This paper proposes a probabilistic vehicle reidentification algorithm for estimating travel time using the image data provided by traffic surveillance cameras. Each vehicle is characterized by its color, type, and length, which are extracted from the video record using image processing techniques. A data fusion rule is introduced to combine these three features to generate a probabilistic measure for a reidentification (matching) decision. The vehicle-matching problem is then reformulated as a combinatorial problem and solved by a minimum-weight bipartite matching method. To reduce the computational time, the algorithm uses the potential availability of historic travel time data to define a potential time window for vehicle reidentification. This probabilistic approach does not require vehicle sequential information and hence allows vehicle reidentification across multiple lanes. The algorithm is tested on a 5-km section of the expressway system in Bangkok, Thailand. The travel time estimation result is also compared with the directly observed data.
With the current popularity of mobile devices with Bluetooth technology, numerous studies have developed methods to analyze the data from such devices to estimate a variety of traffic information, such as travel time, link speed, and origin–destination estimations. However, few studies have comprehensively determined the impact of the penetration rate on the estimated travel time derived from Bluetooth detectors. The objectives of this paper were threefold: (1) to develop a data-processing method to estimate the travel time based on Bluetooth transactional data; (2) to determine the impact of vehicle speeds on Bluetooth detection performance; and (3) to analyze how the Bluetooth penetration rate affected deviations in the estimated travel time. A 28 km toll section in Bangkok, Thailand, was chosen for the study. A number of Bluetooth detectors and microwave radar devices were installed to collect traffic data in October 2020. Five data-processing steps were developed to estimate the travel time. Based on the results, the penetration rate during the day (50 to 90 percent) was higher than during the night (20 to 50 percent). In addition, we found that speed had adverse effects on the MAC address detection capability of the Bluetooth detectors; for speeds greater than 80 km/h, the number of MAC addresses detected decreased. The minimum Bluetooth penetration rate should be at least 1 percent (or 37 vehicles/h) during peak periods and at least 5 percent (or 49 vehicles/h) during the off-peak period.
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