2016 Joint International Conference on Service Science, Management and Engineering (SSME 2016) and International Conference on Information Science and Technology (IST 2016) ISBN: 978-1-60595-379-3

Demarcation of the Hourly Communication Area: A Case Study of Xiamen-Zhangzhou-Quanzhou Metropolitan Area, China Yue-E ZENG 1,a , Shi-Dai WU2,b,* 1College of Resource and Environmental Science, Quanzhou Normal University, Quanzhou, China 2College of Geographical Sciences, Fujian Normal University, Fuzhou, China [email protected], [email protected] *Corresponding author

Keywords: Hourly Communication Area, Traffic Waiting Time, Xiamen-Zhangzhou-Quanzhou.

Abstract. With the advance of urban integration in China, the hourly communication area has attracted significant attention during the development of urban agglomerations. Using ArcGIS 10.1, this study uses the data on traffic networks and data from surveys conducted in Xiamen- Zhangzhou-Quanzhou Metropolitan Area, Fujian Province, in 2014 to demarcate the theoretical HCAs by applying the convex hull method, and establishes the actual HCAs according to the traffic waiting time. The analysis shows that the extent of the theoretical HCAs of XZQ is beyond the scope of the cities’ domains, with areas of 15473.3 km 2, 16356.7 km 2 and 19276.9 km 2, respectively. Furthermore, this paper shows that the traffic waiting time in XZQ ranges from 31 to 61 min, resulting in the reductions in the actual HCAs of 91.7%, 82.9% and 83.9%, compared to the theoretical HCAs. There are only a few areas of intersection between neighbouring cities. Policies should not only pay more attention to the intra-urban public transit systems but also build adequate public transit terminals to facilitate passenger transfers, such as new metro or light rail systems.

Introduction Evolving from the concept of the daily communication area, the hourly communication area (HCA) represents the region that can be accessed within one hour using various means of transport [1]. As a result of rapid development of transportation and increasing commuter demand, HCA has become one of the most important mechanisms of regional development and urban concentration [2] and one of the most documented terms in urban studies because of its important function in shortening the spatial and accelerating the exchange of products and personal labour [3], and it has been extended to other important concepts such as the one-hour metropolitan area, one-hour economic circle and one-hour life sphere. There are two focuses within the conventional literature: one deals with the characteristics of HCAs [1], the other addresses methods of demarcation [4]. However, when introducing traffic waiting time, conventional methods are inadequate for exploring the essence of HCA. Benenson et al. argued that urban access allows a detailed representation of travel times by transit and car; they maintained that an adequate representation of transit travel times is very important [5]. Olaru maintained that a delay in a trip or an early arrival can contribute to changes in timing, and thus proposed the idea of using fuzzy logic rules to explain the effect of variability in travel time [6]. Feng et al. analysed the trip times of a rural population exactly using individual attribution, and described the importance of traffic waiting time [7]. Traffic waiting time, understood as the means of reflecting the effectiveness of the transportation system, is a valuable concept to measure the actual time that the public travel, including the walk time from an origin to a stop, waiting time of vehicle, travel time of vehicle, delay time during a trip, transfer time and walking time from the final stop to the destination. Traffic waiting time is difficult to quantify, because it is affected not just by an individual’s employment structure [8], economic conditions [9] and preference as to traffic mode [10], but also by unavoidable incidents such as road traffic congestion [11], traffic delay, drivers’ physical delay [12] and the convenience of public transportation. Given the boom of city transportation systems and a good grasp of timeliness, the public’s demand for transportation is increasingly strengthened, and places a strong emphasis on the actual range that they can reach in one hour. Therefore, traffic waiting time is becoming a major factor in HCA research. Based on the data from a transportation network and questionnaire survey conducted in 2013, using XZQ as a case study, we will explore the HCAs and its effect to urban integration. The aims of this article are as follows: 1) to demarcate the theoretical HCAs of XZQ using the convex hull method; 2) to establish the actual HCAs according to traffic waiting time.

Study Area, Data Source and Methods Study Area XZQ are located in the south of Fujian Province in southeast China (23°33 ′20 ″–25°56 ′45 ″N, 116°53 ′21 ″–119°01 ′38 ″E), which is a relatively flourishing province in China in terms of economic development and urbanization. XZQ, with a terrestrial area of 25195 km 2 and a resident population of 16.86 million in 2014, is the most densely developed urban area in Fujian Province, and it is also the core area of the Western Taiwan Straits Economic Zone in China. Transportation within XZQ has developed rapidly since the completion of the first highway in Fujian Province in 1997, which is called the Quan-Xia highway. With the completion of the Fu-Xia high-speed railway in 2010, the Long-Xia high-speed railway in 2012 and the Xia-Shen high-speed railway in 2013, XZQ achieved a qualitative leap in transportation. Presently, the transportation of XZQ has formed the bunchy shape, including the Shen-Hai highway, Xia-Rong highway, Xia-Sha highway, Quan-Nan highway, Fu-Xia high-speed railway, Long-Xia high-speed railway, Xia-Shen high-speed railway, Ying-Xia railway, and Zhang-Quan-Xiao railway, G324 national road, and G319 national road. The length of highways in operation in XZQ was 26887 km in 2014, and the density of the road network was 106.7 km/100 km 2. As a result of continuous improvements in the traffic system, economic exchange and interpersonal communication became more frequent in XZQ, and Urban Integration Planning was activated in 2011. Data Sources Two types of data were collected: traffic data and survey data in XZQ. The current traffic data are the 2013 Car GPS traffic data provided by the Fujian Provincial Communications Department. Using this information, we construct a dataset including all information on railways (including high-speed railways), highways, national roads, provincial roads, and country roads (the data update deadline was 31/12/2013). Survey data were collected from June to October 2014 through a questionnaire survey jointly conducted by the authors and the investigation team.

Method We abstract the major stations of the cities (including railway, coach stations) to spatial nodes; thus, we obtain a total of 10 nodes in the study area. We assume that these nodes are the starting points when people travel within XZQ. According to the ‘Industry Standard of the People’s Republic of China: Design Specification for Highway Alignment (JTGD20-2006)’, we set the average speed in all types of road sections; furthermore, in the light of the design speed and the actual operation of the railway in XZQ, we determine the travel speed of each railway (Table 1). We select only railways, highways, national roads, provincial roads and country roads as the traffic network. We did not include waterways and air transportation. Using the diffuse method of isochronal area for the road network, we delineated the boundary points of the theoretical HCAs in XZQ. For high-speed railways and other mass transit, taking into account that transfers can only be accomplished at stations, and using the train timetable provided by the Chinese railway customer service centre website as standard, we obtain the border points at which people can arrive at from various nodes within one hour as the domain of the one-hour railway communication circle. In accordance with the above method, we recognize the boundary point of one theoretical HCAs of each city under the set speed of transportation network, and obtain the one-hour points set of XZQ.

Table 1. Types and velocities of land traffic network in XZQ [km/h].

Fu-Xia Long-Xia Ying-Xia Zhang-Quan-Xiao national provincial county high-speed highway high-speed raiway railway railway road road road railway 200 200 80 70 120 100 80 60

Traffic waiting time is subjective and difficult to quantify compared with the velocity of land traffic network, because of various influencing factors including the time of delay, time of transfer and other preparation times. Thus, we employ the average time taken by a person to get to a station as traffic waiting time, and obtain these data through questionnaires and interviews. Respondents were asked to provide traffic waiting time. The question used to obtain this information was ‘In general, how long do you spend on the way to the station?’ To reflect the means of transport more accurately, we also included questions such as ‘In general, what means of transport do you choose to get to the station?’ ‘In general, what means of transport do you chose to get to Xiamen/Zhangzhou/Quanzhou Cities?’ Before a formal investigation, we conducted a sample test in Huian County, Quanzhou City, in which a total of 80 questionnaires were sent out and 77 were retrieved, an effective rate of 96%. The reliability and validity of the preliminary scale were inspected, eliminating some less reliable indicators to form the final measurement for this study. Multiple stratified sampling procedures were used for selecting respondents whose residence registrations were in the study area. We distributed 2050 questionnaires and collected a total of 2010 valid responses, with 98.05% efficiency. Male (51.19%) and female (48.81%) respondents were almost equally represented, and their residences were in Xiamen City (41.94%), Zhangzhou City (28.36%) and Quanzhou City (29.70%). We used the convex hull method to demarcate the theoretical HCAs of XZQ. On the basis of the boundary point of the theoretical HCAs of XZQ, we construct the theoretical HCAs using the convex hull method. In view of questionnaires and interviews, we calculate and classify the average time for respondents to travel to the railway station/coach station using different types of transport, and take the average time as traffic waiting time for respondents to the railway station/coach station. We regard the proportion of different types of transport chosen by respondents to the railway station/coach station as the amend weight. Then, we amend the theoretical HCAs using traffic waiting time and the amend weight, thus obtaining the actual HCAs, the region that the public can reach in one hour including traffic waiting time.

Results The Traffic Waiting Time in XZQ People in Xiamen City mostly use buses to get to the railway station/coach station. More than 60% of respondents chose buses, a value higher than that for other means of transport. Nevertheless, in Zhangzhou City, large differences exist among public choices of transportation to the railway station/coach station. About 61.3% of people chose taxis to travel to the railway station; roughly equal percentages (about 19%) choose cars or buses, and only 30.1% use taxis to go to the coach station. The largest percentage of people, 37.7%, chose motorcycles to travel to the coach station. In Quanzhou City, the percentage using a motorcycle to go to the coach station is as high as 41.9%, and 32.8% use a bus. Meanwhile, the percentage choosing a car is 45.3%, and only 32.2% of the public in Quanzhou City use a bus to get to a railway station (Table 2). For the question ‘In general, how long do you spend on the way to the station?’, we calculate the average time based on the responses and use that value as traffic waiting time for different means of transport (Table 3). Using the ratios of the trip mode and traffic waiting time for each trip mode, we calculated traffic waiting time in XZQ using the weighted average method (Table 4). From an overall perspective, because of a peculiarity of the rail services (five minutes before the train starts, ticket checking ceases) and the long distance from the centre of the city to a railway station, traffic waiting time to the railway station is approximately 60 minutes, 20 minutes more than the time to the coach station. Therefore, although the railway can provide a more comfortable travel service for longer distances, traffic waiting time to reach the station is one hour, resulting in a greater choice of coach rather than high-speed trains when travelling to Xiamen/Zhangzhou/Quanzhou (Table 5), particularly in Zhangzhou City.

Table 2. Types of transport used for journeys to railway station/coach station in XZQ [%].

Question Xiamen Zhangzhou Quanzhou Car 20.1 19.4 45.3 Bus 61.3 19.3 32.2 The transportation to railway station Taxi 18.6 61.3 19.2 Motorcycle 0 0 3.3 Tricycle 0 0 0 Car 19.6 21.4 14.4 Bus 63.3 10.3 32.8 The transportation to coach station Taxi 17.1 30.1 9.7 Motorcycle 0 37.7 41.9 Tricycle 0 0.5 1.2

Table 3. Traffic waiting time for different means of transport in XZQ [min].

To railway station To coach station City Car Bus Taxi Motorcycle Tricycle Car Bus Taxi Motorcycle Tricycle Xiamen 40 60 45 -- -- 40 25 45 -- -- Zhangzhou 50 90 55 -- -- 35 60 40 35 45 Quanzhou 40 90 55 50 -- 25 55 30 30 40 Note: -- represents that it does not exist.

Table 4. Traffic waiting time in XZQ [min].

Destination Xiamen Zhangzhou Quanzhou To railway station 53.2 60.8 57.7 To coach station 31.4 39.1 37.6

Table 5. Percentage of trip mode to Xiamen/Zhangzhou/Quanzhou Cities [%].

Trip mode Xiamen Zhangzhou Quanzhou Car 26.1 25.1 14.0 Coach 39.4 61.1 44.7 High-speed train 34.5 13.8 41.3 Train 0 0 0 The Theoretical HCAs and Actual HCAs in XZQ To implement the aforementioned framework, we ascertain the boundary points of the theoretical HCAs of XZQ. Railways and other road networks constitute the trunk and branches of the traffic, respectively, which appears as a dendritic diffusion. The theoretical HCAs of XZQ, with areas of 15473.3 km 2 (X), 16356.7 km 2 (Z) and 19276.9 km 2 (Q), extend beyond the city regions. The central cities of XZQ have become the focal points of the transportation axis, constituting the distributed ‘axis’ of the entire road network, and the inter-city transportation axis of each stretch of road network acts as “spokes”. Thus, the entire road network represents a radial expansion from the central cities to the hinterland in an ‘axis–spoke’ structure. The main transportation axis, with a higher connectivity and a stronger ability to control the traffic network (Fu-Xia high-speed railway, Long-Xia high-speed railway and Xia-Shen high-speed railway and Shenhai highway speed), constitute the entire road network space, both sides of which are occupied by a lower-level traffic axis with a lower connectivity that constitutes a stretch of network space (Fig. 1). On the basis of the questionnaires and interviews on traffic waiting time in XZQ, the actual HCAs is obtained by using traffic waiting time and the amend weight to adjust the theoretical HCAs (Fig. 2). The actual HCA of Xiamen City has an area of 1284.4 km2. Its scope stretches to Tongan in the north, and ranges from Xiangan in the east to the area bordering Longwen in Zhangzhou City in the west, although the main part of the actual HCA is within Xiamen City. Obstructed by the Jiulong River and other rivers nearby, the actual HCA of Xiamen City, supported by the Shen-Hai highway and the Xia-Rong highway, expands in a fusiform shape to the northeast and southwest, covering Siming, Huli and partial regions of Xiangan, Tongan, Jimei and Haicang. The actual HCA of Zhangzhou City has an area of 2786.5 km 2, and covers Xiangcheng, Longwen, and part of Longhai, Nanjing, Changtai, and Jimei, and Haicang in Xiamen City. Differently from the coastal location of Xiamen City, the centre of Zhangzhou City is further inland, and the actual HCA of Zhangzhou City can extend in all directions. The actual HCA of Zhangzhou City is shaped like an irregular polygon running along the highway, of which the eastern part is adjacent to Jimei in Xiamen City. In contrast with Xiamen City and Zhangzhou City, the scope of the actual HCA of Quanzhou City is larger, with an area of 3094.3 km 2, covering the areas of Fengze, Licheng, most of Jinjiang, Shishi, Nanan, Luojiang, Quangang, Huian and a small part of Xiangan in Xiamen City. The actual HCA of Quanzhou City is mainly concentrated in its municipal area. Meanwhile, it is noteworthy that there is no significant overlap in the actual HCAs of XZQ, but there are some intersections between neighbouring cities concentrated near highways. Therefore, the regions around high-grade roads show the most significant space-time convergent effects and tend to be the leading areas of urban cooperation and exchange.

Figure 1. The theoretical HCAs in XZQ. Figure 2. The actual HCAs in XZQ.

As shown in figure 1 and figure 2, significant differences are present between the theoretical HCAs and the actual HCAs. Compared to the theoretical HCAs, the actual HCAs is dramatically reduced in size and is shrunk by 91.7% (X), 82.9% (Z) and 83.9% (Q), mainly covering the downtown area of each city. There is no region that is included in all actual HCAs of Xiamen City, Zhangzhou City and Quanzhou City, and no wide overlap in actual HCAs area between two adjacent cities. The actual HCAs appear as a cluster with stations as their origins, while stretching along the high-speed railways and highways.

Conclusions We suggest that expanding the actual HCAs will play an important role in urban integration of XZQ by establishing and improving the intra-urban transportation systems to reduce the public’s traffic waiting time. Policies should not only pay more attention to the intra-urban public transit systems but also build adequate public transit terminals to facilitate passenger transfers, such as new metro or light rail systems. This paper represents a research effort from the perspective of traffic waiting time, and more studies in different settings are needed to achieve a deeper and more complete understanding of this complex issue. Such studies also have great potential to provide new insights into patterns of urban spatial structure division and urban integration in the modern world, as this has become increasingly complicated and diverse due to improved transport and communication and a blurring of the public’s demand.

Acknowledgement This study is funded by the National Natural Science Foundation of China (No. 41171147) and the Key Discipline Development Project for Human Geography of Quanzhou Normal University. The authors would like to thank the investigation team for facilitating this research, and Professor Yongshi Li and Professor Zhiqiang Chen for their research assistance.

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