Ridesharing and Fleet Sizing For On-Demand Multimodal Transit Systems
Ramon Auad, Pascal Van Hentenryck

TL;DR
This paper develops new mixed-integer programming models for designing on-demand multimodal transit systems that incorporate ridesharing and fleet sizing, demonstrating significant cost and time savings in a real-world case study.
Contribution
It introduces novel MIP formulations for ridesharing-enabled ODMTS design and fleet sizing, addressing computational challenges and providing practical solutions.
Findings
Ridesharing reduces costs by 26% compared to individual shuttles.
The proposed ODMTS cuts costs by 35% and transit times by 38% over existing systems.
Fleet-sizing algorithms effectively determine shuttle numbers to meet performance metrics.
Abstract
This paper considers the design of On-Demand Multimodal Transit Systems (ODMTS) that combine fixed bus/rail routes between transit hubs with on-demand shuttles that serve the first/last miles to/from the hubs. The design problem aims at finding a network design for the fixed routes to allow a set of riders to travel from their origins to their destinations, while minimizing the sum of the travel costs, the bus operating costs, and rider travel times. The paper addresses two gaps in existing tools for designing ODMTS. First, it generalizes prior work by including ridesharing in the shuttle rides. Second, it proposes novel fleet-sizing algorithms for determining the number of shuttles needed to meet the performance metrics of the ODMTS design. Both contributions are based on Mixed-Integer Programs (MIP). For the ODMTS design, the MIP reasons about pickup and dropoff routes in order to…
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Taxonomy
TopicsTransportation and Mobility Innovations · Transportation Planning and Optimization · Urban and Freight Transport Logistics
