Abstract Manager

Traffic congestion has long become a major concern in many cities in Canada and around the world. It has been estimated that the annual total economic loss due to traffic congestion in major Canadian urban centers has reached nearly $4 billion. Real-time monitoring of traffic conditions and performance of the underlying traffic management systems is a critical requirement for mitigating, and minimizing the impact of, traffic congestion in an urban road network.  The latest advance in the Connected Vehicle (CV) technology has afforded a new opportunity for making use of the emerging new data source made available by this technology for improving urban traffic monitoring and performance measurement. However, many critical issues still need to be addressed before the potential of CV could be fully realized.  For example, with the CV data, what traffic performance measures could be derived? what types of congestion development could be detected and at what latency? what would be the optimal spatial and temporal data aggregation resolutions? what would be the effect of CV market penetration rate? This research attempts to address some of these questions through a simulation study of a real-world signalized urban arterial corridor under a wide range of conditions, such as congestion level and variation patterns, CV market penetration rates, and signal timing plans. The results from the simulation experiments have underscored the significant potential of CV data, even under the current market penetration rate, for estimating various important traffic performance measures and detecting non-recurrent events or bottlenecks - a basic requirement for implementing Automated Traffic Signal Performance Measures (ATSPM).