Citation

Shafiei S, Mihăiţă AS, Nguyen H, Cai C. Transp. Lett. 2022; 14(6): 629-639.

Copyright

(Copyright © 2022, Maney Publishing, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/19427867.2021.1916284

PMID

unavailable

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.

Language: en

Keywords

Demand estimation and prediction; incident management; machine learning; micro-simulation