Advanced Traffic Signal Control Using Bluetooth Detectors
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Traditionally signal timing plans are developed for expected traffic demands at an intersection. This approach generally offers the best operation for typical conditions. However, when variation in the traffic demand occurs, the signal timing plan developed for typical conditions may not be adequate resulting in significant congestion and delay. There have been many techniques developed to address these variations and they fall into one of two categories: (1) if the variations follow a consistent temporal pattern, then a set of fixed-time signal timing plans can be developed, each for a specific time of the day; (2) if the variations cannot be predicted a priori, then a system that measures traffic demands and alters signal timings in real-time is desired. This research focuses on improving the latter approach with a novel application of Bluetooth detector data. Conventional traffic responsive plan selection (TRPS) systems rely extensively on traffic sensors (typically loop detectors or equivalent) to operate, which are costly to install and maintain, and provide information about traffic only at the points which they are installed. This thesis explores the use of Bluetooth detectors as an alternative data source for TRPS due to their ease of installation and capability to provide information over an area rather than at a single point. This research consists of simulated and field traffic data associated with Bluetooth detectors. The field and simulated traffic data were from a section of Hespeler Road in Cambridge Ontario, bounded by Ontario Highway 401 to the north and Highway 8 to the south. The study corridor is approximately 5.0 kilometres long, and consists of 14 signalized intersections. In order to determine the potential of Bluetooth detectors as a data source, several measures of performance were considered for use in a Bluetooth-based system. The viability of each one was assessed in microsimulation experiments, and it was found that Bluetooth travel time was the most accurate at identifying true traffic conditions. On the basis of the simulation results a field pilot study was designed. Bluetooth detectors and conventional traffic detectors were installed at study intersections along the Hespeler Road corridor to measure real traffic conditions. From these measurements an algorithm was developed to determine when traffic conditions varied from the expected conditions. The final stage of the research evaluated the proposed algorithm using a controlled simulation environment with known atypical traffic patterns. It was found that the algorithm was capable of identifying the atypical conditions that were simulated based on field conditions. The key findings of this research are that (1) Bluetooth detectors are able to provide measured travel times from individual vehicles with sufficient accuracy, and with sufficient sample sizes, that the aggregated travel time information can be used to identify the traffic conditions at a signalized intersections; and (2) these measurements can be used instead of data from conventional traffic detectors, to determine when to switch from time of day fixed time traffic signal control to TRPS control.
Cite this version of the work
Jordan Hart-Bishop (2018). Advanced Traffic Signal Control Using Bluetooth Detectors. UWSpace. http://hdl.handle.net/10012/13037