LAMD$^{2}$: Enabling Economical and Green Travel for Diversified Mobility on Demand Systems

The diversified mobility on demand (MoD) systems integrate both traditional fuel vehicles and green transportation tools (e.g. shared bicycles and shared e-bikes), which can not only reduce the fleet size of traditional fuel vehicles but also address the demand for short-distance travel and alleviate environmental pollution. However, despite having a variety of travel tools, the existing MoD systems neglect the guidance on passengers according to their preferences and travel characteristics and thus lead to the failure of effective cooperation among multiple travel modes and additional waste of resources. This inspired us to design a novel order allocation mechanism for diversified MoD systems. Specifically, we construct a heterogeneous order graph based on the order sets, transform the minimum fleet problem into the minimum trajectory coverage problem on the heterogeneous order graph and propose a learning-based order allocation method LAMD $^{2}$ containing three modules. i) The online breadth-first order search framework fully considers the characteristics of different travel modes and the interaction of multiple vehicles, and then leverages the competitive mechanism to well handle the heterogeneity of travel modes and improve the overall efficiency. ii) The multi-semantic travel mode selection module analyzes users' preferences for diversified travel modes based on multi-semantic historical travel data and then determines the service mode based on the similarity of order spatiotemporal characteristics. iii) The Reinforcement Learning (RL)-based order evaluation module evaluates the long-term benefits of expanding existing For-Hire Vehicle (FHV) trajectories with different orders and updates the behavioral strategies through interactive feedback with the environment. We implement and evaluate the proposed method with a real-world trajectory dataset, demonstrating that LAMD $^{2}$ outperforms all the baselines and reduces the fleet size and energy consumption by the average of 2.93% and 8.01%, respectively, compared to the real-world systems.