Eco-friendly integration of shared autonomous mobility on demand and public transit based on multi-source data

Abstract

Shared Autonomous Mobility on Demand (SAMoD) is considered one of the most efficient modes of transportation for future cities and has thus gained significant attention. However, it may attract the ridership of public transportation (PT) systems, leading to negative externalities such as traffic congestion and environmental pollution. Greater social benefits can only be realized by seamlessly integrating SAMoD with PT systems, leveraging SAMoD’s flexibility and PT’s large-scale transport capacity. Therefore, this study considers the various complex potential interactions between SAMoD and PT (such as subways, BRT, and buses), including first and last-mile services and alternatives, and aims to investigate an optimization framework for network construction and passenger flow allocation in a SAMoD-PT integrated system to achieve an optimal balance between sustainability and efficiency. Specifically, we first applied a hierarchical weighted K-means clustering algorithm to cluster multi-source travel demands and used the Voronoi partition algorithm for regional division. Secondly, potential connections in the multi-modal transportation network were determined using a greedy triangulation algorithm. Subsequently, life cycle assessment and continuous approximation algorithms were employed to quantify environmental costs (including greenhouse gas emissions and energy consumption) as well as passenger and operator costs, respectively. Finally, we constructed a multi-objective optimization model and solved it using the weighted sum method, obtaining the Pareto frontier to balance sustainability and efficiency in the SAMoD-PT integrated system. The results show that the optimized SAMoD-PT integrated system can significantly reduce social costs, mitigate inter-modal competition effects, and ensure the central role of PT. This highlights the great potential of cooperation between SAMoD and PT. These findings provide valuable insights for developing countries on how to plan more efficient and environmentally friendly multi-modal urban transportation systems in the future.