in Figure 5.3, the interviewed respondents observed congestion peaks between 0900 and 1000 in the morning, and between 1800 and 1900 in the evening, for Hong Kong Island. The congestion level reached 2.3 in the morning peak, indicating that most seats were occupied. The average waiting time recorded during the morning congestion peak was 2.88 minutes. Considering that the train frequency for the Island Line during the morning peak is about 1.9 minutes per train, the interviewed tourists waited for one to two trains at the station platform. However, the congestion level experienced by the interviewees increased to an average of 2.8, and even to 3.0 during the evening peak and midnight off-peak times between 2100 and 2200, indicating that all seats were occupied in a medium to high density.

The slightly higher peak occurring from 2100 to 2300 could reasonably be caused by the night-time shopping activities of the tourists at the main tourist zones with luxury malls and department stores, i.e., Zones 2 and 3. As the train frequency drops from 2.1 minutes per train during the evening peak to 3.6–6 minutes per train during the midnight off-peak period, the train service in Hong Kong Island appears to be efficiently utilized, such that passengers are required to wait for at most one more train at station platforms.

4.0 3.5

3.0 3.0 2.8 2.5 2.5 2.3 2.0 1.5

CongestionLevel 1.0 0.5 0.0 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 Hourly Periods of the Day

6.0

5.0

4.0

3.0

2.0

1.0 Average Average Waiting Time (min) 0.0 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 Hourly Periods of the Day

Figure 5.4 Observed handling capacity of the railway on Hong Kong Island

(ii) Kowloon

Compared with Hong Kong Island, the handling capacity in Kowloon reaches its congestion peak earlier, from 0800 to 0900 in the morning (i.e. the same commuting peak as local residents), with a relatively higher congestion level of 2.8, as shown in Figure 5.4. Obviously the tourists generally experienced a lower LOS in Kowloon, as a relatively high congestion level, ranging from 2.8 to 3.0, was maintained during the noon off-peak period until 1200–1700.

In the Kowloon region, the average wait time was 2.83 minutes for the morning peak period, 3.50 minutes for the evening peak period, and around 2.8 minutes for the noon and midnight off-peak periods. Considering that the train frequency during peak periods should be around 2 minutes per train, tourists were likely to wait for one to two trains before boarding.

4.0 3.5 2.8 2.9 2.9 3.0 3.0 2.8 2.5 2.0 1.5

CongestionLevel 1.0 0.5 0.0 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 Hourly Periods of the Day

6.0

5.0

4.0

3.0

2.0

1.0 Average Average Waiting Time (min)

0.0 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 Hourly Periods of the Day

Figure 5.5 Observed handling capacity of the railway in Kowloon

(iii) New Territories

As shown in Table 5.5, the average train frequency is the lowest at railway lines operating in the New Territories. The wait time in the New Territories was thus the highest, with an average of 3.70 minutes (ranging from 3.78 to 5.33 minutes), as shown in Figure 5.5. Compared with the urban areas, such as Hong Kong Island and Kowloon, the New Territories exhibit a more dispersed morning peak period between 0900 and 1100, at a congestion level of 2.5. Another peak period was also found to occur in the afternoon from 1400 to 1700, with a congestion level between 2.4 and 2.7. Tourists from Guangdong Province contributed considerably to the travel demands on the East Rail Line, which connects the land boundary control points at Lo Wu and Lok Ma Chau.

4.0 3.5

3.0 2.7 2.6 2.5 2.5 2.5 2.0 1.5

CongestionLevel 1.0 0.5 0.0 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 Hourly Periods of the Day

6.0

5.0

4.0

3.0

2.0

1.0 Average Average Waiting Time (min) 0.0 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 Hourly Periods of the Day

Figure 5.6 Observed handling capacity of the railway in the New Territories

5.2.3. Handling capacity of franchised bus services

A total of 247 franchised bus trips experienced by the interviewed tourists were recorded to assess the LOS of franchised buses. The distributions of the bus trips were also categorized into three geographical groups, as shown in Table 5.8.

Table 5.8 Franchised bus trip distributions Group Area Traffic zones Number of trips (percentage) 1 Hong Kong Island (HKI) 1–5 114 (46.2%) 2 Kowloon (KLN) 8–17 71 (28.7%) 3 New Territories (NT) 18–40 62 (25.1%)

Compared with the distributions of the railway trips shown in Table 5.6, nearly half of the franchised bus trips were made on Hong Kong Island, while 45.3% of the railway trips were made in Kowloon. At the time of the interviews, Zone 5 (Southern) of Hong Kong Island was not covered by the railway network, hence, the Mainland tourists could only use ground transportation to reach the tourist attractions in Zone 5, such as Ocean Park, Repulse Bay, and Stanley. Thus, about 15% of the franchised bus

trips in Hong Kong Island were made in Zone 5. However, tourists mainly traveled around Kowloon, including Zone 8 (Tsim Sha Tsui), Zone 9 (Jordan/Yau Ma Tei), and Zone 10 (Mong Kok/Prince Edward), which are highly accessible by railway services. Consequently, fewer respondents took franchised buses in Kowloon. The percentage of franchised bus trips in the New Territories was about 25.1%, which was similar to that for railway trips (27.2%).

Table 5.9 The handling capacity of franchised buses experienced by the interviewees (sample size = 247) Congestion level Waiting time (minutes) Group Time period Mean S.D. Mean S.D. Morning peak# 2.42 - 10.00 - Hong Noon off-peak 2.43 0.79 8.67 8.15 Kong Evening peak 2.00 1.00 8.67 2.31 Island Midnight off-peak 3.40 0.89 10.70 5.81 Average 2.42 8.78 Morning peak# 2.00 - 10.0 - Noon off-peak 2.33 0.78 7.01 5.54 Kowloon Evening peak# 1.00 - 1.25 - Midnight off-peak 2.00 0.53 8.31 6.42 Average 2.24 7.08 Morning peak# 3.00 - 15.00 - Noon off-peak 2.26 0.74 8.48 8.65 New Evening peak 2.33 0.82 10.83 3.76 Territories Midnight off-peak 2.00 - 10.00 - Average 2.28 8.84 Notes: (1) S.D. = standard deviation (2) The commuting peaks of local residents are adopted in this LOS assessment, i.e. morning peak: 0800–0900 and evening peak: 1800–1900. # Available samples during the identified period were less than or equal to 5 respondents.

To assess the handling capacity of franchised bus services, the same analytical approach was applied. The average congestion level and wait time experienced by the respondents are presented in Table 5.9. The sample size of the franchised bus trip data was limited, hence figures on the hourly service capacities of franchised buses are not provided. Furthermore, because most of the interviewees preferred to travel by railway and taxi during the morning peak periods (as discussed in the previous section), the valid franchised bus samples for the morning peak periods were constrained in this LOS assessment.

Generally, the congestion level varied between 2 and 3, with average congestion levels of 2.42, 2.24, and 2.28 for Hong Kong Island, Kowloon, and the New Territories, respectively. Most seats were therefore occupied and a few passengers had to stand. During the evening peak period (between 1800 and 2100), the congestion level experienced by the respondents further dropped to 2.0 in Hong Kong Island and 1.0 in Kowloon, so most bus passengers could find seats during the evening peak period in the

urban areas. The average wait time for franchised buses varied from around 7.08 to 8.78 minutes, which was consistent with the service frequencies of most bus routes in Hong Kong. In Kowloon and the New Territories, the tourists experienced better service during the noon off-peak periods (between 1100 and 1800) because the average wait time was normally shorter than in other periods.

The interviewed Mainland tourists reported that the franchised buses they experienced were better than the railway. However, as the destinations of the interviewed tourists were different from those of local residents (mainly traveling on work–home trips), the congestion level and wait times experienced by the tourists may not reflect the general situation in Hong Kong.

5.3. Concluding Remarks

The tourists’ satisfaction with the existing transportation system was studied in this chapter. Two separate assessments were conducted, measuring their level of satisfaction and the LOS of the public transportation systems. Some observations on the tourists’ level of satisfaction are summarized as follows:

- The interviewed Mainland tourists were generally satisfied with the quality of the public transportation service, with an average overall service performance score of 3.87. - Accessibility, safety and security, and travel fare are ranked as the three most important service aspects of public transportation. - Satisfaction levels in terms of travel fare and level of crowding received relatively low scores. However, the level of crowding makes a small contribution to overall service performance. - Customer support has the highest contributing coefficient as evaluated in the ordered probit model. - Based on the IPA results, it was found that the service aspects of travel fares and customer support require immediate attention and hold the top priority for enhancement.

In addition to the tourists’ satisfaction levels on the existing public transport system, the average congestion level and wait time experienced by the respondents were used to represent the LOS of the railway and franchised buses in this study.

- For the railway service, it was generally observed that the existing system is capable of accommodating the travel demands of both local residents and tourists. - The congestion level of Kowloon was the highest during the morning peak period. A low level of congestion was observed for Hong Kong Island even during the peak hour periods.

- Overall, better railway handling capacity was experienced during the off-peak periods along the railway lines of the New Territories, followed by Hong Kong Island, and then Kowloon. - For franchised buses, the service capacities reported by the respondents were better than for the railway service, with a better experience in terms of congestion level and waiting time. - Unlike on the railway system, tourists using buses might have different destinations than local residents, and hence a direct comparison between the railway and franchised buses is not appropriate.

Despite these findings, it was established that the tourists preferred the railway to buses when traveling around Hong Kong, particularly in areas with good accessibility to the railway system. Therefore, it is worth considering a more flexible strategy to improve the quality of the public transportation system, for example encouraging smart tourist travel during off-peak periods, or providing information on bus routes and schedules to improve the accessibility of franchised buses.

6. POLICY IMPLICATIONS FOR TOURISM TRANSPORTATION POLICY MEASURES

Based on the findings in the previous chapters, we have a better understanding of Mainland tourists’ trip characteristics, attitudes, and behavioral performance in terms of public transportation usage, and their needs and satisfaction with the current Hong Kong transportation system. In this chapter, we discuss the effectiveness of the tourism transportation policy measures. We also provide recommendations for authorities in terms of enhancing the sustainability of tourism mobility in Hong Kong.

6.1. Effectiveness of tourism policy measures

The HKSAR government has continued to develop a wide range of tourist attractions, with a view to enhancing Hong Kong’s overall attractiveness as a premier tourist destination. For example, currently popular tourist areas have been further developed through the Stanley Waterfront Improvement Project, the Peak Improvement Project, and the Aberdeen Tourism Project. The government has also promoted activities such as culture and heritage tourism.

These tourism measures certainly enrich the travel experiences of tourists, but opinions on the effectiveness of these measures, particularly for Mainland tourists, have rarely been reviewed. Hence, we adopted a 5-point scale to reveal whether interviewed tourists believed the tourism policy measures were effective in stimulating the Hong Kong tourism industry, with 5 indicating “strongly agree,” 4 indicating “agree,” 3 indicating “neutral,” 2 indicating “disagree,” and 1 indicating “strongly disagree.” The Mainland tourists scored the current and planned tourism policies during the face-to- face interviews.

Table 6.1 tabulates the means and standard deviations of the tourism policies. The interview results indicated that only one tourism policy measure, i.e., reinstatement of “multiple-entry” Individual Visit Endorsements (mean = 4.03), obtained a score of 4, suggesting that the multiple-entry IVS could be an effective measure for attracting Mainland tourists. Provision of boundary shopping centers (3.64), expansion of theme parks (3.54), implementation of new infrastructure (3.36), promotion of self-driving (3.29), and development of arts and culture facilities received satisfaction scores above 3, indicating that the interviewed tourists had a generally positive view of these measures. In contrast, the remaining measures, including promotion of ecotourism (2.90), cruise tourism (2.81), heritage tourism (2.72), and development of spa and resort facilities (2.61) received scores lower than 3, meaning that these measures were less attractive to the Mainland tourists.

Table 6.1 Views on the effectiveness of tourism policy measures Tourism Policy Min Max Mean S.D. Reinstatement of “multiple-entry” Individual 0 5 4.03 1.28 Visit Endorsements Provision of boundary shopping centers 0 5 3.64 1.27 Expansion of theme parks 0 5 3.54 1.26 Implementation of new infrastructure 0 5 3.36 1.30 Promotion of self-driving 0 5 3.29 1.52 Development of arts and culture facilities 0 5 3.11 1.30 Promotion of ecotourism 0 5 2.90 1.37 Promotion of cruise tourism 0 5 2.81 1.39 Promotion of heritage tourism 0 5 2.72 1.39 Development of spa and resort facilities 0 5 2.61 1.44 S.D. = Standard deviation

6.2. Observations and insights of Mainland tourists

6.2.1. Travel characteristics and patterns of Mainland tourists

After the implementation of the IVS in 2003, the number of tourists from the Mainland increased remarkably. In this study, the tourist interview survey data extracted from the 2002 TCS (before the IVS), 2011 TCS (after the IVS), and face-to-face interviews conducted in 2016 were used to reveal the travel characteristics and patterns of Mainland tourists visiting Hong Kong. Based on Chapters 3 and 4, observations on the socio-demographics and travel characteristics of these tourists are tabulated in Table 6.2.

Table 6.2 Trends in the travel characteristics of Mainland tourists Attribute Year Observations Remarks Gender 2002 - Gender distribution was quite even, - No significant variation in gender with slightly more than half being distribution. male (52.5%). 2011 - Gender distribution was quite even, - Refer to Tables 3.1 and 4.6. with slightly more than half being female (56.1%). 2016 - Gender distribution was quite even, with 1.9% more females than males. Age 2002 - Young adults aged 26-35 were the - A gradually declining trend in the largest group (35.5%), followed by age of Mainland tourists over time. those aged 36-45 (27.0%).

2011 - Young adults aged 26-35 were still - Young tourist aged 35 or below has the largest group (41.0%). been increasing from 44.0% in - Tourists were younger than in 2002 to 57.8% in 2011, and further 2002: the percentage of tourists to 69.6% in 2016. aged 18-25 doubled from 8.5% to 16.8%. - Refer to Tables 3.1 and 4.6. 2016 - Young adults aged 26-35 were the largest tourist group (43.9%), followed by 18- to 25-year-olds (25.7%). Trip 2002 - Almost half of the tourists (48.9%) - An obvious change in trip purpose purpose visited Hong Kong for from work/business to sightseeing/shopping. sightseeing/shopping was found. - Work/business trips were the second most common reason, at - Sightseeing/shopping was found to 40.4%. be the most common trip purpose 2011 - Sightseeing/shopping was the main to visit Hong Kong. trip purpose (74.3%). 2016 - Sightseeing/shopping was the main - More Mainland tourists came to trip purpose (84.5%). Hong Kong for shopping, particularly repeat visitors.

- Refer to Tables 3.1 and 4.6. Trip- 2002 - Morning peak (09:00-11:00, with a - Mainland tourists try to avoid the making highest trip-making percentage of commuting morning peak of local periods 7.6%). residents, making their trips later in - Evening peak (18:00-21:00, with a the day. highest trip-making percentage of 8.1%). - A dispersed evening peak period - Tourists made their trips evenly was observed, normally between throughout the day between 09:00 18:00 and 21:00. and 21:00. 2011 - Morning peak (09:00-11:00, with - Refer to Table 3.3 and Figures 3.2 the highest trip-making percentage and 4.3. of 8.1%) - Evening peak (18:00-21:00, with the highest percentage of 9.0%), more dispersed. - Two obvious travel peak periods were observed in the morning and the evening.

2016 - Morning peak (10:00-12:00, with a highest trip-making percentage of 9.7%) - Evening peak (18:00-21:00, with a highest percentage of 7.9%). - Tourists made their trips in the morning, but later in the day to avoid the morning commuting peak of local residents. Travel 2002 - Tourist concentrated in the main - Zones 2, 3, and 8 were identified as patterns tourist zones along the Victoria the main tourist zones, attracting Harbour shore. about 45% of the tourists in both - Less than 1% traveled to the New 2002 and 2011. Territories. 2011 & - Tourists concentrated in the main - Over the years, tourists have 2016 tourists zones along the Victoria become more dispersed throughout Harbour shore. different areas of Hong Kong, and the attractiveness of tourism spots - More tourists traveled to the located in urban areas decreased. northwest and southwest parts of the New Territories. - In contrast, Mainland tourists were looking for experiences rather than simply to tour, so their trip distributions were therefore more dispersed to the rural and remote areas of Hong Kong.

- Possible parallel trade activities may have diverted Mainland tourists away from tourist zones in recent years.

- Refer to Table 3.4 and Figures 3.3 and 3.4.

6.2.2. Use of pubic transport by Mainland tourists

The top three modes of public transport used by tourists, not local residents, were identified as taxis, railways, and tourist coaches. The railway has become a convenient and efficient mode of travel to the city center of Hong Kong, and thus a remarkable increase in railway use was observed. Table 6.3 summarizes the use of public transport by Mainland tourists.

Table 6.3 Summary of the use of public transport by Mainland tourists Attribute Observations Use of public In 2002 transport - Tourist coaches were the most commonly used transportation mode for traveling to different parts of Hong Kong, followed by taxis and railways. - Tourists prefer to use taxis for short journeys in the major and minor tourist zones.

In 2011 - The railway was the dominant mode for reaching tourist zones (Groups 1 and 2), particularly during the morning peak periods. - Travel by rail to non-tourist zones (Group 3) doubled, reaching 31.1%, which was slightly less than tourist coaches (37.8%). - A slight increase in the use of franchised buses was found, and tourists were more likely to make trips to the minor tourist zone (Group 2) and non-tourist zone (Group 3). Nevertheless, this constituted only about 7% of the total trips made. - Tourist coach use reduced remarkably by over 20%. Unlike in 2002, tourists preferred to use coaches to travel to the remote areas of Hong Kong.

In 2016 - Rail was the dominant mode, followed by franchised buses and then taxis. Only 6.9% of Mainland China IVS tourists used tourist coaches.

(Refer to Sections 3.3 and 4.3) Contributory In 2002 factors on mode - Trip characteristics such as travel period, origin, and destination choice made a greater contribution to the mode choice.

In 2011 - Both individual and trip characteristics contributed significantly to the public transport mode choice.

In 2016 - Only the trip characteristics of origin and destination made significant contributions to the mode choice.

(Refer to Tables 3.19 and 4.8)

Local residents vs. Taxis Mainland tourists - The demand for taxis from local residents was the highest in the morning, between 0700 and 0900. However, the overall demand on taxis by tourists was less than 1% during the same period. - Tourists were more likely to travel by taxi at night, when the demands for taxis from local residents and tourists were more comparable, particularly between 2000 and 2300. - In 2002, the overall demand for taxis was higher during the morning peak in Zones 2, 3, and 18, but fortunately the taxi usage proportion for Mainland tourists was relatively low. - The percentages of taxi share observed during the evening peak period in Zones 9 and 10 reached 40% and 26%, respectively. - In 2011, the proportion of taxi usage by tourists generally increased.

Railways - Railway was the dominant transport mode for both local residents and tourists, but the percentages of all railways trips made by tourists were less than 2% (1.3% in 2002 and 1.6% in 2011). - Causeway Bay (Zone 3) and Tsim Sha Tsui (Zone 8) are in general the two areas with the highest proportions of railway usage by Mainland tourists. - There was a general increase in the railway use proportion for Mainland tourists in different areas of Hong Kong in 2011.

Franchised buses - Mainland tourists constituted only 0.3% of the franchised bus users in 2002 and 2011, much lower than the demand tourists placed on the taxi and railway systems. - The influence of this mode was obviously minimal compared with taxis and franchised buses.

Luggage carrying - Almost 50% of the Mainland tourists carried luggage while using public transport, while only 6.9% of the local residents had luggage. - 56.4% of the Mainland tourists had luggage when using railways, followed by franchised buses (51.8%) and then taxis with a relatively low 26.0%, reducing the carrying capacity of the public transportation systems.

(Refer to Section 3.4) Satisfaction level - Mainland tourists were satisfied with the quality of the public transport services, but the aspects of travel fares and customer support were indicated to be the top priorities for improvements. - The congestion level of Kowloon was the highest during the

morning peak period, while in contrast Hong Kong Island had a low congestion level during the morning and evening peak periods. - For the franchised buses, the service capacities reported by the respondents were better than those of the railway service, so a better travel experience was enjoyed.

(Refer to Sections 5.1 and 5.2)

6.2.3. First-time vs. repeat visitors (Refer to Section 4.2)

Over 50% of Mainland IVS tourists were found to be from Guangdong Province (the 21 IVS cities), with others mainly from the southern coastal provinces of Mainland China. In Hong Kong, a substantial proportion of Mainland tourists had visited previously (66%), while only 34% were found to be first-time visitors. The repeat visitors generally spent less than the first-time visitors, possibly because the repeat visitors were more likely to buy necessities such as cosmetics and not high-end products. The repeat visitors therefore made a relatively low economic contribution to Hong Kong, while also causing more social-economic conflicts with the local community. The travel behavior (e.g., destinations, transportation mode, and trip purpose) of first-time and repeat visitors certainly differs significantly (Oppermann, 1997), so a comparison of their travel characteristics is given in the following Table 6.4.

Table 6.4 Travel characteristics of first-time visitors against repeat visitors Attributes First-time visitors Repeat visitors Tourist proportion - 34% of interviewed - 66% of interviewed Mainland tourists. Mainland tourists.

Trip purpose - Sightseeing was the main - Sightseeing was the main purpose (71.1%). purpose (50.8%). - 31.4% visited for shopping, much higher than for first- time visitors (17.8%).

Origin and - Covered more destinations, - Less travel to the main Destination but mainly within the major tourist attraction spots at the and minor tourist zones. Victoria Harbour shore. - Attracted to mega events and special festivals (e.g., Hong Kong Book Fair, Hong Kong Brands and Products Expo Fair) and theme parks (e.g., Hong Kong Disneyland). Day-trip pattern - 21.9% were day trippers. - 35.4% of the repeat

- Half stayed in Hong Kong visitors were day for three or more nights trippers. (51.8%) - About 33% stayed for three or more nights.

Some Mainland tourists came to Hong Kong with the intention of purchasing necessities along the East Rail Line in locations such as Shatin, Fanling, and Sheung Shui and returned to Mainland China on the same day. The proportion of day-trip visitors was therefore relatively high, accounting for 30.8% of the total, who were mainly from Guangdong Province.

6.3. Recommended tourist transportation policy measures

In this study, a full picture of the trends and influences of Mainland tourists on the public transportation system in Hong Kong was provided. Aspects including travel behavior, characteristics, and perception of Chines tourists were explored. Some tourist transportation policy measures are now discussed, with the aim of promoting sustainable tourism in Hong Kong.

6.3.1. Maintenance of existing visa arrangements

The “multiple-entry” measure was replaced by “one trip per week” Individual Visit Endorsements on April 13, 2015 to address the recent influx of Mainland tourists. As some M-Permit visitors acted as parallel traders and their activities led to numerous conflicts with local Hong Kong residents, the adjusted measure was used to combat these activities and help release the pressure on the local community. Indeed, nearly two years after the adjusted measure had been implemented, the number of Mainland tourists to Hong Kong had dropped by over 10%, yet still comprised 70% of the total arrivals.

As specified in Table 6.1, the reinstatement of the M-Permit could be an attractive tourism measure for Mainland tourists (it received the highest average score of 4.03), and therefore some have suggested doing so to reverse the recent recession in Hong Kong tourism. However, one study found that the M-Permit actually generated the lowest economic contribution to Hong Kong, as over 90% of Mainland tourists were day-trip visitors (Sung et al., 2015). Generally, these day-trip visitors appeared to be more dispersed in different parts of Hong Kong, possibly causing more conflicts with local communities, as discussed in Sections 4.2 and 6.3.1. This suggestion may therefore not be a meaningful way to promote sustainable tourism in the long term.

6.3.2. Monitoring of the list of IVS cities

Stakeholders have at times suggested expanding IVS coverage to cater to the tourist demands of other non-IVS cities, but it seems that the HKSAR government has no such plan, given local residents’ generally negative response to it (GovHK, 2017). Although certain tourist demands from non-IVS cities should be considered, research on the influence of possible expansion to the local community has not been conducted. In this study, a total of 1,119 Mainland tourists who self-reported as IVS tourists were interviewed. Of these respondents, about 17% (190 respondents) came from non-IVS cities and exhibited travel characteristics similar to those of IVS tourists (refer to Section 4.1).

On a positive note, the expansion of non-IVS cities is expected to attract limited visitors to Hong Kong, and the transportation effects they induce should be minimal, as most Mainland IVS tourists come from Guangdong Province (the IVS cities). Nevertheless, the actual influence on the transportation demands generated by these extra Mainland tourists should not been overlooked. Authorities or professionals should conduct research to explore this issue.

6.3.3. Promotion of in-depth travel

A high proportion of about 66% of Mainland tourists were repeat visitors who came to Hong Kong mainly for shopping. A proportion of these were day-trip visitors making a relatively low economic contribution (refer to Section 4.2.1). The repeat visitors were more dispersed in different areas of Hong Kong, particularly the northern parts of the New Territories, as observed in Section 3.2.1. The existing tourist spots appear to no longer be attractive for these tourists. Furthermore, research has revealed that discovery of new cultures and landscapes, the contemplation of natural/artistic heritage, and contact with the local community and nature are all important for young tourists (Buffa, 2015). Faced with the increasing trend of young tourists in recent years (refer to Section 6.2.1), new forms of travel experience will be essential for the development of sustainable tourism.

In recent years, there have been discussions of the promotion of in-depth travel to promote Hong Kong (e.g., ecotourism and heritage tourism, as mentioned in Table 6.1). Unlike the traditional travel mode (mainly shopping and sightseeing), in-depth travel allows tourists to plan their own itinerary and focus on the lifestyle and culture of the local community. Tourist packages with professional tour guides and coach services in a type of “one-to-one” (individual tour) or “one-to-small group” (group tour) are recommended. The authorities may also consider providing more limousine licenses, so individual tour guides can take either individual or group tours and travel around the city more easily.

6.3.4. Implementation of boundary shopping malls

Based on the face-to-face interview results, IVS tourists from Guangdong Province, Shanghai, and Beijing comprised almost 80% of visitors (refer to Section 4.1). Of these, 66% were repeat visitors who intended to visit shopping malls unlike the first-time visitors (refer to Table 4.3). This finding is consistent with the assessment conducted by the Commerce and Economic Development Bureau in December 2013, which found that 76% of IVS tourists traveled to Hong Kong for the purpose of shopping (GovHK, 2014). The assessment also found that a number of shops that originally served local residents were replaced by shops targeted at Chinese visitors in the districts along the East Rail Line, thus affecting the daily lives of local residents.

Based on the above observation, the provision of boundary shopping centers at remote areas should be considered. Indeed, Mainland tourists are more dispersed, visiting different parts of the New Territories, as discussed in Section 3.2. Therefore, the development of these boundary shopping centers is an effective and attractive measure to divert the repeat shopping tourists (refer to Table 6.1). Mainland tourists have higher intentions of visiting these boundary shopping centers if discounts are offered regardless of the types of products available (refer to Table 4.12). Therefore, the development of boundary shopping centers for necessities could be considered to release the pressure of Mainland tourists on Hong Kong locals.

6.3.5. Upgrading of railway services

As 40.9% of the local residents use the railway, the system is fully utilized, particularly during peak travel hours. Nevertheless, rail is also the dominant transport mode for Mainland tourists, with an increasing trend in recent years (refer to Sections 3.3 and 4.3). To enhance the travel experience of these tourists when using the railway, the provision of the following facilities could be considered.

A complimentary Airport Express Shuttle-bus is provided for Airport Express passengers traveling to/from the airport stations, i.e., Hong Kong or Kowloon stations, major hotels, and railway interchanges. The shuttle-bus services are currently underutilized because only passengers using the Airport Express are permitted to use this free service. The railway corporation could consider extending the service to all tourists using the railway system. This measure can encourage tourists traveling to/from Hong Kong Station and/or Kowloon Station, releasing the travel demands of those stations with a high share of tourists and local residents (refer to Section 3.4), such as Causeway Bay Station, Tsim Sha Tsui Station, and Mong Kok Station.

As discussed in Section 3.4.4, more than half of the Mainland tourists considered carried heavy luggage when using the railway. By making reference to the luggage storage concept on European trains, providing a “luggage-only” carriage at the end of the train is suggested. Passengers with luggage exceeding the railway requirement (i.e., luggage shall not exceed 170 cm and the length of any one side of the luggage shall not

exceed 130 cm) must then use the “luggage-only” carriage; otherwise, they will receive a penalty.

6.3.6. Enhancement of franchised bus service

To enrich the travel experience of Mainland tourists, the enhancement of customer support and the reduction of travel costs are of paramount importance. Rail is the dominant transport mode used by tourists, and has already been fully utilized by current travel demands (refer to the discussion in Section 5.2). The promotion of franchised buses would thus be a more appropriate method of diverting the travel demands of tourists.

Of the different forms of land transportation, buses are usually considered a comfortable and relaxing mode of travel for tourists to sightsee in a city. In Hong Kong, open-top bus tours (Big Bus Tours) currently provide hop-on-hop-off services for tourists to visit key attractions. Detailed and comprehensive tourist information is provided throughout the bus journey. However, only three fixed routes are currently available: the Hong Kong Island Tour, the Kowloon Tour, and the Stanley Tour (Big Bus, 2017). As the tours offer free admission or discounts to numerous tourist attractions, their prices are somewhat more expensive than those of the franchised buses.

According to the 2002 TCS and 2011 TCS data, less than 10% of the total Mainland tourists used franchised buses to travel around Hong Kong (refer to Tables 3.10 and 3.17). Although a slightly higher percentage of 17.2% was obtained from the 2016 face-to-face interviews (refer to Table 4.7), franchised bus usage was relatively low compared with that of railway usage (almost 80%). Furthermore, Mainland tourists have been more dispersed across different areas of the New Territories in recent years (as discussed in Section 3.2.1). Constrained by the service networks of the railway system in some rural and remote areas, franchised buses that provide extensive network coverage with relatively low travel fares should be an advantageous choice for tourists to reach the remote rural areas, such as the Lam Tsuen Wishing Trees and the Hong Kong Wetland Park. The promotion of franchised buses could thus help to improve tourism mobility.

However, to promote the use of franchised buses, upgrading of facilities and services may be necessary. For example, inadequate tourism customer support may be a reason for the low usage of franchised buses by Mainland tourists (refer to Section 5.1.3). In fact, franchised bus companies have already made improvements in their provision of passenger information through customer service centers and route information panels. Nevertheless, except for the airport coach service (i.e., Cityflyer) between Hong Kong International Airport and urban areas, tourist-specific customer support is not provided. To promote the use of franchised buses, simple tourism guides that highlight tourist attraction spots during the journey could be provided on the information panels at stations or on buses. In addition, tourist-specific customer hotlines

and WeChat and Twitter accounts providing real-time support could be considered to enhance the travel experience of Mainland tourists using franchised buses.

Further to the point discussed in Section 3.4.4 that almost 50% of the Mainland tourists considered carried luggage while using public transport, it is worth studying the feasibility of the provision of luggage compartments on some popular franchised bus routes. This could help to enhance the travel comfort of passengers, releasing the tension between Mainland tourists and local residents. Trial schemes can be implemented in some critical tourist corridors, such as NWFB Route No. 15 (The peak to/from Central), Citybus Route No. 962 (Causeway Bay to/from Tuen Mun, via Golden Beach), or KMB Route No. 299X (Shatin to/from Sai Kung). The arrangement could be similar to the airport coach service, but a smaller luggage compartment region is suggested.

6.3.7. Improvement of taxi service

The promotion of on-demand taxi apps (e.g., HKTaxi, MyTaxi.HK, and Fly Taxi) could be a reasonable way of coping with the additional travel demands of tourists. The Hong Kong Tourism Board could also upgrade the interface of the tourism mobile app My Hong Kong Guide by accessing the relevant transport information (e.g., real-time journey time, routing between origin and destination, and estimated travel cost for the selected transportation modes) from other websites or apps (e.g., HKeTransport) to maximize the function of this application.

6.3.8. Introduction of multimodal tourist travel pass

Unlike other major world cities, there are very few discount cards in Hong Kong for tourists. In terms of transportation, three types of tourist tickets, the Adult/Child Tourist Day Pass, the Airport Express Travel Pass, and the Tourist Cross-boundary Travel Pass are available, providing one to three days’ worth of unlimited travel on the railway (MTR, 2017). As tourists must pay additional fares to take franchised buses or other forms of public transport, they do not find the current tourist tickets attractive. Although tourists can use the Standard Octopus as a travel pass, unlike other tourist travel passes it offers no top-up benefits. The travel fare of the public transportation systems thus requires immediate attention, based on the importance-satisfaction analysis of this study (refer to Section 5.1.3). With reference to the passes offered by other world cities, such as the “myki Explorer pack,” London Travelcards, and the Singapore Tourist Pass Plus (refer to Section 2.5), an unlimited multimodal tourist travel pass should be provided. A free or discounted package for key sightseeing spots could be included to encourage the use of other modes of public transportation with low tourist travel demands (refer to Table 4.5), helping to utilize the overall public transportation system of Hong Kong.

To release the tensions between Mainland tourists and local residents effectively, policy measures encouraging Mainland tourists to make their trips during non-peak

periods will be essential. A proportion of Mainland tourists would like to make their trips during noon off-peak periods (between 11:00 and 18:00), avoiding the local commuting peak periods. Unfortunately, a decreasing trend has been observed: although about 50% of tourists made their trips during noon and off-peak periods in 2002, this figure dropped to 39.7% in 2011 (refer to Table 3.6 of Section 3.2). Extra discounts during off-peak periods could be provided for the proposed tourist travel pass, to encourage Mainland tourists to travel and commute outside the local peak hours.

6.3.9. Scrutinization of self-driving

The promotion of self-driving (which received an average of 3.29, as stated in Table 6.1) has recently been a much-discussed transport measure to attract Mainland tourists. Self-driving travel offers an interesting option for tourists who are looking for experiences rather than simply to tour Hong Kong. For example, self-driving enables tourists to visit the beautiful country parks in the New Territories or to experience the deserted beaches of Hong Kong Island. Although the Chinese middle classes are increasingly seeking out types of tourism that are family friendly and more independent than traditional travel experiences, shopping remains the main travel purpose and is readily accessible by public transport.

Local people have generally expressed concerns about the appropriateness of promoting self-driving in Hong Kong, in terms of the view that Mainland tourists have improper driving attitudes, and also the difficulty of adopting a different driving rule (driving on the right in Hong Kong, while driving on the left in Mainland China), which may lead to potential road hazards. Driving in Hong Kong is relatively difficult and expensive, in terms of congestion, parking, and gasoline fees. For example, the average hourly parking fee in Hong Kong is HK$10-30, and HK$50-160 for a 12-hour day park. Compared with these fees, the fares for most franchised bus urban routes range from HK$2.5 to HK$13.4 and up to HK$52 for a few long routes in the New Territories, and the fares for railways range from HK$4.5 to HK$50 for all lines (MTR, 2017). Hence, the travel cost of using public transportation is much lower than that of self-driving in most cases.

With the imminent opening of the Hong Kong-Zhuhai-Macao Bridge, there will be a higher demand for cross-boundary self-driving licenses. No doubt the commercial cross-boundary drivers will have adequate driving experience to be able to shift between the different traffic rules. Nevertheless, local people are increasingly concerned about the arrangements for cross-boundary vehicles, mainly in terms of self-driving Mainland tourists. Therefore, it may not be appropriate to currently promote self-driving. Instead, government authorities could consider a policy option of discouraging this mode of transport, such as by providing parking facilities with special discounts at the top-side island near the airport or at remote shopping malls (refer to the discussion in Section 6.3.4). Public transport services, such as tourist coaches and franchised buses, could be provided to transport Mainland tourists to the town or other tourist attraction spots of

Hong Kong. This could help to constrain the self-driving Mainland tourists to the boundary area and hence relieve public concern in the initial stage. The driving behavior and attitudes of these Mainland tourists should be measured and examined, providing evidence to ease local concern in the near future.

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APPENDIX A

Face-to-face Interview Questionnaire Script Sample

Department of Civil Engineering The University of Hong Kong

遊客對香港公共交通工具的意見調查

甲部）同意書

香港大學土木工程系黃仕進教授，現正進行關於遊客對香港公共交通工具的學術研究。此調查的目的為：

(一) 了解遊客對公共交通的需求； (二) 評估遊客對選擇公共交通模式的態度與行為；及 (三) 建議適當的針對公共交通系統的旅遊業措施。

是次訪問中，你需要回答與香港公共交通相關的出行習慣、行為及態度的問題。完成此問卷需時約十分鐘。本問卷採用不記名方式進行，個人資料將絕對保密，收集到的資料只會作綜合分析用途。本次研究並不會為閣下提供個人利益，但收集的數據將對研究香港公共交通提供寶貴的資料。你可以隨時終止參與是次訪問，而有關決定將不會引致任何不良後果。

如你對本項研究由任何疑問，歡迎致電李小姐查詢（電話: 2859-2662）。如你想知道更多有關研究參與者的權益，請聯絡香港大學非臨床研究操守委員會（電話: 2241-5267）。

□ 如你明白以上內容，並願意參與是項研究，請在方格內打勾。謝謝。

*(由訪問員填寫)

日期: / / 時間: 上午/ 下午* (日日/月月/年年年)

地點:

訪問員：______參考編號：______(例如：1-123)

乙部）基本資料［請於適當的空格中填上“” 號］

1. 性別（由訪問員填寫） □ 男 □ 女

2. 你來自哪一個國家及城市？ □ 中國省份：______（請列明）城市： ______（請列明） □ 其他國家（訪問結束）

3. 你是持「自由行」簽注入境嗎？ □ 是（如受訪者不清楚，請續問是否持“L 簽注”入境香港，如 □ 否持“L 簽注”，選擇“否”）

4. 年齡： □ 18-25 歲 □ 26-35 歲 □ 36-45 歲 □ 46-55 歲 □ 56-65 歲 □ 65 歲以上

Department of Civil Engineering The University of Hong Kong

5. 你每月的收入是（以人民幣為單位） □ ¥5,000 或以下 □ ¥5,000 - ¥9,999 □ ¥10,000 - ¥19,999 □ ¥20,000 - ¥49,999 □ ¥50,000 - ¥79,999 □ 多於¥80,000 □ 拒絕回應

丙部）是次旅行的性質［請於適當的空格中填上“” 號］

6. 這是你首次來香港嗎？ □ 是 □ 不是 - 你來了香港多少次（不包括本次）？ ______- 距離你第一次訪港有多久？______

7. 請回答下列各項：內容是次第一次（非首次來港旅客） a) 在港逗留時間 (如即日來回，請填寫 “0”) ______晚 ______晚 b) 行程的主要目的 - 悠閒玩樂 □ □ （只可選一項） - 探親訪友 □ □ - 購物 □ □ - 商務 □ □ - 中轉 □ □ - 培訓 □ □ - 其他, 請列明 ______c) 同行旅伴 - 家人 □ □ - 朋友 □ □ - 同事 □ □ - 單獨 □ □ - 其他, 請列明：______d) 旅遊景點 - 山頂 □ □ - 昂坪及大佛 □ □ - 香港迪士尼樂園 □ □ - 海洋公園 □ □ - 旺角市場（女人街、金魚街，雀仔街） □ □ - 金紫荊廣場 □ □ - 鐘樓及星光大道 □ □ - 蘭桂坊 □ □ - 購物中心（如：海港城、時代廣場、 □ □ IFC） □ □ - 其他, 請列明：______

Department of Civil Engineering The University of Hong Kong

8. 請依時間順序記下你過去 24 小時所到訪過的地方：

昨晚凌晨 3 時，你在哪 □ 酒店/旅店 ...... （地點：＿＿＿＿＿＿＿＿＿＿）裡？ □ 家人/ 親戚/ 朋友的住處 ..... （地點：＿＿＿＿＿＿＿＿＿＿） □ 公司 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 購物區 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 食肆 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 旅遊景點 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 主題樂園 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 其他 ...... （地點：＿＿＿＿＿＿＿＿＿＿）

□ 中國內地 ...... （地點： ______途經口岸______)

你第一個目的地是？ □ 酒店/旅店 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 家人/ 親戚/ 朋友的住處 ..... （地點：＿＿＿＿＿＿＿＿＿＿） □ 公司 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 購物區 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 食肆 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 旅遊景點 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 主題樂園 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 其他 ...... （地點：＿＿＿＿＿＿＿＿＿＿）

□ 中國內地 ...... （地點： ______途經口岸______)

乘搭的時間：______＿______上午/ 下午*

乘搭的交通工具：候車時間需等候的鐵路／巴士數擠迫程度（分鐘）目（參考圖片） □ 港鐵 ______□ 公交車（巴士） ______□ 小巴 ______□ 出租車（的士） ______□ 叮叮（電車） ______□ 渡海小輪 ______

□ 觀光車／步行／自行車（單車）／其他*，請列明： ______

* 請刪去不適用

Department of Civil Engineering The University of Hong Kong 你第＿＿個目的地是？ □ 酒店/旅店 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 家人/ 親戚/ 朋友的住處 ..... （地點：＿＿＿＿＿＿＿＿＿＿） □ 公司 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 購物區 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 食肆 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 旅遊景點 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 主題樂園 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 其他 ...... （地點：＿＿＿＿＿＿＿＿＿＿）

□ 中國內地 ...... （地點： ______途經口岸______)

乘搭的時間：______＿______上午/ 下午*

□ 觀光車／步行／自行車（單車）／其他*，請列明： ______

你第＿＿個目的地是？ □ 酒店/旅店 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 家人/ 親戚/ 朋友的住處 ..... （地點：＿＿＿＿＿＿＿＿＿＿） □ 公司 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 購物區 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 食肆 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 旅遊景點 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 主題樂園 ...... （地點：＿＿＿＿＿＿＿＿＿＿） □ 其他 ...... （地點：＿＿＿＿＿＿＿＿＿＿）

□ 中國內地 ...... （地點： ______途經口岸______)

乘搭的時間：______＿______上午/ 下午*

□ 觀光車／步行／自行車（單車）／其他*，請列明： ______

* 重覆使用此表格以紀碌過去 24 小時的行程

Department of Civil Engineering The University of Hong Kong

9. 情景一：假設你正計劃從市區出發前往以下的目的地，請根據已知的路程時間和交通費用考慮會或者不會乘搭公共交通工具前往：

1 - 目的地 = 購物區 □ 會 □ 不會 - 路程時間 = 15 分鐘 - 交通費用 = 10 港元

2 -目的地 =主題公園（例如：迪士尼，海洋公園等） □ 會 □ 不會

-路程時間 = 30 分鐘 - 交通費用 = 20 港元

3 -目的地 = 風景名勝 □ 會 □ 不會版本一 -路程時間 = 60 分鐘 - 交通費用 = 20 港元

4 -目的地 = 食肆 □ 會 □ 不會 -路程時間 = 90 分鐘 - 交通費用 = 10 港元

1 - 目的地 = 食肆 □ 會 □ 不會 - 路程時間 = 15 分鐘 - 交通費用 = 50 港元

2 -目的地= 風景名勝 □ 會 □ 不會

-路程時間 = 30 分鐘 - 交通費用 = 20 港元

3 -目的地 = 主題公園（例如：迪士尼，海洋公園等） □ 會 □ 不會版本二 -路程時間 = 60 分鐘 - 交通費用 = 20 港元

4 -目的地 = 購物區 □ 會 □ 不會 -路程時間 = 90 分鐘 - 交通費用 = 10 港元

1 - 目的地 = 食肆 □ 會 □ 不會 - 路程時間 = 15 分鐘 - 交通費用 = 10 港元

2 -目的地 = 主題公園（例如：迪士尼，海洋公園等） □ 會 □ 不會

-路程時間 = 30 分鐘 - 交通費用 = 30 港元

3 -目的地 = 風景名勝 □ 會 □ 不會版本三 -路程時間 = 60 分鐘 - 交通費用 = 30 港元

4 -目的地 = 購物區 □ 會 □ 不會 -路程時間 = 90 分鐘 - 交通費用 = 50 港元

1 - 目的地 = 購物區 □ 會 □ 不會 - 路程時間 = 15 分鐘 - 交通費用 = 50 港元

2 -目的地= 風景名勝 □ 會 □ 不會

-路程時間 = 30 分鐘 - 交通費用 = 30 港元

3 -目的地 = 主題公園（例如：迪士尼，海洋公園等） □ 會 □ 不會版本四 -路程時間 = 60 分鐘 - 交通費用 = 30 港元

4 -目的地 = 食肆 □ 會 □ 不會 -路程時間 = 90 分鐘 - 交通費用 = 50 港元

Department of Civil Engineering The University of Hong Kong

情景二：假設你正計劃從市區出發前往一個位於偏遠地區的大型商場，請根據已知的購物類別、平均折扣、路程時間及交通車費以及購物考慮會或者不會前往該折扣商場：

1 - 購物類別 = 日用品 - 平均折扣 = 9 折 □ 會 □ 不會 - 路程時間 = 30 分鐘 - 交通費用 = 10 港元

2 - 購物類別 = 珠寶首飾 - 平均折扣 = 9 折 □ 會 □ 不會

- 路程時間 = 30 分鐘 - 交通費用 = 30 港元

3 - 購物類別 = 電器產品 - 平均折扣 = 8 折 □ 會 □ 不會版本一 - 路程時間 = 30 分鐘 - 交通費用 = 50 港元

4 - 購物類別 = 化妝品 - 平均折扣 = 7 折 □ 會 □ 不會 - 路程時間 = 60 分鐘 - 交通費用 = 30 港元

1 - 購物類別 = 化妝品 - 平均折扣 = 8 折 □ 會 □ 不會 - 路程時間 = 30 分鐘 - 交通費用 = 10 港元

2 - 購物類別 = 電器產品 - 平均折扣 = 9 折 □ 會 □ 不會

- 路程時間 = 90 分鐘 - 交通費用 = 30 港元

3 - 購物類別 = 珠寶首飾 - 平均折扣 = 9 折 □ 會 □ 不會版本二 - 路程時間 = 60 分鐘 - 交通費用 = 10 港元

4 - 購物類別 = 奢侈品牌的服裝 - 平均折扣 = 9 折 □ 會 □ 不會 - 路程時間 = 60 分鐘 - 交通費用 = 50 港元

1 - 購物類別 = 奢侈品牌的服裝 - 平均折扣 = 7 折 □ 會 □ 不會 - 路程時間 = 30 分鐘 - 交通費用 = 30 港元

2 - 購物類別 = 電器產品 - 平均折扣 =9 折 □ 會 □ 不會

- 路程時間 = 90 分鐘 - 交通費用 = 30 港元

3 - 購物類別 = 日用品 - 平均折扣 = 8 折 □ 會 □ 不會版本三 - 路程時間 = 60 分鐘 - 交通費用 = 30 港元

4 - 購物類別 = 珠寶首飾 - 平均折扣 = 8 折 □ 會 □ 不會 - 路程時間 = 60 分鐘 - 交通費用 = 50 港元

1 - 購物類別 = 珠寶首飾 - 平均折扣 = 7 折 □ 會 □ 不會 - 路程時間 = 90 分鐘 - 交通費用 = 10 港元

2 - 購物類別 = 日用品 - 平均折扣 = 7 折 □ 會 □ 不會

- 路程時間 = 90 分鐘 - 交通費用 = 50 港元

3 - 購物類別 = 奢侈品牌的服裝 - 平均折扣 = 8 折 □ 會 □ 不會版本四 - 路程時間 = 90 分鐘 - 交通費用 = 10 港元

4 - 購物類別 = 化妝品 - 平均折扣 = 9 折 □ 會 □ 不會 - 路程時間 = 90 分鐘 - 交通費用= 50 港元

Department of Civil Engineering The University of Hong Kong

第四部分）对香港公共交通及旅游业的意见（请于适合的空格中填上“√”号）（请由受访者自行填写）

10. 在本次旅行中，您曾搭乘过以下哪些交通工具？（可选多项） □ 步行 □ 自行车（单车） □ 港铁（地铁） □ 公交车（巴士） □ 小巴 □ 出租车（的士） □ 观光巴士 □ 渡海小轮（渡轮） □ 叮叮（电车） □ 其他，请列明 ______

11. 您对下列各项关于香港公共交通的满意程度是？非常非常满意满意一般不满意不满意没意见 a. 车费 □ □ □ □ □ □ b. 可达性/方便搭乘程度 □ □ □ □ □ □ （例如：车站/出入口位置） c. 行车速度 □ □ □ □ □ □ d. 拥挤程度 □ □ □ □ □ □ e. 安全性 □ □ □ □ □ □ f. 车辆状态 □ □ □ □ □ □ （例如：新旧，性能，整洁等） g. 资讯提供 □ □ □ □ □ □ h. 乘客支援 □ □ □ □ □ □ 整体满意程度(a) □ □ □ □ □ □

12. 根据第 11 题的内容，请列出前三项您认为在香港搭乘公共交通的重要因素。（请以标号“a”至“h”来填写）第一第二第三

13. 您认为下列各项旅游业措施或政策是否对香港旅游业有推动作用？最没有用最有用没意见 0 1 2 3 4 5 a. 推广邮轮旅游 □ □ □ □ □ □ □ b. 推广古迹旅游 □ □ □ □ □ □ □ c. 推广生态旅游 □ □ □ □ □ □ □ d. 发展艺术及文化设施 □ □ □ □ □ □ □ e. 发展水疗及度假村设施 □ □ □ □ □ □ □ f. 扩建主题乐园 □ □ □ □ □ □ □ g. 启用新基建设施 □ □ □ □ □ □ □ h. 兴建边境购物城 □ □ □ □ □ □ □ i. 推广自驾游 □ □ □ □ □ □ □ j. 恢复「一签多行」签注 □ □ □ □ □ □ □

- 问卷结束 – 衷心感谢您的帮助和配合，祝旅途愉快!