Agent based model for dynamic ridesharing

• A new agent based model is proposed for the dynamic ridesharing problem.
• A vicinity approach creates potential matching choice sets for drivers and passengers.
• The agent based model gives close to optimal results within short computation times.
• Higher user cost savings and VKT savings are achieved through multi-passenger rides.
• Short term revenue is maximized by a commission rate of roughly 50%.

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.