Mode-Specific Travel Time Estimation Using Bluetooth Technology. Issue 3 (3rd May 2016)

• Record Type: Journal Article
• Authors: Namaki Araghi, Bahar; Krishnan, Rajesh; Lahrmann, Harry
• Is Part Of: Journal of intelligent transportation systems. Volume 20:Issue 3(2016)
• Page Start: 219
• Page End: 228
• Publication Date: 2016-05-03
• Subjects: Automated Screening Algorithm -- Bluetooth Technology -- Mode Identification -- Mode-Specific Travel Time Estimation; Intelligent transportation systems -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.312
• DOI: 10.1080/15472450.2015.1052906
• Languages: English
• ISSNs: 1547-2450

Abstract

Abstract : The problem of mode-specific travel time estimation is mostly relevant to arterials with different travel modes, including cars, buses, cyclists, and pedestrians. Traditional travel time measurement systems such as automated number plate recognition (ANPR) cameras detect only motor vehicles and provide an estimate of their travel times. Bluetooth technology has been used as an alternative to more expensive ANPR for travel time measurements in the recent past. However, Bluetooth-sensors detect discoverable electronic devices used by all travel modes. Bluetooth-based systems currently use the time stamp of device detection events by two sensors to estimate the travel time, and there is no direct way to estimate mode-specific travel times using this approach. Hence, estimating travel time using Bluetooth technology on urban arterials without classifying the modes of detected devices could provide a biased estimate. A novel method to estimate mode-specific travel times using Bluetooth technology that is capable of estimating mode-specific travel times, specifically distinguishing between the travel time of motor vehicles and bicycles, is presented in this article. The proposed method uses information about type of detected device (class of device, CoD) and radio signal strength indication (RSSI). The proposed method also uses the travel time of the detected device and its detection pattern across the road network by multiple Bluetooth sensors to estimate the travel mode of each detected device. The accuracy of the proposed method was evaluated against the ground truth obtained by manual transcription of traffic video recordings, and was compared against travel times obtained from ANPR, a commercially deployed Bluetooth-based method, and a clustering method. The results show that the proposed method provides travel time estimates using Bluetooth with almost the same level of accuracy as ANPR under mixed traffic conditions.