This paper investigates the day-to-day dynamics model from the perspective of travelers'
actual route choice behaviors, and calibrates and validates the route-based day-to-day …

Calibrating dynamic traffic demand for stochastic traffic simulators is one of the big
challenges due to computational burden. This paper proposes a novel framework to …

Dynamic origin–destination (OD) estimation and prediction is an essential support function
for real-time dynamic traffic assignment model systems for ITS applications. This paper …

This study addresses the problem of calibrating utility-maximizing nested logit activity-based
travel demand model-systems. After estimation, it is common practice to use aggregate …

Traffic state prediction is a key problem with considerable implications in modern traffic
management. Traffic flow theory has provided significant resources, including models based …

Metropolitan scale vehicular traffic modeling is used by a variety of private and public sector
urban mobility stakeholders to inform the design and operations of road networks. High …

Predicting traffic states has gained more attention because of its practical significance.
However, the existing literature lacks a critical review regarding how to address the …

This paper presents a Bayesian inference-based dynamic linear model (DLM) with switching
based on three-phase traffic flow theory to predict online short-term travel time with plate …

Adverse weather has significant impacts on road conditions and traffic dynamics. It is
observed that adverse weather as a set of exogenous factors lowers the free flow speed …

Effective traffic flow prediction is an essential component of any proactive traffic control
system and one of the pillars of an advanced traffic management system. Hence, the main …