Online ad hoc distributed traffic simulation with optimistic execution
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As roadside and in-vehicle sensors are deployed under the Connected Vehicle Research program (formerly known as Vehicle Infrastructure Integration initiative and Intellidrive), an increasing variety of traffic data is becoming available in real time. This real time traffic data is shared among vehicles and between vehicles and traffic management centers through wireless communication. This course of events creates an opportunity for mobile computing and online traffic simulations. However, online traffic simulations require faster than real time running speed with high simulation resolution, since the purpose of the simulations is to provide immediate future traffic forecast based on real time traffic data. However, simulating at high resolution is often too computationally intensive to process a large scale network on a single processor in real time. To mitigate this limitation an online ad hoc distributed simulation with optimistic execution is proposed in this study. The objective of this study is to develop an online traffic simulation system based on an ad hoc distributed simulation with optimistic execution. In this system, data collection, processing, and simulations are performed in a distributed fashion. Each individual simulator models the current traffic conditions of its local vicinity focusing only on its area of interest, without modeling other less relevant areas. Collectively, a central server coordinates the overall simulations with an optimistic execution technique and provides a predictive model of traffic conditions in large areas by combining simulations geographically spread over large areas. This distributed approach increases computing capacity of the entire system and speed of execution. The proposed model manages the distributed network, synchronizes the predictions among simulators, and resolves simulation output conflicts. Proper feedback allows each simulator to have accurate input data and eventually produce predictions close to reality. Such a system could provide both more up-to-date and robust predictions than that offered by centralized simulations within a single transportation management center. As these systems evolve, the online traffic predictions can be used in surface transportation management and travelers will benefit from more accurate and reliable traffic forecast.