Abstract
In ride-sharing, commuters with similar itineraries share a vehicle for their trip. Despite its clear benefits in terms of reduced congestion, ride-sharing is not yet widely accepted. We propose a specific ride-sharing variant, where drivers are completely inflexible. This variant can form a competitive alternative against private transportation, due to the limited efforts that need to be made by drivers. However, due to this inflexibility, matching of drivers and riders can be substantially more complicated, compared to the situation where drivers can deviate. In this work, we propose a four-step procedure to identify the effect of such a ride-sharing scheme. We use a dynamic mesoscopic traffic simulator which computes departure-time choices and route choices for each commuter. The optimal matching of potential drivers and riders is obtained outside the simulation framework through an exact formulation of the problem. We evaluate the potential of this ride-sharing scheme on a real network of the Paris metropolitan area for the morning commute. We show that even with inflexible drivers and when only a small share of the population is willing to participate in the ride-sharing scheme, ride-sharing can alleviate congestion. Further improvements can be obtained by increasing the capacity of the vehicles or by providing small monetary incentives, but without jeopardizing the inflexibility of the drivers. Thereby, we show that ride-sharing can lead to fuel savings, CO\(_{2}\) emission reductions and travel time savings on a network level, even with a low participation rate.
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Notes
In particular, we omit the impact of a decrease of public-transit use on in-vehicle congestion or service quality/frequency.
Although inconvenience in public transit and with ride-sharing are not directly comparable, the value of crowding in public transit can be used as an approximation for the inconvenience cost of ride-sharing. Björklund and Swärdh (2017) estimates that, when sited, the value of time is multiplied by 1.48 when shifting from a situation with no crowding to a situation with overcrowding. In our model, the value of time is 12.96 €/h. A multiplier of 1.48 implies an inconvenience cost of 6.22 €/h.
References
Agatz, N.A.H., Erera, A.L., Savelsbergh, M.W.P., et al.: Dynamic ride-sharing: A simulation study in metro Atlanta. Transp. Res. Part B: Methodol. 45(9), 1450–1464 (2011). https://doi.org/10.1016/j.trb.2011.05.017
Agence de la transition écologique: ADEME - Bilans GES. https://www.bilans-ges.ademe.fr/fr/accueil (2021)
Alisoltani, N., Leclercq, L., Zargayouna, M.: Can dynamic ride-sharing reduce traffic congestion? Transp. Res. Part B Methodol. 145, 212–246 (2021)
Bahat, O., Bekhor, S.: Incorporating ridesharing in the static traffic assignment model. Netw. Spat. Econ. 16(4), 1125–1149 (2016). https://doi.org/10.1007/s11067-015-9313-7
Björklund, G., Swärdh, J.E.: Estimating policy values for in-vehicle comfort and crowding reduction in local public transport. Transp. Res. Part A Policy Practice 106, 453–472 (2017)
Bruck, B.P., Incerti, V., Iori, M., et al.: Minimizing CO2 emissions in a practical daily carpooling problem. Comput. Oper. Res. 81, 40–50 (2017). https://doi.org/10.1016/j.cor.2016.12.003
Chan, N.D., Shaheen, S.A.: Ridesharing in North America: Past, Present, and Future. Transp. Rev. 32(1), 93–112 (2012). https://doi.org/10.1080/01441647.2011.621557
Cici, B., Markopoulou, A., Frias-Martinez, E., et al.: Assessing the potential of ride-sharing using mobile and social data: A tale of four cities. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, New York, NY, USA, UbiComp ’14, pp. 201–211, (2014) https://doi.org/10.1145/2632048.2632055
Cornut, B.: Le Peak car en Ile-de-France: Etude de l’évolution de la place de l’automobile et de ses déterminants chez les franciliens depuis les années 1970. Université Paris-Est, Theses (2017)
Coulombel, N., Boutueil, V., Liu, L., et al.: Substantial rebound effects in urban ridesharing: Simulating travel decisions in Paris, France. Transp. Res. Part D: Transp. Environ. 71, 110–126 (2019). https://doi.org/10.1016/j.trd.2018.12.006
de Palma, A., Lindsey, R.: Comparison of morning and evening commutes in the vickrey bottleneck model. Transp. Res. Rec. 1807(1), 26–33 (2002)
de Palma, A., Nesterov, Y.: Park and ride for the day period and morning-evening commute. In: Mathematical and Computational Models for Congestion Charging. Springer, pp. 143–157 (2006)
de Palma, A., Marchal, F., Nesterov, Y.: METROPOLIS: modular system for dynamic traffic simulation. Transp. Res. Rec. 1607(1), 178–184 (1997). https://doi.org/10.3141/1607-24
de Palma, A., Kilani, M., Lindsey, R.: Congestion pricing on a road network: A study using the dynamic equilibrium simulator METROPOLIS. Transp. Res. Part A Policy Practice 39(7), 588–611 (2005). https://doi.org/10.1016/j.tra.2005.02.018
de Palma, A., Stokkink, P., Geroliminis, N.: Influence of dynamic congestion with scheduling preferences on carpooling matching with heterogeneous users. Transp. Res. Part B Methodol. 155, 479–498 (2022)
Degraeuwe, B., Thunis, P., Clappier, A., et al.: Impact of passenger car NOX emissions on urban NO2 pollution – Scenario analysis for 8 European cities. Atmos. Environ. 171, 330–337 (2017). https://doi.org/10.1016/j.atmosenv.2017.10.040
Delhomme, P., Gheorghiu, A.: Comparing French carpoolers and non-carpoolers: Which factors contribute the most to carpooling? Transp. Res. Part D: Transp. Environ. 42, 1–15 (2016). https://doi.org/10.1016/j.trd.2015.10.014
Di Febbraro, A., Gattorna, E., Sacco, N.: Optimization of Dynamic Ridesharing Systems. Transp. Res. Rec. 2359(1), 44–50 (2013). https://doi.org/10.3141/2359-06
Diao, M., Kong, H., Zhao, J.: Impacts of transportation network companies on urban mobility. Nature Sustain. pp 1–7 (2021)
Enquête Globale Transport: Enquête Globale Transport 2010. Tech. rep, Enquête Globale Transport, Paris (2010)
Friedrich, M., Hartl, M., Magg, C.: A modeling approach for matching ridesharing trips within macroscopic travel demand models. Transportation 45(6), 1639–1653 (2018). https://doi.org/10.1007/s11116-018-9957-5
Galland, S., Knapen, L., Yasar, A.U.H., et al.: Multi-agent simulation of individual mobility behavior in carpooling. Transp. Res. Part C Emerg. Technol. 45, 83–98 (2014). https://doi.org/10.1016/j.trc.2013.12.012
Gheorghiu, A., Delhomme, P.: For which types of trips do French drivers carpool? Motivations underlying carpooling for different types of trips. Transp. Res. Part A Policy Practice 113, 460–475 (2018). https://doi.org/10.1016/j.tra.2018.05.002
Hensher, D.A., Rose, J.M.: Development of commuter and non-commuter mode choice models for the assessment of new public transport infrastructure projects: A case study. Transp. Res. Part A Policy Practice 41(5), 428–443 (2007). https://doi.org/10.1016/j.tra.2006.09.006
Herbawi, W., Weber, M.: The ridematching problem with time windows in dynamic ridesharing: A model and a genetic algorithm. In: 2012 IEEE Congress on Evolutionary Computation, pp 1–8, (2012) https://doi.org/10.1109/CEC.2012.6253001
Île-de-France Mobilités: Enquête Globale Transport H2020 Île-de-France Mobilités-OMNIL-DRIEA / Partial results 2018. Technical Report, Île-de-France Mobilités (2019)
Kleiner, A., Nebel, B., Ziparo, V.: A Mechanism for dynamic ride sharing based on parallel auctions. In: 22th International Joint Conference on Artificial Intelligence, pp 266–272 (2011)
Kong, H., Zhang, X., Zhao, J.: How does ridesourcing substitute for public transit? A geospatial perspective in Chengdu, China. J. Transp. Geogr. 86(102), 769 (2020). https://doi.org/10.1016/j.jtrangeo.2020.102769
Kumar, P., Khani, A.: An algorithm for integrating peer-to-peer ridesharing and schedule-based transit system for first mile/last mile access. (2020) arXiv:2007.07488 [cs, math] https://arxiv.org/abs/arXiv:2007.07488 [cs, math]
Kumar, P., Hama, S., Nogueira, T., et al.: In-car particulate matter exposure across ten global cities. Sci. Total Environ. 750(141), 395 (2021). https://doi.org/10.1016/j.scitotenv.2020.141395
Li, M., Di, X., Liu, H.X., et al.: A restricted path-based ridesharing user equilibrium. J. Intell. Transp. Syst. 24(4), 383–403 (2020). https://doi.org/10.1080/15472450.2019.1658525
Li, Z., Hong, Y., Zhang, Z.: An empirical analysis of on-demand ride sharing and traffic congestion. SSRN Scholarly Paper ID 2843301, Social Science Research Network, Rochester, NY (2016)
Lian, Z., Van Ryzin, G.: Optimal growth in two-sided markets. Manage. Sci. 67(11), 6862–6879 (2021)
Liu, X., Titheridge, H., Yan, X., et al.: A passenger-to-driver matching model for commuter carpooling: Case study and sensitivity analysis. Transp. Res. Part C Emerg. Technol. 117(102), 702 (2020). https://doi.org/10.1016/j.trc.2020.102702
Lo, J.: Morseman S (2018) The Perfect uberPOOL: A Case Study on Trade-Offs. Ethnogr. Praxis Ind. Conf. Proc. 1, 195–223 (2018). https://doi.org/10.1111/1559-8918.2018.01204
Lu, W., Liu, L., Wang, F., et al.: Two-phase optimization model for ride-sharing with transfers in short-notice evacuations. Transp. Res. Part C Emerg. Technol. 114, 272–296 (2020). https://doi.org/10.1016/j.trc.2020.02.020
Ma, R., Yao, L., Song, L., et al.: A novel algorithm for peer-to-peer ridesharing match problem. Neural Comput. Appl. 31(1), 247–258 (2019). https://doi.org/10.1007/s00521-018-3733-5
Ma, T.Y., Rasulkhani, S., Chow, J.Y.J., et al.: A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers. Transp. Res. Part E Logist. Transp. Rev. 128, 417–442 (2019). https://doi.org/10.1016/j.tre.2019.07.002
Malichová, E., Pourhashem, G., Kováčiková, T., et al.: Users’ perception of value of travel time and value of ridesharing impacts on Europeans’ ridesharing participation intention: A case study based on MoTiV European-wide mobility and behavioral pattern dataset. Sustainability 12(10), 4118 (2020). https://doi.org/10.3390/su12104118
Masoud, N., Nam, D., Yu, J., et al.: Promoting peer-to-peer ridesharing services as transit system feeders. Transp. Res. Rec. 2650(1), 74–83 (2017). https://doi.org/10.3141/2650-09
Neoh, J.G., Chipulu, M., Marshall, A.: What encourages people to carpool? An evaluation of factors with meta-analysis. Transportation 44(2), 423–447 (2017). https://doi.org/10.1007/s11116-015-9661-7
Pinto, G.A., Vieira, K.C., Carvalho, E.G., et al.: Applying the lazy user theory to understand the motivations for choosing carpooling over public transport. Sustain. Prod. Consump. 20, 243–252 (2019). https://doi.org/10.1016/j.spc.2019.07.002
Qian, Z., Zhang, H.: Modeling multi-modal morning commute in a one-to-one corridor network. Transp. Res. Part C Emerg. Technol. 19(2), 254–269 (2011). https://doi.org/10.1016/j.trc.2010.05.012
Reck, D.J., Axhausen, K.W.: Subsidized ridesourcing for the first/last mile: how valuable for whom? Eur. J. Transp. Infrastruct. Res. 20(4), 59–77 (2020)
Saifuzzaman, M., de Palma, A., Motamedi, K.: Calibration of METROPOLIS for Ile-de-France, https://hal.archives-ouvertes.fr/hal-00683464, working paper or preprint (2012)
Saifuzzaman, M., Engelson, L., Kristoffersson, I., et al.: Stockholm congestion charging: An assessment with METROPOLIS and SILVESTER. Transp. Plan. Technol. 39(7), 653–674 (2016). https://doi.org/10.1080/03081060.2016.1204089
Schaller, B.: Can sharing a ride make for less traffic? Evidence from Uber and Lyft and implications for cities. Transp. Policy 102, 1–10 (2021). https://doi.org/10.1016/j.tranpol.2020.12.015
Shaheen, S., Cohen, A.: Shared ride services in North America: Definitions, impacts, and the future of pooling. Transp. Rev. 39(4), 427–442 (2019). https://doi.org/10.1080/01441647.2018.1497728
Shaheen, S.A., Chan, N.D., Gaynor, T.: Casual carpooling in the San Francisco Bay Area: Understanding user characteristics, behaviors, and motivations. Transp. Policy 51, 165–173 (2016). https://doi.org/10.1016/j.tranpol.2016.01.003
Standing, C., Standing, S., Biermann, S.: The implications of the sharing economy for transport. Transp. Rev. 39(2), 226–242 (2019). https://doi.org/10.1080/01441647.2018.1450307
Teubner, T., Flath, C.M.: The economics of multi-hop ride sharing. Business Inf. Syst. Eng. 57(5), 311–324 (2015). https://doi.org/10.1007/s12599-015-0396-y
Vickrey, W.: Congestion theory and transport investment. Am. Econ. Rev. 59(2), 251–60 (1969)
Wardman, M.: A review of British evidence on time and service quality valuations. Transp. Res. Part E Logist. Transp. Rev. 37(2), 107–128 (2001). https://doi.org/10.1016/S1366-5545(00)00012-0
Xu, H., Pang, J.S., Ordóñez, F., et al.: Complementarity models for traffic equilibrium with ridesharing. Transp. Res. Part B Methodol. 81, 161–182 (2015). https://doi.org/10.1016/j.trb.2015.08.013
Yan, S., Chen, C.Y.: An optimization model and a solution algorithm for the many-to-many car pooling problem. Ann. Oper. Res. 191(1), 37–71 (2011). https://doi.org/10.1007/s10479-011-0948-6
Yu, X., van den Berg, V.A.C., Verhoef, E.T.: Carpooling with heterogeneous users in the bottleneck model. Transp. Res. Part B Methodol. 127, 178–200 (2019). https://doi.org/10.1016/j.trb.2019.07.003
Acknowledgements
The authors are grateful to Jesse Jacobson for his comments and several discussions. We would also like to thank Catherine Morency, Nikolas Geroliminis and the participants of the CY economics seminar.
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The authors benefited from the support of ANR project AFFINITE (Analytical Framework for Family Interactions in Transport and Energy policies).
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de Palma, A., Javaudin, L., Stokkink, P. et al. Ride-sharing with inflexible drivers in the Paris metropolitan area. Transportation (2022). https://doi.org/10.1007/s11116-022-10361-1
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DOI: https://doi.org/10.1007/s11116-022-10361-1