Abstract
This study used the dynamic algorithm to analyze the ride-sharing potential in New York City (NYC) by comparing the no-sharing scenario with different sharing scenarios. At first, the data set was analyzed and categorized based on the characteristics of trips. Then, no-sharing scenarios were defined as a benchmark against which to compare sharing outcomes. Then, to do sharing scenarios, at each step, a specific percentage is subtracted from the estimated number of taxis in the zero scenarios, and various key performance indicators (KPIs) are compared to determine the optimal number of taxis for the demand of NYC during peak hours and off-peak hours from the viewpoints of customers and taxi companies. The study found that the number of needed vehicles can be reduced to 40% for peak hours on weekends and 50% on weekdays.
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References
Zwick, F., Kuehnel, N., Axhausen, K.W.: Review on theoretical assessments and practical implementations of ride-pooling (2022). https://doi.org/10.3929/ethz-b-000548675
Dai, R., Ding, C., Gao, J., Wu, X., Yu, B.: Optimization and evaluation for autonomous taxi ride-sharing schedule and depot location from the perspective of energy consumption. Appl. Energy 308, 118388 (2022). https://doi.org/10.1016/j.apenergy.2021.118388
Pan, Y., Qiu, L.: How ride-sharing is sha** public transit system: a counterfactual estimator approach. Prod. Oper. Manag. 31(3), 906–927 (2021). https://doi.org/10.1111/poms.13582
Clewlow, R.R.: Disruptive transportation: The adoption, utilization, and impacts of ride-hailing in the United States (UCD-ITS-RR-17–07). Institute of Transportation Studies, UC Davis (2017)
Santi, P., Resta, G., Szell, M., Sobolevsky, S., Strogatz, S.H., Ratti, C.: Quantifying the benefits of vehicle pooling with shareability networks. Proc. Natl. Acad. Sci. 111(37), 13290–13294 (2014). https://doi.org/10.1073/pnas.1403657111
Cai, H., Wang, X., Adriaens, P., Xu, M.: Environmental benefits of taxi ride sharing in Bei**g. Energy 174, 503–508 (2019). https://doi.org/10.1016/j.energy.2019.02.166
Lokhandwala, M., Cai, H.: Dynamic ride sharing using traditional taxis and shared autonomous taxis: a case study of NYC. Transport. Res. Part C: Emerg. Technol. 97, 45–60 (2018). https://doi.org/10.1016/j.trc.2018.10.007
Guo, G., Xu, Y.: A deep reinforcement learning approach to ride-sharing vehicles dispatching in autonomous mobility-on-demand systems. IEEE Intell. Transport. Syst. Maga. 14(1), 128–140 (2022). https://doi.org/10.1109/MITS.2019.2962159
NYC Taxi & Limousine Commission Homepage. https://www.nyc.gov/site/tlc/index.page
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Afsari, M., Ippolito, N., Mistrice, L.M.B., Gentile, G. (2024). Optimizing Ride-Sharing Potential in New York City: A Dynamic Algorithm Analysis of Peak and Off-Peak Demand Scenarios. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds) Reliability and Statistics in Transportation and Communication. RelStat 2023. Lecture Notes in Networks and Systems, vol 913. Springer, Cham. https://doi.org/10.1007/978-3-031-53598-7_3
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DOI: https://doi.org/10.1007/978-3-031-53598-7_3
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