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Navigation-Based Traffic Signal Control in Intelligent Transportation Systems

In smart cities, with large numbers of vehicles on roads, including autonomous and manned ones, congestion is still a significant issue and hence smart traffic signal control is indispensable. Nowadays, navigation apps have become increasingly popular and path information is widely available. Due to the development of various vehicular communication technologies, V2X communications become increasingly stable and efficient. Accurate knowledge of traffic flow is key to effective traffic signal control. Most of the existing adaptive strategies rely on video cameras, radars, or other wireless sensors, which involve a huge investment for measuring or estimating real-time flow. In this paper, based on V2I communications, we propose to make use of navigation information of vehicles to obtain real-time traffic flow around each intersection, thus achieving effective traffic signal control. In undersaturated and oversaturated traffic conditions, we analyze the performance of navigation- based strategy to control traffic signal, study how to obtain optimal cycle duration and compare it with the even-distributed fixed-time strategy. Our results can also provide significant insights on other strategies. We conduct extensive simulations to validate our analysis and evaluate the performance of the navigation-based strategy. The results show that the navigation-based strategy is effective and practical, and can further relieve congestion compared with the even-distributed fixed-time strategy.