The fact that road transportation negatively affects the quality of the environment and
deteriorates its bearing capacity has drawn a wide range of concerns among researchers. In …

This paper presents a review and classification of traffic assignment models for strategic
transport planning purposes by using concepts analogous to genetics in biology. Traffic …

This paper develops a novel linear programming formulation for autonomous intersection
control (LPAIC) accounting for traffic dynamics within a connected vehicle environment …

This paper presents a novel actor-critic deep reinforcement learning approach for metro train
scheduling with circulation of limited rolling stock. The scheduling problem is modeled as a …

This paper focuses on the analytical probabilistic modeling of vehicular traffic. It formulates a
stochastic node model. It then formulates a network model by coupling the node model with …

Traffic management can prevent too many vehicles in a traffic network from reducing traffic
performance. In particular, traffic can be routed so that the bottlenecks are not oversaturated …

Reservation-based intersection control is a revolutionary idea for using connected
autonomous vehicle technologies to improve intersection controls. Vehicles individually …

In this paper we tackle the problem of routing multiple agents in a coordinated manner. This
is a complex problem that has a wide range of applications in fleet management to achieve …

In this study, we develop a stochastic dynamic network loading (DNL) model for the mixed
traffic with autonomous vehicles (AVs) and human-driven vehicles (HVs). The source of …

This paper presents a single-level nonlinear optimization model to estimate dynamic origin–
destination (OD) demand. The model is a path flow-based optimization model, which …