Abstract
Optimizing the use of vehicles is an essential task for sustainable and effective mobility-on-demand services. In a service, a driver aims to accept maximum customers, while a customer wants to minimize his/her waiting time before getting notifications/served. A service platform always faces a trade-off between the two stakeholders and their key performance indicators (KPIs), i.e., the number of accepted customers and waiting times. This paper addresses the problem of maintaining the best possible KPIs by optimizing the use of facilities with solving Dial-a-Ride problems (DARP). We propose a new framework named FORE-SEAQER (FORecast Enhanced StepwisE Allocator with Quick answER), which predicts whether incoming customers can ride in assigned cars using both real and predicted future requests, and decides whether the platform accepts requests as soon as possible. We experimentally evaluate our framework on real-world service log data from Japan and confirm that the proposed framework reasonably works.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
Default penalties are 1500[m]. Weights are implemented like \(100\times 1500\)[m].
- 3.
Since many DARP instances cannot exactly solved in our experiments, the computational upper bound is set to time 600[s].
References
Agatz, N., Erera, A., Savelsbergh, M., Wang, X.: Optimization for dynamic ride-sharing: a review. Eur. J. Oper. Res. 223(2), 295–303 (2012)
Alonso-Mora, J., Samaranayake, S., Wallar, A., Frazzoli, E., Rus, D.: On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Proc. Natl. Acad. Sci. 114(3), 462–467 (2017)
Alonso-Mora, J., Wallar, A., Rus, D.: Predictive routing for autonomous mobility-on-demand systems with ride-sharing. In: Proceedings of IROS2017, pp. 3583–3590 (2017)
Asghari, M., Shahabi, C.: Adapt-pricing: a dynamic and predictive technique for pricing to maximize revenue in ridesharing platforms. In: Proceedings of SIGSPATIAL2018, pp. 189–198 (2018)
Atasoy, B., Ikeda, T., Ben-Akiva, M.E.: Optimizing a flexible mobility on demand system. Transp. Res. Rec. 2563(1), 76–85 (2015)
Balasingam, A., Gopalakrishnan, K., Mittal, R., Arun, V., Saeed, A., Alizadeh, M., Balakrishnan, H., Balakrishnan, H.: Throughput-fairness tradeoffs in mobility platforms. In: Proceedings of MobiSys2021, pp. 363–375 (2021)
Bertsimas, D., Jaillet, P., Martin, S.: Online vehicle routing: The edge of optimization in large-scale applications. Oper. Res. 67(1), 143–162 (2019)
Clewlow, R.R., Mishra, G.S.: Disruptive transportation: The adoption, utilization, and impacts of ride-hailing in the united states. Technical report, UC Davis (2017)
Cordeau, J.F., Laporte, G.: The dial-a-ride problem: models and algorithms. Ann. Oper. Res. 153(1), 29–46 (2007)
Hikima, Y., Kohjima, M., Akagi, Y., Kurashima, T., Toda, H.: Price and time optimization for utility-aware taxi dispatching. In: Proceedings of PRICAI 2021, vol. 13031, pp. 370–381 (2021)
Ho, S.C., Szeto, W.Y., Kuo, Y.H., Leung, J.M., Petering, M., Tou, T.W.: A survey of dial-a-ride problems: literature review and recent developments. Transp. Res. Part B: Methodol. 111, 395–421 (2018)
Lei, Z., Qian, X., Ukkusuri, S.V.: Efficient proactive vehicle relocation for on-demand mobility service with recurrent neural networks. Transp. Res. Part C: Emerg. Technol. 117, 102678 (2020)
Liu, C., Sun, J., Jin, H., Ai, M., Li, Q., Zhang, C., Sheng, K., Wu, G., Qie, X., Wang, X.: Spatio-temporal hierarchical adaptive dispatching for ridesharing systems. In: Proceedings of SIGSPATIAL2020, pp. 227–238 (2020)
Lo, J., Morseman, S.: The perfect uberpool: a case study on trade-offs. In: Ethnographic Praxis in Industry Conference Proceedings, pp. 195–223. No. 1, Wiley Online Library (2018)
Peled, I., Lee, K., Jiang, Y., Dauwels, J., Pereira, F.C.: Preserving uncertainty in demand prediction for autonomous mobility services. In: Proceedings of IEEE ITSC2019, pp. 3043–3048 (2019)
Perron, L., Furnon, V.: Or-tools, https://developers.google.com/optimization/
Shah, S., Lowalekar, M., Varakantham, P.: Neural approximate dynamic programming for on-demand ride-pooling. In: Proceedings of AAAI2020, vol. 34(01), pp. 507–515 (2020)
Spieser, K., Treleaven, K., Zhang, R., Frazzoli, E., Morton, D., Pavone, M.: Toward a systematic approach to the design and evaluation of automated mobility-on-demand systems: a case study in Singapore. In: Road Vehicle Automation, pp. 229–245. Springer (2014)
Yan, A., Howe, B.: Fairness-aware demand prediction for new mobility. In: Proceedings of AAAI 2020, vol. 34 (01), pp. 1079–1087 (2020)
Yang, Y., Shi, Y., Wang, D., Chen, Q., Xu, L., Li, H., Fu, Z., Li, X., Zhang, H.: Improving the information disclosure in mobility-on-demand systems. In: Proceedings of KDD2021, pp. 3854–3864 (2021)
Zhao, B., Xu, P., Shi, Y., Tong, Y., Zhou, Z., Zeng, Y.: Preference-aware task assignment in on-demand taxi dispatching: an online stable matching approach. In: Proceedings of the AAAI 2019, vol. 33 (01), pp. 2245–2252 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Otaki, K., Nishi, T., Shiga, T., Kashiwakura, T. (2022). Optimization-based Predictive Approach for On-Demand Transportation. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13631. Springer, Cham. https://doi.org/10.1007/978-3-031-20868-3_34
Download citation
DOI: https://doi.org/10.1007/978-3-031-20868-3_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20867-6
Online ISBN: 978-3-031-20868-3
eBook Packages: Computer ScienceComputer Science (R0)