CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms

How to optimally dispatch orders to vehicles and how to tradeoff between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-hailing as a large-scale parallel ranking problem and study the joint decision-making task of order dispatching and fleet management in online ride-hailing platforms. This task brings unique challenges in the following four aspects. First, to facilitate a huge number of vehicles to act and learn efficiently and robustly, we treat each region cell as an agent and build a multi-agent reinforcement learning framework. Second, to coordinate the agents from different regions to achieve long-term benefits, we leverage the geographical hierarchy of the region grids to perform hierarchical reinforcement learning. Third, to deal with the heterogeneous and variant action space for joint order dispatching and fleet management, we design the action as the ranking weight vector to rank and select the specific order or the fleet management destination in a unified formulation. Fourth, to achieve the multi-scale ride-hailing platform, we conduct the decision-making process in a hierarchical way where a multi-head attention mechanism is utilized to incorporate the impacts of neighbor agents and capture the key agent in each scale. The whole novel framework is named as CoRide. Extensive experiments based on multiple cities real-world data as well as analytic synthetic data demonstrate that CoRide provides superior performance in terms of platform revenue and user experience in the task of city-wide hybrid order dispatching and fleet management over strong baselines.Comment: CIKM 201

A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers

We propose a ridesharing strategy with integrated transit in which a private on-demand mobility service operator may drop off a passenger directly door-to-door, commit to dropping them at a transit station or picking up from a transit station, or to both pickup and drop off at two different stations with different vehicles. We study the effectiveness of online solution algorithms for this proposed strategy. Queueing-theoretic vehicle dispatch and idle vehicle relocation algorithms are customized for the problem. Several experiments are conducted first with a synthetic instance to design and test the effectiveness of this integrated solution method, the influence of different model parameters, and measure the benefit of such cooperation. Results suggest that rideshare vehicle travel time can drop by 40-60% consistently while passenger journey times can be reduced by 50-60% when demand is high. A case study of Long Island commuters to New York City (NYC) suggests having the proposed operating strategy can substantially cut user journey times and operating costs by up to 54% and 60% each for a range of 10-30 taxis initiated per zone. This result shows that there are settings where such service is highly warranted

Chapter 3 - Mobility on demand (MOD) and mobility as a service (MaaS): early understanding of shared mobility impacts and public transit partnerships

Technology is changing the way we move and reshaping cities and society. Shared and on-demand mobility represent notable transportation shifts in the 21st century. In recent years, mobility on demand (MOD)—where consumers access mobility, goods, and services on-demand by dispatching shared modes, courier services, public transport, and other innovative strategies—has grown rapidly due to technological advancements; changing consumer preferences; and a range of economic, environmental, and social factors. New attitudes toward sharing, MOD, and mobility as a service (MaaS) are changing traveler behavior and creating new opportunities and challenges for public transportation. This chapter discusses similarities and differences between the evolving concepts of MaaS and MOD. Next, it characterizes the range of existing public transit and MOD service models and enabling partnerships. The chapter also explores emerging trends impacting public transportation. While vehicle automation could result in greater public transit competition in the future, it could also foster new opportunities for transit enhancements (e.g., microtransit services, first- and last-mile connections, reduced operating costs). The chapter concludes with a discussion of how MOD/MaaS partnerships and automation could enable the public transit industry to reinvent itself, making it more attractive and competitive with private vehicle ownership and use

How machine learning informs ride-hailing services: A survey

In recent years, online ride-hailing services have emerged as an important component of urban transportation system, which not only provide significant ease for residents’ travel activities, but also shape new travel behavior and diversify urban mobility patterns. This study provides a thorough review of machine-learning-based methodologies for on-demand ride-hailing services. The importance of on-demand ride-hailing services in the spatio-temporal dynamics of urban traffic is first highlighted, with machine-learning-based macro-level ride-hailing research demonstrating its value in guiding the design, planning, operation, and control of urban intelligent transportation systems. Then, the research on travel behavior from the perspective of individual mobility patterns, including carpooling behavior and modal choice behavior, is summarized. In addition, existing studies on order matching and vehicle dispatching strategies, which are among the most important components of on-line ride-hailing systems, are collected and summarized. Finally, some of the critical challenges and opportunities in ride-hailing services are discussed

Benchmarking minimum passenger waiting time in online taxi dispatching with exact offline optimization methods

This paper analyses the use of exact offline optimization methods for benchmarking online taxi dispatching strategies where the objective is to minimize the total passenger waiting time. First, a general framework for simulating dynamic transport services in MATSim (Multi-Agent Transport Simulation) is described. Next, the model of online taxi dispatching is defined, followed by a formulation of the offline problem as a mixed integer programming problem. Three benchmarks based on the offline problem are presented and compared to two simple heuristic strategies and a hypothetical simulation with teleportation of idle taxis. The benchmarks are evaluated and compared using the simulation scenario of taxi services in the city of Mielec. The obtained (approximate) lower and upper bounds for the minimum total passenger waiting time indicate directions for further research

A stochastic user-operator assignment game for microtransit service evaluation: A case study of Kussbus in Luxembourg

This paper proposes a stochastic variant of the stable matching model from Rasulkhani and Chow [1] which allows microtransit operators to evaluate their operation policy and resource allocations. The proposed model takes into account the stochastic nature of users' travel utility perception, resulting in a probabilistic stable operation cost allocation outcome to design ticket price and ridership forecasting. We applied the model for the operation policy evaluation of a microtransit service in Luxembourg and its border area. The methodology for the model parameters estimation and calibration is developed. The results provide useful insights for the operator and the government to improve the ridership of the service.Comment: arXiv admin note: substantial text overlap with arXiv:1912.0198