Abstract
This paper addresses multi-class fleet sizing and vehicle assignment problem where we aim to provide Autonomous Mobility-on-Demand (AMoD) service using a fleet of heterogeneous vehicles. We present a chain of transportation with three classes of autonomous vehicles including cars, buggies and scooters. Each class of vehicle can access a subset of the network, such that, there are some links exclusive for that particular class. Our fleet management system then assigns available vehicles to trips based on the travel time for passenger pick-up and drop-off, their queue time and accessibility of the road network by the vehicle. Each assignment may consist of a set of vehicles allocated for one trip that is composed of multiple-legs served by different vehicles. For example, first mile pick-up by a scooter, middle mile on a car and last-mile trip on a buggy. We apply a genetic algorithm for heterogeneous fleet sizing and propose a hierarchical structure for travel time optimal assignment of the multi-class autonomous vehicles to passengers. We validated our approach with a range of heterogeneous fleet sizes constrained on the given budget. Our approach is more time efficient than taking a ride on a single-class autonomous vehicle for middle mile plus walking during the first and the last miles. Hence, we provide the convenience of autonomously covering the entire journey using multi-class vehicles with no additional travel or transit delays compared to single-class.
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
References
Pendleton, S.D., Andersen, H., Shen, X., Eng, Y.H., Zhang, C., Kong, H.X., Leong, W.K., Ang Jr., M.H., Rus, D.: Multi-class autonomous vehicles for mobility-on-demand service. In: 2016 IEEE/SICE International Symposium on System Integration (SII). IEEE (2016)
Zhang, R., Spieser, K., Frazzoli, E., Pavone, M.: Models , Algorithms, and Evaluation for Autonomous Mobility-On-Demand Systems. In: American Control Conference, pp. 2573–2587 (2015)
Marczuk, K.A., Soh, H.S., Azevedo, C.M., Lee, D.H., Frazzoli, E.: Simulation framework for rebalancing of autonomous mobility on demand systems. In: International Conference on Transportation and Traffic Engineering (2016)
Wang, X.: Optimizing ride matches for the dynamic RDE-sharing systems. Ph.D. thesis, Georgia Institute of Technology (2013)
Meghjani, M., Marczuk, K.: A hybrid approach to matching taxis and customers. In: 2016 IEEE Region 10 Conference (TENCON), pp. 167–169, November 2016
Azevedo, C.L., Marczuk, K., Raveau, S., Soh, H., Adnan, M., Basak, K., Loganathan, H., Deshmunkh, N., Lee, D.H., Frazzoli, E., Ben-Akiva, M.: Microsimulation of demand and supply of autonomous mobility on-demand. J. Transp. Res. Board (2564), 21–30 (2016)
Azevedo, C.L., Marczuk, K., Raveau, S., Soh, H., Adnan, M., Basak, K., Loganathan, H., Deshmunkh, N., Lee, D.-H., Frazzoli, E., Ben-Akiva, M.: Optimizing a flexible mobility on demand system. Transp. Res. Rec. (2536), 76–85 (2015)
Psaraftis, H.N.: A dynamic programming solution to the single vehicle many-to-many immediate request dial-a-ride problem. Transp. Sci. 14(2), 130–154 (1980)
Psaraftis, H.N.: An exact algorithm for the single vehicle many-to-many dial-a-ride problem with time windows. Transp. Sci. 17(3), 351–357 (1983)
Cordeau, J.-F., Laporte, G.: A tabu search heuristic for the static multi-vehicle dial-a-ride problem. Transp. Res. Part B Methodol. 37(6), 579–594 (2003)
Google Inc. Google Self-Driving Car Project
Montella, C., Perkins, T., Spletzer, J., Sands, M.: To the bookstore! autonomous wheelchair navigation in an urban environment. In: Field and Service Robotics (2014)
Pendleton, S., Chong, Z.J., Qin, B., Liu, W., Uthaicharoenpong, T., Shen, X., Fu, G.M.J., Scarnecchia, M., Kim, S.W., Ang, M.H., Frazzoli, E.: Multi-class driverless vehicle cooperation for mobility-on-demand. In: Intelligent Transportation Systems World Congress (ITSWC) (2014)
Dantzig, T., et al.: Number, the language of science: a critical survey written for the cultured non-mathematician (1954)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization & Machine Learning. Addison-Wesley Publishing Company, Inc. (1989)
Allen, A.O.: Probability, Statistics, and Queueing Theory. Academic Press (2014)
Acknowledgment
This research was supported by the National Research Foundation, Prime Minister’s Office, Singapore, under its CREATE programme, Singapore-MIT Alliance for Research and Technology (SMART) Future Urban Mobility (FM) IRG. We would also like to acknowledge the support of NVIDIA Corporation’s NVAIL program.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Meghjani, M. et al. (2019). Multi-class Fleet Sizing and Mobility on Demand Service. In: Cardin, M., Hastings, D., Jackson, P., Krob, D., Lui, P., Schmitt, G. (eds) Complex Systems Design & Management Asia. CSD&M 2018. Advances in Intelligent Systems and Computing, vol 878. Springer, Cham. https://doi.org/10.1007/978-3-030-02886-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-02886-2_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02885-5
Online ISBN: 978-3-030-02886-2
eBook Packages: EngineeringEngineering (R0)