Ridesharing in urban areas: multi-objective optimisation approach for ride-matching and routeing with commuters’ dynamic mode choice

The daily home-office commute of millions of people in crowded cities strains air quality and increases travel time, which motivates the generation of ridesharing. Ridesharing offers many benefits, such as reducing travel costs, congestion, and pollution. Commuter ridesharing is an important theme of urban transportation. This paper studies a ridesharing problem aiming at enlarging the ridesharing market at a limited cost, which enlighten the decision-making problem in city logistics. We establish a novel multi-objective optimisation model based on cumulative prospect theory (CPT) to address the preferred travel mode of commuters. The commuters’ perceived value influences their choice of travel mode. Meanwhile, the perceived value changes with the commuters’ experience of travel mode choice. We give the NP-hardness proof of the ridesharing scheduling problem and develop a heuristic algorithm to solve it in a small-scale scenario. For large-scale problems, a hybrid VNS-NSGAII algorithm combining variable neighbourhood search (VNS) with NSGAII (Non-dominated Sorting Genetic Algorithm II) is proposed to generate an approximate optimal Pareto front. A series of computational experiments are conducted to demonstrate the effectiveness and efficiency of the proposed algorithm based on the actual traffic data in Beijing, China.