Point-based traffic sensors, such as microwave radar and acoustic sensors, provide the valuable capability of sampling the entire traffic stream. However, full network coverage with point sensors requires a significant initial capital investment and ongoing maintenance expenditures. Probe-based sensors can cover an extensive roadway network at a much lower cost because roadway-based field equipment is not required. Decisions regarding the relative level of point sensor- versus probe-based deployment for traffic monitoring involve evaluating the trade-off between the value of comprehensive detection versus total system costs. An essential step in evaluating this trade-off involves directly comparing collocated point sensor and probe vehicle systems to understand how the derived traffic stream measures from the two approaches differ. This study compared 5-min speeds from microwave radar and acoustic sensors with link speeds from Global Positioning System (GPS) probes for both directions at five freeway locations. Systematic differences were found at one location. Floating car GPS runs were performed to confirm that the systematic error lay in the point speeds. The speed differences at all sites were normally distributed, with three locations indicating a mean speed difference greater than 5 mph. Nonsystematic speed differences were identified; the difference was more than 1.5 standard deviations lower than the mean difference. This difference may indicate inherent inaccuracies in reported GPS speeds under heavy congestion, including instances of time lag in recovering from congested speeds.
Diverging diamond interchanges (DDIs) are relatively new in the United States, and signal coordination between the crossovers and adjacent intersections is challenging. This paper provides a method for remotely fine-tuning offsets for a DDI and its adjacent intersections. The proposed method uses the dynamic bandwidth analysis tool (DBAT). The tool uses actuated phase times from the signal controller to optimize the dynamic bandwidth on the basis of that entry data set. Four performance measures evaluated the proposed method: delay, stop severity index, maximum queue, and vehicle trajectory plots. The test results confirmed that DBAT provided a better offset solution than other bandwidth optimization tools that generally optimized programmed bandwidth only and did not account for early return to green caused by skipped or gapped-out movements. Under the DBAT offsets, delay for the through movements on the corridor decreased by 52.8% for northbound vehicles and 46.83% for southbound vehicles. The average delay reduction over all measured paths for uncongested and congested scenarios was 13.88% and 3.50%, respectively. The proposed method and workflow can significantly reduce the offset retiming work process. Normally, this manual process takes more than a day, but the proposed method can be completed in less than an hour without visiting the study site. Furthermore, the proposed method can coordinate any set of movements, as well as multiple travel paths. The authors believe that the proposed method and workflow will significantly help both retiming and new timing of arterial signal coordination along DDI corridors and other signal systems.
Effective management of highway networks requires a thorough understanding of the conditions under which vehicular crashes occur. Such an understanding can and should inform related operational and resource allocation decisions. This paper presents an easily implementable methodology that can classify all reported crashes in terms of the operational conditions under which each crash occurred. The classification methodology uses link-based speed data. Unlike previous secondary collision identification schemes, it neither requires an a priori identification of the precipitating incident nor definition of the precipitating incident’s impact area. To accomplish this objective, the methodology makes use of a novel scheme for distinguishing between recurrent and non-recurrent congestion. A 500-crash case study was performed using a 274 km section of the I-40 in North Carolina. Twelve percent of the case study crashes were classified as occurring in non-recurrent congestion. Thirty-seven percent of the crashes in non-recurrent congestion classified were identified within unreported primary incidents or crashes influence area. The remainder was classified as primary crashes occurring in either uncongested conditions (84%) or recurrent congestion (4%). The methodology can be implemented in any advanced traffic management system for which crash time and link location are available along with corresponding archived link speed data are available.
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