Accuracy Analysis of Freeway Traffic Speed Estimation Based on the Integration of Cellular Probe System and Loop Detectors

Zhang, Jian; He, Shanglu; Wang, Wei; Zhan, Fengping

doi:10.1080/15472450.2014.1000456

Cited by 36 publications

(26 citation statements)

References 16 publications

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“…Data sources like loop detector have been extensively used in past literature due to its high recognition rate and accuracy (Bertini & Leal, ; Dharia & Adeli, ; Van Lint & Hoogendoorn, ; Mo, Li, & Zhan, ; Zhang, He, Wang, & Zhan, ). Nevertheless, there is a high possibility that the sensor devices may malfunction due to various reasons like wearing out of mechanical devices and pavement failure.…”

Section: Literature Reviewmentioning

confidence: 99%

A spatio‐temporal ensemble method for large‐scale traffic state prediction

Liu

et al. 2019

Computer aided Civil Eng

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How to effectively ensemble multiple models while leveraging the spatio‐temporal information is a challenging but practical problem. However, there is no existing ensemble method explicitly designed for spatio‐temporal data. In this paper, a fully convolutional model based on semantic segmentation technology is proposed, termed as spatio‐temporal ensemble net. The proposed method is suitable for grid‐based spatio‐temporal prediction in dense urban areas. Experiments demonstrate that through spatio‐temporal ensemble net, multiple traffic state prediction base models can be combined to improve the prediction accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

A spatio‐temporal ensemble method for large‐scale traffic state prediction

Liu

et al. 2019

Computer aided Civil Eng

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“…See, for example, Herrera and Bayen [10], Work et al [11], Yuan et al [12], Seo et al [13], Astarita et al [14], and Bekiaris-Liberis et al [15]. Other traffic state estimation methods making use of connected vehicle data without an underlying traffic model are presented by Herring et al [16], Herrera et al [17], Van Lint and Hoogendoorn [18], Qiu et al [19], Ma et al [20], Bhaskar et al [21], Zhang et al [22], Seo et al [23], and Montero et al [24].…”

Section: Traffic State Estimation Using Connected Vehiclesmentioning

confidence: 99%

“…For the combined methods, a weighted estimate based on both traditional detector measurements and connected vehicle measurements of, for example, speed, travel time, and/or location is used. Examples are the methods presented by Astarita et al [14], Qiu et al [19], Ma et al [20], Bhaskar et al [21], Zhang et al [22], and Bekiaris-Liberis et al [15]. The method by Qiu et al [19], which was later extended by Ma et al [20], detects the number of vehicles located within a segment.…”

Section: Traffic State Estimation Using Connected Vehiclesmentioning

confidence: 99%

“…According to Qiu et al [19], the accuracy of the density estimates is better than when only stationary detector data is used to estimate density. Zhang et al [22] use probe vehicle data and detector stations in order to estimate the space mean speed, and not density, on a road stretch. Astarita et al [14] and Bekiaris-Liberis et al [15] develop macroscopic cell transmission type models for the dynamics of the percentage of connected vehicles along the considered road.…”

Section: Traffic State Estimation Using Connected Vehiclesmentioning

confidence: 99%

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Traffic State Estimation Using Connected Vehicles and Stationary Detectors

Grumert

Tapani

2018

Journal of Advanced Transportation

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Real-time traffic state estimation is of importance for efficient traffic management. This is especially the case for traffic management systems that require fast detection of changes in the traffic conditions in order to apply an effective control measure. In this paper, we propose a method for estimating the traffic state and speed and density, by using connected vehicles combined with stationary detectors. The aim is to allow fast and accurate estimation of changes in the traffic conditions. The proposed method does only require information about the speed and the position of connected vehicles and can make use of sparsely located stationary detectors to limit the dependence on the infrastructure equipment. An evaluation of the proposed method is carried out by microscopic traffic simulation. The traffic state estimated using the proposed method is compared to the true simulated traffic state. Further, the density estimates are compared to density estimates from one detector-based method, one combined method, and one connected-vehiclebased method. The results of the study show that the proposed method is a promising alternative for estimating the traffic state in traffic management applications.

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“…There are several kinds of cell phone probe-based techniques, that is, assisted global positioning system (A-GPS) method, angle of arrival (AOA) technique, time difference of arrival (TDOA) technique, handoffbased method, and cell phone activity data-based method. 1,[7][8][9][10] Among them, the cellular handoff probe system (CHPS) is extensively studied and tested for the collection of traffic speeds. A handoff event is one kind of cell phone communication records.…”

Section: Introductionmentioning

confidence: 99%

A data-driven study on the sample size of cellular handoff probe system

Cheng

Zhong

et al. 2017

Advances in Mechanical Engineering

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The popularity of big data from cell phone communication records makes cellular handoff probe system become an optimal choice for the collection of traffic speeds, especially for the large-scale freeway network. As a probe-based technique, the issues related to sample size are vital for the accuracy of cellular handoff probe system. Based on the current findings from simulation and field test, this study made an extensive discussion about the sample size problems using the field data. The subsequent investigation began with the correlation analysis between the sample size and the potentially influencing factors (the length of handoff link and traffic volume). Furthermore, a Weibull distribution model was set up to fit sample size for different handoff links. And then, the impact of sample size on the accuracy of cellular handoff probe system was following. Finally, based on the above explorations, several concluding remarks were presented as a reference for determining appropriate sample size to achieve high precision.

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Accuracy Analysis of Freeway Traffic Speed Estimation Based on the Integration of Cellular Probe System and Loop Detectors

Cited by 36 publications

References 16 publications

A spatio‐temporal ensemble method for large‐scale traffic state prediction

A spatio‐temporal ensemble method for large‐scale traffic state prediction

Traffic State Estimation Using Connected Vehicles and Stationary Detectors

A data-driven study on the sample size of cellular handoff probe system

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