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

Heatmap-based Decision Support for Repositioning in Ride-Sharing Systems

In ride-sharing systems, platform providers aim to distribute the drivers in the city to meet current and potential future demand and to avoid service cancellations. Ensuring such distribution is particularly challenging in the case of a crowdsourced fleet, as drivers are not centrally controlled but are free to decide where to reposition when idle. Thus, providers look for alternative ways to ensure a vehicle distribution that benefits both users and drivers, and consequently the provider. We propose an intuitive means to improve idle ride-sharing vehicles\u27 repositioning: repositioning opportunity heatmaps. These heatmaps highlight driver-specific earning opportunities approximated based on the expected future demand, fleet distribution, and location of the specific driver. Based on the heatmaps, drivers make decentralized yet better-informed repositioning decisions. As our heatmap policy changes the driver distribution, we propose an adaptive learning algorithm for designing our heatmaps in large-scale ride-sharing systems. We simulate the system and generate heatmaps based on previously learned repositioning opportunities in every iteration. We then update these based on the simulation\u27s outcome and use the updated values in the next iteration. We test our heatmap design in a comprehensive case study on New York ride-sharing data. We show that carefully designed heatmaps reduce service cancellations therefore revenue loss for platform and drivers significantly

Smart balancing of E-scooter sharing systems via deep reinforcement learning: a preliminary study

Nowadays, micro-mobility sharing systems have become extremely popular. Such systems consist in fleets of dockless electric vehicles which are deployed in cities, and used by citizens to move in a more ecological and flexible way. Unfortunately, one of the issues related to such technologies is its intrinsic load imbalance, since users can pick up and drop off the electric vehicles where they prefer. In this paper we present ESB-DQN, a multi-agent system for E-Scooter Balancing (ESB) based on Deep Reinforcement Learning where agents are implemented as Deep Q-Networks (DQN). ESB-DQN offers suggestions to pick or return e-scooters in order to make the fleet usage and sharing as balanced as possible, still ensuring that the original plans of the user undergo only minor changes. The main contributions of this paper include a careful analysis of the state of the art, an innovative customer-oriented rebalancing strategy, the integration of state-of-the-art libraries for deep Reinforcement Learning into the existing ODySSEUS simulator of mobility sharing systems, and preliminary but promising experiments that suggest that our approach is worth further exploration

Dynamic Control and Modelling of Ride-Sourcing Systems in Large Urban Cities

The goal of the thesis is to develop a holistic framework to improve the service quality of a ride-sourcing system in terms of reducing waiting and unassigned time of the ride-sourcing vehicles and reducing waiting and unassigned time the passengers. To this end, the research approach deals with three main intertwine problems: designing a vehicle-passenger matching method, modelling of a ride-sourcing system in a macroscopic level, and designing a proactive controller for repositioning of idle vehicles. The specific objectives of this thesis are (i) designing an adaptive spatio-temporal matching method to dynamically find optimum values for vehicle-passenger maximum matching distance and the frequency of the matchings, (ii) developing a validated macroscopic model with capabilities of considering vehicle-passenger matching method and repositioning of idle vehicles to predict the evolution of the state of ride-sourcing system, and (iii) designing a Nonlinear Model Predictive Controller (NMPC) for repositioning of the idle vehicles proactively to the locations with higher probability of being matched to the waiting passengers. The microsimulation results demonstrate accuracy of the model in predicting the evolution of the number of the ride-sourcing vehicles in different states (e.g. idle, transferred, dispatched, and occupied) and passengers (e.g. waiting and assigned) in each region of the network. Furthermore, the proposed matching method pinpoints its effectiveness by reducing reserved and delay times of ride-sourcing vehicles and passengers. In microsimulation experiments, the designed controller improves the performance of the ride-sourcing system by reducing passengers’ average unassigned time (-20.4%) and waiting times (-12.4%), vehicles’ average waiting times (-8.8%), the number of the fleet size (-18.6%) and increasing the number of the served trip requests (9.7%)

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig