A novel algorithm for urban traffic congestion detection based on GPS data compression

Xu, Xiujuan; Gao, Xiaobo; Zhao, Xiaowei; Xu, Zhenzhen; Chang, Huajian

doi:10.1109/soli.2016.7551670

Cited by 11 publications

(2 citation statements)

References 7 publications

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“…In the literature [31], congestion levels are estimated by using clustering and find out the road area per capita along with the ownership of vehicles per thousand people, which affects the ratio of congestion. In [32], a novel traffic congestion detection algorithm is designed from two aspects. One is the offline traffic data processing, and the other is congestion mode judgment by online monitoring, but the system has very low support for real-time implementation.…”

Section: Congestion Detection Systemsmentioning

confidence: 99%

An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking

et al. 2021

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Automatic incident detection (AID) plays a vital role among all the safety-critical applications under the parasol of Intelligent Transportation Systems (ITSs) to provide timely information to passengers and other stakeholders (hospitals and rescue, police, and insurance departments) in smart cities. Moreover, accurate classification of these incidents with respect to type and severity assists the Traffic Incident Management Systems (TIMSs) and stakeholders in devising better plans for incident site management and avoiding secondary incidents. Most of the AID systems presented in the literature are incident type-specific, i.e., either they are designed for the detection of accident or congestion. While traveling along the road, one may come across different types of traffic incidents, such as accidents, congestion, and reckless driving. This necessitates that the AID system detects and classifies not only all the popular traffic incident types, but severity as well that is associated with these incidents. Therefore, this study aims to propose an efficient incident detection and classification (E-IDC) framework for smart cities, by incorporating the efficacy of model stacking, to classify the incidents with respect to their types and severity levels. The experimental results showed that the proposed E-IDC framework achieved performance gains of 5%–56% in terms of incident severity classification and 1%–14% in terms of incident type classification when applied with different classifiers. We have also applied the Wilcoxon test to benchmark the performance of our proposed framework that reflects the significance of our approach over existing individual incident predictors in terms of severity and type classification. Moreover, it has been observed that the proposed E-IDC framework outperforms the existing ensemble technique, such as XGBoost used for the classification of incidents.

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Section: Congestion Detection Systemsmentioning

confidence: 99%

An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking

et al. 2021

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“…A novel stereo vision-based system for vehicle speed measurement was proposed in [ 8 ], which was set in a fixed location to capture two-view stereo videos of the passing vehicles by a calibrated binocular stereovision system. [ 9 ] proposed a method on the basis of multisource global positioning system (GPS) data, which takes the k-means algorithm to cluster the data, obtain the average speed within the cluster, and determine the traffic congestion state.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Congestion Detection at Urban Intersections

Yang

Wang

Bai

et al. 2021

Sensors

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In this paper, a deep learning-based traffic state discrimination method is proposed to detect traffic congestion at urban intersections. The detection algorithm includes two parts, global speed detection and a traffic state discrimination algorithm. Firstly, the region of interest (ROI) is selected as the road intersection from the input image of the You Only Look Once (YOLO) v3 object detection algorithm for vehicle target detection. The Lucas-Kanade (LK) optical flow method is employed to calculate the vehicle speed. Then, the corresponding intersection state can be obtained based on the vehicle speed and the discrimination algorithm. The detection of the vehicle takes the position information obtained by YOLOv3 as the input of the LK optical flow algorithm and forms an optical flow vector to complete the vehicle speed detection. Experimental results show that the detection algorithm can detect the vehicle speed and traffic state discrimination method can judge the traffic state accurately, which has a strong anti-interference ability and meets the practical application requirements.

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Trajectory Data-Driven Pattern Recognition of Congestion Propagation in Road Networks

Gao

Yang

Huang

et al. 2018

Algorithms and Architectures for Parallel Processing

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A novel algorithm for urban traffic congestion detection based on GPS data compression

Cited by 11 publications

References 7 publications

An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking

An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking

Deep Learning-Based Congestion Detection at Urban Intersections

Trajectory Data-Driven Pattern Recognition of Congestion Propagation in Road Networks

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