A generalized ride-matching approach for sustainable shared mobility
Seyed Mehdi Meshkani, Bilal Farooq

TL;DR
This paper introduces GMOMatch, a graph-based algorithm for dynamic many-to-one ride-matching in shared mobility, improving service quality and reducing congestion in simulations of Toronto.
Contribution
The paper presents a novel GMOMatch algorithm that enhances ride-matching efficiency and service quality in shared mobility systems with traffic congestion considerations.
Findings
GMOMatch outperforms existing algorithms in service rate and travel efficiency.
Vehicle capacity significantly impacts service quality improvements.
The algorithm effectively reduces traffic congestion in simulated urban environments.
Abstract
On-demand shared mobility is a promising and sustainable transportation approach that can mitigate vehicle externalities, such as traffic congestion and emission. On-demand shared mobility systems require matching of one (one-to-one) or multiple riders (many-to-one) to a vehicle based on real-time information. We propose a novel Graph-based Many-to-One ride-Matching (GMOMatch) algorithm for the dynamic many-to-one matching problem in the presence of traffic congestion. GMOMatch, which is an iterative two-step method, provides high service quality and is efficient in terms of computational complexity. It starts with a one-to-one matching in Step 1 and is followed by solving a maximum weight matching problem in Step 2 to combine the travel requests. To evaluate the performance, it is compared with a ride-matching algorithm developed by Simonetto et al. (2019). Both algorithms are…
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Taxonomy
TopicsTransportation and Mobility Innovations · Transportation Planning and Optimization · Urban and Freight Transport Logistics
Methodstravel james · Emirates Airlines Office in Dubai
