Vehicle traffic congestion is an increasing concern in metropolitan areas, with negative
health, environment and economical implications. In recent times, computational intelligence …

The paper presents a generic second order family, which generalizes the ARZ (Aw-Rascle-
Zhang) model. This family, called EARZ (Extended Aw-Rascle-Zhang) is characterized by a …

Calibration of car-following models against trajectory data has been widely applied as the
basis for studies ranging from model investigation and benchmarking to parameter …

We propose a kinematic wave-based Deep Convolutional Neural Network (Deep CNN) to
estimate high-resolution traffic speed fields from sparse probe vehicle trajectories. We …

Accurate calibration of car-following models is essential for understanding human driving
behaviors and implementing high-fidelity microscopic simulations. This work proposes a …

The problem of traffic state estimation for large-scale urban networks modeled with MFD
dynamics is studied here. Given a network partitioned in a number of regions, aggregated …

The ability of microscopic (simulation) models to represent lane-changing behavior
according to reality has recently been questioned. In this paper the merging maneuver (a …

Recent experimental analysis has shown that some types of urban networks exhibit a low
scatter reproducible relationship between average network flow and density, known as the …

Systematic lane changes can seriously deteriorate traffic safety and efficiency inside lane-
drop, merge, and other bottleneck areas. In our previous studies (Jin, 2010a, Jin, 2010b), a …

This paper studies a discontinuous lane reservation problem considering the
heterogeneous traffic demand containing both vehicles with general purposes and vehicles …