The estimation of up-to-date origin-destination matrix (ODM) from an obsolete trip data,
using current available information is essential in transportation planning, traffic …

Rapid urbanization is placing increasing stress on already burdened transportation
infrastructure. Ubiquitous mobile computing and the massive data it generates presents new …

In this research, we propose a methodology to develop OD matrices using mobile phone
Call Detail Records (CDR) and limited traffic counts. CDR, which consist of time stamped …

To assess the vulnerability of congested road networks, the commonly used full network
scan approach is to evaluate all possible scenarios of link closure using a form of traffic …

This paper deals with the problem of predicting traffic flows and updating these predictions
when information about OD pairs and/or link flows becomes available. To this end, a …

Traffic congestion, volumes, origins, destinations, routes, and other road-network
performance metrics are typically collected through survey data or via static sensors such as …

This paper deals with the problem of trip matrix and path flow reconstruction and estimation
based on plate scanning and link flow observations. To solve the problem, the following …

In this article, we propose a new method for OD (Origin–Destination)​ matrix prediction
based on traffic data using deep learning. The input values of the developed model were …

During the last decade, there has been a substantial interest in how to determine the optimal
number and locations of traffic counters for origin–destination (OD) trip matrices estimation …

The network sensor location problem (NSLP) for origin–destination (OD) estimation
identifies the optimal locations for sensors to estimate the vehicular flow of OD pairs in a …