Ride hailing service in vietnam market and its impacts on travel behavior of local people

VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY NGUYEN DANH MINH RIDE-HAILING SERVICE IN VIETNAM MARKET AND ITS IMPACTS ON TRAVEL BEHAVIOR OF LOCAL PEOPLE MASTER'S THESIS Hanoi, 2019 VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY NGUYEN DANH MINH RIDE-HAILING SERVICE IN VIETNAM MARKET AND ITS IMPACTS ON TRAVEL BEHAVIOR OF LOCAL PEOPLE MAJOR: INFRASTRUCTURE ENGINEERING CODE: PILOT RESEARCH SUPERVISOR: Prof HIRONORI KATO Dr PHAN LE BINH Hanoi, 2019   ACKNOWLEDGMENT After years at the Vietnam-Japan university, I had many great experiences, new friends, helpful knowledge, and above all, I learned at the international study environment of the top university in Vietnam First of all, I would like to express my endless thanks and gratefulness to my research supervisor Professor Hironori Kato and Doctor Phan Le Binh for his kind support and continuous advice during research time Their encouragement and comments had significantly enriched and improved my research process Without their motivation and instructions, my research would have been impossible to be done Furthermore, from bottom of my heart, I would like to thanks Program Director of Infrastructure Engineering Program of Vietnam-Japan University (VJU), Prof Nguyen Dinh Duc who always has encouraged and deeply care about me; As of last, my deepest thanks come to the rest of the teachers and staff of Vietnam Japan University Their kindly help and guidance has inspired me and helped me to overcome the challenges which I faced during the period of study at Vietnam Japan University Although I tried to complete this research by all my effort, however, there are still many errors and shortcomings for many reasons I look forward to receiving comments and suggestions which could present me with new sources of inspiration as I develop in my ability to research and learn Thank you sincerely! Nguyen Danh Minh i     TABLE OF CONTENTS ACKNOWLEDGMENT   i  TABLE OF CONTENTS   ii  LIST OF FIGURES  . iv  LIST OF TABLES   v  LIST OF ABBREVIATIONS   vi  ABSTRACT   1  CHAPTER 1. INTRODUCTION   2  CHAPTER 2. LITERATURE REVIEW   5  2.1. Grab company  . 5  2.2. Some key finding from studies about ride‐hailing service in South East Asia countries   6  2.3. Some key finding from studies about ride‐hailing service in developed countries   7  CHAPTER 3. METHODOLOGY   9  CHAPTER 4. INTRODUCING OF RHS, LEGAL MECHANISM AND CONTROVERSY IN OPERATION OF  GRABCAR IN VIETNAM   10  4.1. The history of introducing RHS in Vietnam   10  4.2. Legal mechanism for operation of GrabCar service in Vietnam   11  4.3. Controversy among stakeholders regarding operation of GrabCar service  . 12  4.3.1. Lawsuit between traditional taxi and Grab   12  4.3.2. Opinions of stakeholders relating operation of GrabCar service in Vietnam   15  4.4. Conclusion   18  CHAPTER 5. THE IMPACT OF RHS ON LOCAL PEOPLE AND DYNAMIC MOVEMENTS OF RIDE‐ HAILING CAR   19  5.1. Survey description  . 19  5.1.1. Data collection by group discussion (qualitative interviews)   19  5.1.2. Data collection by Grab app   21  5.2. Group discussion (qualitative interviews)   25  5.2.1. Group discussion record   25  5.2.2. Key finding   33  5.3. Dynamic characteristic of RHS car   34  5.3.1. The fluctuation of the price over time   34  5.3.2. The relationship between the availability and the waiting time   49  5.3.3. The surge pricing   52  CHAPTER 6. DISCUSSION AND CONCLUSION   54  ii     REFERENCES   56      iii     LIST OF FIGURES Page Figure Traditional taxi usage change in last 12 month 3  Figure History of introducing RHS in Vietnam 11  Figure Allegation of Vinasun about operation of Grab .13  Figure Image of the interview at VJU 19  Figure The availability of ride-hailing car 21  Figure The waiting time and the price of trip 21  Figure The location of districts in the city 23  Figure The fluctuation of the price on weekdays in zone 35  Figure The fluctuation of the price on Saturday in zone 35  Figure 10 The fluctuation of the price on Sunday in zone 36  Figure 11 The fluctuation of the price on Monday and Wednesday in zone .40  Figure 12 The fluctuation of the price on Friday in zone 41  Figure 13 The fluctuation of the price on Saturday in zone .41  Figure 14 The fluctuation of the price on Sunday in zone 42  Figure 15 The fluctuation of the price in zone .48  Figure 16 The frequency of the availability in weekdays in zone .50  Figure 17 The frequency of the waiting time in weekdays in zone .50  Figure 18 The frequency of the availability in days of the week in zone 51  Figure 19 The frequency of the waiting time in days of the week in zone 51  Figure 20 The frequency of the waiting time at the peak-hour in zone .53      iv     LIST OF TABLES Page Table Information of plaintiff and defendant in lawsuit 12  Table The detailed information of survey days 22  Table The characteristic of zone 22  Table The O-D pair 23  Table The fluctuation of price between time group (21h-22h) and (19h-21h) 36  Table The fluctuation of price between time group (22h-23h10) and (23h1024h) 38  Table The fluctuation of price between time group (7h-14h) and (14h-22h) 39  Table The fluctuation of price in zone 42  Table The fluctuation of price between time group (16h30-19h30) and (15h3016h30) 43  Table 10 The fluctuation of price between time group (18h-19h) and (15h3018h) 44  Table 11 The fluctuation of price between time group (22h-22h30) and (22h3024h) 45  Table 12 The fluctuation of price between time group (21h-22h10) and (19h1021h) 46  Table 13 The fluctuation of price in zone 48  Table 14 The regression statistics table displays the relationship between the availability and the waiting time of zone and zone 49  Table 15 The regression statistics table displays the relationship between the availability and the waiting time of zone .52      v     LIST OF ABBREVIATIONS RHS Ride-hailing service RHCs Ride-hailing companies RHAs Ride-hailing apps ICT Information&Communication Technology AI Artificial intelligence MOT Ministry of Transport CBD Central business district       vi     ABSTRACT Ride-hailing service (RHS) is a new travel mode in urban transport system, it is called as the technology taxi service in Vietnam Ride-hailing is the act of requesting a ride from a private passenger vehicle by an app on a smartphone RHS has appeared in Vietnam from 2014, however, it has spread rapidly and affect significantly to Vietnam transport market and travel habit of people Until now, there has been still no completed regulation for the operation of RHS in Vietnam RHS has caused big controversy with the domestic taxi industry, traditional taxi companies claim that RHS is no different from taxis, and that ride-hailing companies (RHCs) should be held to the same operating requirements and regulations as taxi companies, including licensing, fare regulation, and vehicle and driver safety standards in order to maintain an equal and fair playing field The research has also evaluated qualitatively impacts of RHS on local people The results show that people usually use Grab for picking up/sending off their children Some people use Grab to replace their private vehicle to go to work or use it as a paratransit mode After examining the dynamic characteristic of the RHS car, some interesting findings have been revealed The fluctuation of the price of Grab depends on the demand of the market When the demand is high, the price of the trip will high and vice versa The great fluctuation of price frequently happens at the morning peak – hour (6h-9h) on weekdays when people have a high demand for going to work On the weekend, the price frequently fluctuates widely in the afternoon (16h30-19h), in the evening (19h-22h) and at night (22h-23h) when people have a high demand for leisure purpose and going back their home The waiting time for a RHS car does not only depend on the number of the available car around but also depends on the distance between the vehicle and the customer A potential assumption also has been proposed that the algorithm of Grab app automatically raises prices to balance between demand and supply because when bad conditions happen     CHAPTER INTRODUCTION Ride-hailing also is known as technology taxi in Vietnam – is the act of requesting a ride from a private passenger vehicle by an app on a smartphone This type of system is built, managed, and operated by ride-hailing companies (RHCs), Grab (Malaysia), Uber (United States of American), or Gojek (Indonesia) are some well-known RHCs in South East Asia countries area These RHCs serve as the broker between the customer who has ride demand and the driver who possesses and operates his/her own private vehicle After launch, Ride-hailing service (RHS) has immediately redefined the individual public transport industry (taxi or motorcycle-taxi in some South East Asia countries such as Vietnam, Indonesia, Thailand, and Cambodia) with its user-friendly platform, which includes extra convenience, variety payment method, completed door-to-door service, maximal reduced waiting time RHS has appeared in Vietnam transport market in 2014 with RHCs – Uber and Grab Until 4/2018, Uber sold the whole market share for Grab, stopped providing all services of Uber in Vietnam After Uber was taken over, Grab has occupied a large share of the passenger transport market Within years, Grab almost has dominated the individual public transport market in Vietnam, it has supplied service to a majority of metropolitan regions spanning throughout 36 provinces/cities across the country with 175.000 partners (drivers), has become a important portion of urban transport and affect significantly to operation of other types of transport business, especially type of traditional taxi business as well as travel behavior of people According to Vietnam Parliament Television, by the end of 2017, in Hanoi and Ho Chi Minh City, the number of traditional taxi car is only 23.000, reduced 8000 vehicles compared with transportation planning, meanwhile number of Grab car, from vehicles, increased up to 37000 vehicles The frequency of riding traditional taxi also goes down, 61% of users confirmed that they used     The independent-samples T test is used to verify the fluctuation of price over time On Friday, the period from 16h30 to 19h30 will be compared with the period from 15h30 to 16h30 The result are shown below Table The fluctuation of price between time group (16h30-19h30) and (15h3016h30) Group Statistics VAR00002 VAR00001 N Mean Std Error Mean Std Deviation 15h30-16h30 13 43.6154 65044 18040 16h30-19h30 36 46.7500 6.20311 1.03385 Independent Samples Test Levene's Test for Equality t-test for Equality of Variances of Means F VAR00001 Equal variances assumed Sig 6.852 Equal variances not assumed t 012 df -1.806 47 -2.987 37.064 Independent Samples Test t-test for Equality of Means Sig (2tailed) Mean Std Error 95% Difference Difference Confidence Interval of the Difference Lower 43     VAR00001 Equal variances assumed 077 -3.13462 1.73535 -6.62569 Equal variances not assumed 005 -3.13462 1.04947 -5.26093 There is the significant fluctuation in the price at the period from 16h30 to 19h30 (Std= 6.20) on Friday, as well as the significant difference of mean between time groups (Sig.= 0.005 0.05) On Sunday, the period from 21h to 22h10 will be compared with the period from 19h10 to 21h The result are shown below Table 12 The fluctuation of price between time group (21h-22h10) and (19h1021h) Group Statistics VAR00002 VAR00001 N Mean Std Error Mean 21h-22h10 15 58.7333 21.72381 5.60906 19h10-21h 23 39.6957 92612 19311 46   Std Deviation   Independent Samples Test Levene's Test for Equality t-test for Equality of Variances of Means F VAR00001 Equal variances assumed Sig 24.458 Equal variances not assumed t 000 df 4.228 36 3.392 14.033 Independent Samples Test t-test for Equality of Means Sig (2tailed) Mean Std Error 95% Difference Difference Confidence Interval of the Difference Lower VAR00001 Equal variances assumed 000 19.03768 4.50246 9.90626 Equal variances not assumed 004 19.03768 5.61239 7.00298 There is the great fluctuation in the price at the period from 21h00 to 22h10 (Std= 21.72) on Sunday, as well as the significant difference of mean between time groups (Sig.= 0.004 0.5), when the availability is low, the waiting time will be longer, and vice versa 49     800 90.00% 78.89% 80.00% 700 70.00% 600 60.00% Frequency 500 50.00% 400 40.00% 300 30.00% 200 20.00% 11.65% 100 0.12% 0.12% 0.81% 1.50% 1.73% 1.27% 10.00% 3.92% 0.00% 10 More The availability (the number of car) Figure 16 The frequency of the availability in weekdays in zone 700 80.00% 71.51% 70.00% 600 60.00% Frequency 500 50.00% 400 40.00% 300 30.00% 200 100 8.42% 20.00% 13.49% 6.46% 10.00% 0.12% 0.00% 1.5 More The waiting time (minutes) Figure 17 The frequency of the waiting time in weekdays in zone 50     1000 70.00% 61.87% 900 60.00% 800 50.00% Frequency 700 600 40.00% 500 30.00% 400 300 20.00% 200 1.31% 2.63% 100 7.47% 5.33% 4.71% 5.05% 4.43% 3.32% 3.88% 10.00% 0.00% 10 More The availability (the number of car) Figure 18 The frequency of the availability in days of the week in zone 500 35.00% 31.49% 450 30.00% 400 25.00% Frequency 350 18.96% 300 20.00% 250 200 15.00% 10.87% 150 7.20%6.23% 5.95% 4.50%5.54% 2.01% 50 0.62%1.59% 100 2.63%1.94% 0.48% 10.00% 5.00% 0.00% 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5 16.5 More The waiting time (minutes) Figure 19 The frequency of the waiting time in days of the week in zone Based on Figure.16;17;18;19, we can realize that in zone1 and zone 3, the available car which is 10 cars occupies the high frequency (78,89% in zone and 61.87% in zone 2) However, while in zone 1, the time which the customer must wait for a car is about minutes, the waiting time in zone is about 10.5 minutes In zone 3, the distance between GrabCar and the location of the customer is quite far, while the distance in zone is much shorter than Therefore, the waiting time 51     does not only depend on availability, but also depends on the distance between the vehicle and the customer  Zone Table 15 The regression statistics table displays the relationship between the availability and the waiting time of zone The period of time Regression Statistics P-value R-square Monday 3.5E-97 0.78 Wednesday 1.9E-98 0.79 Friday 4E-103 0.80 Saturday 8.3E-84 0.73 Sunday 2.8E-88 0.75 The Table.17 shows result: In zone 2, the waiting time depends on the availability (R-square > 0.5) When the availability is low, the waiting time will be longer, and vice versa 5.3.3 The surge pricing According to the opinion of respondents in the qualitative interview, the surge pricing always happens at the peak-hour Aim to find out the reason of this problem, based on the data of the waiting time in zone 2, the writer built a diagram of the frequency of the waiting time at the morning peak-hour in all the day of the week (except Saturday because the surge pricing does not happen at the morning peakhour on Saturday) The result are shown below 52     30.00% 40 25.00% 35 25.00% 20.27% Frequency 30 25 18.92% 15.54% 20.00% 14.19% 20 15.00% 15 10.00% 6.08% 10 5.00% 0.00% 1.5 More The waiting time (minutes) Figure 20 The frequency of the waiting time at the peak-hour in zone At the peak-hour, the wait time always be from to minutes When bad conditions happen, demand for using GrabCar is higher, maybe demand will be greater than supply, and waiting time become longer Therefore, a potential assumption is given that algorithm of Grab application will automaticaly raise prices to balance between demand and supply (when the surge pricing happens, the customer will not choose GrabCar service for their trip and the demand for using service will reduce significantly), ensure the waiting time not to be too long                   53     CHAPTER DISCUSSION AND CONCLUSION Ride-hailing service is a new travel mode in urban transport system in Vietnam However, it has spread rapidly and affect significantly to Vietnam transport market, especially the traditional taxi The appearance of RHS has redefined the taxi industry, Grab has transported tens of millions of passengers while the Government has not lost money to finance for development of this type of service, because it uses socialization source The relationship between Grab and the car driver is a partnership, it is not the relationship between boss and employee Grab does not possess and manage any car The success of RHS in transport market has implied that RHS conforms to the trend of the consumer in the technology era, as well as, indicated that some current business conditions of the traditional taxi service have been too old, too complicated and need to be removed Ride-hailing service also has impacted significantly on the travel habit of local people After the launch of Grab, it make accompanying trips of parents with their children are less than before, people usually use Grab for sending off/picking up their children due to its outstanding features such as tracking journey on the app, cashless payment method, knowing price in advance, knowing car driver information Some people have used Grab replace the private vehicle to go to work because they prefer to be driven by someone rather than driving by themselves and the cheap expense People also have used it as a paratransit vehicle to take them to the bus/BRT station From here, people will use bus, BRT to come to their destination In this case, RHS has encouraged people using the public transportation vehicle The fluctuation of the price of Grab depends on the demand of the market When the demand is high, the price of the trip will high and vice versa The great fluctuation of price frequently happens at the morning peak – hour (6h-9h) on weekdays when people have a high demand for going to work On the weekend, the price frequently fluctuates widely in the afternoon (16h30-19h), in the evening 54     (19h-22h) and at night (22h-23h) when people have a high demand for leisure purpose and going back their home The research has shown that the waiting time for a GrabCar does not only depend on the number of the available car around but also depends on the distance between the vehicle and the customer Finding for the reason of the surge pricing at the bad conditions such as the peak-hour, the research proved that, at the peak-hour, the waiting time always be very short (about 2-4 minutes) Therefore, a potential assumption is given that the algorithm of Grab app automatically raises prices to balance between demand and supply because when bad conditions happen, demand for using GrabCar is higher, maybe demand will be greater than supply, and waiting time become longer If the demand and the supply are balanced, the short waiting time always be ensured From all findings of this research, Grab is supplying an extremely useful RHS to the customer, has efficiently addressed the inadequacies of the current urban transportation system Besides that, the launch of Grab in Vietnam has forced the traditional taxi to change their business strategy, offer a better quality service to customer Grab also contribute to encouraging people using the public transportation vehicle and decreasing demand for using the private vehicle The fluctuation of price over time or the surge pricing does not affect negatively to the transport market and local people because Grab is not an only RHS supplier in the Vietnam market Besides the traditional taxi, there are also other RHS suppliers such as Be, GoViet, Fast-Go, etc People have many other options for their trip if the surge pricing happens, Grab cannot manipulate the Vietnam transport market However, there have been still no regulations for the operation of Grab or RHS in Vietnam, therefore building an appropriate legal mechanism for its operation is an urgent mission at now to manage and control efficiently this new type of service     55     REFERENCES Ackaradejruangsri, P. (2015). Insights on GrabTaxi: An Alternative Ride Service in Thailand (Vol. 4).  Beer, R., Brakewood, C., Rahman, S., & Viscardi, J. (2017). Qualitative Analysis of Ride‐Hailing  Regulations in Major American Cities. Transportation Research Record, 2650(1), 84–91.  https://doi.org/10.3141/2650‐10  Contreras, S. D., & Paz, A. (2018). The effects of ride‐hailing companies on the taxicab industry in  Las Vegas, Nevada. Transportation Research Part A: Policy and Practice, 115, 63–70.  https://doi.org/https://doi.org/10.1016/j.tra.2017.11.008  de Souza Silva, L. A., de Andrade, M. O., & Alves Maia, M. L. (2018). How does the ride‐hailing  systems demand affect individual transport regulation? Research in Transportation  Economics, 69, 600–606. https://doi.org/https://doi.org/10.1016/j.retrec.2018.06.010  Haddad, E., Schwambach Vieira, R., Stevanato Jacob, M., Waksberg Guerrini, A., Germani, E.,  Barreto, F., … Levy Sayon, P. (2019). A socioeconomic analysis of ride‐hailing emergence and  expansion in São Paulo, Brazil. In Transportation Research Interdisciplinary Perspectives.  https://doi.org/10.1016/j.trip.2019.100016  Hall, J. D., Palsson, C., & Price, J. (2018). Is Uber a substitute or complement for public transit?  Journal of Urban Economics, 108, 36–50.  https://doi.org/https://doi.org/10.1016/j.jue.2018.09.003  Joia, L. A., & Altieri, D. (2018). Antecedents of continued use intention of e‐hailing apps from the  passengers’ perspective. The Journal of High Technology Management Research, 29(2), 204– 215. https://doi.org/https://doi.org/10.1016/j.hitech.2018.09.006  Lavieri, P. S., & Bhat, C. R. (2019). Investigating objective and subjective factors influencing the  adoption, frequency, and characteristics of ride‐hailing trips. Transportation Research Part C:  Emerging Technologies, 105, 100–125.  https://doi.org/https://doi.org/10.1016/j.trc.2019.05.037  Mayne, P., & Poulos, H. (1999). Approximate Displacement Influence Factors for Elastic Shallow  Foundations. Journal of Geotechnical and Geoenvironmental Engineering ‐ J GEOTECH  GEOENVIRON ENG, 125. https://doi.org/10.1061/(ASCE)1090‐0241(1999)125:6(453)  Nguyen‐Phuoc, D. Q., Nguyen, H. A., De Gruyter, C., Su, D. N., & Nguyen, V. H. (2019). Exploring  the prevalence and factors associated with self‐reported traffic crashes among app‐based  motorcycle taxis in Vietnam. Transport Policy, 81, 68–74.  https://doi.org/https://doi.org/10.1016/j.tranpol.2019.06.006  Phun, V. K., Kato, H., & Chalermpong, S. (2019). Paratransit as a connective mode for mass transit  systems in Asian developing cities: Case of Bangkok in the era of ride‐hailing services.  Transport Policy, 75, 27–35. https://doi.org/https://doi.org/10.1016/j.tranpol.2019.01.002  Su, Q., & Wang, D. Z. W. (2019). Morning commute problem with supply management considering  parking and ride‐sourcing. Transportation Research Part C: Emerging Technologies.  https://doi.org/https://doi.org/10.1016/j.trc.2018.12.015  Sui, Y., Zhang, H., Song, X., Shao, F., Yu, X., Shibasaki, R., … Li, Y. (2019). GPS data in urban online  ride‐hailing: A comparative analysis on fuel consumption and emissions. Journal of Cleaner  56     Production, 227, 495–505. https://doi.org/https://doi.org/10.1016/j.jclepro.2019.04.159  Vivoda, J. M., Harmon, A. C., Babulal, G. M., & Zikmund‐Fisher, B. J. (2018). E‐hail (rideshare)  knowledge, use, reliance, and future expectations among older adults. Transportation  Research Part F: Traffic Psychology and Behaviour, 55, 426–434.  https://doi.org/https://doi.org/10.1016/j.trf.2018.03.020  Young, M., & Farber, S. (2019). The who, why, and when of Uber and other ride‐hailing trips: An  examination of a large sample household travel survey. Transportation Research Part A:  Policy and Practice, 119, 383–392. https://doi.org/https://doi.org/10.1016/j.tra.2018.11.018  ポン ヴェン キエン V. K., Masui, R., & Yai, T. (2018). Operational Characteristics of Paratransit  Services with Ride‐Hailing Apps in Asian Developing Cities: The Phnom Penh Case. In Journal  of Transportation Technologies (Vol. 08). https://doi.org/10.4236/jtts.2018.84016      57   .. .VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY NGUYEN DANH MINH RIDE- HAILING SERVICE IN VIETNAM MARKET AND ITS IMPACTS ON TRAVEL BEHAVIOR OF LOCAL PEOPLE MAJOR: INFRASTRUCTURE... Origin: 64 Nguyen Khiet Destination: Hanoi Tower Origin Destination Origin: Royal city Destination Destination: 278 Ton Duc Thang Origin Origin: 20 Phu Minh Road Origin Destination: Market Destination... qualitatively impacts of RHS on local people Finally, the research will evaluate the dynamic movements of ride- hailing vehicles relied on data of the ridehailing app (RHA) on Information & Communication