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 (many- to-one) to a vehicle based on real-time information. We propose a novel graph-based algorithm (GMO- Match) for 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 is step 2 to combine the travel requests. To evaluate the performance of the proposed algorithm, it is compared with a ride-matching algorithm by IBM (Simonetto et al., 2019). Both algorithms are implemented in a micro-traffic simulator to assess their performance and also their impacts on traffic congestion. Downtown Toronto road network is chosen as the study area. In comparison to IBM algorithm, GMOMatch improves the service quality and traffic travel time by 32% and 4%, respectively. A sensitivity analysis is also conducted over different parameters to show their impacts on the service quality.