This report summarizes the results of a one-year project aimed at exploiting vehicle-to-infrastructure (V2I) communication to enhance the effectiveness of real-time adaptive traffic signal control systems. As originally formulated, the project’s goal was to explore the potential of using the sensing capabilities of connected autonomous vehicles (CAVs) to detect other vehicles in close proximity and use this information to “virtually increase” the level of penetration of connected vehicles in the traffic network, and enhance the predictive accuracy of real-time traffic signal control. However, following initial discussions with project partners Rapid Flow Technologies Inc., provider of the surtrac adaptive traffic signal control system , and Argo AI, an autonomous vehicle technology company, the project focus was shifted to a problem of more immediate and pragmatic importance: a field demonstration and analysis of the ability to further optimize traffic signal control performance through vehicle-to-infrastructure (V2I) communication of real-time CAV route information. In [Hawkes 2016], a mechanism was proposed for incorporating this information into surtrac to reduce uncertainty and generate more accurate predictions of vehicle flows through a controlled traffic network. A benefits analysis of this mechanism, conducted using a microscopic traffic simulation of various traffic networks, showed that network delay was substantially reduced for those vehicles willing to share their routes, and moreover, there was little adverse effect (and even some benefit) to those vehicles not sharing route information.
The report is available through the Carnegie Mellon University web site at https://ppms.cit.cmu.edu/media/project_files/Final_Report_-_294.pdf
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