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Integrated Traffic Simulation-Prediction System using Neural Networks with Application to the Los Angeles International Airport Road Network

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Document pages: 19 pages

Abstract: Transportation networks are highly complex and the design of efficienttraffic management systems is difficult due to lack of adequate measured dataand accurate predictions of the traffic states. Traffic simulation models cancapture the complex dynamics of transportation networks by using limitedavailable traffic data and can help central traffic authorities in theirdecision-making, if appropriate input is fed into the simulator. In this paper,we design an integrated simulation-prediction system which estimates theOrigin-Destination (OD) matrix of a road network using only flow rateinformation and predicts the behavior of the road network in differentsimulation scenarios. The proposed system includes an optimization-based ODmatrix generation method, a Neural Network (NN) model trained to predict ODmatrices via the pattern of traffic flow and a microscopic traffic simulatorwith a Dynamic Traffic Assignment (DTA) scheme to predict the behavior of thetransportation system. We test the proposed system on the road network of thecentral terminal area (CTA) of the Los Angeles International Airport (LAX),which demonstrates that the integrated traffic simulation-prediction system canbe used to simulate the effects of several real world scenarios such as laneclosures, curbside parking and other changes. The model is an effective toolfor learning the impact and possible benefits of changes in the network and foranalyzing scenarios at a very low cost without disrupting the network.

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