Agent based model for dynamic ridesharing |
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| Institution: | 1. NICTA, Australian National University, Australia;2. H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, United States;1. Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, USA;2. Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697, USA;1. Faculty of Economics, University of Ljubljana, Kardeljeva ploscad 17, 1000 Ljubljana, Slovenia;2. Rotterdam School of Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA Rotterdam, The Netherlands;3. H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, 765 Ferst Drive, Atlanta, GA, USA;1. Telecom Italia, Corso Duca degli Abruzzi 24, 10129 Torino, Italy;2. Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy |
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| Abstract: | Dynamic ridesharing involves a service provider that matches potential drivers and passengers with similar itineraries allowing them to travel together and share the costs. Centralized (binary integer programming) and decentralized (dynamic auction-based multi-agent) optimization algorithms are formulated to match passengers and drivers. Numerical experiments on the decentralized approach provides near optimal solutions for single-driver, single-passenger cases with lower computational burden. The decentralized approach is then extended to accommodate both multi-passenger and multi-driver matches. The results indicate higher user cost savings and vehicle kilometers traveled (VKT) savings when allowing multi-passenger rides. Sensitivity analysis is conducted to test the impact of the service provider commission rate on revenue and system reliability. While short term revenue can be maximized at a commission rate of roughly 50% of each trip’s cost, the resulting drop in system reliability would be expected to reduce patronage and revenues in the longer term. |
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| Keywords: | Ridesharing Agent based modeling Decentralized optimization Carpool |
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