Evaluation of Wide-Area Traffic Monitoring Technologies for Travel Time Studies

Omrani, Reza; Izadpanah, Pedram; Nikolic, Goran; Hellinga, Bruce; Hadayeghi, Alireza; Abdelgawad, Hossam

doi:10.3141/2380-12

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…Due to luxury vehicles equipped with Bluetooth modules for communication purposes, some researchers have installed roadside Bluetooth sensors and utilized Bluetooth fingerprints to collecte real time freeway travel time [12], to calculate the origin–destination matrix [13], and to estimate traffic speed [14]. A study proved no statistically significant difference between the performance of applying GPS data and Bluetooth data when observing highway travel time [15]. However, the sampling rate of Bluetooth data was found to be within the relatively low range of 2.0–3.4%, in terms of the main traffic stream [12].…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, installation of these devices requires coordination with roadside power lines, increasing the costs of installation and maintenance [5]. In recent decades, data sources such as GPS data [5,6,7], cellular data [8,9,10,11], Bluetooth data [12,13,14,15,16], and Wi-Fi signal data [17,18,19] have gained attention from the community for traffic state monitoring. Conventionally, traffic characteristics are directly collected from roadside equipment.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of a Wi-Fi Signal Based System for Freeway Traffic States Monitoring: An Exploratory Field Test

Ding

Chen

et al. 2019

Sensors

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Monitoring traffic states from the road is arousing increasing concern from traffic management authorities. To complete the picture of real-time traffic states, novel data sources have been introduced and studied in the transportation community for decades. This paper explores a supplementary and novel data source, Wi-Fi signal data, to extract traffic information through a well-designed system. An IoT (Internet of Things)-based Wi-Fi signal detector consisting of a solar power module, high capacity module, and IoT functioning module was constructed to collect Wi-Fi signal data. On this basis, a filtration and mining algorithm was developed to extract traffic state information (i.e., travel time, traffic volume, and speed). In addition, to evaluate the performance of the proposed system, a practical field test was conducted through the use of the system to monitor traffic states of a major corridor in China. The comparison results with loop data indicated that traffic speed obtained from the system was consistent with that collected from loop detectors. The mean absolute percentage error reached 3.55% in the best case. Furthermore, the preliminary analysis proved the existence of the highly correlated relationship between volumes obtained from the system and from loop detectors. The evaluation confirmed the feasibility of applying Wi-Fi signal data to acquisition of traffic information, indicating that Wi-Fi signal data could be used as a supplementary data source for monitoring real-time traffic states.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of a Wi-Fi Signal Based System for Freeway Traffic States Monitoring: An Exploratory Field Test

Ding

Chen

et al. 2019

Sensors

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“…Probe vehicles are often used as an effective means for obtaining arterial travel times (Omrani et al, 2013;Remias et al, 2013). The probe vehicle data typically come from vehicles already traveling the network, and either employ on-board devices broadcasting the probe vehicle's positioning information for fleet management, e.g., automatic vehicle location, (AVL) (Bertini and Tantiyanugulchai, 2003;Cathey and Dailey, 2002;Hall and Vyas, 2000); cell phone tracking for location based applications (Bar-Gera, 2007;Herrera et al, 2010;Liu et al, 2008); or wayside sensors tracking the vehicle through the network, e.g., Bluetooth ID (Saeedi et al, 2013;Wasson et al, 2008), automatic number plate recognition (ANPR) (Hasan et al, 2011;Takaba et al, 1991), and magnetic signature based vehicle re-identification (Kwong et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Signal Progression Impacts on Transit Buses As Travel Time Probes

Thornton

Coifman

2015

J. Transp. Eng.

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With the deployment Automatic Vehicle Location systems to monitor transit vehicles (and other fleet vehicles), there have been several efforts in recent years that seek to leverage these data to also obtain arterial travel times for the private vehicle population. The fleet vehicle data capture the traffic conditions, but they also capture behavior unique to the fleet, e.g., servicing passengers at a bus stop. There are several strategies used in conventional practice to eliminate the biases that occur in the vicinity of the bus stop. While investigating the benefits of using a perception sensor to identify ambient traffic conditions around the bus and correct for transit operations, the work revealed that even a perfect correction for local conditions resulted in large deviation between the travel time measured from the bus and the actual travel time experienced by the surrounding private vehicles. This research uncovered a fundamental issue affecting almost all systems that use buses as arterial travel time probes: the fact that transit operations inevitably pull the bus out of the traffic signal progression no matter what corrections are made locally at the bus stop. The impact at subsequent traffic signals far downstream of a bus stop can be much larger than the local effects at the bus stop itself. This point is an important finding for any system that seeks to use transit vehicle probes to estimate the private vehicle travel times. To date the literature has made little consideration of the signal progression biases relative to the private vehicles that occur far beyond the bus stop. Finally, though the focus the present work is buses, the basic findings should apply to other fleets used as travel time probes as well if the given fleet behavior differs from the private automobiles.

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