Topic: Modeling for the Ride Sharing Problems in Urban Cities
EXECUTIVE SUMMARY
Ride sharing is becoming a popular travel mode in an increasing number of big cities. Apart from reducing traffic emissions and ameliorating traffic congestion, Ride sharing can bestow personal benefits, including cost reduction, trip convenience, etc. Car Sharing and Taxi Sharing are the main forms in the study of ride sharing. There are three aspects throughout the car sharing or taxi sharing procedure: matching, e routing and pricing.
Ride share matching occurs in very diverse circumstances. To better understand these matching patterns, we divide ride share matching into two categories: passenger-based or driver-based according to the party who initiates the ride sharing.
Ride share routing mainly focuses on the dynamic optimization model: these models aim to save travel time and minimize the distance travelled. Due to the complexity of the models used, some heuristic algorithms are used as solution methods. Those algorithms mainly include the Particle Swarm Optimization (PSO) algorithm, genetic algorithms, simulated annealing algorithms, Lagrangian decomposition and the greedy randomized adaptive search procedure (GRASP) algorithm.
Ride sharing pricing is to benefit both sharing passengers and the driver. A win-win pricing pattern is necessary. Equity is an important ingredient in this win-win game: whose destination is the first choice, and how much each one should pay when their travel distances are different remain as open questions. To ensure equity, various pricing methods are proposed: based on the number of passengers, travel distance, and trip urgency.
A basic requirement of each passenger on the ride sharing is to gain from the sharing rather than to lose. Hence, a concise allocation method which is convenient for practical use should be developed for the optimal ride sharing plan. Two key questions will be addressed: who should share a vehicle (the matching and the routing problem) and how much each of them should pay (the pricing problem).
Regarding the second question, for any two given passengers, a fair fare allocation method will be developed first. Then, based on this method, the optimal sharing plan of two different charging types will be investigated: deterministic fare (only charged on trip distance) and stochastic fare (trip time is also charged). A system optimal static model and an efficient dynamic model will be developed for the case of deterministic fare, both of which give the optimal solutions.
Its contributions include: (i) unlike matching agencies that require a large volume of participants of drivers and passengers, ride sharing simply works in a “business as usual” manner if the number of passengers is small; (ii) unlike service operators that use their own vehicles and drivers which usually require advanced booking, demand for ride sharing at the roadside can be satisfied in real time.
This research proposal provides an initial step on the optimal determination of ride sharing scheme, where some practical issues (fairness, sharing in team, queuing, payment method, etc.) for real implementations will be discussed as well. However, the practical situations are more complicated. Efforts are needed to further address other practical issues and incorporate them into the model; for instance, the ride sharing scheme has reduced each passenger’s trip expenses, thus it would change the modal split and increase the mode share of users. Hence, for the sake of modelling, the demand elasticity and multimodal equilibrium should be further investigated.
Reference
1. Agatz, N., Erera, A., Savelsbergh, M., & Wang, X. (2012). Optimization for dynamic ride-sharing: A review. European Journal of Operational Research, 223(2), 295-303.
2. Hosni, H., Naoum-Sawaya, J., & Artail, H. (2014). The shared-taxi problem: Formulation and solution methods. Transportation Research Part B: Methodological, 70, 303-318.
3. Sayarshad, H., & Chow, J. (2015). A scalable non-myopic dynamic dial-a-ride and pricing problem. Transportation Research Part B: Methodological. DOI: 10.1016/j.trb.2015.06.008.
4. Xu, H., Pang, J., Ordóñez, F., & Dessouky, M. (2015). Complementarity models for traffic equilibrium with ridesharing. Transportation Research Part B: Methodological, 81, 161-182.
Dr. Dong Xiao (2024) Monash University
