Speed estimation and length based vehicle classification from freeway single-loop detectors

Travel time estimation is an integral part of Intelligent Transportation Systems, and has been an important component in traffic management and operations for many years. Travel time, being spatial in nature, requires spatial sensors to measure it accurately. Bluetooth is emerging as a promising technology for the direct measurement of travel time, and is reported in a few studies from homogenous traffic conditions. At the same time, there have been no studies on the applicability of Bluetooth for travel time estimation in heterogeneous traffic seen in Istanbul and even that Turkey. Bluetooth data collected from a busy urban road in Istanbul city have been analyzed and the penetration rate was found to be about 5 %. Two wheelers and light motor vehicles have been detected using the Bluetooth sensor and the data have been extrapolated to estimate travel times of other classes of vehicles. The study developed linear relationships between speeds of different classes of vehicles through weighted linear regression methods and were used for the estimation of stream travel time. The results obtained were promising and show that Bluetooth is a cost-effective technology for estimation of travel time for heterogeneous traffic conditions.

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“…Models have been proposed to estimate travel time (or speeds) [6,7,[8][9][10][11] and density [12][13][14] from loop detector data.…”

Section: Background and Scopementioning

confidence: 99%

Bluetooth as a traffic sensor for stream travel time estimation under Bogazici Bosporus conditions in Turkey

Erkan

Haştemoğlu

2016

J. Mod. Transport.

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“…Typically, an operating agency will set average vehicle length, L A , to a constant value and use Eq. (1) to estimate speed from single-loop detector measurements (Coifman and Kim, 2009). …”

Section: Vehicle Length and Speed Estimationmentioning

confidence: 99%

“…For length based classification from loop detectors, there are three interrelated parameters that can be measured or estimated for each passing vehicle, namely effective vehicle length (L eff ) which is the sum of the physical vehicle length and the size of the loop's detection zone, speed (V) and the amount of time the detector is ''on", i.e., the on-time (T on ). These parameters are related by the following equation (Coifman and Kim, 2009):…”

Section: Length Based Vehicle Classificationmentioning

confidence: 99%

“…The event-based loop detector data contains every vehicle detector actuation and de-actuation ''event'', therefore time gaps between consecutive vehicles and detector occupation time for each vehicle can be easily derived. Coifman and Kim (2009) used the event-based data from a single loop detector to identify vehicle length following a statistical based method. They studied the probability distribution of the detector occupation time (i.e., the detector on-time actuated by individual vehicles), and classified a vehicle by associating its detector actuation time with that distribution.…”

Section: Single Loop Detectormentioning

confidence: 99%

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Average Vehicles Length in Two-lane Urban Roads: A Case Study in Budapest

Zefreh

Török

Mészáros

2017

Period. Polytech. Transp. Eng.

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“…The information about the vehicle speed in connection with the signature duration provides information about the vehicle length that may be utilized in the classification process [15].…”

Section: Introductionmentioning

confidence: 99%

Automatic Vehicle Classification in Systems with Single Inductive Loop Detector

Gajda¹,

MIELCZAREK²

2014

Metrology and Measurement Systems

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The work proposes a new method for vehicle classification, which allows treating vehicles uniformly at the stage of defining the vehicle classes, as well as during the classification itself and the assessment of its correctness. The sole source of information about a vehicle is its magnetic signature normalised with respect to the amplitude and duration. The proposed method allows defining a large number (even several thousand) of classes comprising vehicles whose magnetic signatures are similar according to the assumed criterion with precisely determined degree of similarity. The decision about the degree of similarity and, consequently, about the number of classes, is taken by a user depending on the classification purpose. An additional advantage of the proposed solution is the automated defining of vehicle classes for the given degree of similarity between signatures determined by a user. Thus the human factor, which plays a significant role in currently used methods, has been removed from the classification process at the stage of defining vehicle classes. The efficiency of the proposed approach to the vehicle classification problem was demonstrated on the basis of a large set of experimental data.

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Speed estimation and length based vehicle classification from freeway single-loop detectors

Cited by 99 publications

References 10 publications

Bluetooth as a traffic sensor for stream travel time estimation under Bogazici Bosporus conditions in Turkey

Bluetooth as a traffic sensor for stream travel time estimation under Bogazici Bosporus conditions in Turkey

Average Vehicles Length in Two-lane Urban Roads: A Case Study in Budapest

Automatic Vehicle Classification in Systems with Single Inductive Loop Detector

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