1. Home
  2. Vol 9, No 5 (2023)
  3. Sugiyanto

Demand Modeling for Taxi and Ride-hailing Transport Services (RTS)

Gito Sugiyanto, . Yanto, Aris Wibowo, Toni Tauladan, Tory Damantoro

Abstract


The rapid growth of Ride-hailing Transport Services (RTS) demand is found to have caused a fierce market share battle with conventional taxis in previous decades. In selecting a taxi or RTS, understanding the factors affecting passenger’s decisions is substantial for better development and more reliable transit service. The aims of this study to evaluate the demand for taxis and RTS in the Jakarta Greater Area, Indonesia, using the demand-supply and dynamic models. It has been conducted by using 519 respondents, with the model inputs consisting of waiting and travel time, trip costs, and the destination of the conventional passengers. Moreover, the choice between taxi and RTS was analyzed based on the stated preferences of respondents. The results showed that the waiting and travel time, as well as costs per trip of RTS, were 1.49 and 2.67 minutes lower and IDR10,902 cheaper than a taxi, respectively. The factors influencing the demand for these transport modes were also the number of trips per-day, mode share, the average vehicle occupancy, operating hours/day, passengers and driver waiting time, as well as travel period. In the dynamic model, the addition of variable service area, peak hour, and average vehicles speed was subsequently observed. Based on the results, the requests for these transport modes in the Greater Area of Jakarta were 64,494 and 55,811 vehicle units for the demand-supply and dynamic models, respectively. This proved that the dynamic model was better than the demand-supply, due to the added parameters representing the area’s traffic characteristics. Additionally, subsequent future research are expected to focus on modeling of taxi and RTS demands through the global positioning system data, as well as analysis using machine learning and deep learning.

 

Doi: 10.28991/CEJ-2023-09-05-03

Full Text: PDF


Keywords


Ride-Hailing Transport Services; Waiting Time; Travel Time; Travel Cost; Dynamic Model; Demand-Supply.

References


Dal Fiore, F., Mokhtarian, P. L., Salomon, I., & Singer, M. E. (2014). “Nomads at last”? A set of perspectives on how mobile technology may affect travel. Journal of Transport Geography, 41, 97–106. doi:10.1016/j.jtrangeo.2014.08.014.

Shaheen, S., Cohen, A., & Martin, E. (2017). Smartphone App Evolution and Early Understanding from a Multimodal App User Survey. Disrupting Mobility. Lecture Notes in Mobility. Springer, Cham, Switzerland. doi:10.1007/978-3-319-51602-8_10.

Csiszar, C., & Foldes, D. (2015). Analysis and modelling methods of urban integrated information system of transportation. 2015 Smart Cities Symposium Prague (SCSP). doi:10.1109/scsp.2015.7181574.

Circella, G., & Mokhtarian, P. L. (2017). Impacts of information and communication technology. The Geography of Urban Transportation. Guildford Press, New York, united States.

Kim, J., Rasouli, S., & Timmermans, H. (2017). Satisfaction and uncertainty in car-sharing decisions: An integration of hybrid choice and random regret-based models. Transportation Research Part A: Policy and Practice, 95, 13–33. doi:10.1016/j.tra.2016.11.005.

Shaheen, S., Cano, L., & Camel, M. (2016). Exploring electric vehicle carsharing as a mobility option for older adults: A case study of a senior adult community in the San Francisco Bay Area. International Journal of Sustainable Transportation, 10(5), 406–417. doi:10.1080/15568318.2014.962675.

Shaheen, S., Cohen, A., & Zhody, I. (2016). Smartphone applications to influence travel choices: U.S. Department of Transportation Federal Highway Administration, Washington, United States.

Rayle, L., Dai, D., Chan, N., Cervero, R., & Shaheen, S. (2016). Just a better taxi? A survey-based comparison of taxis, transit, and ride sourcing services in San Francisco. Transport Policy, 45, 168–178. doi:10.1016/j.tranpol.2015.10.004.

Brodeur, A., & Nield, K. (2018). An empirical analysis of taxi, Lyft and Uber rides: Evidence from weather shocks in NYC. Journal of Economic Behavior & Organization, 152, 1–16. doi:10.1016/j.jebo.2018.06.004.

Schaller, B. (2005). A Regression Model of the Number of Taxicabs in U.S. Cities. Journal of Public Transportation, 8(5), 63–78. doi:10.5038/2375-0901.8.5.4.

Nelson, L. (2016). Uber and Lyft have devastated L.A.’s taxi industry, city records show. In Los Angeles Time. Available online: http://www.latimes.com/local/lanow/la-me-ln-uber-lyft-taxis-la-20160413-story.html (accessed on April 2023).

Uber Technologies Inc. (2023). Available online: https://www.uber.com/en-GB/newsroom/company-info/ (accessed on April 2023).

Jaked, J. D. (2014). Uber Has Pretty Much Destroyed Regular Taxis in San Francisco. Money. Available online: http://money.com/money/3397919/uber-taxis-san-francisco/ (accessed on April 2023).

Nie, Y. M. (2017). How can the taxi industry survive the tide of ride sourcing? Evidence from Shenzhen, China. Transportation Research Part C: Emerging Technologies, 79, 242–256. doi:10.1016/j.trc.2017.03.017.

Gonzales, E. J., Yang, C., Morgul, E. F., Ozbay, K., & Mineta National Transit Research Consortium. (2014). Modeling taxi demand with GPS data from taxis and transit (No. CA-MNTRC-14-1141). Mineta National Transit Research Consortium, San Jose, United States.

Sugiyanto, G. (2016). The Impact of congestion pricing scheme on the generalized cost and speed of motorcycle to the City of Yogyakarta, Indonesia. Journal of Engineering and Applied Sciences, 11(8), 1740–1746. doi:10.3923/jeasci.2016.1740.1746.

Cici, B., Markopoulou, A., Frias-Martinez, E., & Laoutaris, N. (2014). Assessing the potential of ride-sharing using mobile and social data: A tale of four cities. UbiComp 2014 - Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 201–211. doi:10.1145/2632048.2632055.

Liu, X., Gong, L., Gong, Y., & Liu, Y. (2015). Revealing travel patterns and city structure with taxi trip data. Journal of Transport Geography, 43, 78–90. doi:10.1016/j.jtrangeo.2015.01.016.

Li, Z., Hong, Y., & Zhang, Z. (2017). An Empirical Analysis of On-demand Ride-sharing and Traffic Congestion. Proceedings of the Annual Hawaii International Conference on System Sciences. doi:10.24251/hicss.2017.002.

Chan, N. D., & Shaheen, S. A. (2012). Ridesharing in North America: Past, Present, and Future. Transport Reviews, 32(1), 93–112. doi:10.1080/01441647.2011.621557.

Sugiyanto, G. (2018). The effect of congestion pricing scheme on the generalized cost and speed of a motorcycle. Walailak Journal of Science and Technology, 15(1), 95–106. doi:10.48048/wjst.2018.2347.

Qian, X., & Ukkusuri, S. V. (2017). Taxi market equilibrium with third-party hailing service. Transportation Research Part B: Methodological, 100, 43–63. doi:10.1016/j.trb.2017.01.012.

He, F., Wang, X., Lin, X., & Tang, X. (2018). Pricing and penalty/compensation strategies of a taxi-hailing platform. Transportation Research Part C: Emerging Technologies, 86, 263–279. doi:10.1016/j.trc.2017.11.003.

Chen, F., Wu, J., Chen, X., & Wang, J. (2017). Vehicle kilometers traveled reduction impacts of Transit-Oriented Development: Evidence from Shanghai City. Transportation Research Part D: Transport and Environment, 55, 227–245. doi:10.1016/j.trd.2017.07.006.

Garikapati, V. M., Pendyala, R. M., Morris, E. A., Mokhtarian, P. L., & McDonald, N. (2016). Activity patterns, time use, and travel of millennials: a generation in transition? Transport Reviews, 36(5), 558–584. doi:10.1080/01441647.2016.1197337.

Sugiyanto, G., & Malkhamah, S. (2018). Determining the maximum speed limit in urban road to increase traffic safety. Jurnal Teknologi, 80(5). doi:10.11113/jt.v80.10489.

Alonso, B., Barreda, R., dell’Olio, L., & Ibeas, A. (2018). Modelling user perception of taxi service quality. Transport Policy, 63, 157–164. doi:10.1016/j.tranpol.2017.12.011.

Paronda, A. G. A., Regido, J. R. F., & Napalang, M. S. G. (2016). Comparative Analysis of Transportation Network Companies (TNCs) and Conventional Taxi Services in Metro Manila. 23rd Annual Conference of the Transportation Science Society of Philippines, 8 August, Quezon City, Philippines.

Wang, M., & Mu, L. (2018). Spatial disparities of Uber accessibility: An exploratory analysis in Atlanta, USA. Computers, Environment and Urban Systems, 67, 169–175. doi:10.1016/j.compenvurbsys.2017.09.003.

Alemi, F., Circella, G., Handy, S., & Mokhtarian, P. (2018). What influences travelers to use Uber? Exploring the factors affecting the adoption of on-demand ride services in California. Travel Behaviour and Society, 13, 88–104. doi:10.1016/j.tbs.2018.06.002.

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. doi:10.1016/j.trc.2019.05.037.

Zhang, Y., Guo, H., Li, C., Wang, W., Jiang, X., & Liu, Y. (2016). Which One is More Attractive to Traveler, Taxi or Tailored Taxi? An Empirical Study in China. Procedia Engineering, 137, 867–875. doi:10.1016/j.proeng.2016.01.327.

Etminani-Ghasrodashti, R., & Hamidi, S. (2019). Individuals’ demand for ride-hailing services: Investigating the combined effects of attitudinal factors, land use, and travel attributes on demand for app-based taxis in Tehran, Iran. Sustainability (Switzerland), 11(20), 5755–5773. doi:10.3390/su11205755.

Sugiyanto, G., Santosa, P. B., & Santi, M. Y. (2019). Determining the Criteria for an Air Hub in Indonesia using an Analytical Hierarchy Process (AHP). Advances in Engineering Research, 187, 20–24. doi:10.2991/icosite-19.2019.4.

Etminani-Ghasrodashti, R., & Ardeshiri, M. (2015). Modeling travel behavior by the structural relationships between lifestyle, built environment and non-working trips. Transportation Research Part A: Policy and Practice, 78, 506–518. doi:10.1016/j.tra.2015.06.016.

Yang, Z., Franz, M. L., Zhu, S., Mahmoudi, J., Nasri, A., & Zhang, L. (2018). Analysis of Washington, DC taxi demand using GPS and land-use data. Journal of Transport Geography, 66, 35–44. doi:10.1016/j.jtrangeo.2017.10.021.

Weng, G. S., Zailani, S., Iranmanesh, M., & Hyun, S. S. (2017). Mobile taxi booking application service’s continuance usage intention by users. Transportation Research Part D: Transport and Environment, 57, 207–216. doi:10.1016/j.trd.2017.07.023.

Altshuler, T., Altshuler, Y., Katoshevski, R., & Shiftan, Y. (2019). Modeling and Prediction of Ride-Sharing Utilization Dynamics. Journal of Advanced Transportation, 2019, 1–18. doi:10.1155/2019/6125798.

Hughes, R., & MacKenzie, D. (2016). Transportation network company wait times in Greater Seattle, and relationship to socioeconomic indicators. Journal of Transport Geography, 56, 36–44. doi:10.1016/j.jtrangeo.2016.08.014.

Cirillo, C., Liu, Y., & Tremblay, J. M. (2017). Simulation, numerical approximation and closed forms for joint discrete continuous models with an application to household vehicle ownership and use. Transportation, 44(5), 1105–1125. doi:10.1007/s11116-016-9696-4.

Akbari, M., Amiri, N. S., Zúñiga, M. Á., Padash, H., & Shakiba, H. (2020). Evidence for Acceptance of Ride-Hailing Services in Iran. Transportation Research Record, 2674(11), 289–303. doi:10.1177/0361198120942224.

Schreffler, E. N. (2018). Better integrating travel choices into future urban mobility systems: The day the highways stood still. Journal of Public Transportation, 21(1), 82–91. doi:10.5038/2375-0901.21.1.9.

Salanova, J. M., Romeu, M. E., & Amat, C. (2014). Aggregated Modeling of Urban Taxi Services. Procedia - Social and Behavioral Sciences, 160(2014), 352–361. doi:10.1016/j.sbspro.2014.12.147.

Frei, C., Hyland, M., & Mahmassani, H. S. (2017). Flexing service schedules: Assessing the potential for demand-adaptive hybrid transit via a stated preference approach. Transportation Research Part C: Emerging Technologies, 76, 71–89. doi:10.1016/j.trc.2016.12.017.

Ma, X., Tao, Z., Wang, Y., Yu, H., & Wang, Y. (2015). Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies, 54, 187–197. doi:10.1016/j.trc.2015.03.014.

Wang, C., Hou, Y., Barth, M. (2020). Data-Driven Multi-step Demand Prediction for Ride-Hailing Services Using Convolutional Neural Network. Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, 944. Springer, Cham, Switzerland. doi:10.1007/978-3-030-17798-0_2.

Wang, R. (2017). Supply-demand Forecasting For a Ride-Hailing System. Master Thesis, University of California, Irvine, United States.

Ke, J., Zheng, H., Yang, H., & Chen, X. (Michael). (2017). Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach. Transportation Research Part C: Emerging Technologies, 85, 591–608. doi:10.1016/j.trc.2017.10.016.

Lam, C., & Liu, M. (2017). Demand and Consumer Surplus in the On-Demand Economy: The Case of Ride Sharing. SSRN Electronic Journal. doi:10.2139/ssrn.2997190.

Oviedo, D., Granada, I., & Perez-Jaramillo, D. (2020). Ride sourcing and travel demand: Potential effects of transportation network companies in Bogotá. Sustainability (Switzerland), 12(5), 1732–1747. doi:10.3390/su12051732.

Do, M., Byun, W., Shin, D. K., & Jin, H. (2019). Factors influencing matching of ride-hailing service using machine learning method. Sustainability (Switzerland), 11(20), 5615–5627. doi:10.3390/su11205615.

Hossain, S., & Habib, K. N. (2021). Inferring the purposes of using ride-hailing services through data fusion of trip trajectories, secondary travel surveys, and land use data. Transportation Research Record, 2675(9), 558–573. doi:10.1177/03611981211003593.

Dey, B. K., Tirtha, S. D., & Eluru, N. (2021). Transport networking companies demand and flow estimation in New York City. Transportation Research Record, 2675(9), 139–153. doi:10.1177/03611981211000752.

Shoman, M., & Moreno, A. T. (2021). Exploring preferences for transportation modes in the city of Munich after the recent incorporation of ride-hailing companies. Transportation Research Record, 2675(5), 329–338. doi:10.1177/0361198121989726.

Wilkes, G., Engelhardt, R., Briem, L., Dandl, F., Vortisch, P., Bogenberger, K., & Kagerbauer, M. (2021). Self-regulating demand and supply equilibrium in joint simulation of travel demand and a ride-pooling service. Transportation Research Record, 2675(8), 226–239. doi:10.1177/0361198121997140.

JICA. (2019). Jabodetabek Urban Transportation Policy Integration (JUTPI) Project Phase 2 in the Republic of Indonesia. Coordinating Ministry for Economic Affairs Republic of Indonesia, Jakarta, Indonesia.

Tamin, O. Z. (2020). Transportation Planning and Modeling: Example Problems and Applications. ITB, Bandung, Indonesia. (In Indonesian).

Kalouptsidis, N., & Psaraki, V. (2010). Approximations of choice probabilities in mixed logit models. European Journal of Operational Research, 200(2), 529–535. doi:10.1016/j.ejor.2009.01.017.

Adisasmita, S. A., & Caroles, L. (2021). Multi-Airport System Development Model: Case Study of Airports in Indonesia. Civil Engineering Journal, 7, 182-193. doi:10.28991/CEJ-SP2021-07-013.

Sugiyanto, G., Santosa, P. B., Wibowo, A., & Santi, M. Y. (2015). Analysis of hub-and-spoke airport networks in Java Island, based on cargo volume and freight ratio. Procedia Engineering, 125, 556–563. doi:10.1016/j.proeng.2015.11.061.

Karanki, F., & Lim, S. H. (2023). Spatial dependence and competition between U.S. airports. Journal of Air Transport Management, 110, 102355. doi:10.1016/j.jairtraman.2022.102355.

Puzis, R., Altshuler, Y., Elovici, Y., Bekhor, S., Shiftan, Y., & Pentland, A. (2012). Augmented Betweenness Centrality for Environmentally Aware Traffic Monitoring in Transportation Networks. Journal of Intelligent Transportation Systems, 17(1), 91–105. doi:10.1080/15472450.2012.716663.

Gehrke, S. R., Felix, A., & Reardon, T. G. (2019). Substitution of Ride-Hailing Services for More Sustainable Travel Options in the Greater Boston Region. Transportation Research Record, 2673(1), 438–446. doi:10.1177/0361198118821903.

ATTN (2018). Research and Development Agency of the Ministry of Transportation of the Republic of Indonesia. (2018). National Transportation Origin Destination Survey of 2018 Passengers Based on Big Data. (In Indonesian).

Sugiyanto, G., Yanto, Y., Wibowo, A., & Astoto, T. W. (2023). Service quality of transit and demand-supply forecasting for ride-hailing in the Jakarta Greater Area, Indonesia. Journal of Engineering Research. doi:10.36909/jer.14725.

Pan, R., Yang, H., Xie, K., & Wen, Y. (2020). Exploring the equity of traditional and ride-hailing taxi services during peak hours. Transportation Research Record, 2674(9), 266–1278. doi:10.1177/0361198120928338.

Wang, F., & Ross, C. L. (2019). New potential for multimodal connection: exploring the relationship between taxi and transit in New York City (NYC). Transportation, 46(3), 1051–1072. doi:10.1007/s11116-017-9787-x.

Alonso-Mora, J., Samaranayake, S., Wallar, A., Frazzoli, E., & Rus, D. (2017). On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Proceedings of the National Academy of Sciences, 114(3), 462–467. doi:10.1073/pnas.1611675114.

Tirachini, A. (2020). Ride-hailing, travel behaviour and sustainable mobility: an international review. Transportation, 47(4), 2011–2047. doi:10.1007/s11116-019-10070-2.

Pant, P., & Harrison, R. M. (2013). Estimation of the contribution of road traffic emissions to particulate matter concentrations from field measurements: A review. Atmospheric Environment, 77, 78–97. doi:10.1016/j.atmosenv.2013.04.028.

Carrion, C., & Levinson, D. (2012). Value of travel time reliability: A review of current evidence. Transportation Research Part A: Policy and Practice, 46(4), 720–741. doi:10.1016/j.tra.2012.01.003.

Hennessy, D. A., & Wiesenthal, D. L. (1999). Traffic congestion, driver stress, and driver aggression. Aggressive Behavior, 25(6), 409–423. doi:10.1002/(SICI)1098-2337(1999)25:6<409::AID-AB2>3.0.CO;2-0.

Sugiyanto, G., Fadli, A., & Santi, M. Y. (2017). Identification of black spot and equivalent accident number using Upper Control Limit method. ARPN Journal of Engineering and Applied Sciences, 12(2), 528–535.

Gehrke, S., Niemz, S., Wenige, L., & Ruhland, J. (2022). Investigation of Senior IT Management Skills Using COBIT Enablers and Social Media Platform. Journal of Human, Earth, and Future, 3(1), 69-81. doi:10.28991/8-2022-03-01-05.

Wiengdee, S. (2019). An analysis of the industry and business strategy of ride-hailing transport service in Thailand: A case study of Grab business. Course No. 751409, Research Exercise in Current Economics Issues, Chiang Mai University, Chiang Mai, Thailand.

Ben-Akiva, M., McFadden, D., & Train, K. (2019). Foundations of stated preference elicitation: Consumer Behavior and Choice-based Conjoint Analysis. Foundations and Trends in Econometrics, 10(1–2), 1–144. doi:10.1561/0800000036.

Gerte, R., Konduri, K. C., & Eluru, N. (2018). Is there a limit to adoption of dynamic ridesharing systems? Evidence from analysis of uber demand data from New York City. Transportation Research Record, 2672(42), 127–136. doi:10.1177/0361198118788462.

Chang, H. Wen, Tai, Y. Chin, & Hsu, Y. Jen J. (2010). Context-aware taxi demand hotspots prediction. International Journal of Business Intelligence and Data Mining, 5(1), 3–18. doi:10.1504/IJBIDM.2010.030296.

Moreira-Matias, L., Gama, J., Ferreira, M., & Damas, L. (2012). A predictive model for the passenger demand on a taxi network. 15th International IEEE Conference on Intelligent Transportation Systems. doi:10.1109/itsc.2012.6338680.


Full Text: PDF

DOI: 10.28991/CEJ-2023-09-05-03

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2023 Gito Sugiyanto, Yanto Yanto, Aris Wibowo

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
x
Message