In the field of traffic operations, accurate performance measures are crucial for many of the intelligent transportation systems applications. Achieving this accuracy and quality requires that network‐based roadway sensors are allocated in locations beneficial to traffic operations. However, with the budgetary restrictions most transportation agencies face, these roadway sensors cannot be placed as thoroughly as obligatory for ideal accuracy, requiring these agencies to select a limited number of installments that produce the most optimal results. In this article, a nonlinear integer program is proposed to optimally allocate point sensors along a one‐directional freeway corridor, given that any pair of adjacent sensors can produce a benefit for bottleneck identification. The objective of this model is to optimize the accuracy of bottleneck identification subject to resource and monetary constraints. This model is nonlinear and, due to a non‐differentiable function, genetic algorithm is applied to find a solution. We demonstrate that on a case study network with bottlenecks at unknown locations, the model successfully allocates sensors in a manner that optimizes bottleneck identification accuracy.

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For decades, traffic conditions have been measured using aggregated point measurements from loop detectors. However, new technologies have become available recently that can assess traffic conditions by tracking vehicle trajectories and travel times. Among these new technologies is cell phone tracking, a concept that has received strong interest from the transportation community. AirSage, Inc., a private firm in the United States, has constructed a proprietary system that, using the Sprint PCS network, can track cell phone movements in Minneapolis, Minnesota, and deliver travel times for most of the urban roads, including both limited access freeways and signalized arterials. A University of Minnesota research team was consulted to evaluate the system's travel times against measured conditions and assess the level of accuracy and reliability of the technology through statistical analysis. The system's performance during its first-stage deployment in May and early June of 2007 was evaluated, including the system's accuracy on a limited access freeway and a signalized arterial during the peak hours over a 16-day period. The technology produced results with varied accuracies. Whether this system would produce acceptable margins of error for speeds and travel times depends on the guidelines set forth by interested transportation agencies.

This paper discusses the observed evolution of traffic in the Minneapolis-St Paul (Twin Cities) region road network following the unexpected collapse of the I-35W Bridge over the Mississippi River. The observations presented within this paper reveal that traffic dynamics are potentially different when a prolonged and unexpected network disruption occurs rather than a preplanned closure. Following the disruption from the I-35W Bridge's unexpected collapse, we witnessed a unique trend: an avoidance phenomenon after the disruption. More specifically, drivers are observed to drastically avoid areas near the disruption site, but gradually return after a period of time following the collapse. This trend is not observed in preplanned closures studied to date. To model avoidance, it is proposed that the tragedy generated a perceived travel cost that discouraged commuters from using these sections. These perceived costs are estimated for the Twin Cities network and found to be best described as an exponential decay cost curve with respect to time. After reinstituting this calibrated cost curve into a mesoscopic simulator, the simulated traffic into the discouraged areas are found to be within acceptable limits of the observed traffic on a weekby-week basis. The proposed model is applicable to both practitioners and researchers in many traffic-related fields by providing an understanding of how traffic dynamics will evolve after a long-term, unexpected network disruption.