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Graph-based many-to-one dynamic ride-matching for shared mobility services in congested networks

On-demand shared mobility systems require matching of one (one-to-one) or multiple riders (manyto-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. The proposed algorithm, which is an iterative two-step method, provides high service quality and is efficient in terms of computational complexity. GMOMatch 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 of the proposed algorithm, it is compared with a ride-matching algorithm developed by IBM (Simonetto et al., 2019). Both algorithms are implemented in a micro-traffic simulator to assess their performance and their impact on traffic congestion. Downtown Toronto road network was chosen as the study area. In comparison to the IBM algorithm, GMOMatch improved the service quality and traffic travel time by 32% and 4%, respectively. The sensitivity analysis indicated that in downtown Toronto, utilizing vehicles with a capacity of 10, can achieve 25% improvement in comparison to a capacity of 4, which also meant a lower fleet size on the network.