Method to detect malfunctioning traffic count stations

Oña, Juan de; Gómez, Penélope; Mérida-Casermeiro, Enrique

doi:10.1049/iet-its.2011.0102

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…The historical consistency correction method corrects the error based on past historical traffic volume. But there are limits to this method as past historical traffic volume is not always available such as when traffic patterns change due to traffic accidents or new road construction [9]. The network consistency method requires accurate actual traffic volume information from the main line or ramp, but this can be difficult to collect [13].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In other words, the traffic volume entering and exiting the road does not satisfy the traffic volume conservation law, which must be consistent with the traffic volume remaining in the corresponding section. Considering these limits, in some studies the measured traffic volume from the detector is assumed to be the actual traffic volume, and the measured traffic volume is corrected to satisfy the requirement of traffic volume balance within a certain [9]- [12]. However, since the measured traffic volume collected from the detector includes errors, the corrected measured traffic volume may cause larger errors when the traffic volume is corrected to achieve a traffic volume balance.…”

Section: Introductionmentioning

confidence: 99%

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Correction of Measured Traffic Volume on Expressways Based on Traffic Volume Balancing

Kim

Shin

Park

et al. 2018

WIT Transactions on the Built Environment

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This study developed an optimization method to correct the measured expressway traffic volume that minimizes measurement errors and satisfies the traffic balancing with TCS. For this purpose, the model constructed in this study was compared with the true traffic volume and verified. Through the verification of the model, it was found that the measurement error is reduced when the measured traffic volume is corrected for the traffic volume balance. By applying it to 21 links of the Gyeongbu expressway, the measured traffic volume was corrected by -8.1%~9.6% and the measurement error was decreased as much as the corrected traffic volume. This research is meaningful in terms of improving the accuracy of the measured traffic volume of the expressway, which is significant considering that the role of the expressway are increasing.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Correction of Measured Traffic Volume on Expressways Based on Traffic Volume Balancing

Kim

Shin

Park

et al. 2018

WIT Transactions on the Built Environment

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“…A method to detect faulty sensors solely on the basis of currently observed data without relying on historical data was proposed by de Ona et al [12]. This method, which uses linear optimisation, has been applied to a small urban network with 7 intersections and 86 sensors.…”

Section: Related Work On Spatial Consistency Based Traffic Data Validmentioning

confidence: 99%

Method for accuracy assessment of aggregated freeway traffic data

Jagadeesh¹,

Dhinesh²,

Srikanthan³

2014

IET Intelligent Transport Systems

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“…It is important to clearly distinguish between data “accuracy” and “validity.” Although both terms relate to data quality, accuracy in this article is defined as how close the measured value is to the actual value (e.g., the number of vehicles that cross the sensors versus the number counted), and validity is defined as whether the data record itself is correctly associated with the subject. For example, it is possible to automate the detection of malfunctioning sensors ( 29 ) and even cyberattacks ( 30 , 31 ), but a malfunctioning sensor in this context can still be “valid” even if it is so inaccurate that it cannot be used. In contrast, it is possible for a sensor to be operating normally and accurately but to be labeled incorrectly in a database and, thus, be invalid and accurate.…”

Section: Introductionmentioning

confidence: 99%

Erroneous High Occupancy Vehicle Lane Data: Detecting Misconfigured Traffic Sensors With Machine Learning

Fournier

Farid

Patire

2022

Transportation Research Record: Journal of the Transportation R

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Quality data are vital to the planning and operation of traffic systems. High occupancy vehicle (HOV) lanes, for instance, must comply with federal performance standards. If an agency fails to meet the standards, the facility is considered to be “degraded” and the agency is required to undertake actions that would return the facility to satisfactory operation. This could include removing exempted vehicles (e.g., low-emission vehicles) or increasing toll prices and passenger occupancy limits. Such policy changes may be costly, and may affect related policy goals, such as promoting clean air vehicles. Owing to constant changes in the system (e.g., roadwork, system upgrades), some of the thousands of HOV sensors in California’s transportation system are misconfigured, such as being labeled as general-purpose lanes. In this situation, HOV lane data may be mistakenly aggregated with general-purpose lane data and vice versa, causing a HOV facility to be erroneously reported as degraded and requiring unnecessary policy action. Detecting these misconfigurations is challenging and labor-intensive to accomplish manually. The purpose of this research was to utilize machine learning techniques to detect sensor misconfigurations and to understand the extent to which they affect performance reporting of HOV lanes. The results for Caltrans District 7 (Los Angeles and Ventura counties) showed that about 5% to 8% of Performance Measurement System HOV sensors are misconfigured. Therefore approximately 27 to 44 mi of HOV lanes are erroneously measured, with approximately 10 to 16 mi (38%) of those reporting an erroneously high degradation rating.

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Method to detect malfunctioning traffic count stations

Cited by 4 publications

References 16 publications

Correction of Measured Traffic Volume on Expressways Based on Traffic Volume Balancing

Correction of Measured Traffic Volume on Expressways Based on Traffic Volume Balancing

Method for accuracy assessment of aggregated freeway traffic data

Erroneous High Occupancy Vehicle Lane Data: Detecting Misconfigured Traffic Sensors With Machine Learning

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