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Abstract:
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Urban transportation networks are complex systems that consist of many components, including traffic signals, cars, buses, pedestrians, and special vehicles (ambulances, fire trucks and police cars). Travel time from origin to destination is one of the most significant measures in assessing the performance of transportation networks. It is important for urban planners and transportation engineers to give accurate predictions of travel times, especially during disruptions to the network. Modeling urban traffic networks may help address the question of how to enhance transportation network's resilience and efficiency, which has been a long-time research topic in the field of transportation engineering. To build an accurate model of traffic flow, it is important to insure that the data used for the estimation of the model include network link data that are statistically independent. In this work, we develop a general rule for collecting link data such that the dependence among links is minimized. We collect link data using Google maps, then apply MLE to obtain estimates of the model's parameters, and perform a simulation of the dynamic traffic network within Markov framework.
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