@article{ref1,
title="Integrating data-driven and simulation models to predict traffic state affected by road incidents",
journal="Transportation letters",
year="2022",
author="Shafiei, Sajjad and Mihăiţă, Adriana-Simona and Nguyen, Hoang and Cai, Chen",
volume="14",
number="6",
pages="629-639",
abstract="Predicting the traffic conditions in urban networks is a priority for traffic management centres. This becomes very challenging, especially when the network is affected by traffic incidents that vary in both time and space. Although data-driven modelling can be considered an ideal tool for short-term traffic predictions, its performance is severely degraded if little historical traffic information is available under incident conditions. This paper addresses this challenge by integrating data-driven and traffic simulation modelling approaches. Instead of directly predicting the traffic states using limited historical data, we employ a traffic simulation reinforced by data-driven models. The traffic simulation uses newly reported incident information and the estimated origin-destination (OD) demand flows to capture the complex interaction between drivers and road network, and predicts traffic states under extreme conditions. We showcase the capability of the proposed data-driven enforced traffic simulation platform for incident impact analysis in a real-life sub-network in Sydney, Australia.<p /> <p>Language: en</p>",
language="en",
issn="1942-7867",
doi="10.1080/19427867.2021.1916284",
url="http://dx.doi.org/10.1080/19427867.2021.1916284"
}