1 VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY TRAN THE HUY IMPACTS OF BRT INTRODUCTION ON COMMUTER TRAVEL BEHAVIOR IN HANOI MASTER’S THESIS Hanoi, 2019 2 ANNEX 2 LIST OF FORMS FOR MANA[.]
VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY TRAN THE HUY IMPACTS OF BRT INTRODUCTION ON COMMUTER TRAVEL BEHAVIOR IN HANOI MASTER’S THESIS Hanoi, 2019 VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY TRAN THE HUY IMPACTS OF BRT INTRODUCTION ON COMMUTER TRAVEL BEHAVIOR IN HANOI MAJOR: INFRASTRUCTURE ENGINEERING CODE: PILOT RESEARCH SUPERVISOR: Dr NGUYEN HOANG TUNG ANNEX LIST OF FORMS FOR MANAGEMENT Hanoi, 2019 ACKNOWLEDGEMENT I would like to spend the very first words of this report to indicate my deeply thankful to some people because without them, I would not complete my master’s thesis Firstly, I would like to express my sincere gratitude to my advisor Dr Nguyen Hoang Tung of the University of Transport and Communications for the continuous support of my master study and related research, for his patience, motivation, and immense knowledge His guidance during research time leaded me to the right approach for the whole research Besides my advisor, I would like to thank Prof Hinoroni Kato of the University of Tokyo, who provided me an opportunity to join his team as intern in Japan and gave access to the international laboratory and research facilities I have reached so many valuable articles for my thesis Prof Kato also gave me precious advices, directions and commends which helped me to shape a reasonable framework of my thesis Without his great support it would not be possible to conduct this research My sincere thanks also goes to Dr Phan Le Binh of the Vietnam Japan University, long-term expert of JICA, who gave me his kindly support when I struggled with the survey data collected and urged me to accelerate the process of making thesis I thank my fellow classmates and the people of Vietnam Japan University, for the all the things we have shared in the last two years Finally, but by no means least, thanks go to my parents, my brother and my lover Thuy Tran for spiritually and unbelievable support As a special mention, I dedicate this thesis to them Sincerely, Tran The Huy i ABSTRACT Bus Rapid Transit is a preferred worldwide transit mode In recent years, BRT has been introduced and operated in Hanoi However, unlike the world, the effect of BRT in Hanoi somehow remains unclear In this context, studies about the impacts of BRT introduction has the necessity, scientific significance and practicality The objective of this study is aim to understand the commuter behavior after BRT introduction in Hanoi, to see if BRT introduction has change the commuter travel behavior (mode choice and walking behavior) and evaluate quantitatively the effects of Hanoi BRT on commuter behavior through data collected by surveying In order to achieve the objective, Difference – In – Difference estimation is chosen to identify and obtain the effectiveness of BRT by comparing the observed changes of BRT commuter (treatment group) and bus commuter (comparison group) before and after BRT introduction The Difference – In – Difference estimation in this study is conducted under regression framework which includes time dummy, group dummy and the interaction term between them Some important findings from the analysis that BRT implementation in Hanoi did have positive impacts on these following aspects: (1) reducing travel time over travel distance of normal bus commuters; and (2) increasing the maximum acceptable walking distance to the station (the willing to walk longer distance for public transportation); and (3) attracting commuters to change their mode choice towards public transport ii TABLE OF CONTENTS ACKNOWLEDGEMENT i ABSTRACT ii TABLE OF CONTENTS iii LIST OF FIGURES v LIST OF TABLES vi LIST OF ABBREVIATIONS vii CHAPTER INTRODUCTION 1.1 1.2 Research background Research Framework CHAPTER LITERATURE REVIEWS 2.1 2.2 Studies on BRT effects in some countries Articles and studies about BRT effects in Vietnam CHAPTER HYPOTHESES, METHODOLOGY AND APPROACH 10 3.1 Hypotheses 10 3.2 Methodology 11 3.4 Difference-In-Difference in a regression framework 15 3.3 Difference-in-Difference (DID) Estimation Approach 12 CHAPTER SURVEY DATA 17 4.1 Survey design 17 4.3 Data collection 19 4.2 4.4 Questionnaire design 17 Descriptive Statistic of survey data 22 CHAPTER DATA ANALYSIS AND DISCUSSION 32 5.1 Simple analysis 32 5.1.1 Hypothesis 32 5.1.3 Hypothesis 34 5.1.2 5.1.4 5.2 Hypothesis 33 Hypothesis 35 Difference-in- difference analysis on the data 36 5.2.1 Stata Software 36 iii 5.2.2 Sample analysis and Structural Equation Modeling (SEM) 39 5.3.1 Hypothesis 42 5.3.3 Hypothesis 46 5.3 Analysis results and discussions 42 5.3.2 5.4 Hypothesis 44 Further discussion 48 CHAPTER CONCLUSION 52 REFERENCES 54 iv LIST OF FIGURES Figure 1.1 Deployment of Hanoi BRT Figure 1.2 Route alignment of Hanoi BRT Figure 3.1 Graphical explanation of DID explanation 12 Figure 4.1 Route alignment of Bus 01 20 Figure 4.2 Route alignment of Bus 32 20 Figure 4.3 Route alignment of Bus 30 21 Figure 5.1 Proportion of commuter accepted walking longer for transportation 33 Figure 5.2 Respondents’ used rates of different types of transportation in 2016 34 Figure 5.3 Respondents’ used rates between different types of private vehicle in 2016 35 Figure 5.4 Import survey data into Stata using excel form 38 Figure 5.5 Example of Stata input data 38 Figure 5.6 Regression model in SEM 41 Figure 5.7 Route alignment interaction between BRT and bus routes 50 v LIST OF TABLES Table 3.1 Groups comparison illustration 13 Table 4.1 Questionnaire form of survey 18 Table 4.2 Selected common bus routes for the survey 19 Table 4.3 The Socio-Demographic Profile of Sample Respondents 22 Table 4.4 Descriptive Statistic results of BRT 23 Table 4.5 Descriptive Statistic results of Bus 01 25 Table 4.6 Descriptive Statistic results of Bus 30 27 Table 4.7 Descriptive Statistic results of Bus 32 29 Table 5.1 Average travel time/ travel distance of bus/BRT users 32 Table 5.2 Simple linear regression calculation results on hypothesis 1, group 39 Table 5.3 Covariance between variables generated by SEM 41 Table 5.4 DID analysis results on hypothesis 1, group 43 Table 5.5 DID analysis results on hypothesis 1, group 43 Table 5.6 DID analysis results on hypothesis 1, group 44 Table 5.7 DID analysis results on hypothesis 2, group 45 Table 5.8 DID analysis results on hypothesis 2, group 45 Table 5.9 DID analysis results on hypothesis 2, group 46 Table 5.10 DID analysis results on hypothesis 3, group 47 Table 5.11 DID analysis results on hypothesis 3, group 47 Table 5.12 DID analysis results on hypothesis 3, group 48 Table 5.13 BRT effects comparison between treatment-comparison groups 51 vi LIST OF ABBREVIATIONS BRT: Bus Rapid Transit CFI: Comparative Fit Index LOS: Level of Service BHLS: DID: Bus of High Level of Service Difference-in-Difference Max: Maximum Min: Minimum (s): minute (s) MRT: Mass Rapid Transit MNB: Minibus RMSEA: Root Mean Squared Error of Approximation SEM: Structural Equation Modeling SD: SP: Standard Deviation Stated Preference vii of personal vehicle user between minibus (MNB), BRT without P&R and feeder (BRT) and BRT with P&R and feeder (BRTS) by modeling The results showed that BRT had potential to attract notably personal vehicle users to change the mode choice Hypothesis 3: Hanoi BRT has attract a number of commuters to switch from their private vehicle to use the public transport (BRT) (changed their mode choice) Satiennam and Jaensirisak (2013) also detailed that travel time and travel cost has importantly affect the modal shift to BRT To be more specific, travel cost has bigger effect on motorbike users’ mode choice meanwhile travel time dominant in encouraging car users to change theirs Hypothesis 4: The proportion of car users shift towards BRT is higher than the portion of motorcycle users that change their mode choice (due to travel time reduction) 3.2 Methodology With the objective of evaluating the impact of BRT introduction, commuter behavior is observed at two different periods, before (Y| t=1) and after (Y| t=2) The change of commuter travel behavior during these times could be identified as: λ = (Y| t=2) - (Y| t=1) However, besides the effect of BRT introduction, there are probably many factors could affect the commuter behavior between the two before and after periods Therefore, it is not reasonable to simply calculate the difference between before and after BRT implementation and consider that change is BRT effect on 11 commuter travel behavior (which mean λ cannot be considered to be the BRT effect) When it comes up to the task of evaluating the effect of a specific intervention or treatment, Difference-in-Difference (DID) Estimation is one of the best solution DID estimation could be able to obtain an appropriate counterfactual to estimate a causal effect by comparing the changes in outcomes over time between a population that is enrolled in a program (treatment or intervention group) and a population that is not (comparison or control group) For that reason, DID estimation is chosen to identify and obtain the effectiveness of BRT in this study The influence of BRT implementation on commuter behavior will be identified by observing and analyzing the changes between the changes of BRT user (treatment group) and normal bus user (comparison group) before and after BRT introduction 3.3 Difference-in-Difference (DID) Estimation Approach In this section, we will point out why the results of the DID estimation could be considered to be the effect of a specific treatment (which is BRT effect in this study) It could be proved as explained below: Figure 3.1 Graphical explanation of DID explanation 12 Table 3.1 Groups comparison illustration Treatment Comparison Pre-Program Y Y Post-Program Y Y There is only one cell among those four cells that is truly treated: Y As DID estimation assumption, without the program, independent i’s outcome at time t is given by: [ | = 0, = ] = + where: The selection bias related to determinate characteristics of independent i (which is not changing overtime) Time trend ( which is same for the treatment and comparison groups) These conditions of and assumption is needed to represent of DID estimation’s Without the program, i’s outcome at the time τ is: [ | = 0, = ] = + Outcomes in the comparison group: Y = [ | = 0, = 1] = [ | = 0] + Y = [ | = 0, = 2] = [ | = 0] + The difference between pre and post program of the comparison group is: Y − Y = [ | 13 = 0] + −( [ | = 0] + ) = Call − is the program’s true impact, then we have: = [ | = 1, = ] − [ | = 1, = ] which does not depend on the i’s characteristics or time trend Outcomes in the intervention group: Y = [ | = 1, = 1] = [ | = 1] + Y = [ | = 1, = 2] = [ | = 1] + + Differences in outcomes pre-treatment versus post-treatment cannot be attributed to the program because the treatment (program) effect are conflated with time trend have: Y If we calculate the difference between pre-treatment and post-treatment, we − Y = [ | = have: Y + = 1] + + −( [ | = 1] + ) − If we determine the treatment group and comparison group difference, we − Y = [ | = = 1] + + [ | 14 + −( [ | = 1] − [ | = 0] = 0] + ) Substituting in the term from our model: =Y = [ = [ | −Y | − Y [ = 1, = 2] − −( [ −[ [ | = 0, = 2] − | = 1] + | + −( [ | = 0] + −Y = 1, = 1] [ = 0, = 1]) | = 1] + −( [ | ) = 0] + )] = Therefore, DID estimation does give the true effect of the program on participants (with the constraint that the assumption conditions are not violated) 3.4 Difference-In-Difference in a regression framework The DID estimation will be implemented using Stata Software It will be implemented as a regression model with an interaction term between time dummy variables and group dummy variables The formula of the regression model for DID analysis of my thesis: , = + + ( + ∗ )+ , where: , Outcome of the interest (representative function which capable of considering each investigated aspect through the relevant coefficients resonance) is the time dummy which indicated the data is pre/post treatment ( which = if in 2016 and = if in 2018) is the group dummy which shown the data in comparison or treatment group (which = for bus commuter and = for BRT commuter) 15 is the coefficient of the interest (the program effect) = [ | = [ | = − ∗ = 0] + pre-program mean in comparison group = 1] − [ | = 0] selection bias time trend Interaction term (which use to indicate who is truly received the effect of BRT implementation) In chapter 7, we will use the Structural Equation Modeling in Stata Software to create the model of this DID regression framework to acquire the results 16 CHAPTER SURVEY DATA 4.1 Survey design As mentioned in Section 5.1, normal bus users are selected as comparison group and BRT users will be the treatment group for the DID estimation However, conducting DID estimation with BRT route and only one normal bus route would not be sufficient enough to point out the BRT effect because the changing of bus users themselves and the context different existence which possibly also cause some effects (mainly or partly) Therefore, different normal bus routes in Hanoi city were selected to address this problem The chosen bus routes ought to have the similar characteristics about the area it goes through (high demand area), road’s cross-section of the route (number of lane) and ticket fee in order to have the highest similarity possible 4.2 Questionnaire design There are two types of questionnaire designed One type is for BRT route and the other one is for the normal bus routes However, they have exactly the same questions, only the target respondent is different Most of the questions ask people about information in two period of time which is 2016 (before BRT introduction) and 2018 (after BRT introduction) There are also other types of question needed to further gather the information about travel behavior Completed questionnaires were developed by Mr.Luu Duy, the student of the first intake of Master of Infrastructure Engineering Program of Vietnam Japan University The questions included in the questionnaire are categorized by part, information collected type and question type in detail as indicated in the table below: 17 Table 4.1 Questionnaire form of survey Information type Question Variable Question type Gender Age Part Preliminary survey Basic respondent information Occupation Income Vehicle using 2016 Travel time Open-ended Question Travel distance Bus using frequency Acceptable travel time Punctuality Security LOS Perception Part of Respondent Safety Comfortable Suitable for children Satisfy BRT/Bus vs Motor Additional (Faster, Higher Questions security, More satisfy) Walking distance Walking behavior acceptable Walking in hurry Walking prefer 18 Likert Scale Question (from – strongly disagreed, – disagreed, 3- neither agreed or disagreed, – agreed, 5- strongly agreed) 4.3 Data collection The survey has been done in June, 2018 with commuters of BRT and common bus route Three common bus route selected are Route 01, 30 and 32 which have their characteristics and route alignments as shown in the table and figure below, respectively: Table 4.2 Selected common bus routes for the survey Bus routes Route ends Route through the districts* Routes length Roads cross section Ticket Fee 01 Yen Nghia Station – Gia Lam Station Ha Dong, Thanh Xuan, Dong Da, Hai Ba Trung, Hoan 30 My Dinh Station – Mai Dong Nam Tu Liem, Cau Giay, Dong Da, Hai Ba Trung Kiem, Long Bien 32 Giap Bat Station Nhon Hoang Mai, Hai Ba Trung, Dong Da, Ba Dinh, Cau Giay, Nhon 22.4 km 15.4 km 18.5 km lanes - lanes lanes 7000VND 7000VND 7000VND *The districts that connected by those routes have difference main function such as: administrative, entertainment and residential areas This has created a very high demand for travel between these districts 19 Figure 4.1 Route alignment of Bus 01 Figure 4.2 Route alignment of Bus 32 20 Figure 4.3 Route alignment of Bus 30 The questionnaires were distributed randomly irrespective of the sex (gender) of the respondents and also irrespective of the purpose The survey was carried out in a week during rush and normal hours No extra explanations were given to avoid the introduction of biases There were 200 samples of questionnaire distributed for each normal bus route and BRT route There were total 750 valid Tải FULL (65 trang): https://bit.ly/3TZdlc4 responded which is different for each route: Dự phòng: fb.com/TaiHo123doc.net 182 samples responded for BRT route 192 samples responded for Bus route 01 184 samples responded for Bus route 30 192 samples responded for Bus route 32 21 Table 4.3 The Socio-Demographic Profile of Sample Respondents Factor % Sample Respondents % Hanoi Population Gender* Male 44.40% 49.30% Female 55.60% 50.70% Age Group* 15 – 25 years old 24.40% 23.71% 26 – 64 years old 60.07% 69.24% > 64 years old 6.53% 7.05% Income ≤ millions 65.73% N/A – 10 millions 24.36% N/A 10 – 15 millions 5.89% N/A 15 – 50 millions 4.02% N/A *Source: Forecasting the size and structure of Hanoi's population by 2020 According to the table of Socio-Demographic Profile of Sample Respondents, it is reasonable to state that the survey data is able to represent the Hanoi population characteristics for further analyzing Tải FULL (65 trang): https://bit.ly/3TZdlc4 Dự phòng: fb.com/TaiHo123doc.net 4.4 Descriptive Statistic of survey data The descriptive statistic is conducted for each data set of BRT and common bus routes It is used to present quantitative descriptions in a manageable form and simplify large amounts of data in a sensible way thereby allowing to observe and assess the consistency of the data and check if the data is normally distributed or not It is also a necessary step to assess whether data can be used for further analysis Each route’s descriptive statistic is shown in the tables below, respectively: 22 Table 4.4 Descriptive Statistic results of BRT Variable 1.53 SD 0.50 Min 25% 50% 75% Max Age 30.29 12.02 12.00 21.00 28.00 35.00 78.00 Job 1.81 0.98 1.00 1.00 1.00 3.00 4.00 Vehicle 1.82 0.92 1.00 1.00 2.00 2.00 4.00 Income 8.32 6.26 0.00 3.00 8.00 10.00 50.00 2.68 1.46 1.00 2.00 2.00 2.50 10.00 2.27 0.98 1.00 2.00 2.00 2.00 10.00 5.28 1.82 1.00 5.00 6.00 7.00 7.00 4.93 1.69 1.00 5.00 5.00 6.00 7.00 km 8.45 3.82 1.00 6.00 8.00 10.00 25.00 km 7.42 3.20 1.00 5.00 7.00 10.00 15.00 32.48 16.26 3.00 20.00 30.00 40.00 105.00 26.82 13.15 2.00 15.00 25.00 37.25 60.00 3.53 0.68 2.00 3.00 4.00 4.00 5.00 rated 4.22 0.67 1.00 4.00 4.00 5.00 5.00 Gender Bus using per day 2016 Bus using per day 2018 Day using bus per week 2016 Day using bus per week 2018 Travel distance 2016 Travel distance 2018 Travel time 2016 Travel time 2018 Unit time (s) time (s) time (s) time (s) (s) (s) Mean 1.00 1.00 2.00 2.00 2.00 LOS Perception Acceptable travel time 2016 Acceptable travel time 2018 rated (1- 5) Punctuality 2016 rated 3.17 0.77 1.00 3.00 3.00 4.00 5.00 Punctuality 2018 rated 4.18 0.65 1.00 4.00 4.00 5.00 5.00 Security 2016 rated 3.39 0.75 1.00 3.00 3.00 4.00 5.00 Security 2018 rated 4.24 0.68 1.00 4.00 4.00 5.00 5.00 Safety 2016 rated 3.46 0.73 1.00 3.00 4.00 4.00 5.00 23 Table 4.4 Descriptive Statistic results of BRT - Continued Variable 4.22 SD 0.68 Min 25% 50% 75% Max rated 3.34 0.73 1.00 3.00 3.00 4.00 5.00 rated 4.23 0.72 1.00 4.00 4.00 5.00 5.00 rated 2.96 0.81 1.00 2.00 3.00 3.00 5.00 rated 4.00 0.75 1.00 4.00 4.00 4.00 5.00 Satisfy 2016 rated 3.39 0.68 1.00 3.00 3.00 4.00 5.00 Satisfy 2018 rated 4.27 0.53 3.00 4.00 4.00 5.00 5.00 rated 2.35 0.66 1.00 2.00 2.00 3.00 5.00 rated 3.57 0.93 1.00 3.00 4.00 4.00 5.00 rated 3.75 0.64 2.00 4.00 4.00 4.00 5.00 rated 4.19 0.63 1.00 4.00 4.00 5.00 5.00 rated 3.67 0.71 1.00 3.75 4.00 4.00 5.00 rated 4.21 0.67 1.00 4.00 4.00 5.00 5.00 250.00 300.00 500.00 1000.00 300.00 500.00 800.00 1000.00 Safety 2018 Comfortable 2016 Comfortable 2018 Suitable for children 2016 Suitable for children 2018 Faster than motor 2016 Faster than motor 2018 Higher security than motor 2016 Higher security than motor 2018 More satisfy than motor 2016 More satisfy than motor 2018 Walking Behavior Unit Mean rated 1.00 4.00 4.00 5.00 5.00 Walking distance accepted 2016 Walking distance accepted 2018 meter 413.03 224.45 200.00 (s) meter 526.65 265.55 100.00 (s) rated 3.41 0.87 1.00 3.00 4.00 4.00 5.00 In hurry 2018 rated 2.74 0.97 2.00 2.00 2.00 4.00 5.00 rated 3.36 0.77 2.00 3.00 4.00 4.00 5.00 rated 3.79 0.72 2.00 4.00 4.00 4.00 5.00 In hurry 2016 Like to walk 2016 Like to walk 2018 24 Table 4.5 Descriptive Statistic results of Bus 01 Variable 1.58 SD 0.50 Min 25% 50% 75% Max Age 30.29 14.87 13.00 21.00 23.00 35.00 77.00 Job 2.45 1.03 1.00 1.00 3.00 3.00 4.00 Vehicle 1.60 1.07 1.00 1.00 1.00 2.00 4.00 Income 5.02 3.76 0.00 3.00 4.00 6.00 25.00 2.58 1.09 1.00 2.00 2.00 3.00 8.00 2.69 1.28 1.00 2.00 2.00 3.50 8.00 5.38 1.68 1.00 5.00 6.00 7.00 7.00 5.38 1.63 1.00 5.00 6.00 7.00 7.00 km 7.88 3.91 2.00 5.00 8.00 10.00 17.00 km 9.16 5.18 1.00 5.00 8.00 11.50 40.00 33.56 15.95 6.00 20.00 30.00 45.00 75.00 38.87 17.78 3.00 25.00 40.00 50.00 120.00 Gender Bus using per day 2016 Bus using per day 2018 Day using bus per week 2016 Day using bus per week 2018 Travel distance 2016 Travel distance 2018 Travel time 2016 Travel time 2018 LOS Perception Unit time (s) time (s) time (s) time (s) (s) (s) Mean 1.00 1.00 2.00 2.00 2.00 Acceptable travel time 2016 Acceptable travel time 2018 rated (1- 5) 3.78 0.64 1.00 4.00 4.00 4.00 5.00 rated 3.94 0.70 2.00 4.00 4.00 4.00 5.00 Punctuality 2016 rated 3.70 0.67 2.00 3.00 4.00 4.00 5.00 Punctuality 2018 rated 3.81 0.84 2.00 3.00 4.00 4.00 5.00 Security 2016 rated 3.72 0.83 1.00 3.00 4.00 4.00 5.00 Security 2018 rated 3.83 0.85 1.00 4.00 4.00 4.00 5.00 Safety 2016 rated 4.00 0.61 2.00 4.00 4.00 4.00 5.00 25 6796202 ... is aim to understand the commuter behavior after BRT introduction in Hanoi, to see if BRT introduction has change the commuter travel behavior (mode choice and walking behavior) and evaluate quantitatively... effectiveness of BRT by comparing the observed changes of BRT commuter (treatment group) and bus commuter (comparison group) before and after BRT introduction The Difference – In – Difference estimation in. .. development of BRTS and the cities growth where BRT is in operation in China The performance of BRT system in major cities in China was also evaluated by Li and Hino (2013) The performance of BRT system