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This paper presents a framework for online highway travel-time prediction using traffic measurements that are likely to be available from vehicle infrastructure integration (VII) systems, in which vehicle and infrastructure devices communicate to improve mobility and safety. In the proposed intelligent VII system, two artificial intelligence (AI) paradigms, i.e., artificial neural networks (ANNs) and support vector regression (SVR), are used to determine future travel time based on such information as the current travel time and VII-enabled vehicles' flow and density. The development and performance evaluation of the VII-ANN and VII-SVR frameworks, in both the traffic and communications domains, were conducted using an integrated simulation platform for a highway network in Greenville, SC. In particular, the simulation platform allows for implementing traffic surveillance and management methods in the traffic simulator PARAMICS and for evaluating different communication protocols and network parameters in the communication network simulator, Network Simulator version 2 (ns-2). This paper's findings reveal that the designed communications system can support the travel-time prediction functionality. The findings also demonstrate that the travel-time prediction accuracy of the VII-AI framework was superior to a baseline instantaneous travel-time prediction algorithm, with the VII-SVR model slightly outperforming the VII-ANN model. Moreover, the VII-AI framework was shown to perform reasonably well during nonrecurrent congestion scenarios, which have traditionally challenged sensor-based highway travel-time prediction methods.
Intelligent Transportation Systems, IEEE Transactions on (Volume:13 , Issue: 3 )
Date of Publication: Sept. 2012