INTRODUCTION
Over the years, Hanoi has witnessed a rapid development of the urbanization that leads to the increasing demand in travelling, thus to the rapid increase of the transportation means, in particular private vehicles In December 2015, it was reported that the total of private vehicles in Hanoi was 5.8 million units including 376,417 cars and 5.4 million motorbikes (Ngoc, 2016), with the exception of the large number of vehicles from other provinces going in and out the city during the day Such a massive private vehicles population has caused various transportation issues such as road traffic accidents, traffic jam, and air pollution To deal with those problems, Public transportation systems in Hanoi were paid attention to be invested and developed, focusing on buses (only one bus rapid transit (BRT) route is operating and urban railway are under construction in Hanoi) However, public transportation meets only partially the travelling demand of urban people, specifically, public transportation system transports only less than 10 percent demand in Hanoi city (World Bank, 2018) Even recent years, the productivity of buses in Hanoi tends to be restrain and even going down in some years (reduced 0,35 percent in 2015 compared to that of 2014) (Figure 1.1) In particular, the progress of construction and operation for the Mass Rapid Transit system (BRT, urban railway) in Hanoi missed the deadline Therefore, how to encourage and attract people to use public transportation modes is a more and more urgent topic
There are various factors that influence the use of public transportation modes, including cost, level of service of the existing system, physical accessibility, spatial access, etc all of which contribute to each person’s motivation and ability to decide to use public transportation This research focuses on walking distance to transit stop because understanding influences on walking distance to transit stops of riders is an important indicator of a transit system’s ability to attract people The public transportation services are widely used when more and more people live and/or work in close proximity to transit stops (Murray et al., 1998) Moreover, walking distance to transit stops is a very important aspect to define stop catchment areas, which are essential for the planning process of new public transportation lines (Andersen &
Landex, 2008), and to evaluate the impacts of transit infrastructure on land-use, and to design policies for transit-oriented development (TOD) Most studies and transit planners usually assume distance thresholds of 400 meters for access to bus stops (Queensland Government, 1997; Murray, 2001; Biba, Curtin, & Manca, 2010)
However, these distance thresholds have not yet been justified with an empirical basis in Hanoi In addition, potential factors affecting the different in walking distance to public transport of public transportation users such as demographics, trip purpose, built environment, perceived neighbourhood walkability are still unclear so it is necessary to be clarified
Figure 1.1: Bus passenger ridership in Hanoi
The purpose of this study is to analyze empirically the associations between the density and diversity of the built environment (BE) with walking distance to public transit, using On-board Survey data for Hanoi, Vietnam, in 2019 This analysis particularly pays attention to use measurement objective distance for analyses relationship with BE In order to compute the BE variables in different geographic
Year units, detailed geospatial data for Hanoi measured in 2010 were installed into a Geographic information system (GIS) database Finally, some implications are made based on estimation results
Based on the background presented before, the research questions this study pursue to answer are: i What is the actual distance walked to public transit stops in Hanoi? ii How do the built environment characteristics affect the walking distance to public transit stops?
This research makes significant contributions to the body of knowledge for several reasons This research contributes to enhancing our understanding about the relationship between BE and the walking distance to transit stops in case of a developing city like Hanoi and providing implications for land use planning and the proper location of transit stops in a public transportation system More specifically, this research aims to offer planning implications based on the following two aspects
Firstly, with regards to the stop area planning aspect, the BE attributes that positively impact on the actual walking distance of public transportation users need to be identified This will suggest transit planners, policy makers and researchers how to motivate people to walk farther for using public transportation Second, with regards to the new stop location choice, this study wants to determine BE attributes that tend to shorten public transportation's walking distance Consequently, the new transit stops for new routes or relocate exist transit stop could be located in the places with these BE attributes by transit planners Therefore, this study fulfils the research gap in giving more evidences to support for both promoting riders walking longer and minimize rider's walking distance In addition, to the best of my knowledge, this study is the first study using GIS-based database to investigate the relationship between BE and walking distance in Hanoi
This thesis consists of five chapters This chapter focuses on the background, problems statement as well as the main objectives of this research The following chapters are written as follows:
Chapter two reviews previous researches in the literature to clarify the research objectives in accordance with current studies This chapter aims to recognize the research gap that exists in the literature and needs to fulfil
Chapter three describes the data source as well as procedures for collecting them This chapter also shows the procedure to develop GIS-based database in Hanoi and establish the measurement of BE variables in GIS-based database
Chapter four aims to develop a model to analyse the relationship of BE on walking distance to transit stop in Hanoi A Poison Regression model is adopted to analyse the potential impact of BE on walking distance This chapter includes the data characteristics, descriptive analysis, methodology for model analysis and its results
Chapter five displays the estimation results of the walking distance model and explanation of these results
Chapter six concludes the discussion and the key findings obtained from the estimation Finally, research limitations and future research directions were discussed The research flow diagram is presented in Figure 1.2 below
LITERATURE REVIEW
The relationship between walking and BE
There were a large number of papers which have studied on the relationship between walking and built environment To investigate this connection between walking and the BE, we need to have a clear understanding of the meaning of walking and built environment In general, walking recognized as a movement by foot which is one types of the man’s transportation Broadly speaking, in urban context, walking is explained as short distance moving from one point to the other point (Rafiemanzelat et al., 2017) As defined by Davison and Lawson (2006), The built or physical environment is objective and perceived characteristics of the physical context in which people spend their time (e.g., home, school) including aspects of urban design (e.g., presence and structure of sidewalks), traffic density and speed, distance to and design of venues for physical activity, crime, safety and weather conditions On the whole, the BE includes three characteristics, there are: Design, Density, and Diversity Based on these atributes of BE and the purpose of the trips, many studies focused on the impact of BE to walking Ferrer et al., (2015) divided five main categories of built environmental factors influencing walking for transportation including: Safety from crime (street lighting, other people, cleanliness, etc.); Traffic safety (traffic volume, traffic speed, crossing waiting times, etc.);
Walking facilities (sidewalk width, obstacles, etc.): Aesthetics (presence of green elements, buildings, noise, etc.); and Convenience and other perceptions (availability of car parking, hills and pedestrian volume, open and wide spaces and length perception) According to these BE characteristics, the role of BE for short walking trips were investigated (Ferrer et al., 2015) Furthermore, several studies mentioned the correlation between BE and walking is moderated based on the varied factors of
BE and trip characteristic (Lovasi et al., 2009).The spatial links between the built environment and walking are further explored with the purpose of the trip such as walking for errands and leisure (Feuillet, et al., 2016) or determined the contribution revealed the effectiveness of different BE attributes on improving walking by determining the general and specific features of the major built environment attributes of residential neighborhoods which could help overcome varied barriers and enhance walking and cycling activity levels (Wang et al., 2016) They comprise Greenery, public leisure space (e.g roof garden, fitness club, public space), specific road, trail and path design, safety and security provisions, a wider choice of facilities (e.g sidewalks, cycling paths, treadmills, stairs) as well as some specific design provisions inside buildings can help overcome some barriers that hinder walking and cycling activities within a residential neighbourhood Ariffin and Zahari (2013) found that the proximity of destinations, good weather condition, safety and well-designed pedestrian facilities can significantly contribute to better perceptions of the walking environment Through these studies, it showed that the BE factors have a strong relationship with walking Nevertheless, a number of research gaps still remain, most studies revealing the association of BE and walking were originated from the various fields Consequently, there are only a limited number of studies could provide valuable finding on the design and planning of BE For instance, it has been confirmed that scenery made positive effects on walking activities; but there is a lack of researches revealing which type of landscape design could induce activities
Similarly, there is still a lack of evidence demonstrating the complex relationships and interdependent between walking conditions, facilities, BE atributes and walking within a residential neighborhood Accordingly, further studies are needed to provide more appropriate understanding of the association between BE and walking activities.
Distance walking and walking to public transit
Public transit (e.g buses, BRT, MRT) is generally not a point-to-point mode of travel; it requires another mode to reach a pick up point and to get from a drop off point to the passenger’s final destination The majority of transit users walk to reach to transit systems For example, According to Daniels & Mulley (2013), by synthesizing the data from the Bureau of Transport Statistics, in Sydney, they revealed that walking is the access mode for nearly 90% of bus trips from home to transit stops (Table 2.1)
Table 2.1: Access modes from home to bus stops in Sydney
Access mode from home To bus
(Source: Bureau of Transport Statistics)
Walking distance is especially important for at least two reasons Firstly, the distance walked by public transport users to transit stops is a major element of a transit system’s capacity to attract pessengers in its service area , hence walking distance has a significant influence on public transport use (Daniels et al., 2013)
Ewing and Cervero (2010) using a meta-analysis revealing a 10 percent increase in walking distance to a transit stop would decrease public transport use by roughly 3 percent Other papers have found that for every additional 500 m to reach a station, the probability that a rider will walk to transit system decreases by 50%
(Loutzenheiser, 1997), and similarly every 10% decline in transit use when 10% increase in walking distance ( Dill, 2003) Durand, et al., (2016) have identified the correlation between distance to a transit stop and the probability it will be accessed by walking In this study, they also examined even at three kilometres from a transit stop, there is still roughly a 50% possibility a rider will walk to the transit stop versus using a motorized mode Consequently, the distance people are willing to walk to stops seem to be much higher than the frequently cited rules of thumb of 400 meters
Data for this study was collected from the California Household Travel Survey 2012 by the survey questionnaires (both the individual and household-level were conducted) and the single-day travel diary via computer-assisted telephone interviewing, online survey, or mailed survey Durand, et al., (2016) revealed the limitations of the study are difficulties in generalizing the dataset, the lacking of detailed address data which make the study further unable to explain the effect of walking distance factors on active access to transit
Secondly, it is admitted that planning for public transport system entails finding a feasible alignment that maximizes population accessibility to transit stops The population with access to transit are an important indicator to estimates of transit use
The ability to precisely measure walking distances to transit facilities has been elusive given the large number of possible walking paths for the population and given the quality of network data available for analysis (Biba, Curtin, & Manca, 2010)
Transportation planners often assume the distance which people will walk to access to public transportation or “rules of thumb” to determine stop spacing, particularly for buses as these are more flexible but also by land-use planners for urban design to achieve walkable cities and plan transit-oriented developments (TODs)
In this section, the literature reviews the influences on walking distance to public transport to determine possible explanatory variables for use in the analysis, including trip characteristics, socio-demographic attributes, the BE factors, and perceived walking conditions For example, El-Geneidy et al., (2014) found that there is an oppositional relationship between the number of transfers and walking distance, whereas the total trip length is positively correlated with walking distance O'Sullivan
& Morrall (1996) and Alshalalfah & Shalaby (2007) found that there is a positively associated between walking distance and transit services with high level and short waiting time Transit riders' demographic attributes, such as age, gender, occupation, income, the number of vehicles, and driver’s lisence are also important indicators of walking distance (García-Palomares et al., 2013; El-Geneidy et al., 2014) Previous studies have explored the influences of BE characteristics on walking distance because they are important for walking distance and public transportation use ( Agrawal, Schlossberg, & Irvin, 2008) In addition, El-Geneidy et al., (2014) and Jiang et al., (2012) found that walking distance has a positively association with population density, intersection density, and sidewalk density Jiang et al (2012) concluded that public transport users willing to walk further when the walking environment is highly walkable Aesthetics and amenity are also potential determinant of walking distance
However, in a study evaluating the effect of a range of factors including amenity and aesthetics on deciding a route for walking, Agrawal et al, (2008) revealed that the fundamental consideration for riders walking to transit stations in the study in California and Oregon was minimizing the walking time and walking distance Safety (from traffic accident, rather than crime) was a secondary determinant in route choice, whereas amenity and aesthetics appearance was less of a concern
In summary, there have been clear calls in the literature for relationship of distance walking and use of public transit The influences on distance walking to public transit have also been highlighted Previous studies indicated that walking distance to public transport may be affected by demographic factors (particularly income, age and gender), by the trip characteristics such as purpose of the overall trip, and by the location of the access trip in terms of BE, although the influences are variable Overall, it shows that the ease of walking elements is impacted by built environment attributes but it might be taken into account more factors in determining how far people walk to public transport once they decide to walk to public transport.
Studies on walking distance and built environment in the world and Vietnam
There are several studies on walking distance to transit and BE in Context of large cities Wang and Cao (2017) explored that BE correlates strongly with walking distance of the egress segments (between transit stops and non-home destinations)
Study area of this research is the Mineapolis-St.Paul (Twin Cities) metropolitan area,
US By using the results of the 2010 Transit Onboard Survey with capturing following information of respondents: trip purposes, origin and destination addresses, access and egress modes, transit routes, and demographic characteristics, this study developed four models to compare the effects of the built environment around transit stops upon walking distance of transit egress trip Focusing on walking distance of egress trip (between transit stops and non-home-ends), and using ArcGIS which is a geographic information system (GIS) for working with maps and geographic information to measure dependent variable based on shortest path in street network, Wang and Cao (2017) found that, in term of transit egress, (1) the employment factor has a stronger effect on the walking distance to transit stops than the population factor, (2) the number of intersections has a negative correlation with walking distance to stops within downtown areas, (3) the number of transit stops positively associated with walking distance to stops within downtown whereas is negatively correlated with walking distance to stops outside of downtown areas, (4) The diversity in land use (land use mix) around transit stops has a significant and positive relationship with walking distance to stops which outside of downtown and suburban centers and (5) compared to the transit stops outside of downtown areas, the walking distacne to transit stops within downtown areas is much more affected by the job accessibility Kamruzzaman et al, (2016) examined the relationship between urban form and time spent (minutes) walking to transit in Brisbane, Australia, using both cross-sectional and longitudinal research design frameworks This study concluded both cross-sectional and panel assessment methods confirmed that the built environment influences walking participation, but it might take into consideration of perceptual and attitudinal factors is also important for understanding these relationships
One of the most recent studies on walking in Vietnam is written by Minh Tu Tran et al, (2015) In this study, researchers have indicated that perceived neighbourhood walkability has a significant influence on mode choices of short- distance trips in context of Hanoi, the capital of Vietnam They also assess the walkability of different types of residential neighborhood including: downtown, mixed and new urban Data for this study was collected from a household face-to-face interview survey included a set of questions regarding residential neighborhood walkability: 1) residential density, 2) distance to various places, 3) street connectivity,
4) access to services, 5) walking facilities, 6) traffic safety and 7) crime safety and 8) aesthetics and household and individual attributes: gender, age, employment, education level, household income, household size, vehicle ownership and so on
They found that people are uncomfortable to walk if trip distance is more than 500 meters; The share of walking in downtown neighborhood was highest, implying that residents in this neighborhood are more likely to walk for short-distance trips; The influences of perceived neighborhood walkability on mode choice of short-distance trips were empirically confirmed Specifically, accessibility-by-foot, the fear of crime, walking facilities and traffic conditions were found to have significant influences on mode choice In another study, Tran et al, (2016) found that the more diversity of land use at residence and at working place, the more likely peoplelike to walk
So at the end of it, this section comes to the conclusion that there is few research exploring the built environment correlates of walking distance of both access trip and egress trip In addition, studies on walking in Hanoi, reliance on self-reported walking distance measures has been a weakness because of respondents’ abilities to accurately estimate walking distance Accordingly, the study have been conducted to fills these gaps in the literature and extends the stream of these studies by (1) calculating the actual distance walked to public transit stops to define a catchment area for public transport in Hanoi and (2) determining how built environment characteristics influence walking distance to transit stops of both access segment from origins to transit stops and egress segment from transit stops to final destinations.
DATA
Survey Questionnaire
Hanoi’s public transportation system operates over 115 bus routes and 1 BRT line In addition, eight urban mass rail transit lines will be constructed in the future, of these, line 2a will start in summer 2019 and line 3 is under construction
Consequently, this research will be primarily focused on bus system to represent the public transportation system in Hanoi The respondents who were riding on a bus were chosen randomly in the south of Hanoi city, which comprise ten urban inner districts The areas surveyed were ten inner districts: Ba Dinh district, Hoan Kiem district, Dong Da district, Tay Ho district, Hai Ba Trung district, Cau Giay district, Thanh Xuan district, Hoang Mai district, Bac Tu Liem district and Nam Tu Liem district Located in the south side of Red River, the above districts are the most developed areas in Hanoi Metropolitan The survey area had the population about 2,705,000 persons in an area of approximately 195.74 square kilometer in total According to Hanoi People Committee 2016
Based on data from Hanoi Bus Map and latest Hanoi bus routes database of Hanoibus, 15 bus lines that get good coverage of the entire main streets of Hanoi city were selected, including routes 02, 03A, 08A, 09, 14,16, 19, 22A, 23, 26, 27, 30, 32,
44, 50 (Figure 3.1) According to Hanoi urban transport management and operation center (TRAMOC) data, these fifteen bus lines account for approximately 20% of the total passengers of Hanoi's public transport system Each route has a large volume of passengers, for example, line 02 has 2,795.419 passengers in the first quarter of 2019, line 3A is 1,100,677 passengers, etc By selecting these survey routes, this study may mitigate spatial aggregation bias compared to the other kind of survey such as household survey or bus stop survey which need to have a much larger sample
Furthermore, thanks for permission and support from TRAMOC, the survey was very successful and received cooperation from many passengers
Figure 3.1: Survey bus route in Hanoi
(Source: Created by the author, using source map from Hanoibus)
Figure 3.1: Survey bus route in Hanoi
(Source: Created by the author, using source map from Hanoibus)
This survey was designed and conducted by writer with the support of Vietnam Japan University A questionnaire was designed for an interview-based Onboard survey Specifically, a set of survey questionnaire consists of three parts The first part is about socio-demographic information of respondents including 14 questions such as gender, age, household income, household size and workers, vehicles availability and driver license These information provide an understanding of public transportation users and were used to improve the explanatory power of walking distance model Part two begins from question 15 to question 28 focusing on transit trip information of respondents including origin and destination Address, origin and destination activity, boarding stop and alighting stop location, number of transfers, transit usage history, frequency of transit Use and the reasons for choosing transit In addition to provide an in-depth understanding of how people use the public transportation system, these information is very important to calculate objectively walking distance to public transit of respondents The last part of the survey questionnaire is Perceived neighborhood walkability The Likert scale was used to measure and evaluate respondent sentiment on: conflict with other mode, cleanliness, level road, cross the street, drainage, step up and down, and walking amenities
Respondents must choose a positive or negative answer with 5-point Likert scale corresponding to statements “Very Poor – Poor – Fair – Good – Excellent” In particular, each situation has an illustration so that respondents can easily imagine (Figure 3.2)
The survey was conducted from May 10 th , 2019 to May 15 th , 2019 A total of
609 passengers had participated in this survey Twelve surveyors from a local professional survey team were divided into 3 groups (4 surveyors/a group) performed
15 different bus routes At each bus line, the survey questionnaire will be distributed directly to the passengers by surveyors With a desirable total of over 600 random samples and divided by 15 bus routes, each bus route was implemented about 40 to
50 respondents In which, 20 samples at peak hours (17h - 18h and 7h15-1h15) and 20-25 samples at off-peak hours (the remaining hours)
Figure 3.3: A group of the survey and an interview on a bus route
Figure 3.2: An illustration in the survey questionnaire
In the week before the survey begin, surveyors were interviewed, hired and trained In the survey process, surveyors interviewed passengers face-to-face and interpreted for respondents to answer all questions correctly In order to ensure the simplest and least expensive way to complete the necessary assignments, Survey was distributed and collected as soon as passengers get on the bus because it is more convenient when people have more free time to answer questions (limited time on a bus) In particular, in this study, the walking distance data will be collected objectively Accordingly, the address of an origin, destination, first bus stop location and last bus stop location of respondents paid special attention to record in the survey in detail and clearly whereby the walking distance will be measured objectively on the ArcGIS tool
Table 3.1 summarizes the descriptive statistics of the sample dataset with the socio-economic profiles of the survey areas Female respondents account for 54.20% of the total, reflecting the proportion of riders using buses by gender distribution in Hanoi The average age of respondents is only approximately 33 years of age, which reflects the fact that the young people dominate the use of public transportation in Hanoi The most common job type occupies the highest proportion in the use of bus are knowledge-intensive labour (54.98%), including government officer (14.3%), private-company officer (31.5%), university researcher (2.1%), doctor (1.57%), and school teacher (5.51%) Service workers, unskilled workers, and street vendors/shopkeepers account for 9.45%, 6.56%, and 6.69%, respectively while housewives/unemployed/retired people and pupils/students account for 4.86% and 11.55%, respectively In relation to motorbikes, 92.52% of respondents are owners, and 95.54% have a motorbike license These results reflect the fact that most Hanoi people use motorbikes for commuting In addition, 11.02 % of respondents own cars, and 22.18% of respondents have a car license, which may mean that the number of cars will grow in the future The results also show that the average number of household members is 3.23 while the average number of working members is 2.09
This means that most respondents belong to quite small families, typically nuclear families The average monthly household income is around 20 million VND, which is slightly higher than the average household income in Hanoi The average numbers of motorbikes and bicycles owned by respondents’ households are 2.19 and 1.26, respectively The average width of the road accessing their place of residence is 3.78m (one-lane road) This reflects the poor condition of streets in Hanoi, with narrow lanes and substandard road space, which could be one of major factors leading to the dominant share of motorbikes in the city
Table 3.1: The descriptive statistics of attributes of respondents (N`9)
Minimum Maximum Mean Median Std
General staff in private company
Labourer & Unskilled occupation Medical doctor No job/Retired
Officer in governmental organization or equivalent
Teacher (high school or lower)
Senior staff in private company
Service worker, Shop and Market Worker
Student Researcher/Teacher at university Free
Household size Minimum Maximum Mean Median Std
Working person Minimum Maximum Mean Median
Car driver's licence Yes No Motorcycle driver's licence
Usable bicycles Minimum Maximum Mean Median
Usable motorcycles Minimum Maximum Mean Median Std
Usable car Minimum Maximum Mean Median Std
Bicycle Motorbike Bus/BRT Car
Picked up by someone Walk
Pick-up children Yes No
Reasons for using Bus/BRT
Safe (accident) No need to drive
Good connection with existing bus Timesaving Have no other choice
There are still barriers to collecting the necessary information Firstly, there are many participants who do not want to provide details of special housing-related addresses In the data entry process, we need to consider and filter out the most relevant and detailed answers Secondly, the survey has divided the time frame including peak hours and off-peak hours During rush hour, there were many difficulties because the number of people getting into the bus was very high, there was a lack of space for convenient interviews
GIS-based database
In order to compute objectively walking distance and BE variables, it is essential to have a complete GIS-base database of Hanoi city This research used GIS-base database developed by Nguyen et al., (2018) This is a very valuable database because there was no GIS based system for Hanoi before In terms of creating this GIS-base database, Nguyen et al., (2018) revised the primary data source from current maps formatting in DWG files which developed by Hanoi Urban Planning Institute and converted to GIS by using AutoCAD software After that, these maps were converted to ArcGIS by using DWG-to-GIS convert tool After converting these maps to GIS, they transformed these maps into a global geographical coordinate system because the primary maps are developed in VN2000, which is a local projected coordinate system All the maps were transformed to global geographical coordinate system by using ArcGIS 10.4 software provided by ERSI
To complete this database, the data of spatial land use and transportation system attributes were collected and coded into a GIS-based database Once the database was available, the respondent's addresses were inserted into Google Earth Pro software via “Add Placemark” tool and transferred to GIS-based databased for computing variables.
Measurement of walking distance to public transit
The On-board survey in Hanoi recorded the detailed address of origin, the first bus stop, last bus stops and destination which were then geocoded to GIS-based database
On a bus, the surveyor asked respondents for these location and recorded them into questionnaire sheet Of the total respondents who participated the survey, there were
290 samples with detailed addresses for access trips and 299 samples with detailed addresses for egress trip (Figure 3.4) Because of privacy and confidentiality reasons, these addresses and bus stops location were not provided in the dataset, only the estimated walking distance was showed
Calculation of walking distance to public transit stop is a significant issue for this research Objective distance can be measured in two ways without using tracking devices: the shortest distance walking route along the street network (calculated using a network analysis function) and the straight-line or Euclidean distance (calculated using a distance measuring tool) Both are measured objectively The first one is more
(Source: created by writer, using source map from open street map) exactly than the other one The origin address, used bus stop and destination address were located clearly All these locations will be geo-coded as x, y coordinates into a Geographic Information System (GIS), after that, a network analysis tool in ArcGIS 10.6 provided by ERSI was used It will analyse to calculate the shortest route and the objective distance to public transit of each respondent According to Daniels &
Mulley (2013), this is an approximation of the actually walking distance for several reasons Riders might walk through open space and park rather than the main road network, which reduce their walking distance Otherwise, people might also choose a longer path than the shortest road distance because the longer path is more attractive, facilities or avoids negative elements However, the walking distance to the bus stop is not too long Therefore, the walking distance which was measured based on the shortest path in street network using ArcGIS is accurate and close to the most actual walking distance.
Measurement of BE variable
Each BE variable were computed for four different buffer including 100, 200,
500, and 1000 meters This ensures that the parameters are calculated on a diverse range of spaces
The population in different buffering scales are computed using the ward-scale (the smallest administrative boundary in Hanoi) data in 2010 due to the constraints of data availability, as shown below:
𝐴 𝑖𝑅 (1) where XiPD,R represents the population density in a buffering zone (R) for the individual i; ZPDk represents the average population density in a ward k; Ak is the area in ward k; and AiR is the area of the buffering zone (R) for the individual i Figure
5 illustrates the ward-scale population density distribution in Hanoi
Figure 3.3: Population density by ward
(Created by the writer, using database: Nguyen et al., (2018); base map: Open street map)
Data about employment density in Hanoi is provided by Hanoi statistical Office (HSO) in 2017 This data consists of number of employment in both state and private companies in ward-scale level Employment density is measured following the same formulation as population density measurement, as shown below:
𝐴 𝑖𝑅 (2) where XiJD,R represents the employment density in a buffering zone (R) for the individual i; ZJDk represents the average employment density in a ward k; Ak is the
Figure 3.4: Job density by ward
(Created by the writer, using database: Nguyen et al., (2018); base map: Open street map) area in ward k; and AiR is the area of the buffering zone (R) for the individual n
Figure 3.6 illustrates the ward-scale Job density distribution in Hanoi
Entropy Index of land use mix Land use mix is measured by using entropy index which measures the heterogeneity of the spatial unit A land use map in 2010 was used to compute the entropy index The map categorizes the land-use patterns into 3 different types: residential land, public-used land (such as offices, market, hospital, department store, etc.) and other purposes (such as industrial land, transportation) The entropy index is estimated based on following equation (Frank et al., 2005):
Where Pi,R is the percentage of each land use type i in the buffering (R); k,R is the number of land use types in the buffering zone (R)
Number of bus stops and bus frequencies
By using the GIS-base database which all bus stops were pointed on the map, the number of bus stops of each buffer were counted The bus stop map provided by Hanoibus – Transerco – the local bus operator in Hanoi Bus frequency was computed based on the bus schedule provided by Hanoibus – Transerco, that equal to the total number of bus passes all bus stations in a day within each buffer
Figure 3.7 shows the bus network and bus stop distribution in Hanoi
Number of 4-way road intersection is computed by counting total number of 4- way or more within a buffer area This variable explains the street connectivity and accessibility for riders
A source maps about current land use in Hanoi was used to show current public spaces The footmark of each public space was pointed by using a Feature-to-Point tool in ArcGIS Afterward, the number of public facilities is computed by counting total number of public facilities such as market, hospital, department store and so on within a buffer area
Figure 3.5: Current bus network and bus stops distribution
(Created by the writer, using database: Nguyen et al., (2018); base map: Open street map)
WALKING DISTANCE MODEL DEVELOPMENT
Overview
This chapter develops a model for walking distance to public transit in Hanoi
Three sections will be displayed in this chapter including: overview, explanation of the variables and methodology for walking distance model development The purposes for developing walking distance functions in Hanoi is to examine an association of built environment with walking distance.
Explanatory variables
Table 4-1 shows the explanatory variables used for the walking distance model development The explanatory variable can be categorized into four groups: built environment, individual characteristics, trip characteristics and perceived neighborhood walkability The built environment consists of seven variables: population density, employment density, land use mix, bus stops, bus frequency and intersections These are continuous variables measured objectively by ArcGIS analysis tool The individual characteristics includes dummy variables named male, young people, senior people, motorbike license, car license Beside, ranks of household income (low income, middle income and high income) was created
Perceived neighborhood walkability is a series of questions: conflict with other mode, cleanliness, level road, cross the street, drainage, step up and down and walking amenities which used a Likert scale to assess the current walking condition of respondents Finally, trip characteristics consist of a continuous variable and two dummy variables These are total buses, main transportation: motorbike and Other vehicle available, respectively
Table 4.1: Potential explanatory variables for walking distance model
The number of people per square meter in a buffer around origin or destination
Job density The number of jobs per square meter in a buffer around origin or destination
ArcGIS analysis Land use mix Land use entropy index is measured
Bus stops The number of bus stops within a buffer of origin or destination
Bus frequency The total number of bus passes all bus stations in a day within a buffer
Intersections The number of intersections within a buffer of a transit stop around origin or destination
Male A dummy variable indicating a respondent is male
Young people A dummy variable indicating a respondent is
Senior people A dummy variable indicating a respondent is
Low household in come Less than 10 million VND Mid household income Between 10 million VND - 30 million VND High household income More than 30 million VND Motorbike
License Indicating that a respondent has a vehicle to use
Car License A dummy variable indicating that a respondent has a car driver's license
A dummy variable indicating the current walking condition is poor and very poor
A dummy variable indicating the current walking condition is poor and very poor
Drainage Step up and down Walking amenities
Total buses Total number of buses for this trip On-board survey Main transportation: motorbike
A dummy variable indicating main transportation of a respondent is motorbike
Other vehicle A dummy variable indicating a respondent has another vehicle
Table 4.2 and Table 4.3 shows the descriptive statistics of potential explanatory variables for walking distance function Both are relatively similar in values Note that the “Perceived neighbourhood walkability” is only for access trip because survey questionnaires only require respondents to assess walking conditions around their homes
First, in terms of demographic characteristics, the average “male” are 0.438 for access trip (AC) and 0.395 for egress trip (EG), which means that the over majority of using bus is Female who often afraid of driving themselves The average “young people” and “senior people” are 0.545 and 0.224 (AC); 0.542 and 0.174 (EG), respectively It shows that young people and senior are the two dominant groups
There is a gradual decrease in household income and possession in of driver’s license of bus users The average of low income, middle income and high income are 0.507, 0.393, 0.1 (AC) and 0.462, 0.499, 0.107 (EG) respectively An average Motorbike driver’s license are 0.576 (AC) and 0.619 (EG) Car driver’s license are 0.076 (AC) and 0.104(EG)
Second, on the subjects of trip characteristics, a large part of respondents often chooses motorbikes as the main means of transportation, an average “Main transportation mode: motorbike” is 0.138 (AC) 0.147 (EG) The complexity of the trip of respondents is low because bus riders tend to choose a trip with less than one transit, an average “total buses” is 1.224 (AC) 1.284(EG) The average “other vehicles” is 0.403 (AC) and 0.445 This shows that Nearly half of bus riders still have other means to choose instead of buses
Third, the “average perceived neighbourhood walkability” including conflict with other mode, cleanliness, level road, cross the tress, drainage, step up and down, walking amenities is moderate, from 3.01 to 3.786 (AC) This reflects the fact that for those who are using the bus, the current walking condition is at an average level
Finally, in terms of built environment, the average population density (PD) measured in 4 buffering zone is from 277.76 to 290.20 (AC) and from 266.94 to 271.25 (EG) people per hectare An average “employment density” (ED) is from 238.93 to 254.63 (AC) and from 269.41 to 296.05 (EG) employment per hectare
Both of them vary in a very wide range, they reflect the complex characteristic of Hanoi Metropolitan Area The average of “Entropy index” is different according to each buffer, varying from 0.66 to 0.87 a(AC) and 0.64 to 0.88 (EG) This reflects that the current land-use pattern in Hanoi is fairly mixed and equally distributed An average “Number of public facilities” varying from 0.7 to 54.98 This implies the unequal distribution of public facilities in Hanoi The average “Number of intersections” ranges from 0.18 to 20.8 (AC) and 0.35 to 30.82 (EG) The “Bus Frequency” per day within four buffer has an average ranging from 51.53 to 2737.59 (AC) and from 81.45 to 3060.3 (EG)
Table 4.2: Descriptive statistics of variables for access trip (N)0)
Variables Type Min Max Mean Median Std
Total buses for this trip
Conflict with other mode Dummy 0 1 0.203 0 0.403
Step up and down Dummy 0 1 0.110 0 0.314
Land use entropy Continuo us 0.66 0.25 0.76 0.19 0.85 0.12 0.87 0.09
Bus frequency Continuo us 51.53 93.80 173.77 179.14 764.72 377.46 2737.59 963.30 Numbers of public facilities
Table 4.3: Descriptive statistics of variables for egress trip (N)9)
Variables Type Min Max Mean Median
Total buses for this trip
Land use entropy Continuo us 0.64 0.26 0.79 0.20 0.88 0.13 0.88 0.09
Methodology
In the study, the dependent variable is walking distance which is a count data
Therefore, a Poisson models was adopted Because the Poisson regression is used to predict a dependent variable that consists of "count data" given one or more independent variables Poisson regression is similar to regular multiple regression except that the dependent (Y) variable is an observed count that follows the Poisson distribution Thus, the possible values of Y are the nonnegative integers: 0, 1, 2, 3, and so on It is assumed that large counts are rare Hence, Poisson regression is similar to logistic regression, which also has a discrete response variable However, the response is not limited to specific values as it is in logistic regression
In Poisson regression, we suppose that the Poisson incidence rate à is determined by a set of k regressor variables (the X’s) The expression relating these quantities is:
Note that often, 𝑋 1 = 1 and β1 is called the intercept The regression coefficients β1 β2 …β k are unknown parameters that are estimated from a set of data
Their estimates are labelled b1, b2 , bk Using this notation, the fundamental Poisson regression model for an observation i is written as
𝜇 𝑖 = 𝑡 𝑖 𝜇(𝑥 𝑖 ′ 𝛽) = 𝑡 𝑖 exp (𝛽 1 𝑋 1 + 𝛽 2 𝑋 2 + ⋯ + 𝛽 𝑘 𝑋 𝑘 ) That is, for a given set of values of the regressor variables, the outcome follows the Poisson distribution
Furthermore, in order to control for mild violation of the distribution assumption that the variance equals the meal, Cameron & Trivedi (2009) recommended using robust standard errors for the parameter estimates Accordingly, in this study, Poisson models with robust errors was adopted
Using R 3.5.3 which is a free software environment for statistical computing and graphics, this study developed two models to examine the impacts of built environment attributes on walking distance to stops within four buffers for both access trip and egress trip We used R package “sandwich” to obtain the robust standard errors and calculated the p-values accordingly.
ESTIMATION RESULTS
Estimation results of access trip
Table 5.1 summarizes the estimation results of access segment Four regression models which examining the association between BE attributes and walking distance to transit stop within four different buffers are estimated for each segment A model that represents a buffer is selected based on the following steps including: selecting AIC, analysis of deviance table by Anova, and chiquare test Based on this result, we can choose four model four each segment as the optimal models to use for interpretation Note that “Male” is a dummy variable, defined to be 1 if the respondent is male and 0 is female; “Low income”, “High income”, “Middle income” are defined to be 1 if the household income of respondents are low, high, and middle respectively, and 0 otherwise; and “Motorbike driver's license” and “Car driver's license” is defined to be 1 if the respondent has a license and 0 otherwise “Main transportation mode: motorbike” is define to be 1 if the main transportation mode of respondent is motorbike and 0 otherwise The conditions of Perceived neighbourhood walkability are defined to be 1 if the point is less than 3 point (poor and very poor) and 0 otherwise
The estimation results in all models consistently show that “Male” has a significantly positive association with walking distance This is understandable because male generally have better physical attributes than female, they are able to walk a farther distance to bus stops This is also the reason for the explanation of
“Young people” and “Senior people” “Young people” has a positive association with walking distance in model 4, whereas “Senior people” has a negative association
The dummy variables related to household income have different correlations with walking distance “Low income” has negative correlation with the walking distance, whereas “Middle income” and “High income” has a positive association, which means that middle and high income people walk to the bus stop farther than low-income people To explain this, we need to know that most low-income people here are students who come from other provinces and rent a house to live in Hanoi
They always prefer to select areas near bus stops to facilitate going to their university
Many high-income people live in new urban areas where the density of bus stops is low, and most of them do not choose buses as a major means of transportation
Next, the estimation results in all models consistently show that “Motorbike driver's license” and “Car driver's licence” have a significantly negative association with walking distance to public transit This is reasonable because people who have a driver's license tend to use private vehicles more than walking in general and walking to the bus stop in particular This is also the same reason why “Main transportation mode: motorbike” and “Private vehicle available” have negative association with walking distance to transit stop
“Total Transfers” has a negative correlation with walking distance This reasonable because the complexity of the trip could make passengers unwilling to walk too far to make this trip If the distance to bus stop is long and the trip has many transfer, they can give up and choose other means of transportation
In terms of perceived neighbourhood walkability, outside of the “Step up and down”, all other dummy variables including “Conflict with other mode”, “Level road”, “Cross the street”, “Drainage”, and “Walking amenities” have the negative association with the walking distance to transit stop These negative coefficient indicates that in good walking conditions transit users walk farther to bus stop than poor walking conditions “Step up and down” has a positive correlation with walking distance This is understandable because when transit users have to walk a lot to go up or down stairs (pedestrian bridge, walkway), their walking distance is also longer
However, in general, perceived neighbourhood walkability have weak correlations with walking distance
Finally, BE attributes have the association with walking distance “Population density” in all four model is estimated to be significantly negative, which means in denser areas transit users walk shorter to public transit This is reasonable in Hanoi because the public transport bus stops are distributed very much in the central areas of Hanoi, where the population density is very high That is reason why residents in these areas could easily access the bus stops It is worth noting that “Job density” is frequency” is estimated to be significantly negative This means that better accessibility to public transit gives shorter walking distance This is similar to
“Numbers of public facilities” Finally, “Land use mix” is estimated to be significantly negative, which suggests that transit users’ walking distance in higher level of land use mix is shorter
Table 5.1: Estimation result of Poisson model for walking distance of access trip
Explanatory variables Model 1 (buffer 100) Model 2 (buffer 200) Model 3 (buffer 500) Model 4 (buffer 1000)
Motorbike driver's license -0.1015*** 0.2503 -0.0749*** 0.3928 -0.0527*** 0.5254 -0.0859*** 0.3239 Car driver's licence -0.2460*** 0.1105 -0.1961*** 0.1260 -0.2213*** 0.0936 -0.1897*** 0.2286 Main transportation mode: motorbike -0.2788*** 0.0380 -0.2039*** 0.0984 -0.2314*** 0.0706 -0.2248*** 0.0616
Total transfers -0.0933*** 0.2188 -0.1813*** 0.0177 -0.2482*** 0.0062 -0.1793*** 0.0701 Private vehicles available -0.0917*** 0.3010 -0.0997*** 0.2363 -0.0985*** 0.2281 -0.1124*** 0.1907 Conflict with other mode -0.1838*** 0.0462 -0.1580*** 0.0579 -0.1168*** 0.2269 -0.1176*** 0.2371
Table 5.2: Estimation result of Poisson model for walking distance of egress trip
Explanatory variables Model 1 (buffer 100) Model 2 (buffer 200) Model 3 (buffer 500) Model 4 (buffer 1000)
Total Transfers -0.2205*** 0.0245 -0.2021*** 0.0313 -0.1665*** 0.0901 -0.1676*** 0.1013 Private vehicles available 0.1008*** 0.2780 0.1387*** 0.1269 0.0580*** 0.5265 0.0704*** 0.5088
Estimation results of egress trip
Table 5.2 summarizes the estimation results of egress segment Four regression models which examining the association between BE attributes and walking distance to transit stop within four different buffers are estimated for egress segment
In terms of demographics and trip characteristic, most variables have a similar association with walking distance like access segment “Male”, “Young people”, and
“High income” has a significantly positive association with walking distance to transit stop “Senior people”, “Motorbike driver's license”, and “Total Transfers” is estimated to be negative However, there are some differences between access segment and egress segment “Low income” has positive association with walking distance in egress trip As explained in access section, low-income people prefer to choose renting a house near a bus, therefore, they could accept walking farther on the remaining trip “Main transportation mode: motorbike” and “Private vehicles available” also has positive correlation with walking distance This is the opposite of access segment because many respondents who use motorbike as a main transportation mode and have a private vehicle select bus for the purpose of leisure and shopping, therefore, distance walking of segment is longer
Similar to access segment, most BE attributes has a negative association with walking distance including “Population density”, “Land use mix”, “Numbers of bus stop”, “Numbers of intersection”, and “Bus frequency” The differences here are “Job density” has a negative association with walking distance, and “Numbers of public facilities” has significantly positive association with walking distance to transit stop
These are explained by the fact that many of theareas with high job density are located in the centre of Hanoi city, where is easily accessible by public transit
“Numbers of public facilities” is significantly positive, which means that the more public facilities (such as shops, markets), the farther walking distance to transit stop.
DISCUSSION AND CONCLUSION
Walking distance to public transit
Analysis of the Hanoi on-board survey data with more than 600 respondents who were bus riders showed that walking is the majority mode of both bus trips from home to transit stop and from transit stop to final destination in Hanoi (Table 6.1) which accounts for more than 95 percent of the total modes This result is similar to previous studies in other study, such as in Sydney city, walking accounted for 89% of the total modes from home to the bus stop (Daniels et al., 2013) It asserted that walking is the primary mode for both access trip from home to transit stop and egress trip from transit stop to the destination and therefore walking distance has a significant impact on the use of public transportation because when choosing walking routes to public transportation system, transit users often give priority to walking time or walking distance to transit stops (Agrawal et al., 2008)
Table 6.1: Access modes from home to bus stops and from final stops to destinations in Hanoi
Mode Access trip Egress trip
Drive or rode with someone and park vehicle 0.80% 0.30%
Rode motorbike by myself and park 0.50% -
(Source: On-board Survey in Hanoi, May 2019) One of the main goals of this study was to attempt to compute the actual distance walked to public transit stops of transit riders in Hanoi As mentioned in the literature review section, transportation planners often use assumptions about distances that people will be willing to walk to transit stop to determine the location and catchment area of the transit stops In addition, this “rules of thumb” was also used by land use planners for designing policies for transit-oriented development (TOD) For the bus system, the majority of studies often use distance threshold of 400 meters to reach the bus stops
In the context of the Hanoi city, this study computed the average distance walked for access trip from home to bus stops is 397.083 meters and for egress trip from bus stop to destinations is 325.197 meters This result has shown that transit rider tends to walk from home to the bus stop farther than walking from the bus stop to the destination This is understandable because people can easily select the nearest bus stop to the destination and choose a relevant bus stop to depart from home after that In terms of the assumptions about walking distance to public stops, the “rules of thumb” that previous studies have given are consistent with the Hanoi context, because the average of walking distance to bus stop for both access trip and egress trip is slightly shorter than the distance threshold of 400 meters (AC is 397.083 meters and EG is 325.197 meters)
Table 6.2: Statistics on walking distances for access trip and egress trip
Walking distance (meters) Minimum Maximum Mean Median Std
(Source: On-board Survey in Hanoi, May 2019)
Association between BE and walking distance
This study examines the association of the BE with walking distance to transit stop of both transit egress and access in Hanoi city We found that the current walking condition near transit user’s house affect their walking distance The poor conditions of walking path including conflict with other mode, cleanliness, level road, cross the street, drainage, step up and down, and walking amenities shorten the walking distance of transit users Accordingly, in addition to improving the walking conditions to increase users' walking distance and serve more people by enlarging catchment area, when choosing stop locations for new transit services, the priority should be given to areas where have poor walking conditions
Furthermore, in the downtown areas where there are favourable conditions for access to public transit stops such as the denser population, high job density, high bus frequency, the transit user’s walking distance tends to be shorter Thus, transit planners should focus on transit stops outside of downtown areas to facilitate transit users' walking to their destinations
Stop locations for new transit services within downtown areas should be prioritized in areas with good street connectivity, as the number of intersections has the largest effect on minimizing walking distance In areas where there are many public facilities, transit stops should be located to increase users' walking distance and serve more people by enlarging catchment areas.
Limitations
This study showed empirical evidences about the association between BE and the walking distance to transit stop in Hanoi
However, this research still has limitations which raise the gap for future research
Firstly, the walking distance to transit stop was only calculated for bus stops because the current public transportation system is only operating the bus system and only one BRT route It is afraid that the mass rapid transit (MRT) will be operational in the near future (MRT line 2A will be open in 2019), which may require another analysis on it
The second limitation is the shortage of data availability It is difficult to access the latest data sources such as population density by ward, and official GIS based system for Hanoi
Finally, under the allowed conditions of this research, the number of surveyed samples is still small (more than 600 samples) It is not enough to represent the whole of Hanoi city, so there may still be some biased results
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A set of survey questionnaire in English
A set of survey questionnaire in Vietnamese
Correlation between potential explanatory variables
Young_p eople M ale Low_inc ome
M ain_tr ansporta tion_mo de_M ot orbike
Conflict _with_ot her_mod e
Level_ro ad cross the street Drainage
Step_up _and_do wn
Number _of_publ ic_faciliti es_100
Private_vehicles_available -.172 ** -.011 110 -.215 ** 158 ** 101 083 137 * 181 ** 089 1 195 ** 161 ** 081 174 ** 095 137 * -.002 046 -.098 -.066 065 029 -.068 027 Conflict_with_other_mode -.046 -.054 -.049 -.067 084 -.026 -.080 035 021 315 ** 195 ** 1 401 ** 167 ** 374 ** 277 ** 150 * 269 ** 051 065 -.112 030 -.039 -.026 004 Cleanliness 014 -.057 -.087 -.045 050 -.007 -.007 -.103 120 * 184 ** 161 ** 401 ** 1 313 ** 299 ** 261 ** 262 ** 081 031 -.078 -.163 ** 028 -.004 -.144 * -.015
Level_road 017 -.134 * 007 -.085 060 045 025 -.152 ** 049 -.014 081 167 ** 313 ** 1 196 ** 269 ** 034 121 * 010 -.046 074 135 * 078 009 043 cross the street -.031 -.051 -.116 * -.106 073 057 004 -.043 -.053 210 ** 174 ** 374 ** 299 ** 196 ** 1 375 ** 040 260 ** -.052 -.031 -.083 096 066 -.018 035
Number_of_intersection_100 137 * -.143 * 045 -.078 052 046 121 * 008 -.007 020 027 004 -.015 043 035 -.087 004 009 -.100 175 ** 178 ** 177 ** 004 245 ** 1 Walking_distance_for_access_tr ip_meter
Correlations of buffer 100 - Access trip
** Correlation is significant at the 0.01 level (2-tailed)
* Correlation is significant at the 0.05 level (2-tailed)
Car_dri ver_lis ence
Main_t ranspo rtation _mode _Moto
Private _vehicl es_ava ilable
Numbe r_of_in tersecti on_200 Senior_people 1 -.588 ** -.008 -.181 ** 058 207 ** 065 -.007 -.167 ** 062 -.172 ** -.046 014 017 -.031 -.077 022 -.088 000 045 -.022 -.002 -.013 148 * 111 Young_people -.588 ** 1 -.031 456 ** -.342 ** -.203 ** -.261 ** -.098 -.156 ** -.097 -.011 -.054 -.057 -.134 * -.051 -.013 -.010 026 014 080 050 022 -.014 -.152 ** -.100
Low_income -.181 ** 456 ** -.103 1 -.816 ** -.338 ** -.186 ** -.149 * -.146 * -.060 -.215 ** -.067 -.045 -.085 -.106 -.005 -.027 106 030 114 069 052 024 -.034 -.028 Mid_income 058 -.342 ** 115 -.816 ** 1 -.268 ** 063 162 ** 210 ** -.009 158 ** 084 050 060 073 -.025 055 -.060 040 -.039 -.012 -.086 -.004 068 039 High_income 207 ** -.203 ** -.016 -.338 ** -.268 ** 1 208 ** -.016 -.100 113 101 -.026 -.007 045 057 049 -.044 -.078 -.116 * -.127 * -.097 054 -.035 -.054 -.016 Car_driver_lisence 065 -.261 ** 141 * -.186 ** 063 208 ** 1 246 ** 263 ** -.027 083 -.080 -.007 025 004 011 -.018 051 -.014 069 089 057 096 086 156 ** Motorcycle _driver_lisence -.007 -.098 279 ** -.149 * 162 ** -.016 246 ** 1 262 ** -.037 137 * 035 -.103 -.152 ** -.043 -.058 057 -.052 074 090 -.013 011 110 072 059 Main_transportation_mode_
-.167 ** -.156 ** 191 ** -.146 * 210 ** -.100 263 ** 262 ** 1 -.043 181 ** 021 120 * 049 -.053 031 178 ** 042 -.024 -.022 006 036 145 * 034 -.021 Total_buses 062 -.097 -.053 -.060 -.009 113 -.027 -.037 -.043 1 089 315 ** 184 ** -.014 210 ** 044 -.004 -.012 -.089 -.156 ** -.170 ** -.070 -.192 ** -.047 -.001 Private_vehicles_available -.172 ** -.011 110 -.215 ** 158 ** 101 083 137 * 181 ** 089 1 195 ** 161 ** 081 174 ** 095 137 * -.002 046 -.103 -.006 128 * 040 -.078 -.037 Conflict_with_other_mode -.046 -.054 -.049 -.067 084 -.026 -.080 035 021 315 ** 195 ** 1 401 ** 167 ** 374 ** 277 ** 150 * 269 ** 062 035 -.055 005 -.036 -.005 -.043 Cleanliness 014 -.057 -.087 -.045 050 -.007 -.007 -.103 120 * 184 ** 161 ** 401 ** 1 313 ** 299 ** 261 ** 262 ** 081 016 -.094 -.045 -.008 -.011 -.085 -.026 Level_road 017 -.134 * 007 -.085 060 045 025 -.152 ** 049 -.014 081 167 ** 313 ** 1 196 ** 269 ** 034 121 * 001 -.054 077 -.003 021 052 051 Across -.031 -.051 -.116 * -.106 073 057 004 -.043 -.053 210 ** 174 ** 374 ** 299 ** 196 ** 1 375 ** 040 260 ** -.041 -.062 -.023 038 -.015 -.019 053 Drainage -.077 -.013 026 -.005 -.025 049 011 -.058 031 044 095 277 ** 261 ** 269 ** 375 ** 1 134 * 290 ** -.036 -.038 006 -.051 -.011 -.088 -.087 Step_up_and_down 022 -.010 -.022 -.027 055 -.044 -.018 057 178 ** -.004 137 * 150 * 262 ** 034 040 134 * 1 067 -.078 005 030 -.003 099 -.128 * 007 Walking_amenities -.088 026 014 106 -.060 -.078 051 -.052 042 -.012 -.002 269 ** 081 121 * 260 ** 290 ** 067 1 -.043 -.007 032 -.013 -.001 -.035 022 Population_Density _200 000 014 140 * 030 040 -.116 * -.014 074 -.024 -.089 046 062 016 001 -.041 -.036 -.078 -.043 1 229 ** 178 ** 101 103 240 ** -.090 Job_Density_200 045 080 064 114 -.039 -.127 * 069 090 -.022 -.156 ** -.103 035 -.094 -.054 -.062 -.038 005 -.007 229 ** 1 405 ** 230 ** 225 ** 329 ** 289 ** Entropy_Index_200 -.022 050 -.019 069 -.012 -.097 089 -.013 006 -.170 ** -.006 -.055 -.045 077 -.023 006 030 032 178 ** 405 ** 1 250 ** 283 ** 290 ** 194 ** Number_of_bus_stop_200 -.002 022 060 052 -.086 054 057 011 036 -.070 128 * 005 -.008 -.003 038 -.051 -.003 -.013 101 230 ** 250 ** 1 626 ** 057 204 ** Bus_frequency_200 -.013 -.014 086 024 -.004 -.035 096 110 145 * -.192 ** 040 -.036 -.011 021 -.015 -.011 099 -.001 103 225 ** 283 ** 626 ** 1 076 030 Number_of_public_facilities
Correlations of buffer 200 - Access trip
** Correlation is significant at the 0.01 level (2-tailed)
* Correlation is significant at the 0.05 level (2-tailed)
Car_dri ver_lise nce
Main_tr ansport ation_m ode_M otorbik
Private _vehicl es_avai lable
Number _of_pu blic_fac ilities_5 00
Number _of_int ersectio n_500 Senior_people 1 -.588 ** -.008 -.181 ** 058 207 ** 065 -.007 -.167 ** 062 -.172 ** -.046 014 017 -.031 -.077 022 -.088 -.019 062 -.020 064 048 232 ** 192 ** Young_people -.588 ** 1 -.031 456 ** -.342 ** -.203 ** -.261 ** -.098 -.156 ** -.097 014 -.054 -.057 -.134 * -.051 -.013 -.010 026 027 053 083 -.040 -.090 -.208 ** -.115 *
High_income 207 ** -.203 ** -.016 -.338 ** -.268 ** 1 208 ** -.016 -.100 113 127 * -.026 -.007 045 057 049 -.044 -.078 -.119 * -.129 * -.069 -.065 -.086 -.058 -.035 Car_driver_lisence 065 -.261 ** 141 * -.186 ** 063 208 ** 1 246 ** 263 ** -.027 069 -.080 -.007 025 004 011 -.018 051 007 097 065 081 110 077 151 ** Motorcycle _driver_lisence -.007 -.098 279 ** -.149 * 162 ** -.016 246 ** 1 262 ** -.037 148 * 035 -.103 -.152 ** -.043 -.058 057 -.052 079 107 017 107 156 ** 100 075 Main_transportation_mode_
-.167 ** -.156 ** 191 ** -.146 * 210 ** -.100 263 ** 262 ** 1 -.043 156 ** 021 120 * 049 -.053 031 178 ** 042 003 017 010 014 131 * 004 -.034 Total_buses 062 -.097 -.053 -.060 -.009 113 -.027 -.037 -.043 1 069 315 ** 184 ** -.014 210 ** 044 -.004 -.012 -.101 -.172 ** -.230 ** -.149 * -.304 ** -.087 -.082 Private_vehicles_available -.172 ** 014 104 -.198 ** 125 * 127 * 069 148 * 156 ** 069 1 199 ** 137 * 068 148 * 117 * 117 * 051 032 -.078 -.015 052 004 -.111 -.064 Conflict_with_other_mode -.046 -.054 -.049 -.067 084 -.026 -.080 035 021 315 ** 199 ** 1 401 ** 167 ** 374 ** 277 ** 150 * 269 ** 068 005 -.009 076 -.029 -.023 -.057 Cleanliness 014 -.057 -.087 -.045 050 -.007 -.007 -.103 120 * 184 ** 137 * 401 ** 1 313 ** 299 ** 261 ** 262 ** 081 015 -.100 -.050 006 -.002 -.083 -.068
Population_Density _500 -.019 027 143 * 013 060 -.119 * 007 079 003 -.101 032 068 015 019 -.022 -.045 -.048 -.036 1 296 ** 244 ** 265 ** 237 ** 301 ** -.074 Job_Density_500 062 053 107 073 005 -.129 * 097 107 017 -.172 ** -.078 005 -.100 -.048 -.089 -.046 002 -.016 296 ** 1 500 ** 492 ** 394 ** 469 ** 398 ** Entropy_Index_500 -.020 083 012 037 004 -.069 065 017 010 -.230 ** -.015 -.009 -.050 024 002 021 027 027 244 ** 500 ** 1 422 ** 491 ** 204 ** 136 * Number_of_bus_stop_500 064 -.040 177 ** 009 030 -.065 081 107 014 -.149 * 052 076 006 -.033 -.001 013 032 053 265 ** 492 ** 422 ** 1 604 ** 454 ** 479 **
Bus_frequency_500 048 -.090 142 * 008 045 -.086 110 156 ** 131 * -.304 ** 004 -.029 -.002 051 -.015 -.003 053 -.005 237 ** 394 ** 491 ** 604 ** 1 333 ** 208 ** Number_of_public_facilities
Correlations of buffer 500 - Access trip
** Correlation is significant at the 0.01 level (2-tailed)
* Correlation is significant at the 0.05 level (2-tailed)
Young_ people Male Low_in come
Car_dri ver_lise nce
Main_tr ansport ation_m ode_M otorbike
Conflict _with_o ther_mo de
Level_r oad cross the sstreet
Number _of_pu blic_fac ilities_5 00
High_income 207 ** -.203 ** -.016 -.338 ** -.268 ** 1 208 ** -.016 -.100 113 101 -.026 -.007 045 057 049 -.044 -.078 -.112 -.106 -.071 -.116 * -.107 -.031 -.003 Car_driver_lisence 065 -.261 ** 141 * -.186 ** 063 208 ** 1 246 ** 263 ** -.027 083 -.080 -.007 025 004 011 -.018 051 043 122 * 092 075 102 137 * 162 ** Motorcycle _driver_lisence -.007 -.098 279 ** -.149 * 162 ** -.016 246 ** 1 262 ** -.037 137 * 035 -.103 -.152 ** -.043 -.058 057 -.052 099 108 020 066 092 135 * 046 Main_transportation_mode_
-.167 ** -.156 ** 191 ** -.146 * 210 ** -.100 263 ** 262 ** 1 -.043 181 ** 021 120 * 049 -.053 031 178 ** 042 059 047 025 -.031 020 -.005 -.020 Total_buses 062 -.097 -.053 -.060 -.009 113 -.027 -.037 -.043 1 089 315 ** 184 ** -.014 210 ** 044 -.004 -.012 -.142 * -.193 ** -.269 ** -.190 ** -.261 ** -.124 * -.174 ** Private_vehicles_available -.172 ** -.011 110 -.215 ** 158 ** 101 083 137 * 181 ** 089 1 195 ** 161 ** 081 174 ** 095 137 * -.002 023 -.054 -.003 -.047 -.059 -.087 -.082 Conflict_with_other_mode -.046 -.054 -.049 -.067 084 -.026 -.080 035 021 315 ** 195 ** 1 401 ** 167 ** 374 ** 277 ** 150 * 269 ** 058 016 -.001 047 005 -.037 -.066 Cleanliness 014 -.057 -.087 -.045 050 -.007 -.007 -.103 120 * 184 ** 161 ** 401 ** 1 313 ** 299 ** 261 ** 262 ** 081 016 -.090 -.068 -.014 -.072 -.078 -.101
Entropy_Index_500 -.051 105 013 036 007 -.071 092 020 025 -.269 ** -.003 -.001 -.068 -.022 -.042 052 061 -.019 288 ** 639 ** 1 620 ** 690 ** 277 ** 318 ** Number_of_bus_stop_500 003 135 * 090 094 -.026 -.116 * 075 066 -.031 -.190 ** -.047 047 -.014 -.043 -.058 014 053 050 394 ** 777 ** 620 ** 1 880 ** 566 ** 626 **
Correlations of buffer 1000 - Access trip
Young_pe ople M ale Low_inco me
M ain_tras portation_ mode_M o torbike
Other_veh icles_avail able
M ain_trasportation_mo de_M otorbike -.141 * -.186 ** 070 -.195 ** 267 ** -.113 230 ** 307 ** 1 -.067 274 ** -.047 029 027 043 046 028 019
Number_of_public_facil ities_100 089 -.132 * -.001 -.022 -.029 082 005 040 028 -.064 -.043 117 * 284 ** 238 ** 055 012 1 254 **
Corre lations of buffe r 100 - Egre ss
** Correlation is significant at the 0.01 level (2-tailed)
Main_tras portation_ mode_Mo torbike
Private_ve hicles_ava ilable
Main_tras portation_ mode_Mo torbike
Other_ve hicles_av ailable
Main_trasportati on_mode_Motor -.141 * -.186 ** 070 -.195 ** 267 ** -.113 230 ** 307 ** 1 -.067 274 ** -.053 080 003 083 052 091 103
Number_of_publ ic_facilities_500 133 * -.164 ** 021 -.045 026 031 -.005 047 091 -.128 * -.058 181 ** 594 ** 184 ** 416 ** 338 ** 1 732 **
** Correlation is significant at the 0.01 level (2-tailed)
Car_dri ver_lice ns e
Motorcy cle_driv er_licen s e
Main_tr as portat ion_m o de_Mot orbike
Num ber _of_bus _s top_1 000
Num ber _of_pub lic_facili ties _10 00
Num ber _of_inte rs ection _1000 Senior_people 1 -.499 ** 081 -.159 ** 081 127 * 047 -.039 -.141 * -.068 -.180 ** -.023 007 -.133 * -.001 -.037 140 * 144 * Young_people
127 * -.116 * 052 -.321 ** -.302 ** 1 024 -.040 -.113 129 * 126 * -.034 -.002 -.051 -.039 -.046 -.010 012 Car_driver_licens e 047 -.282 ** 174 ** -.227 ** 213 ** 024 1 244 ** 230 ** 115 * 159 ** 035 -.035 -.096 -.058 -.059 -.020 018 Motorcycle_driver_lic ens e
-.039 -.114 * 282 ** -.143 * 169 ** -.040 244 ** 1 307 ** 061 148 * -.042 037 -.033 -.008 025 060 082 Main_tras portation_ m ode_Motorbike -.141 * -.186 ** 070 -.195 ** 267 ** -.113 230 ** 307 ** 1 -.067 274 ** -.062 067 001 053 028 084 129 * Total_bus es -.068 026 048 -.085 004 129 * 115 * 061 -.067 1 112 -.131 * -.116 * -.105 -.111 -.123 * -.167 ** -.144 * Private_vehicles _av ailable -.180 ** -.055 062 -.221 ** 144 * 126 * 159 ** 148 * 274 ** 112 1 -.078 -.031 004 -.065 -.054 -.071 -.003 Population_Dens ity
-.133 * 123 * -.032 044 -.012 -.051 -.096 -.033 001 -.105 004 402 ** 570 ** 1 633 ** 710 ** 219 ** 088 Num ber_of_bus _s t op_1000 -.001 -.022 022 045 -.021 -.039 -.058 -.008 053 -.111 -.065 443 ** 793 ** 633 ** 1 875 ** 498 ** 440 **
Num ber_of_public_f acilities _1000 140 * -.146 * 046 -.033 040 -.010 -.020 060 084 -.167 ** -.071 278 ** 713 ** 219 ** 498 ** 624 ** 1 844 **
Num ber_of_inters ec tion_1000 144 * -.183 ** -.009 -.019 011 012 018 082 129 * -.144 * -.003 -.016 557 ** 088 440 ** 473 ** 844 ** 1