sustainability

Article Measurements of Pedestrian Friendliness of Residential Area: A Case Study in Hexi District of Nanjing

Caiyun Qian *, Difei Zhu ID , Yang Zhou and Jiadeng Chen

School of Architecture, Nanjing Tech University, Nanjing 211800, China; [email protected] (D.Z.); [email protected] (Y.Z.); [email protected] (J.C.) * Correspondence: [email protected]; Tel.: +86-137-7058-4818 or +86-025-58139459



Received: 14 May 2018; Accepted: 10 June 2018; Published: 13 June 2018


Abstract: As part of urban transportation, walking plays an important role in promoting the development of urban green transportation systems. Through the analysis of resident travel preference and the influence factors on travel choice of land use and related effects, this paper puts forward the walking accessibility, pedestrian route directness (PRD), street walking popularity and the ratio of green interface as a quantitative index of the measurement system of pedestrian friendliness, to evaluate the effect of land use and urban space on the residents’ walking. Based on this, this paper takes Nanjing Hexi District as an example, using ArcGIS software to quantify the city residents’ travel purpose. Two-dimensional space characteristic analysis verifies that the high density and high degree of mixing, high accessibility blocks with good street environmental quality and open environment on the pedestrian block has a good role in promoting walking, and then puts forward optimization suggestions to promote the pedestrian friendliness in Hexi District.

Keywords: pedestrian friendliness; accessibility; potential model; pedestrian route directness; flexible travel

1. Introduction Walking is of great significance to the development of low-carbon cities. Advocating walking in cities is also a good policy to ease urban problems such as traffic congestion and environmental pollution. Since the 1990s, many countries have made great efforts to promote walking in urban development. For example, Germany has set up a central pedestrian zone in all cities and a vast majority of towns with populations of over 50,000; and in the United States, Portland has reduced the per capita number of kilometers traveled by cars by controlling the growth of urban boundaries, integrated development of transport and land use and by encouraging walking and cycling trips [1]. Some local governments and research institutes in the world are also pushing forward the evaluation research on the development of walking trips and setting the city’s pedestrian friendliness as the standard for assessing the livability of cities. For example, the Scottish Walkability Assessment Tool has been used to evaluate the city’s pedestrian friendliness level [2]. In 2007, American scholars put forward the “Walk Score”, a city walk friendliness score system, which has been widely accepted internationally and has been practiced in many countries [3]. Many scholars have conducted extensive research on the pedestrian friendliness of cities. The commonality of these studies is to build a suitable walking environment in the city, thus promoting more walking behavior. Some scholars believe that land use patterns often affect the choice of residents’ travel mode. High-density mixed land use patterns can significantly promote pedestrian trip. Generally,

Sustainability 2018, 10, 1993; doi:10.3390/su10061993 www.mdpi.com/journal/sustainability Sustainability 2018, 10, 1993 2 of 22 the higher the density means the smaller the distance the public travel for different purposes such as living, working and leisure shopping. Cervero found that higher density, diverse land use and pedestrian-oriented design could reduce motor vehicle trips and encourage non-motorized trips [4]. Ewing believed that residents living in compact communities walked more [5]; Lu and Ding et al. advocated high-intensity development and encouraged public transport and believed that it would effectively reduce the total demand for travel and shorten the residents travel distance, thereby promoting residents to walk and use other forms of green travel [6]. Many theoretical studies and empirical analyses show that the intensive land development and the spatial pattern of compact growth can reduce people’s travel distance, thereby reducing their dependence on car travel and promoting the pedestrian trip. The overall purpose of the above study is to reduce the distance of daily travel behavior, optimize the urban construction environment, improve the quality of walking travel, and ultimately improve the proportion of people walking in daily travel. Therefore, this paper takes Hexi District in Nanjing as an example to study the influence of land use and the spatial pattern of neighborhoods on pedestrian travel and conducts a quantitative analysis on the relevant indicators by establishing a model, so as to provide effective reference for the study on improving the city’s pedestrian friendliness.

2. The Impact Factors of Built Environment Based on Flexible Trip Perspective Travel behavior can be divided into the necessary travel and flexible travel. The former one refers to commuting travel behavior, which is characterized by fixed travel time, routes and ways and is often related with the distance between office location, residence and urban transportation. The flexible travel is characterized by shopping, fitness, leisure and communication, etc. Its travel time, route and way are more flexible, and the behavior itself will be influenced by the travel distance, travel time, the built environmental quality, street popularity and many other factors. In order to explore which factors affect the flexible travel of residents, the author selected the Hexi District of Nanjing as the research area and studied the travel preferences and travel mode choice of residents’ flexible travel behavior. Based on the study, the impact factors were selected for analysis.

2.1. Overview of Research Area The study area is the central part of the Hexi District of Nanjing, the provincial capital of Jiangsu Province. As the center of the new city, the Hexi District of Nanjing has gathered many urban functions such as business, residence, exhibition, culture and sports. The study area is the central part of the Hexi District, with Fengtai South Road in the east, Yangzijiang Avenue in the west, Yingtian Street in the north, and Jiangshan Street in the south, all surrounded by urban expressways. Since 2000, after more than 10 years of construction, the district has formed a relatively complete urban spatial form and infrastructure, which is mainly divided into four types including residential lands, commercial lands, public service lands, and areas to be built (Figure1). To note here, in recent years, China’s real estate development has mostly adopted the construction mode of gated residential blocks. The residential blocks adopting this development mode are often surrounded by streets and roads. Most of them are isolated from the outside by fences and gates. The internal roads of these blocks are not open to the public, only for the use of the owner. Therefore, the roads within the block are not concerned as urban public roads. Sustainability 2018, 10, 1993 3 of 22 Sustainability 2018, 10, x FOR PEER REVIEW 3 of 22 Sustainability 2018, 10, x FOR PEER REVIEW 3 of 22

FigureFigure 1.1. Status map map of of land land use use in in Hexi Hexi District District.. Figure 1. Status map of land use in Hexi District. TheThe author author divides divides thethe researchresearch area into 29 29 traffic traffic analysis analysis zones zones (TAZ) (TAZ) using using the the public public urban urban The author divides the research area into 29 traffic analysis zones (TAZ) using the public urban mainmain roads roads as as the the standard standard ofof divisiondivision (Figure 22).). main roads as the standard of division (Figure 2).

Figure 2. Zoning map of Hexi District. Figure 2. Zoning map of Hexi District. Figure 2. Zoning map of Hexi District. 2.2. Impact Factors of Pedestrian Travel Based on Residents’ Travel Preference 2.2. Impact Factors of Pedestrian Travel Based on Residents’ Travel Preference 2.2. ImpactThe Factorspreliminary of Pedestrian data collection Travel Based of this on studyResidents’ is mainly Travel dividedPreference into two parts: A survey of The preliminary data collection of this study is mainly divided into two parts: A survey of residents’ travel preferences and travel concern factors and a survey of residents’ travel mode residentsThe preliminary’ travel preferences data collection and travel of this concern study factors is mainly and divideda survey into of residents two parts:’ travel A survey mode of choice. residents’choice. travel preferences and travel concern factors and a survey of residents’ travel mode choice. 2.2.1. Data Collection 2.2.1.2.2.1 Data. Data Collection Collection  Survey on Residents’ Travel Preferences and Travel Concern Factors •  SurveySurvey on Residents’on Residents Travel’ Travel Preferences Preferences and and Travel Travel Concern Concern Factors Factors This survey is mainly aimed at surveying the three types of travel behaviors of residents in the This survey is mainly aimed at surveying the three types of travel behaviors of residents in the HexiThis District, survey including is mainly daily aimed commuter at surveying traffic, the shopping three types and consumption of travel behaviors travel, and of residents leisure and in the HexiHexi District, District, including including daily daily commuter commuter traffic,traffic, shopping and and consumption consumption travel, travel, and and leisure leisure and and Sustainability 2018, 10, 1993 4 of 22 entertainment travel. The questionnaires were sent to residents to investigate their preferred travel mode among the above three types of travel behaviors, as well as the factors that are of concern when travelling on a daily basis and scoring the factors that they think affect their travel experience. At the same time, a personal attribute survey was set up in the questionnaire to exclude non-local residents’ questionnaires. The main contents of the investigation include: 1 Survey of residents’ travel preferences: According to the purpose of travel, it is divided into three categories: commuting, daily shopping and consumer travel and leisure and entertainment travel. The frequency and transportation modes of residents in the above mentioned three kinds of travel are recorded, among which the alternative modes of transportation include walking, non-motor vehicles, public transportation and motor vehicles. 2 The survey of residents’ travel concern factors: Through the questionnaire, the investigated individuals selected their concern factors that have an impact on their walking travel and rated the satisfaction of these factors between 1 and 10 points, so as to obtain the satisfaction of residents on the built environment of Hexi District. • Survey of Residents’ Travel Mode Choice In order to obtain the actual measurement data of residents’ travel behavior in Hexi, this study used camera counting to record residents’ non-commuting travel. It can be seen from Section 2.1, that currently China adopts the model of gated residential block to construct residential land. Therefore, in this study, in order to effectively distinguish between local residents and outsiders and also because it is convenient to explore the relationship between the amount of travel behavior and the resident population, 46 residential blocks were selected for survey of residents’ travel mode in the 14 main residential TAZs of NO. 1, NO. 2, NO. 4, NO. 5, NO. 7, NO. 8, NO. 9, NO. 10, NO. 15, NO. 18, NO. 21, NO. 23, NO. 26, and NO. 28. On a fine weather day, the author selected 9:30–16:30 and 18:00–20:00 to set up a fixed camera to record the residents’ trips in the area and obtain residents’ videos during non-commuting hours, using OpenCV and Matlab software to track and count the moving objects in the video. Because this study uses a fixed camera to collect residents’ travel data, the background changes little, so the moving objects are identified and counted by using the inter-frame difference method and the background difference method. The specific process includes: 1 Firstly, Frame processing is performed on the original video; 2 Select several frames without moving objects to compose a static reference background frame; 3 Performed grayscale processing on each frame so as to reduce the amount of computation; 4 Set different target-scale adaptive algorithms for moving objects; 5 Eliminate boundary effects; 6 Use the inter-frame difference method and the background difference method to identify the features of moving objects, so that the moving objects are tracked and identified.

2.2.2. Data Analysis • Analysis of Residents’ Travel Preferences and Travel Concern Factors After sorting out the collected questionnaires, a total of 1099 valid questionnaires were obtained. From the perspective of residents’ travel preferences (Figure3), in non-communicative transportation, 56.41% of residents preferred to walk. In the two main types of non-commuting traffic behaviors, the residents’ preference for walking in leisure and entertainment travel is 58.66%, which is slightly higher than 54.13% in shopping and consumption travel. It can be seen that walking is absolutely dominant in the flexible trip. More than half of the residents will choose to walk in non-commuting travel. There are also some respondents who declared that in addition to outings or vacations, almost all of the daily flexible trip is walking, it can be seen that walking has almost become a “rigid demand” in non-commuting traffic for residents. Sustainability 2018, 10, 1993 5 of 22

Sustainability 2018, 10, x FOR PEER REVIEW 5 of 22

Sustainability 2018, 10, x FOR PEERPreference REVIEW of travel mode choices 5 of 22

Preference of travel mode choices 27.80% leisure and entertainment 58.66%

leisure and entertainment 22.20%27.80% shopping and consumption 54.13%58.66%

shopping and consumption 22.20%25.02% non-commuting 54.13%56.41%

25.02% non-commuting 33.09% 56.41% commuting 11.28% 33.09% commuting0.00% 10.00%11.28%20.00% 30.00% 40.00% 50.00% 60.00% 70.00%

preference on0.00% motor10.00% Vehicle20.00% 30.00%preference40.00% 50.00% on60.00% walk70.00% preference on motor Vehicle preference on walk Figure 3. Ratio of travel mode choice preference of residents. Figure 3. Ratio of travel mode choice preference of residents. Figure 3. Ratio of travel mode choice preference of residents. In the survey of the concern factors, the author summarized the factors that concern residents duringIn the In walking. surveythe survey ofIt theisof mainlythe concern concern divided factors, factors, into the the five authorauthor categories: summarized street thelandscape the factors factors that planting that concern concern, street residents residentsleisure duringfacilities,during walking. garbage walking. It iscansIt mainly is mainlyand billboards divided divided into and into fiveother five categories: categories: facilities, streetpublic street landscape transportation landscape planting planting, station, street streetlayout leisureleisure and facilities,conveniencefacilities, garbage garbage of stores cans cans andalong and billboards the billboards street. and and otherother facilities,facilities, public public transportation transportation station station layout layout and and convenienceconvenienceBy summarizing of stores of stores along the along scores the the street. ofstreet. residents rated on the above factors (Figure 4), it can be seen that residentsBy summarizingBy havesummarizing a good the overall the scores scores evaluation of of residents residents of the ratedrated built on environment the the above above factors factors quality (Figure (Figure of Hexi 4), 4it ),District can it can be .seen beOnly seen that TAZ that residentsNo.residents 10 haveand haveNo. a good 23a good have overall overall residents evaluation evaluation scoring of o fless the the builtthanbuilt environment6 points on thequality quality street of Hexileisure of Hexi District facilities, District.. Only while Only TAZ all TAZ No. 10 and No. 23 have residents scoring less than 6 points on the street leisure facilities, while all No.other 10 and TAZs No. have 23 have scored residents more than scoring 6 points, less than and 6many points zone ons the have street scored leisure more facilities, than 8 points while on all the other other TAZs have scored more than 6 points, and many zones have scored more than 8 points on the TAZsfactors have. scored more than 6 points, and many zones have scored more than 8 points on the factors. factors.

Score of Concern factors of residents' walking behavior Score of Concern factors of residents' walking behavior No.28 No.28 No.26No.26 No.23No.23 No.21No.21 No.18No.18 No.15No.15 No.10No.10 No.9No.9 No.8No.8 No.7No.7 No.5No.5 No.4No.4 No.2No.2 No.1No.1 0.00 2.00 4.00 6.00 8.00 10.00 12.00 0.00 2.00 4.00 6.00 8.00 10.00 12.00 Convenience of stores Convenience of stores Garbage can, billboards and other facillities Garbage can, billboards and other facillities Street leisure facility Street leisure facility Street landscape planting Street landscape planting Public transportation layout Public transportation layout

Figure 4. Score of concern factors of residents’ walking behavior. FigureFigure 4. 4Score. Score of of concern concern factorsfactors of residents’ walking walking behavior behavior.. Sustainability 2018, 10, 1993 6 of 22 Sustainability 2018, 10, x FOR PEER REVIEW 6 of 22

 • AnalysisAnalysis of of Survey Survey Data Data of of Resident Residents’s’ Travel Travel Mode Mode Choic Choicee A Atotal total of of23,760 23,760 samples samples of travel of travel behavior behavior have have been been obtained obtained by using by using the above the above algorithm algorithm in Sectionin Section 2.2.1 . 2.2.1Through. Through the summary the summary of the oftravel the travelquantity quantity of each of residential each residential block in block the study in the area, study thearea, author the obtained author obtained the resident the trip resident data ( tripFigure data 5). (Figure It can be5). seen It can from be Figure seen from 5 that Figure the difference5 that the of differenceabsolute number of absolute of travel number amount of travel between amount each between residential each block residential is huge. block The is maximum huge. The number maximum of numberpedestrian of pedestriantrips in these trips blocks in these exceeded blocks 900 exceeded people 900 per people day, and per the day, minimum and the minimum number is number less thanis less20 people. than 20 By people. comparing By with comparing the size with of the the block size and of the the blocktotal travel and the amount, total travelthe absolute amount, valuethe absoluteof walking value travel of walkingis influenced travel by is the influenced popularity by of the each popularity residential of each block residential and the surrounding block and the environment.surrounding environment.

The travel quantity of each rensidential block

No.28c No.26c No.23d No.23a No.21b No.18e No.18b No.15d No.15a No.8f No.8c No.7b No.5c No.4c No.2 No.1a 0 200 400 600 800 1000 1200

Motor vehicle Non-motor vehicle Walking

Figure 5. The travel quantity of each residential block. Figure 5. The travel quantity of each residential block. In order to further understand the choice of each traffic mode in daily life, the author summarizesIn order the to above further survey understand data and the obtains choice the of each ratio traffic of travel mode mode in daily choices life, in the the author non-commuter summarizes periodthe above of the survey main data residential and obtains TAZs. the From ratio Figure of travel 6, mode it can choices be seen in that the non-commuter the proportion period of actual of the walkingmain residential trips in most TAZs. TAZs From meets Figure or is6, close it can to be the seen proportion that the proportion of walk (56.41%) of actual in walking the surve tripsy of in residentsmost TAZs’ preferences meets or is to close travel. to the Among proportion them, of the walk highest (56.41%) percentage in the survey of walking of residents’ in TAZ preferences No. 21 reachesto travel. 75.52%; Among however, them, TAZ the highest No. 5 (40.59%), percentage No. of 7 walking(33.15%), in No. TAZ 23 No. (37.00%) 21 reaches and No. 75.52%; 26 (31.03%) however, wereTAZ significantly No. 5 (40.59%), lower No. than 7 (33.15%), the walking No. 23ratio (37.00%) of 56.41% and in No. the 26 residents (31.03%)’ werepreference significantly survey lower. than the walking ratio of 56.41% in the residents’ preference survey. Sustainability 2018, 10, 1993 7 of 22 Sustainability 2018, 10, x FOR PEER REVIEW 7 of 22

Ratio of travel mode choice in non-commuter period

NO.28 59.04% NO.26 31.03% NO.23 37.00% NO.21 75.52% NO.18 54.86% NO.15 65.10% NO.10 56.10% NO.9 61.19% NO.8 55.64% NO.7 33.15% NO.5 40.59% NO.4 60.76% NO.2 61.20% NO.1 54.36% 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00%

Ratio of motor vehicle Ratio of non-motor vehicle Ratio of walk

Figure 6. Ratio of travel mode choice in non-commuter period. Figure 6. Ratio of travel mode choice in non-commuter period. From this it indicates that although the actual walking ratio in most areas has reached or exceededFrom this the it pedestrian indicates thatratio although in the residents the actual’ preference walking survey, ratio in there most are areas still has some reached issues or in exceeded some theTAZs pedestrian that have ratio hindered in the residents’ residents preference’ willingness survey, to travel there are by still walk, some resulting issues in some a substantial TAZs that haveproportion hindered of residents’their actual willingness trips being tofar travel below by the walk, expected resulting value. in a substantial proportion of their actual trips being far below the expected value. 2.2.3. Correlation Analysis of Residents’ Concern Factors and Residents’ Flexible Trip Mode Choice 2.2.3. Correlation Analysis of Residents’ Concern Factors and Residents’ Flexible Trip Mode Choice Using the data collected above, the author used the SPSS statistical software to analyze the correlationUsing the between data collected the residents’ above, travel the preference author used data the and SPSS the statisticalresidents’ softwarenon-commuting to analyze travel the correlationmode choices between; the theSpearman residents’ correlation travel preference analysis about data andthe residents’ the residents’ travel non-commuting selection data separately travel mode choices;along the with Spearman the scoring correlation of street analysis greening, about street the leisure residents’ facilities, travel trash selection cans data and billboards,separately alongthe withconvenience the scoring of oflayout street of greening, public transportation, street leisure and facilities, the convenience trash cans andof shops billboards, along the the street convenience, was ofconducted layout of public to identify transportation, variables that and have the an convenience impact on residents of shops’ flexible along thetrip. street, was conducted to identifyThrough variables the that correlation have an analysis impact onof residents’the residents’ flexible preference trip. to travel and the residents’ choice of flexible trip mode selection data, we can see (Table 1) that the urban construction variables that Through the correlation analysis of the residents’ preference to travel and the residents’ choice are significantly related to residents’ choices of flexible trip modes include street landscape planting of flexible trip mode selection data, we can see (Table1) that the urban construction variables that and convenience of stores. Three items of street rest facilities: Garbage can, billboards and public are significantly related to residents’ choices of flexible trip modes include street landscape planting transportation station layout did not have a significant correlation with the choice of residents’ mode and convenience of stores. Three items of street rest facilities: Garbage can, billboards and public of flexible trip. The higher the convenience of business along the street and degree of satisfaction transportationwith greening station along layoutthe street, did the not more have residents a significant choose correlation to travel by with walking. the choice It can of be residents’ inferred t modehat ofresidents flexible trip. are more The higherconcerned the about convenience whether ofthey business can easily along obtain the commercial street and degreeand public of satisfaction services within non greening-commuting along thetraffic, street, and the whether more there residents is good choose landscaping to travel to by ensure walking. the quality It can beof inferredwalking. that residents are more concerned about whether they can easily obtain commercial and public services in non-commuting traffic,Table and 1. whetherCorrelation there of residents’ is good preference landscaping and travel to ensure mode the choice. quality of walking.

Street Street Garbage Can, Public Travel Mode Convenience Street Table 1.LandscpeCorrelation and ofLeisure residents’ Billboards preference and Other and travelTransportation mode choice. Choices of Stores Popularity Planting Facilities Facilities Station Layout Correlation 0.642 * −0.080 −0.347 −0.215 0.747 ** 0.709 ** Ratio of Walking Street Garbage Can, Public Travel Mode Significance Street0.018 Landscpe 0.796 0.246 0.481 Convenience0.003 0.007Street Leisure Billboards and Transportation Choices and Planting of Stores Popularity Ratio of Motor Correlation −0.591 * 0.083Facilities Other0.322 Facilities Station0.360 Layout −0.416 −0.566 * Vehicle Significance 0.033 0.789 0.284 0.226 0.157 0.044 Ratio of Correlation 0.642 * −0.080 −0.347 −0.215 0.747 ** 0.709 ** ** Correlation is significant at the 0.01 level (2 tailed); * Correlation is significant at the 0.05 level (2 Walking Significance 0.018 0.796 0.246 0.481 0.003 0.007 tailed). Ratio of Motor Correlation −0.591 * 0.083 0.322 0.360 −0.416 −0.566 * Vehicle Significance 0.033 0.789 0.284 0.226 0.157 0.044 ** Correlation is significant at the 0.01 level (2 tailed); * Correlation is significant at the 0.05 level (2 tailed). Sustainability 2018, 10, 1993 8 of 22

3. Quantitative Indicators Affecting Flexible Walking Travel In the study above, the factors related to the choice of flexible travel mode are determined. From the perspective of flexible travel behavior, the author considered that the flexible travel can mainly be divided into purposeful, flexible travel behavior and non-purposeful, flexible travel behavior. The former includes shopping, going to the gym or restaurant, etc., and the latter includes strolls, walking the dog, etc. Therefore, the author discusses the influence on flexible travel from four aspects of travel time, travel distance, street popularity and the built environmental quality. The author will discuss the influence on flexible travel from four aspects of travel time, travel distance, street popularity and the built environmental quality. This chapter will further quantify the above factors so as to obtain indicators that can measure the pedestrian friendliness.

3.1. Travel Time In terms of travel cost, the cost of money and the cost of time are the main factors that affect the travel mode choices of residents. Through quantitative research on transportation cost, Pan found in his research that the most influential factor to travel cost was travel time [7]. Since walking is a human-driven mode of transportation, walking duration is often related to the feeling of the pedestrian. Horning found in her study that within 5 min, it was the best time for residents to walk [8]. In most studies based on walking, the walking distance of 5 min (400–600 m) is often taken as the reference for the suitable walking distance. However, for different travel behaviors, there are also differences in residents’ travel habits. Lu found in his research that residents have different tolerable limit travel times for different travel purposes, with a commuting time of 45 min, shopping of 35 min and leisure and entertainment time of 85 min [9]. Yang and Xu use time cost accessibility to evaluate the level of public services in different residential areas. They believe that the purpose of facility planning is to ensure equitable access to different classes of residents (with equal results), rather than to satisfy the fairness of geographical space (with equal forms) [10]. Through the analysis in Section 2.2.3, for the purpose of flexible travel, the convenience of stores down the street was significantly associated with residents’ travel mode choices, which indicates that the accessibility of stores and public service facilities around the community is an important factor influencing residents’ choice of flexible travel. Considering the effect of time cost on walking travel, the weighted average travel time based on the potential model is taken as the measurement index. The potential model was first proposed by Lagrange by referring to Newton’s law of gravity, and then developed into a classical model to study the interaction between urban spaces [11]. Hansen also introduced the potential model when analyzing the relationship between urban population activities and residential land development, he defined accessibility as the potential to obtain interaction opportunities [12]. The analytical model used in this article is based on Geertman’s formula of the potential model [13], which takes time as an expression for accessibility, so that it is easy to understand the meaning of the accessibility value. The potential model is regarded as the basis for the analysis to describe the relationship between supply and demand and spatial relations between two locations in urban space. By quantizing the characteristics of the two-dimensional streets and classifying the departure and destination of residents’ walking trips, an analysis model is established, coupled with the space resistance surface, to simulate the walking behavior in the area. Therefore, the model can better reflect the situation of considering travel destinations in actual traffic trips. The specific formula is shown as follows:

n ti = ∑ (pijtij) j = 1 j 6= 1

Aij Pij = Ai Sustainability 2018, 10, 1993 9 of 22

Aij = Mj/ f dij

k Ai = ∑ Aij j=1

ti represents the weighted average travel time of the demand point; Pij is the probability distribution function of the spatial strength of the resource provided by the supply point j to the demand point i; tij is the transit time between the demand point i and the supply point j; Aij is the spatial intensity between demand point I and the supply point j; Mj is the supply intensity of supply point j; dij is the spatial distance between point i and point j, The distance in this paper is the actual shortest path distance. f () is the distance attenuation function; Ai is the supply intensity of all points to demand point i in the calibration range; and k is the number of supply points.

3.2. Travel Distance Absolute distance to the destination is the most influential factor that affects pedestrian travel; the importance of travel distance is much greater than factors such as weather, the body situation and the fear of crime [14]. The travel distance is not only an expression of the spatial relationship between the origin and the destination in the actual physical environment, but also a reflection of the personal willingness of residents in their travel behaviors. Generally speaking, travel behavior is dependent on urban road network and road facilities, the length of most routes is often greater than the space linear distance between the corresponding origin and destination. Daff’s study finds that only 27 percent of walkers are willing to make long detours to use the crossing street facilities [15]. It was observed in the study by Jan Gehl that the majority of pedestrians prefer to take a risk from the ground directly to get over the street and are unwilling to use underground access [16]; Considering that walking behavior is greatly influenced by the physical strength of walkers, most walkers prefer more direct paths. Therefore, many scholars have introduced the Pedestrian Route Directness (PRD) to describe the ratio of the path length and the spatial linear distance between origin and destination, so as to express the extra distance required for walkers to reach their destination. In order to further consider the effect of travel distance on the purposeful flexible travel, the author introduces the relationship between the supply and demand of the origin and destination in the above potential model in the traditional pedestrian route directness to get the integrated pedestrian route directness of the area. That means to calculate the ratio of the straight-line distance to the actual shortest walking distance in the space based on the spatial relationship of origin and destination in the potential model, it is necessary take the attraction probability function of the supply point to the demand point as the weight of the segment path and then obtain the integrated pedestrian route directness of a region by weighting the pedestrian route directness of all the starting and ending points.

n ri = ∑ (pijrij) j = 1 j 6= 1

ri is the integral pedestrian route directness of demand point i. pij is the probability distribution function in the above potential model. rij is the pedestrian route directness between the demand point i and supply point j. In summary, the integral pedestrian route directness in this paper is a pedestrian route directness that takes into account the service of the travel destinations for different travel destinations. This factor can not only evaluate the linear coefficients of a road network in a certain area of the city, but also explore the influence of the spatial distribution of travel destinations on the pedestrian route directness. Sustainability 2018, 10, 1993 10 of 22

3.3. Street Popularity As a slow mode traffic in an open environment, walking is not only a kind of urban traffic mode, but also a part of urban life. Communicative activities and social activities will also stimulate the generation of walking behaviors, especially the generation of non-purposeful walking behaviors (that is, the travel itself is the purpose). Good street popularity can effectively improve the pedestrian density of streets, thus promoting the generation of Communicative activities and social activities. Studies have shown that there are many links between street popularity and walking behavior. As the origin-destination for most traffic behaviors in cities, the residential area is the most important urban function to hold the population, and it is also the most direct source of street popularity. Agrawal and Schimek believe that when the population density reaches 9700 people per km2, the residents have more trips [17]; Mouden found through their studies that when the density of living exceeded 21.7 households per acre, residents had the longest walking travel time [18]. Therefore, the number of the resident population in the city is also an important measure of walking travel behavior. In order to verify the relationship between the residents’ walking travel and the street popularity, the author established the index of street walking popularity according to the relevant research, and the calculation formula is as follows. PW = dr pr

Among them, PW represents the potential pedestrian popularity in the region, dr indicates the residence density of area r (unit: Household/square kilometer), and pr indicates the ratio of daily average pedestrian travel amount and resident population of plot r.

3.4. Built Environment Quality The built environmental quality of streets also has many influences on residents’ travel. A superior walking environment not only attracts people to participate in walking, but also enables people to accept longer walking distance. According to the research in Section 2.2.3 of this paper, landscape planting along the street is an influential factor that residents in Hexi District generally pay attention to and it has a significant correlation with residents’ flexible travel. Chen Yong believed that the density of a public green space and the ratio of transparent (green) interface have influences on residents’ travel [19]. Considering that most of the landscape greening layout forms in the study area are attached to roads, there is basically no current situation of greening groups and parks, so this paper uses the index of the ratio of greening interface along the street to quantify the variable of greening along the street; the specific formula is as follows: Gi = gi/Si

Among them, Gi represents the proportion of green interface along the street in the i area; gi represents the greening interface length in the i area; Si represents the total length of the street in the i area.

4. Case Study in Hexi District The impact factors and quantitative indicators related to residents’ walking trips of flexible travel have been basically determined in the above article. This chapter will measure the current status of pedestrian friendliness in Hexi District through the above-mentioned quantitative indicators and verify the reliability of the model based on the actual survey data of residents’ travel.

4.1. Analysis of Travel Time Based on Potential Model

4.1.1. Acquisition of Research Data This paper studies the impact of land use and urban spatial morphology on residents’ flexible travel. Therefore, the author obtained the travel destination data through the POI retrieval. POI means Sustainability 2018, 10, 1993 11 of 22

“Points of Interest” and is a variety of living, entertainment and service facilities that are attractive to residents’ activities and trips. Each POI data includes sub-data such as name, category, latitude and longitude. It is the open-source data based on location services and has been widely applied in today’s Sustainability 2018, 10, x FOR PEER REVIEW 11 of 22 electronic map services for its high timeliness. The spatial clustering and density of POI coordinate dataof POI on coordinate the 2D map data can reflecton the the 2D type map and can degree reflect of the mixing type of and urban degree land of to mixing some extent of urban (as is land shown to insome Figure extent7). (as is shown in Figure 7).

FigureFigure 7.7. Status quoquo of Hexi District,District, Nanjing.Nanjing.

AtAt thethe samesame time,time, the the author author obtains obtains the the coordinate coordinate data data of theof the main main residential residential area area through through the the online map software and transforms the urban road network into two-dimensional plane vector online map software and transforms the urban road network into two-dimensional plane vector data data that can be quantitatively analyzed through the AutoCAD software. Lastly, the author uses the that can be quantitatively analyzed through the AutoCAD software. Lastly, the author uses the open open information of housing rental websites to estimate the resident population data in the study information of housing rental websites to estimate the resident population data in the study area. area. 4.1.2. Overview of Facilities Distribution Based on Kernel Density 4.1.2. Overview of Facilities Distribution Based on Kernel Density As is acquired from the above POI data, the author has retrieved a total of 4142 facilities including food,As entertainment, is acquired shopping,from the above living POIservices data, and the public author facilities has retrieved in this research a total area.of 4142 The facilities author carriedincluding out food, a kernel entertainment, density analysis shopping, by ArcGIS living softwareservices and to study public the facilities spatial in distribution this research of thesearea. commercialThe author carried facilities. out Kernela kernel density density analysisanalysis isby basedArcGIS on software the kernel to study function the spatial of Silverman distribution [20]. Thisof these study commercial considers facilities. the spatial Kernel distribution density betweenanalysis facilities.is based on Therefore, the kernel the function Population of Silverman fields of each[20]. pointThis study are set considers to None. the Because spatial this distribution paper takes between walking facilities. travel Therefore, as the research the Pop object,ulation the fields walk advantageof each point travel are set distance to None. 500 Because m is used this as paper the bandwidth. takes walking According travel as to the the research kernel density object, the analysis walk ofadvantage the coordinate travel pointsdistance of 500 these m facilitiesis used as (as the is shownbandwidth. in Figure According8), the TAZto the No. kernel 17 and density No. 20 analysis in the centralof the coordinate area is the points central of business these faci district,lities (as with is shown a strikingly in Figure obvious 8), the combined TAZ No. effect 17 and of No. the facilities.20 in the Thecentral facilities area is in the other central TAZs business mainly district, depend onwith the a distributionstrikingly obvious of residential combined areas, effect of whichof the thefacilities. main residentialThe facilities TAZ in other No. 8 TAZ has as largemainly number depend of on commercial the distribution service of facilities, residential with are anas, obvious of which combined the main effect.residential TAZ TAZ No.23 No. has 8 has very a fewlarge internal number facilities; of commercial the rest service of the residentialfacilities, with zones an haveobvious a fair combined number ofeffect. internal TAZ facilities, No. 23 has with very heterogeneous few internal distribution,facilities; the relativelyrest of the loose residential overall zones layout have and a a fair small-scale number combinedof internal effect facilities, generating with in heterogeneous some zones; other distribution, non-residential relatively TAZs also loose have overall low facilities layout density. and a small-scale combined effect generating in some zones; other non-residential TAZs also have low facilities density. Sustainability 2018, 10, 1993 12 of 22

Sustainability 2018, 10, x FOR PEER REVIEW 12 of 22

FigureFigure 8. Kernel 8. Kernel density density map map of of the the distribution distribution of service service facilities facilities in inHexi Hexi District, District, Nanjing Nanjing..

4.1.3. Data Processing Based on Accessibility Models 4.1.3. Data Processing Based on Accessibility Models The above kernel density analysis has made a preliminary study on the spatial distribution of Thecommercial above kerneland public density service analysis facilities has in madeHexi District a preliminary, so the following study on is thefurther spatial simulated distribution and of commercialanalyzed and for the public relationship service between facilities commercial in Hexi District, and public so service the following facilities isand further residents simulated’ travel. and analyzedThe for theauthor relationship uses the existing between CAD commercial road network and vector public data, service which facilities stores the and status residents’ quo of road travel. Thetraffic author network, uses road the length, existing road CAD intersections road network and other vector information. data, which With stores ArcGIS the status10.2 software, quo of road trafficthe network, following road steps length, can be followed road intersections to compare the and accessibility other information. of the domain With values: ArcGIS 10.2 software, (1) First, add the existing CAD data in ArcGIS, export the “dwg polyline” data and store it in the following steps can be followed to compare the accessibility of the domain values: the feature class in the new personal geodatabase; re-import the shape file in the above database and (1) First, add the existing CAD data in ArcGIS, export the “dwg polyline” data and store it in add the field of “walking time” and use the “field computer” function to calculate the walking time the feature(refer to class the av inerage the new adult personal walking geodatabase;speed 75 m/min). re-import the shape file in the above database and add the field(2) Create of “walking a new networktime” and dataset use the under “field the computer” data set and function establish to a calculate traffic network the walking dataset time (referbased to the on average this area. adult Create walking a new speed OD cost 75 matrix m/min). through Network Analyst and load the position (2)information Create a newwith network the gate entrances dataset under of all the the data communities set and establish as the starting a traffic point network of the dataset network. based on thisMeanwhile, area. Create load a new the locationOD cost information matrix through with Network commercial Analyst and public and load service the position facilities information as the with thedestination gate entrances point for of the all the traffic communities network, set as the the impedance starting point as the of travel the network. time (Min), Meanwhile, and run loadthe the locationcurrent information analysis. withThe analytical commercial model and defines public th servicee parameters facilities in the as theabove destination formula and point outputs for the the traffic average traffic travel time based on each starting point (or destination point) by establishing a spatial network, set the impedance as the travel time (Min), and run the current analysis. The analytical model analysis network. After considering the travel probability, the weighted average time (average travel defines the parameters in the above formula and outputs the average traffic travel time based on each time * travel probability) and weighted average rounding coefficient (average rounding coefficient * startingtravel point probability) (or destination are regarded point) as bytheestablishing standard of pedestrian a spatial analysisfriendliness. network. After considering the travel probability,(3) The ArcCatalog the weighted is used average to establish time (average Spatial Topological travel time Relations * travel by probability) solving the and network weighted averagestructure, rounding and topology coefficient information (average such rounding as traffic coefficient routes and* road travel network probability) are calculated, are regarded and logi asc the standardassociated of pedestrian information friendliness. of traffic routes and road locations are obtained based on travel time. Then, (3)based The on ArcCatalog the current situation is used of to la establishnd and space Spatial development Topological and Relationsutilization, byselect solving the reasonable the network structure,pedestrian and topologyfriendliness information to calculate such the source as traffic points routes according and road to the network comprehensive are calculated, situation and of logic associatedurban economic information trade, of employment traffic routes distribution, and road construction locations are and obtained population based density, on travelpublic time.service Then, facilities and so on. based on the current situation of land and space development and utilization, select the reasonable (4) The ArcGIS Spatial Analyst Tools, grid computing module, inverse distance weighting pedestrian friendliness to calculate the source points according to the comprehensive situation of method (IDW) and spline function method in interpolation analysis (use two-dimensional minimum urbancurvature economic spline trade, method employment to interpolate distribution, data points construction into grid surface and population domain-like density, data) andpublic other service facilities and so on. (4) The ArcGIS Spatial Analyst Tools, grid computing module, inverse distance weighting method (IDW) and spline function method in interpolation analysis (use two-dimensional minimum curvature Sustainability 2018, 10, 1993 13 of 22

splineSustainability method 2018 to, 10 interpolate, x FOR PEER dataREVIEW points into grid surface domain-like data) and other functions13 of 22 are usedSustainability to stretch 2018 and, 10, displayx FOR PEER the REVIEW residents’ traffic accessibility at each traffic network point according13 of 22 tofunctions the weighted are used average to stretch time ofand each display starting the residents’ point as welltraffic as accessibility the current at situation each traffic of roadnetwo trafficrk functions are used to stretch and display the residents’ traffic accessibility at each traffic network inpoint the source according area. to the Then weighted a visual average grid graphtime of iseach generated, starting point followed as well by as the the quantitativecurrent situation analysis of roadpoint traffic according in the to sourcethe weighted area. Then average a visual time gridof each graph starting is generated, point as well followed as the bycurrent the quantitative situation of (subarea averaging). analysisroad traffic (subarea in the averaging). source area. Then a visual grid graph is generated, followed by the quantitative 4.1.4.analysis Analysis (subarea of Accessibility averaging). of Commercial and Public Service Facilities Based on Weighted Average Travel4.1.4 Time. Analysis of Accessibility of Commercial and Public Service Facilities Based on Weighted Average4.1.4. Analysis Travel of Time Accessibility of Commercial and Public Service Facilities Based on Weighted AverageAs we Travel can see Time from Figure9, the dark blue areas with very low accessibility are mainly distributed in TAZAs No. we 9, No.can 22, see No.from 23, Figure and No. 9, the 27, and dark are blue located areas in with the peripheralvery low accessibility area of the research are mainly area; As we can see from Figure 9, the dark blue areas with very low accessibility are mainly A smalldistributed amount in TAZ of the No. light 9, No. blue 22, areas No. with23, and low No. accessibility 27, and are also located appeared in the onperipheral the TAZ area No. of 1, No.the 2, researchdistributed area; in ATAZ small No. amount 9, No. 22, of theNo. light23, and blue No. areas 27, and with are low located accessibility in the peripheral also appeared area on of thethe No. 15 and No. 28; the rest of the TAZs have better accessibility. The author calculated the weighted TAZresearch No. area; 1, No. A 2, small No. amount15 and No. of the 28 ;light the blue rest of areas the withTAZs low ha veaccessibility better accessibility. also appeared The author on the average travel time in the study area according to the average value of the divided zones. According to calculatedTAZ No. 1,the No. weighted 2, No. average15 and No.travel 28 ;time the in rest the of study the TAZs area accordinghave better to accessibility.the average value The authorof the Figure 10, it can be seen that: dividedcalculated zone thes. weightedAccording average to Figure travel 10, it time can inbe theseen study that: area according to the average value of the divided zones. According to Figure 10, it can be seen that:

Figure 9. Thermodynamic diagram of accessibility in Hexi District, Nanjing. Figure 9. Thermodynamic diagram of accessibility in Hexi District, Nanjing. Figure 9. Thermodynamic diagram of accessibility in Hexi District, Nanjing.

Figure 10. Accessibility zoning map of Hexi District, Nanjing. FigureFigure 10. 10.Accessibility Accessibility zoningzoning map of Hexi District, District, Nanjing Nanjing.. Overall, the weighted average travel time values of TAZ No. 7 and No. 20 as the CBD are the smallest;Overall, and thethe TAZweighted No. 9 average(13.36 min) travel and time No. values23 (12.28 of min)TAZ withNo. 7the and less No. internal 20 as commercialthe CBD are and the smallest; and the TAZ No. 9 (13.36 min) and No. 23 (12.28 min) with the less internal commercial and Sustainability 2018, 10, 1993 14 of 22

Overall, the weighted average travel time values of TAZ No. 7 and No. 20 as the CBD are the smallest; and the TAZ No. 9 (13.36 min) and No. 23 (12.28 min) with the less internal commercial and service facilities are the highest. Among them, lots of lands in No. 9 zone are still under construction. Therefore, there is still the possibility of optimization; with the kernel density analysis, we can see that all the lands with a weighted average travel time below 5 min are located with a high kernel density. By comparing the accessibility value with the current land use situation in the site, it can be found that the spatial distribution characteristics of commercial service facilities and the urban spatial structure have a significant impact on accessibility: TAZ No. 8 has the smallest scale and high development intensity. The internal commercial and public service facilities are characterized by various types, high density, and wide distribution. Therefore, the weighted average travel time is 4.96 min. TAZ No. 7 and No. 20 are located in the center of the entire area and are the main commercial land in the Hexi area. All kinds of commercial and public service facilities are complete, and the density of commercial and public service facilities is also the highest. The accessibilities of the adjacent TAZs ((No. 14, No. 16, No. 18 and No. 21,) to No. 7 and No. 20 are better than the TAZs (No. 13, No. 15, No. 19, No. 22) farther away from the central area, indicating that the influence of high-density commercial facilities in the central area began to gradually decline. Therefore, the author believes that within a reasonable walking range, high-density commercial land development has a certain impact on pedestrian accessibility. TAZ No. 1, No. 2, No. 3, No. 4, No. 5, and No. 6 located at the north, and the No. 18 located at the south are far away from the central business area. The impact of high-density commercial service facilities on these three regions has been significantly reduced. At the same time, the streets within these three regions have more street-level businesses, and accessibility has the tendency to develop inwards within the neighborhood. TAZ No. 23 has a large number of residential areas, but the low commercial density leads to low internal accessibility, and the larger block size also blocks the impact of external commercial service facilities on internal residential areas.

4.2. Analysis of Walking Distance Based on Integral Pedestrian Route Directness Many scholars studied the reasonable recommended value of the PRD. Hess argued that pedestrians were reluctant to bypass on circuitous and discontinuous sidewalks and that his recommended PRD values were generally 1.2–1.7 and those that are greater than 1.8 may find the road too tortuous [21]; Randell and Baetz found in their study that the PRD coefficient tends to be between 1.4 and 1.5 for the roads with the grid-pattern network and the street-side with the shorter side. That for more curvilinear roads and end-of-the-road streets is usually 1.6–1.8 [22]. In Portland of the United States, the PRD coefficient has been regarded as the main reference for the design of the maximum length of the block, and it should be controlled at 1.2–1.5, and 1.6–1.8 can be considered as detour roads [23]. From Figure 11, it can be seen that the minimum value of the Pedestrian Route Directness in each section is 1.26, and the maximum value of the Pedestrian Route Directness is 2.11. Among them, the Pedestrian Route Directness around the center of the Olympic sports center is the highest, and there are a large number of road sections with a Pedestrian Route Directness exceeding 1.66. It indicates that the super-scale blocks like the Olympic Sports Center have a great influence on the Pedestrian Route Directness between the origin-destination. Through the statistics of the average integrated Pedestrian Route Directness within the given area (Figure 12), we found that the integral Pedestrian Route Directness of all residential zones in the Hexi District with a grid-pattern road system basically meets the above recommended value of the Pedestrian Route Directness 1.5, but the distribution of comprehensive Pedestrian Route Directness in different regions has certain differences and connections: Sustainability 2018, 10, 1993 15 of 22 Sustainability 2018, 10, x FOR PEER REVIEW 15 of 22 Sustainability 2018, 10, x FOR PEER REVIEW 15 of 22

FigureFigure 11.11. Thermodynamic diagramdiagram ofof pedestrianpedestrian routeroute directnessdirectness inin HexiHexi District,District, Nanjing.Nanjing. Figure 11. Thermodynamic diagram of pedestrian route directness in Hexi District, Nanjing.

Figure 12. Zoning map of pedestrian route directness of Hexi District, Nanjing. Figure 12. Zoning map of pedestrian route directness of Hexi District, Nanjing. Figure 12. Zoning map of pedestrian route directness of Hexi District, Nanjing. (1) As a whole, the Pedestrian Route Directness of the zones adjacent to the TAZ No. 14 are generally(1) As higher a whole, than the the Pedestrian Pedestrian RouteRoute DirectnessDirectness ofof the zones faradjacent away tofrom the the TAZ No. No. 14, 14which are (1) As a whole, the Pedestrian Route Directness of the zones adjacent to the TAZ No. 14 generallyindicates thathigher the than Olympics the Pedestrian center within Route TAZ Directness No. 14 ofis athe huge zones scale far construction away from the and No. has 14 a , greaterwhich are generally higher than the Pedestrian Route Directness of the zones far away from the No. 14, indicatesimpact on that the the Pedestrian Olympics center Route within Directness, TAZ No. resulting 14 is a inhuge an scale increase construction in the Pedestrianand has a greater Route whichimpact indicates on the thatPedestrian the Olympics Route center Directness, within resulting TAZ No. in 14 an is a increase huge scale in constructionthe Pedestrian and Route has a Directness of the surrounding area. As a public service facility, the Olympic Sports Center has less greater impact on the Pedestrian Route Directness, resulting in an increase in the Pedestrian Route Directnessinternal functions of the surrounding and lacks commer area. Ascial a publicvibrancy service along facility, the street the andOlympic cannot Sports provide Center more has travel less Directness of the surrounding area. As a public service facility, the Olympic Sports Center has less internaloptions forfunctions nearby and residents. lacks commercial vibrancy along the street and cannot provide more travel internal functions and lacks commercial vibrancy along the street and cannot provide more travel options(2) forThe nearby Pedestrian residents. Route Directness in commercial land is generally high. There are six zones options(2) forThe nearby Pedestrian residents. Route Directness in commercial land is generally high. There are six zones exceeding the recommended value of 1.5, and the highest is TAZ No. 20, which has reached 1.67. It (2) The Pedestrian Route Directness in commercial land is generally high. There are six zones exceedingcan be seen the by recommended comparison withvalue t heof 1.5, current and roadthe highest network, is TAZ like No. the 20, shape which of thehas pedestrianreached 1.67. road It exceeding the recommended value of 1.5, and the highest is TAZ No. 20, which has reached 1.67. cannetwork be seen inside by comparisonTAZ No. 16, withNo. 17 the and current No. 20 road is mostly network, a rectangle like the shapewith a of high the ratio pedestrian of length road to It can be seen by comparison with the current road network, like the shape of the pedestrian road networkwidth. When inside the TAZ origin No.-destination 16, No. 17 isand located No. 20on is the mostly two long a rectangle sides of witha rectangular a high ratio block of withlength such to network inside TAZ No. 16, No. 17 and No. 20 is mostly a rectangle with a high ratio of length to width. width.a block When pattern the, itorigin is easy-destination to form ais windinglocated on path the aroundtwo long a sides single of neighborhood a rectangular block when with the routesuch Whena block the pattern origin-destination, it is easy to is for locatedm a winding on the two path long around sides ofa single a rectangular neighborhood block with when such the a blockroute directness of the path will be high. directness(3) In ofthe the residential path will areasbe high., the Pedestrian Route Directness of TAZ No. 8 is significantly lower than(3) that In of the other residential zones. Itareas can ,be the found Pedestrian by comparison Route Directness with the ofkernel TAZ density No. 8 is maps significantly of commercial lower than that of other zones. It can be found by comparison with the kernel density maps of commercial Sustainability 2018, 10, 1993 16 of 22 pattern, it is easy to form a winding path around a single neighborhood when the route directness of theSustainability path will 2018 be, 10 high., x FOR PEER REVIEW 16 of 22 (3) In the residential areas, the Pedestrian Route Directness of TAZ No. 8 is significantly lower thanand public that of services other zones. and the It can status be foundmap (Figure by comparison 8), that although with the there kernel are density many mapsroad ofnetwork commercial types and publicthey are services not regular and thesquare statuss, its map high (Figure-density8), internal that although coverage there of area wide many range road of networkbusinesses types can andprovide they aare wealt not regularh of travel squares, options its high-density within a reasonable internal coverage distance of within a wide the range area of; businessesso the author can providebelieves athat wealth a goodof travel commercial options within and a public reasonable service distance facilities within layout the area;has so a thecertain author impact believes on thatshorten a gooding the commercial residents andPedestrian public Route service Directness facilities layoutand improving has a certain accessibility impact in on neighborhood shortening thes. residents Pedestrian Route Directness and improving accessibility in neighborhoods. 4.3. Analysis of Street Popularity 4.3. Analysis of Street Popularity In the main residential zones in this study, the TAZ No. 9 is still under construction, so it is excludedIn the from main the residential calculation zones of the in street this study, walking the popularity TAZ No. 9 value is still. T underhrough construction, the statistics so of itthe is excludedresidential from land the in calculationthe area, combined of the street with walking the data popularity provided value. by the Throughhousing therental statistics website, of the the residentialstreet walk landing popularity in the area, incombined the main with residential the data provided area is estimated by the housing based rental on the website, formulas the in street the walkingSection 3.3 popularity. As shown in thein the main Figure residential 13, TAZ area No. is 15 estimated has the hig basedhest on street the formulaswalking popularity in the Section value, 3.3. Asand shown the average in the daily Figure pedestrian 13, TAZ No.population 15 has thedensity highest has street reached walking 839.61 popularity people/km value,2, followed and theby 2 averageTAZ No. daily 8 (743.73 pedestrian people/ populationkm2), and density No. 2 (636.61 has reached people 839.61/km2) people/km is third; the, followedaverage daily by TAZ walking No. 8 2 2 (743.73population people/km density ),of and the No.No. 223 (636.61 block people/kmis the lowest,) is only third; 117.28 the average people/ dailykm2; the walking value population of highest 2 densityzone is of7.16 the times No. 23 that block of isNo. the 23 lowest,, while only the 117.28 remaining people/km street ;v thealues value are of within highest the zone range is 7.16 240 times–540 2 thatpeople of No.per 23,km while2. the remaining street values are within the range 240–540 people per km .

Street Walking Popularity

1000.00 839.61 743.73 800.00 639.61 534.67 600.00 390.68 348.04 349.34 400.00 288.84 308.27 296.80 290.92 241.32

kilometer 200.00 117.28

0.00 unit: people / square people unit:

Figure 13. Street walking Popularity of Hexi District, Nanjing. Figure 13. Street walking Popularity of Hexi District, Nanjing. 4.4. Analysis of Ratio of Green Interface 4.4. Analysis of Ratio of Green Interface Through the formulas in Section 3.4, the author calculates the percentage of the green interface Through the formulas in Section 3.4, the author calculates the percentage of the green interface along the street in the main residential area (Figure 14). The area with the lowest ratio of greening along the street in the main residential area (Figure 14). The area with the lowest ratio of greening along the street is TAZ No. 8, which is only 17.20%; the proportion of the greening interface in the along the street is TAZ No. 8, which is only 17.20%; the proportion of the greening interface in the TAZ TAZ No. 23 is preliminary. The calculated result is 44.1%, which is the highest in all zones. We found No. 23 is preliminary. The calculated result is 44.1%, which is the highest in all zones. We found that the that the Yangtze River Avenue on the west side of TAZ No. 23 could not meet the requirements of Yangtze River Avenue on the west side of TAZ No. 23 could not meet the requirements of pedestrian pedestrian traffic after the field research. Therefore, the author excluded the green interface of the traffic after the field research. Therefore, the author excluded the green interface of the road as it cannot road as it cannot be used by pedestrians from the calculation of the ratio of the green interface; the be used by pedestrians from the calculation of the ratio of the green interface; the adjusted proportion adjusted proportion of the green interface in the TAZ No. 23 is only 17.30%; the TAZ with the of the green interface in the TAZ No. 23 is only 17.30%; the TAZ with the highest proportion of green highest proportion of green interfaces is No. 10 area, reaching 38.84% and TAZ No. 15 is followed by interfaces is No. 10 area, reaching 38.84% and TAZ No. 15 is followed by 32.53%; the green interface of 32.53%; the green interface of the remaining area has a smaller proportional gap and is concentrated the remaining area has a smaller proportional gap and is concentrated in the range of 17–26%. in the range of 17–26%. Sustainability 2018, 10, 1993 17 of 22 Sustainability 2018, 10, x FOR PEER REVIEW 17 of 22

Ratio of green interface

45.00% 38.84% 40.00%

35.00% 32.53%

30.00% 25.72% 25.33% 24.19% 25.00% 23.26% 23.52% 21.52% 20.11%20.83% 19.01% 20.00% 18.00% 17.20% 17.30%

15.00%

10.00%

5.00%

0.00% No.1 No.2 No.4 No.5 No.7 No.8 No.9 No.10 No.15 No.18 No.21 No.23 No.26 No.28

Figure 14. Ratio of green interface of Hexi District, Nanjing. Figure 14. Ratio of green interface of Hexi District, Nanjing. 4.5. Correlation Analysis 4.5. Correlation Analysis In the above chapter, the pedestrian friendliness in Hexi District was analyzed by four In the above chapter, the pedestrian friendliness in Hexi District was analyzed by four quantitative quantitative indicators: weighted average travel time, the Pedestrian Route Directness, street indicators: weighted average travel time, the Pedestrian Route Directness, street walking popularity walking popularity and the ratio of green interface. In order to verify the credibility of the above and the ratio of green interface. In order to verify the credibility of the above indicators and the model indicators and the model constructed by the author, the Spearman correlation analysis was constructed by the author, the Spearman correlation analysis was conducted on the above indicators conducted on the above indicators and the choice of residents’ travel mode data for flexible trip by and the choice of residents’ travel mode data for flexible trip by using SPSS statistical software. using SPSS statistical software. 4.5.1. Process of Correlation Analysis 4.5.1. Process of Correlation Analysis The author carried out a Spearman correlation analysis of the average value of the above indicators The author carried out a Spearman correlation analysis of the average value of the above and the data of the residents’ travel mode choice in non-commuting periods according to the survey indicators and the data of the residents’ travel mode choice in non-commuting periods according to in Section 2.2 in each TAZ, so as to test the validity of the model, and verify whether the evaluation the survey in Section 2.2 in each TAZ, so as to test the validity of the model, and verify whether the indicators in the model established by the author have a significant relationship with the residents’ evaluation indicators in the model established by the author have a significant relationship with the flexible travel. residents’ flexible travel. 4.5.2. Results of Analysis 4.5.2. Results of Analysis As it can be seen from Table2, the weighted average travel time, the Pedestrian Route Directness, the streetAs it walking can be popularity seen from andTable the 2 ratio, the of weighted green interface average are travel all significantly time, the Pedestrian correlated toRoute the ratioDirectness of walking, the ofstreet residents’ walking flexible popularity travel. Theand weightedthe ratio average of green travel interface time andare Pedestrian all significantly Route Directnesscorrelated to are the significantly ratio of walking negatively of residents’ correlated flexible with the travel ratio. The of walking. weighted This average shows travel that thetime lower and thePedestrian Pedestrian Route Route Directness Directness are in significantly the area and negatively the higher correlated the accessibility with the to commercialratio of walking. and public This serviceshows that facilities, the lower the morethe Pedestrian residents Route choose Directness to walk. in The the ratio area ofand green the higher interface, the streetaccessibility walking to popularity,commercial and and the public ratio of service pedestrian facilities, travel the showed more a residents significant choose positive to correlation, walk. The indicatingratio of green that theinterface, higher street resident walking population popularity, in the and area, the the ratio higher of pedestrian the street popularitytravel showed and a thesignificant more landscape positive greencorrelation, along the indicating street, the that more the conducive higher resident it is to stimulating population the in production the area, of the walking higher behavior. the street popularity and the more landscape green along the street, the more conducive it is to stimulating the production of walking behavior.

Sustainability 2018, 10, 1993 18 of 22

Table 2. Correlation of pedestrian friendliness indicators and residents’ travel mode choice.

Travel Mode Average Weighted Travel Mode Walking Ratio of Street Choices Travel Time Choices Popularity Greening Ratio of Correlation −0.306 * −0.444 ** 0.487 ** 0.381 ** Walking Significance 0.039 0.002 0.001 0.009 Ratio of Motor Correlation 0.466 ** 0.465 ** −0.499 ** −0.240 Vehicle Significance 0.001 0.001 0.000 0.108 ** Correlation is significant at the 0.01 level (2 tailed).; * Correlation is significant at the 0.05 level (2 tailed).

At the same time, it can be seen from the correlation analysis of the ratio of motor vehicle trips, apart from the ratio of green interfaces, the remaining indicators are significantly correlated to the ratio of motor vehicle trips. Among them, the weighted average travel time and the Pedestrian Route Directness are positively related to the ratio of motor vehicle trips, which indicates that in the area with high Pedestrian Route Directness and low accessibility to commercial and public service facilities, residents prefer to use motor vehicles to travel. The street walking popularity and motor vehicles trips showed a significant negative correlation, indicating that residents in areas with higher popularity are more likely to abandon motor vehicle travel. The reason for this is that high street popularity also represents a possibility of congestion for motor vehicles. It is concluded from the above correlation analysis that there are significant correlations between the four quantitative indicators in this study and the residents’ walking behavior in the flexible trip. Therefore, the walking friendliness index of this study has high credibility and scientificity. Through the correlation analysis of the residents’ choices of walking and motor vehicle travel modes and the above indicators, we can see that walking as a human-driven open transportation mode has been affected by many aspects of the current state of the urban construction environment; the accessibility requirements of the function to the city are even higher than other traffic modes, meanwhile, they are more sensitive to the linearity of the route. A good street-built environment can also stimulate the residents’ pedestrian travel behavior; at the same time, as part of the communication and urban life, high street popularity is obviously a generation of pedestrian travel behavior. Therefore, the above indicators should be used as a reference in building a pedestrian-friendly neighborhood.

5. Optimization Suggestions for Hexi District Based on Pedestrian Friendliness In Section 2.2.2, we found that the proportion of pedestrians in the flexible trip of residents in TAZ No. 5, No. 7, No. 23, and No. 26 is significantly lower than the proportion of pedestrians’ travel preferences. By comparing the above indicators of these four zones with the average of other residential areas, we can see from Figure 15 that there are three indicators in TAZ No. 5, No. 7 and No. 26 that do not reach the average level, and all four indicators in TAZ No. 23 do not reach the average level. Therefore, based on the data obtained in this study, the author put forward optimization proposals for the Hexi District. Sustainability 2018, 10, 1993 19 of 22 Sustainability 2018, 10, x FOR PEER REVIEW 19 of 22

Figure 15. Comparison with average value. Figure 15. Comparison with average value. 5.1. Optimization of Land Use Patterns 5.1. Optimization of Land Use Patterns Scholars believe that the high density and high degree of mixed use of land use patterns in the Scholars believe that the high density and high degree of mixed use of land use patterns in neighborhood is conducive to the formation of a high accessibility pedestrian neighborhood. This the neighborhood is conducive to the formation of a high accessibility pedestrian neighborhood. study found that high density residential function can provide fine support for travel, and high Thisdensity study commercial found that and high public density service residential land function can improve can provide the accessibility fine support of residents for travel, to and various high densityurban functio commercialns. For and urban public residential service functions, land can improvereasonable the commercial accessibility layout of residents along the to street various can urbangreatly functions. shorten the For travel urban time residential and distance functions, of residents reasonable in non commercial-commuting layout trips. along At the the same street time, can greatlyhigh density shorten commercial the travel timefacilities and distancesuch as commercial of residents complex in non-commutinges also have trips. a certain At the attraction same time, for highresidents’ density trip. commercial Therefore, facilities for the land such use as commercialwithin the neighborhood, complexes also attention have a certainshould attractionbe directed for to residents’the high trip.-density Therefore, development for the land in use both within horizontal the neighborhood, and vertical attention directions should be to directed promote to the the high-densityconcentration development of different in urban both horizontal functions and in space, vertical which directions will to shorten promote the the travel concentration distance of of differentresidents urban effectively functions and inprovide space, residents which will with shorten more theurban travel functions distance in ofreasonable residents walking effectively range. and provideIn residentsthe spatial with layout more of urbanfacilities, functions high-density in reasonable commercial walking and range. public service facilities need to focusIn on the fairness spatial and layout accessibility. of facilities, It is high-density suggested that commercial they should and be public arranged service in the facilities core area need of the to focusneighborhood. on fairness At and the accessibility. same time, it It should is suggested be integr thatated they with should rail be services arranged and in equipped the core area with ofcommunity the neighborhood.-level service At the facilities same time,to provide it should services be integrated including with the rail daily services life function and equipped such as withshopping, community-level catering, education service facilities and medical to provide care, culture services and including sports, the postal daily and life financial function services, such as shopping,and the administra catering, educationtion and utilitiesand medical sectors, care, thus culture forming and sports, an accessible postal and community financial center services, by andwalking. the administration The survey results and utilities show sectors,that the thusmain forming pedestrian an accessible entrances community and exits of center residential by walking. areas Theshould survey be arranged results show for convenience that the main stores, pedestrian housing entrances rental, convenience and exits of residentialservices, restaurants areas should and beother arranged food businesses for convenience along stores, the streets, housing thereby rental, reducing convenience the daily services, travel restaurants distance of and residents. other foodTherefore, businesses the author along proposes the streets, to therebyadjust and reducing optimize the the daily layout travel of distancecommercial of residents. and service Therefore, facilities thearound author TAZ proposes No. 23 to so adjust as to better and optimize meet th thee functional layout of requirements commercial and of residents’ service facilities walking. around TAZ No. 23 so as to better meet the functional requirements of residents’ walking. 5.2. Moderate Adjustment of Neighborhood Scales and Road Network Morphology 5.2. Moderate Adjustment of Neighborhood Scales and Road Network Morphology The author believes that the rational scale of the neighborhood division can not only increase The author believes that the rational scale of the neighborhood division can not only increase the the choices of walking path, but also can reduce the walking distance, thereby enhancing the choices of walking path, but also can reduce the walking distance, thereby enhancing the accessibility accessibility within the district; at the same time, a small neighborhood scale can lead to a high road intersection density and reduce the speed of motor vehicles inside the neighborhood, which is more conducive to the safety of pedestrians. In addition, it can also provide more commercial capacity Sustainability 2018, 10, 1993 20 of 22 within the district; at the same time, a small neighborhood scale can lead to a high road intersection density and reduce the speed of motor vehicles inside the neighborhood, which is more conducive to the safety of pedestrians. In addition, it can also provide more commercial capacity along the streets in the area, which helps to create a continuous business climate and enhance the popularity of walking. According to the analysis in Sections 4.1 and 4.2, it can be seen that large-scale public buildings such as stadiums and convention centers can easily have a negative impact on the walking of residents, and the neighborhood morphology with too large an aspect ratio can also cause high pedestrian route directness. Therefore, the author proposes to increase the branch network for walking according to the actual situation in the area, trying to keep the distance between walking roads within 200 m and adopting a street shape with an aspect ratio close to 1 to reduce the route directness.

5.3. Promoting the Formation of an Open Neighborhood Type Although it is more difficult to adjust the scale of the built-up neighborhood, the internal roads of some areas are in an open state for the pedestrians in later use. Therefore, by increasing the degree of openness of the inner roads to pedestrians in residential areas, the density of pedestrian roads can be increased, which can shorten the distance traveled by residents and increase the ratio of pedestrians in the area. In the survey, the author finds that in Hongyuan New Apartment located in TAZ No. 8, it only restricts the passage of motor vehicles, not pedestrians not living here and non-motor vehicles using the internal roads, although it is equipped with an access control system. As a result, the internal roads have become public sidewalkd, resulting in a significantly higher percentage of walking and non-motor vehicles trips in the area than in other areas. This proves that the degree of opening up of residential areas and the scale of the neighborhood are equally important. Therefore, the author suggests opening some of the internal roads to pedestrians on a conditional basis, especially in the residential areas located in the central part of the neighborhood or public buildings, to improve the internal pedestrian friendliness.

5.4. Strengthening the Construction of Street Greening Interface A good street environmental quality has a certain impact on the residents’ walking travel, and the good green interface has a significant role in promoting the residents’ walking behavior in flexible travel. As far as the current situation of the Hexi District is concerned, besides the road isolation belt, the main urban greening is attached to the urban landscape construction, and the continuous green footpath has not been formed. For the urban residential function land in Hexi District, there are few greening group and public green spaces for leisure and entertainment. Therefore, the author suggests that the construction of public green space can be strengthened by the existing unbuilt land, so as to strengthen the quality of walking travel. It can be seen from Figure 14 that the proportion of green interface of all the zones that reach the proportion of pedestrians’ travel preferences in this study is between 20.83% and 38.84%. Therefore, the author believes that the ratio of greening interface construction of the zones with less walking behavior in Hexi District should be strengthened and promoted to a reasonable range so as to create a good and fair environment for residents’ walking behavior.

5.5. Promote the Formation of Better Street Popularity As for street popularity, it basically stems from the number of the resident population in the neighborhood. However, urban construction in China has controlled the volume ratio of residential land in the initial stage of planning. We found through investigation that, for example, residential buildings in the No. 23 area, most of them are 150 m2 or even 250 m2 per household, and the number of houses in the No. 8 area with the earlier building is mostly 60 m2 per household, which also causes a huge difference in street popularity between the two. Therefore, developers should also weigh the relationship between residents’ requirements for living space and the urban environment. Sustainability 2018, 10, 1993 21 of 22

6. Conclusions This paper, with Hexi District in Nanjing as a case study, analyzes the influence of the quantitative indexes of land use and urban spatial morphology on residents’ choices of travel means, and puts forward the optimization suggestions based on the pedestrian friendliness. It can be found from the study that there is an important influence among the above-mentioned construction environment variables. In the study, we found that there are many factors affecting the walking travel, such as the traffic location in the city, the feature of the block, the road paving and so on. Because it is difficult to quantify or lack of data, this paper did not carry out analysis. The author will make further improvements in the follow-up study on these issues.

Author Contributions: C.Q. designed the analytical framework and revised the paper. D.Z. constructed the model, analyzed the data and co-wrote the paper. Y.Z. co-wrote the paper and revised the paper. J.C. carried out the video counting process. Acknowledgments: This study is supported by National Natural Science Foundation of China (No. 51578282 and No. 51508265); Natural Science Foundation of Jiangsu Province, China (No. BK20151538); Science and Technology Project of Ministry of Housing and Urban-Rural Development of China (2016-K2-027); and Foundation of the “333 High-Level Talents” of Jiangsu Province (No. BRA2016417); Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX17_0910). Conflicts of Interest: The authors declare no conflict of interest.

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