Association of built environment with walking distance to public transit stops in Hanoi

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 [r]

VIETNAM NATIONAL UNIVERSITY, HANOI

VIETNAM JAPAN UNIVERSITY

CHU ANH TUAN

ASSOCIATION OF BUILT ENVIRONMENT WITH WALKING DISTANCE TO PUBLIC

TRANSIT STOPS IN HANOI

MASTER'S THESIS

MASTER OF INFRASTRUCTURE ENGINEERING

Hanoi, 2019

ANNEX 2 LIST OF FORMS FOR MANAGEMENT

VIETNAM NATIONAL UNIVERSITY, HANOI

VIETNAM JAPAN UNIVERSITY

CHU ANH TUAN

ASSOCIATION OF BUILT ENVIRONMENT WITH WALKING DISTANCE TO PUBLIC TRANSIT STOPS

ACKNOWLEDGMENT

This research would not have been possible without the support, contribution and collaboration of others

First of all, I would like to convey my greatest appreciation and deepest attitude

to my supervisors, Prof Hironori Kato and Dr Phan Le Binh for their support and constant supervision which contributed immensely to my personal development for his precious advice and helpful guidance in research work Without their patience and continuous support, I cannot fulfil my research

I sincerely thank to Prof Nguyen Dinh Duc - the Program Director of Infrastructure Engineering Program of Vietnam- Japan University (VJU) who always has encouraged and deeply care about me

In addition, I sincerely thank to all members in MIE’s office Specially, I would like to give my greatest thanks to Dr Nguyen Tien Dung and Mr Bui Hoang Tan who always give me strong support and insightful advice

My deep thank to Mrs Nguyen Thi Mai Chi about her precious support for my data collection

Furthermore, I sincerely thank to the rest of the teachers and staffs of Vietnam Japan University for their support and guidance which has inspired me and helped

me to overcome the challenges I faced during the period of study at Vietnam Japan University To all of you, I extend my deepest gratitude

Finally, I would like to send my deepest love and gratitude to my family, who always love, support and encourage me every time

Sincerely,

Chu Anh Tuan

ABTRACT

Similar to many other developing cities, Hanoi, the capital city of Vietnam, has also faced many serious problems caused by spontaneous urban development and massive private vehicles As a part of solutions, public transportation systems in Hanoi were paid attention to be invested and developed However, Public transportation meets only partially the travelling demand of urban people Even recent years, the productivity of buses in Hanoi tends to be restrain and even going down in some years Therefore, how to encourage and attract people to use public transportation modes is a more and more urgent topic This research focuses on a key factor which greatly influences the use of transit services This is Walking distance

to transit stop and impacts of built environment on this walking distance of riders

A Poison Regression model is estimated to analyze the associations of built environment and walking distance to public transit stop using a dataset collected from

a questionnaire-based Onboard survey in May 2019 with the geospatial data of BE variables where the dataset contains 609 respondents of the Hanoi Metropolitan Area The results unveiled that: (1) Walking is the majority mode of both bus trips from home to transit stop and from transit stop to final destination; (2) The assumptions about walking distance to public stops (threshold of 400 meters) that planners have given are consistent with the Hanoi context; (3) BE variables may have both positive/ negative impacts on walking distance to public transit stops

Finally, some policy recommendations were produced to support for the use of the public transportation modes in Hanoi

Keywords: Walking; Walking distance; Built environment; Transit stop

TABLE OF CONTENTS

ACKNOWLEDGMENT i

TABLE OF CONTENTS ii

LIST OF FIGURES v

LIST OF TABLES vi

LIST OF ABBREVIATIONS vii

CHAPTER 1 INTRODUCTION 1

CHAPTER 2 LITERATURE REVIEW 6

2.1 The relationship between walking and BE 6

2.2 Distance walking and walking to public transit 7

2.3 Studies on walking distance and built environment in the world and Vietnam 10

CHAPTER 3 DATA 13

3.1 Survey Questionnaire 13

3.1.1 Survey area 13

3.1.2 Survey implemention 15

3.2 GIS-based database 20

3.3 Measurement of walking distance to public transit 20

3.4 Measurement of BE variable 22

CHAPTER 4 WALKING DISTANCE MODEL DEVELOPMENT 27

4.1 Overview 27

4.2 Explanatory variables 27

4.3 Methodology 33

CHAPTER 5 ESTIMATION RESULTS 35

5.1 Estimation results of access trip 35

5.2 Estimation results of egress trip 40

CHAPTER 6 DISCUSSION AND CONCLUSION 41

6.1 Walking distance to public transit 41

6.2 Association between BE and walking distance 42

6.3 Limitations 43

REFERENCES 45

APPENDIX 48

A set of survey questionnaire in English 48

A set of survey questionnaire in Vietnamese 55

Correlation between potential explanatory variables 62

LIST OF FIGURES

Page

Figure 1.1: Bus passenger ridership in Hanoi 2

Figure 1.2: Flow of Research 5

Figure 3.1: Survey bus route in Hanoi 14

Figure 3.2: A group of the survey and an interview on a bus route 16

Figure 3.3: An illustration in the survey questionnaire 16

Figure 3.4: Locations of respondents 21

Figure 3.5: Population density by ward 23

Figure 3.6: Job density by ward 24

Figure 3.7: Current bus network and bus stops distribution 26

LIST OF TABLES

Page

Table 2.1: Access modes from home to bus stops in Sydney 8

Table 3.1: The descriptive statistics of attributes of respondents (N=609) 18

Table 4.1: Potential explanatory variables for walking distance model 28

Table 4.2: Descriptive statistics of variables for access trip (N=290) 31

Table 4.3: Descriptive statistics of variables for egress trip (N=299) 32

Table 5.1: Estimation result of Poisson model for walking distance of access trip 38 Table 5.2: Estimation result of Poisson model for walking distance of egress trip 39 Table 6.1: Access modes from home to bus stops and from final stops to destinations in Hanoi 41

Table 6.2: Statistics on walking distances for access trip and egress trip 42

LIST OF ABBREVIATIONS

CHAPTER 1 INTRODUCTION

1.1 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

(Source: TRANSERCO) 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

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?

1.2 Contributions of this research

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

1.3 Research Framework

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

1 Introduction

2 Literature Review

3 Data

4 Walking Distance Model Development

6 Discussion and Conclusions

5 Estimation Results

Figure 1.2: Flow of Research

CHAPTER 2 LITERATURE REVIEW

2.1 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

of the BE to explaining age differences in walking (Ghani et al., 2018) Some of them

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

2.2 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

(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

2.3 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

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)

3.1.2 Survey implemention

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 10th, 2019 to May 15th, 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

(Photo taken by author)

Figure 3.2: An illustration in the survey questionnaire

(Source: Created by author)

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=609)

Farmer, Forestry &

Fishermen

General staff in private

company

Housewife

Labourer & Unskilled

No job/Retired /Dependent

Officer in governmental organization or equivalent

Shop keeper/

Street Vender

Teacher (high school or lower)

Senior staff in private company

Service worker, Shop and Market Worker

40 - 45 million VND

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

3.2 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

3.3 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

Figure 3.2: Locations of respondents

(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

3.4 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

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)

Job density

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:

𝑋𝑖𝐽𝐷,𝑅 =∑𝑘∈𝐾𝑖,𝑅𝑍𝐽𝐷𝑘𝐴𝑘

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):

𝐿𝑎𝑛𝑑 𝑈𝑠𝑒 𝐸𝑛𝑡𝑟𝑜𝑝𝑦 𝐼𝑛𝑑𝑒𝑥 (𝑅) = − ∑ 𝑃𝑖,𝑅𝐿𝑛(𝑃𝑖,𝑅)

𝐿𝑛(𝑘,𝑅)

𝑘 𝑖=1 (3)

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 road intersection

Number of way road intersection is computed by counting total number of way or more within a buffer area This variable explains the street connectivity and accessibility for riders

4-Number of public facilities

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)

CHAPTER 4 WALKING DISTANCE MODEL DEVELOPMENT

4.1 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

4.2 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

Built environment attributes

around origin or destination

ArcGIS analysis

Land Use Entropy Index

origin or destination

ArcGIS analysis Bus frequency The total number of bus passes all bus stations in

a day within a buffer

ArcGIS analysis Intersections The number of intersections within a buffer of a

transit stop around origin or destination

ArcGIS analysis

Individual characteristics

male

On-board survey

“between 18 to 25 years old”

“over 55 years old”

has a car driver's license

Perceived neighbourhood walkability

Conflict with

other mode

A dummy variable indicating the current walking

condition is poor and very poor

On-board survey Cleanliness

Level road

Across

A dummy variable indicating the current walking

condition is poor and very poor

On-board survey

another vehicle

On-board survey

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)