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Real-Time Highway Traffic Condition Assessment Framework Using Vehicle–Infrastructure Integration (VII) With Artificial Intelligence (AI)

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4 Author(s)
Yongchang Ma ; IEM, Inc., Morrisville, NC, USA ; Chowdhury, M. ; Sadek, A. ; Jeihani, M.

This paper presents a framework for real-time highway traffic condition assessment using vehicle kinetic information, which is likely to be made available from vehicle-infrastructure integration (VII) systems, in which vehicle and infrastructure agents communicate to improve mobility and safety. In the proposed VII framework, the vehicle onboard equipment and roadside units (RSUs) collaboratively work, supported by an artificial intelligence (AI) paradigm, to determine the occurrence and characteristics of an incident. Two AI paradigms are examined: 1) support vector machines (SVMs) and 2) artificial neural networks (ANNs). Each RSU then assesses the traffic condition based on the information from multiple vehicles traveling on its supervised highway segment. As a case study, this paper developed a model of the VII-SVM framework and evaluated its performance in a microscopic traffic simulation environment for a highway network in Spartanburg, SC. The performance of the VII-SVM was compared with the performance of the corresponding VII-ANN framework, and both frameworks were found to be capable of classifying the travel experience using the kinetic data generated by each vehicle. The performance of the VII-SVM framework, in terms of its detection rate, false-alarm rate, and detection times, was also found to be superior to a baseline California-type incident-detection algorithm. Moreover, the framework provided additional information, including an estimate of the incident location and the likely number of lanes blocked, which will be helpful for implementing an appropriate response strategy. The proposed VII-AI framework thus provides a reliable alternative to traditional traffic sensors in assessing traffic conditions.

Published in:

Intelligent Transportation Systems, IEEE Transactions on  (Volume:10 ,  Issue: 4 )