Impacts of BRT introduction on commuter behavior in Hanoi

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 Figure 5.6 Regression model in SEM (Source: author) Performing estimation with the same case of data set (hypothesis of Group 1), SEM gives exactly the same results with Table 7.2 and the covariance between DBRT, D2018 and DBRT*D2018 These covariance is listed in Table 7.3 Table 5.3 Covariance between variables generated by SEM Time Distance cov (DBRT , D2018) cov (DBRT , DBRTXD2018) cov (D2018 , DBRTXD2018) Coefficient SD t-Stat P-value Standardized Coefficient -7.61e-17 0.0105512 -0.00 1.000 -3.05e-16 0.1246875 0.0104118 11.98 0.000 0.5867387 0.11875 0.0102969 11.53 0.000 0.5580998 According to table 7.3, there is covariance between DBRT and DBRT*D2018, D2018 and DBRT*D2018 with significantly high influence which accordance with DID estimation assumption In other words, it confirms that DBRT and D2018 have a 41 indirectly affect to the dependent value through interaction term D BRT*D2018 Therefore, they could not be drop out of the formula even though they might not satisfy the p-value condition of regular regression One more thing, table 7.3 also shows that there is no covariance between DBRT and D2018 which is rational and consistent with the explanation because time dummy and group dummy are truly independent variable With all the observed possible unusual parameters are explained as above, the next section 7.3 will only inform the results of DID analysis on all hypotheses with all treatment-comparison groups and some discussions SEM is used to analyze all the cases to show both coefficient of independent variables (normal and standardized) and covariance between them 5.3 Analysis results and discussions All of the analysis cases in this are also checked for goodness of fit with SEM Two important index to determine the goodness of fit are RMSEA (Root mean squared error of approximation) and CFI (Comparative fit index) All the cases of analysis, the fit statistics have the RMSEA = and CFI = which is indicated very good model fit 5.3.1 Hypothesis The results of DID analysis of hypothesis on all groups are shown in Table 7.4, Table 7.5 and Table 7.6 42 Table 5.4 DID analysis results on hypothesis 1, group Time Distance Coefficient SD t-Stat P-value DBRT -0.4358479 0.1350858 -3.23 0.001 DBRTXD2018 -0.5925879 0.1910402 -3.10 D2018 _cons cov (DBRT , D2018) cov (DBRT , DBRTXD2018) cov (D2018 , DBRTXD2018) 0.1179145 0.1316653 0.90 0.370 4.423102 0.0931015 -7.61e-17 Standardized Coefficient -0.1816012 0.049192 47.51 0.002 0.000 -0.2104078 0.0105512 -0.00 1.000 -3.05e-16 0.1246875 0.0104118 11.98 0.000 0.5867387 0.11875 0.0102969 11.53 0.000 0.5580998 3.690493 Table 5.5 DID analysis results on hypothesis 1, group Time Distance Coefficient SD t-Stat P-value DBRT -0.0487837 0.1403327 -0.35 0.728 DBRTXD2018 -0.4686673 0.1984603 -2.36 0.018 D2018 _cons cov (DBRT , D2018) cov (DBRT , DBRTXD2018) cov (D2018 , DBRTXD2018) -0.0060061 0.1427687 -0.04 0.966 Standardized Coefficient -0.0212746 -0.0026209 -0.1791317 4.036038 0.1009527 39.98 0.000 2.70e-34 0.0110203 0.00 1.000 1.08e-33 0.1248467 0.011113 11.23 0.000 0.5704904 0.1293774 0.0112184 11.53 0.000 0.5908311 43 3.522394 Table 5.6 DID analysis results on hypothesis 1, group Time Distance Coefficient SD t-Stat P-value DBRT -0.3713122 0.1453486 -2.55 0.011 DBRTXD2018 -0.5642851 0.205554 -2.75 D2018 _cons cov (DBRT , D2018) cov (DBRT , DBRTXD2018) cov (D2018 , DBRTXD2018) 0.0896117 0.1416683 4.358567 Standardized Coefficient -0.1461602 0.63 0.527 0.1001746 43.51 0.000 -7.61e-17 0.0105512 -0.00 1.000 -3.05e-16 0.1246875 0.0104118 11.98 0.000 0.5867387 0.11875 0.0102969 11.53 0.000 0.5580998 0.006 0.0353182 -0.189284 3.435637 Hypothesis is about assessing the effects of BRT on the average travel time/travel distance reduction of the commuters Coefficient of DBRT*D2018 ( ) < in all groups show that BRT have significantly decreased the average time/distance The coefficient in each group are also satisfy the p-value condition Therefore, BRT does have effect on the travel time reduction of commuters Hypothesis is supported 5.3.2 Hypothesis Table 7.7, 7.8 and 7.9 are illustrated the DID analysis results of groups with hypothesis 2, respectively 44 Table 5.7 DID analysis results on hypothesis 2, group Time Distance Coefficient SD t-Stat P-value DBRT -18.09805 29.23794 -0.62 0.536 DBRTXD2018 104.0778 41.34869 2.52 0.012 D2018 _cons cov (DBRT , D2018) cov (DBRT , DBRTXD2018) cov (D2018 , DBRTXD2018) -7.202797 26.20586 -0.27 0.783 Standardized Coefficient -0.0395482 -0.0160531 0.1858602 431.1189 18.53034 23.27 0.000 1.921702 3.72e-17 0.0112115 0.00 1.000 1.52e-16 0.120166 0.0105312 11.41 0.000 0.6118378 0.1004184 0.0102489 9.80 0.000 0.5013072 Table 5.8 DID analysis results on hypothesis 2, group Time Distance Standardized Coefficient Coefficient SD t-Stat P-value DBRT -73.67272 33.60484 -2.19 0.028 DBRTXD2018 116.4718 47.52442 2.45 0.014 0.1928165 D2018 _cons cov (DBRT , D2018) cov (DBRT , DBRTXD2018) cov (D2018 , DBRTXD2018) -19.59677 31.3936 -0.62 0.532 -0.1464508 0.039275 486.6935 22.19863 21.92 0.000 -9.51e-34 0.0118214 -0.00 1.000 -3.83e-33 0.1229752 0.0113895 10.80 0.000 0.6003875 0.1090909 0.011134 9.80 0.000 0.528705 45 1.950819 Table 5.9 DID analysis results on hypothesis 2, group Time Distance Coefficient SD t-Stat P-value Standardized Coefficient DBRT -13.3125 28.29243 -0.47 0.638 -0.0296281 D2018 3.333333 24.99501 0.13 0.894 0.0076041 DBRTXD2018 93.54167 40.01153 2.34 0.019 0.1691305 _cons 426.3333 17.67414 24.12 0.000 1.945127 cov (DBRT , D2018) 2.32e-34 0.010996 0.00 1.000 9.49e-34 cov (DBRT , DBRTXD2018) 0.1189768 0.0102336 11.63 0.000 0.6154575 cov (D2018 , DBRTXD2018) 0.097561 0.009573 9.80 0.000 0.492366 Hypothesis suggests that there is influence of BRT on increasing the maximum acceptable walking distance to station of commuters The coefficient of DBRT*D2018 ( ) > in all groups give evidence of the BRT's positive impact on the acceptable walking distance increasing of commuters from 2016 to 2018 The coefficient in each group are also satisfy the p-value condition Thus, BRT’s effect on this aspect is determined Hypothesis is supported 5.3.3 Hypothesis Hypothesis on the treatment-comparison groups is analyzed by using DID estimation and had its results listed in Table 7.10, 7.11 and 7.12 46 Table 5.10 DID analysis results on hypothesis 3, group Time Distance Coefficient SD t-Stat P-value DBRT 0.2194368 0.0725604 3.02 0.002 DBRTXD2018 -0.2194368 0.1026159 -2.14 0.032 D2018 _cons cov (DBRT , D2018) cov (DBRT , DBRTXD2018) cov (D2018 , DBRTXD2018) -0.59375 0.0715837 -8.29 Standardized Coefficient 0.1392222 0.000 -0.3768407 2.02304 -0.1195187 1.59375 0.0506173 31.49 0.000 -1.55e-33 0.0091376 -0.00 1.000 -6.18e-33 0.1249106 0.0090747 13.76 0.000 0.5824282 0.1216578 0.0090179 13.49 0.000 0.567058 Table 5.11 DID analysis results on hypothesis 3, group Time Distance DBRT Coefficient SD t-Stat P-value 0.1012303 0.0756582 1.34 0.181 -0.1012303 0.1069969 -0.95 0.344 D2018 -0.7119565 _cons DBRTXD2018 cov (DBRT , D2018) cov (DBRT , DBRTXD2018) cov (D2018 , DBRTXD2018) 0.0754512 -9.44 1.711957 0.0533521 6.44e-33 Standardized Coefficient 0.0618215 0.000 -0.4347992 32.09 0.000 2.091019 0.0092401 0.00 1.000 2.58e-32 0.1249963 0.0092274 13.55 0.000 0.578399 0.1243169 0.009215 13.49 0.000 0.575247 47 -0.0534419 Table 5.12 DID analysis results on hypothesis 3, group Time Distance Coefficient SD t-Stat P-value DBRT 0.1100618 0.0782315 1.41 0.159 DBRTXD2018 -0.1100618 D2018 _cons cov (DBRT , D2018) cov (DBRT , DBRTXD2018) cov (D2018 , DBRTXD2018) Standardized Coefficient 0.0649903 -0.703125 0.0771786 -9.11 0.000 -0.4153361 1.703125 0.0545735 31.21 0.000 2.012073 4.55e-33 0.0091376 0.00 1.000 1.82e-32 0.1249106 0.0090747 13.76 0.000 0.5824282 0.1216578 0.0090179 13.49 0.000 0.567058 0.1106361 -0.99 0.320 -0.0557925 Hypothesis predicts that BRT with its advantages has positive impacts on encouraging commuters to change their mode choice In the survey data, private vehicle (motorbike, car, others) is marked as 2,3,4 and bus/BRT is marked as Therefore, the negative values of the coefficient will point out the shifting from personal vehicle towards public transport In all comparison – treatment groups, the coefficient of DBRT*D2018 ( ) < which verify BRT’s influence on attracting private vehicle users to switch to public transports Hypothesis is declared to be supported 5.4 Further discussion Normally, different locations possibly bring some differences in various aspect such as people’s income, technical infrastructure, development strategy… which could affect the effect of introducing a new type of public transport In the methodology section, a location dummy was intended to put into the regression formula besides the time dummy and group dummy However, this idea has 48 encountered some obstacles and has not been developed Firstly, the locations difference is likely included in the difference between group (different treatment- comparison group) Secondly, the bus routes are located in the same city, and not quite far from each other They likely will share almost the similar characteristics instead of the differences that different locations would bring Therefore, the effect caused by the different between them is considered to be hard to observe even though it exists However, the correlation between BRT and bus routes about route direction is noticeably different (which could possibly lead to some comments about the location effect):  The route direction of BRT and bus route 01 start from the same station and have its major part parallel with each other They are also located near each other It is reasonable to consider that who use BRT is not using route 01 and vice versa (competitive relations)  BRT and bus route 30 have different route direction but have connection (share a part with each other) People using BRT and bus route 30 could transit between the two routes (support relations)  BRT and bus route 32 have different route direction and no connections It is considered that BRT and bus route 32 have no effect on each other 49 Figure 5.7 Route alignment interaction between BRT and bus routes with BRT route is the red line Bus route 01 is the blue line Bus route 30 is the green line Bus route 32 is the pink line The comparison between the coefficient of each aspect of these routes is carried out using treatment-comparison groups to observe the interaction of BRT with each other bus route (location effect) However, normal coefficient of each aspect has a big gap between each other, which makes it difficult to compare because they are regressed from quantities with different units Maximum acceptable walking distance, travel time over travel distance and mode choice have different units On the other hand, standardized coefficients' advocates note that the coefficients ignore the independent variable's scale of units, which makes 50 comparisons easy Therefore, the standardized coefficient of each aspect are used for comparison This comparison is indicated in Table 7.13 The negative and positive signs, which indicated the trend of aspect (negative is decrease and positive is increase), is exclude in this table Table 5.13 BRT effects comparison between treatment-comparison groups BRT effects on Increase the maximum accepted walking distance BRT & Bus 01 BRT & Bus 30 BRT & Bus 32 0.186 0.193 0.169 0.210 0.179 0.189 0.120 0.053 0.056 Decrease the average travel time/travel distance Attract commuters to change their mode choice According to the table, it is reasonable to consider the interaction between BRT and different bus route has some affects to BRT effects The group with support relation (BRT & Bus 30) shows the strongest BRT effects on increase the maximum accepted walking distance The group with competitive relation (BRT & Bus 01) shows the strongest BRT effects on decrease the average travel time/travel distance and attract commuters to change their mode choice BRT and Bus 32 not have any interaction to each other and therefore BRT effects are average in all aspects considered It could be concluded that the BRT effects on commuter travel behavior are higher with bus route users that have location relation to BRT (which is considerable as a type of location effect) 51 CHAPTER CONCLUSION This study has assessed the impacts of BRT introduction on commuter travel behavior in Hanoi using DID estimation It was found that BRT implementation in Hanoi did have positive impact to commuters The positive impacts are proved and shown through some aspects Firstly, BRT introduction with the advantage of its exclusive bus lane has improved efficiency of public transportation by reducing travel time over travel distance of normal bus commuters It means that with the same travel distance, commuter using BRT will have their travel time smaller than using normal bus Secondly, the advantages that benefits from BRT introduction have increased the walking for transportation of commuter More specifically, it is proved in this study that BRT implementation has the positive influence on increasing the maximum acceptable walking distance to the station (the willing to walk longer distance for public transportation) Finally, the last proved impact of BRT introduction in this thesis is the ability to attract commuters to change the mode choice Besides the normal bus commuter, Hanoi BRT has attracted a number of private vehicle users to switch from their mode towards BRT These impacts on reducing travel time, increasing the walking for transportation and mode choice towards BRT are noticeably related to each other in many ways and through that the rationale when confirming the influence of BRT on commuter travel behavior could be further reinforced Furthermore, the impact of location to Hanoi BRT introduction’s effectiveness is also discussed and partly confirmed when comparing the effect of Hanoi BRT with normal bus routed which have the type of interaction of their routes and BRT route differently Discussing the impact of the location on the BRT effect may be a possible idea for future feasibility studies Additionally, the survey actually also provides good information and data but has not been used completely which possibly use for further feasibility studies For example, the level of service perception data can be used to evaluate the commuter satisfaction with BRT 52 compared to normal bus or other kind of transport In addition, it can be used to rank criteria that affect the satisfaction of BRT users and then compare with others type of transport This research, however, is subject to several limitations The first possible limitation in this study is about the surveying The survey was conducted with commuter of BRT and normal bus routes Although the Socio-Demographic Profile of Sample Respondents show the similarity of the survey respondents to the population of Hanoi city, there still might be a chance that sample does not reflect the general population or appropriate population concerned well enough This results in limitations for the study known as “sample bias” or “selection bias” The next limitation is also about the survey but related to the methodology The treatment and comparison group definition in DID estimation should be “people who live within the chosen radius of BRT/Bus Station” which is normally used However, the information gathered by the survey is not included the address or location of respondents Therefore, their distance to the station are unknown and the regular definition is impracticable Then the definition of the treatment and comparison will be defined by what respondents use (BRT/bus) The final limitation mentioned is few unusual values noted to be different from regular regression conditions of DID estimation under regression framework results This limitation has been discussed and explained carefully in Section 7.2.2 which suggested that these difference come from the DID estimation’s specific assumption and 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