Multi-Objective Optimization for Multi-Modal Route Planning Integrating Shared Taxi and Bus

Abstract

Multi-modal transportation, emerging as a sustainable travel option, has shown immense promise in reducing passengers’ travel expenses and vehicles’ energy consumption, while simultaneously easing traffic congestion. To further promote green travel, this work studies a multi-modal route planning problem, focusing on the integration of shared taxis and buses. Its objective is to devise an innovative route planning approach for shared taxis, enabling passengers to seamlessly transition between the two modes and arrive at their destinations within designated timeframes. It designs a new pricing rule and establishes a multi-objective optimization that takes into account both the interests of passengers and shared taxi operators. The objectives are minimizing the aggregate cost incurred by all passengers and the overall travel distance traversed by shared taxis, and maximizing the revenue earned per kilometer by shared taxi operators. A novel nondominated linear sorting genetic algorithm (NLSGA) is introduced to tackle the problem. This algorithm incorporates innovative evolution and selection strategies to preserve solution diversity and enhance convergence speed. NLSGA demonstrates superior performance compared to several widely used multi-objective optimization algorithms, including NSGA-II, MOPSO, and MOGWO. Experimental results reveal that the proposed algorithm effectively reduces passengers’ cost and shared taxis’ travel distance while simultaneously maximizing revenue per kilometer for shared taxi operators.