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Open Access Publications from the University of California

Dynamic Traffic Routing and Adaptive Signal Control in a Connected Vehicles Environment


This dissertation aims to study effective and efficient ways for both travelers and transportation authorities to consider the actions of the other side when they make their corresponding travel or management decisions, such that certain common goals, such as mitigating congestion, reducing cost in travel expenses and improving the overall reliability of the transportation system can be achieved.

A novel dynamic traffic routing (DTR) with an adaptive signal control framework is developed to utilize the fast developing wireless communication technologies that makes V2X (Vehicle To Everything) possible. The hyper-path based dynamic traffic routing method takes stochasticity of link travel time into consideration, which ensures robust and reliable routing decisions. In addition, online travel time updating is incorporated into the DTR model. The online updating presented in this dissertation uses both historical information (a priori knowledge) and new information, thanks to the V2X system, to form a posteriori knowledge about the link travel time. Various distributed traffic signal control methods are proposed and tested concurrently with the DTR model to cope with the different levels of the traffic demand. Simulation models are built to test and compare the models developed in this dissertation against the traffic routing methods and traffic signal control models in the literature. In the extensive simulation tests, the author discovers that enabling vehicle re-routing in the network can reduce the average travel time as well as reduce the average queue length at the intersections.

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