In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement
learning approaches to decision optimization problems in a typical ridesharing system …

Solving link-based route guidance problems for large-scale networks is computationally
challenging and faces practical issues, such as spatial–temporal data coverage. Thus …

Route choice models are a vital tool for evaluating the impact of transportation policies and
infrastructure improvements, such as the addition of new roads, tolls, or congestion charges …

This paper aims to develop a paradigm that models the learning behavior of intelligent
agents (including but not limited to autonomous vehicles, connected and automated …

In this paper, the Bush–Mosteller (BM) reinforcement learning (RL) scheme is introduced to
model the route choice behaviors of the travelers in traffic networks, who aim to seek the …

Road congestion induces significant costs across the world, and road network disturbances,
such as traffic accidents, can cause highly congested traffic patterns. If a planner had control …

This research investigates the effectiveness of information provision for congestion reduction
in Connected Autonomous Vehicle (CAV) systems. The inherent advantages of CAVs, such …

Abstract The RIde-hail VEhicle Routing (RIVER) problem describes how drivers in a ride-
hail market form a dynamic routing strategy according to the expected reward in each zone …

We explore the complex design space of behaviour planning for autonomous driving.
Design choices that successfully address one aspect of behaviour planning can critically …

Introduction Locomotor adaptation is a motor learning process used to alter spatiotemporal
elements of walking that are driven by prediction errors, a discrepancy between the …