Exploring the feasibility of high-capacity vehicle strategy on ridesharing service from the perspective of economic, social, and environmental sustainability

Abstract

The emergence of ridesharing services is poised to significantly reshape the urban sustainability paradigm. With ever-growing demand, the adoption of high-capacity vehicles in shared mobility is becoming increasingly feasible. In this study, we present a high-capacity vehicle strategy to improve the economic, social, and environmental sustainability of the ridesharing system. To optimize the initial solution, deep reinforcement learning (DRL) is introduced into the framework of the NSGA-II algorithm. Based on the priority order, we apply lexicographic optimization to achieve the final solution. Ultimately, a case study is investigated to simulate the system performance on a realistic network of Chicago area. The results suggest that the high-capacity strategy with an incentive mechanism can substantially benefit all the stakeholders. Per-vehicle profit has drastically promoted from $82.50 to $95.00, meanwhile regret utility and carbon emissions decline to some extent. The best incentive discount for the high-capacity vehicle strategy is approximately 0.2. Overall, this study is significantly conducive to providing a new strategy to promote economic-social-environmental sustainability.