Evolution and Future of Urban Road Incident Detection Algorithms

Evans, Jonny; Waterson, Ben; Hamilton, Andrew

doi:10.1061/jtepbs.0000362

Cited by 10 publications

(5 citation statements)

References 21 publications

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“…But the traffic parameters often contain unpredictable random fluctuation components, which will influence the prediction stability and affect the accuracy of incident evaluation. Directly setting the threshold for the forecast error will have a high false alarm rate ( 15 ); the threshold setting needs to be adjusted in a targeted manner. This will reduce the generality and universality of the model.…”

Section: Methodsmentioning

confidence: 99%

“…To sum up, the AID algorithm based on predictive regression is the mainstream trend in current research. However, the incident detection task is different from the common prediction task, and requires the algorithm not only to have good prediction performance under normal traffic conditions, but also to be able to make stable predictions according to the laws of normal traffic patterns, even after an abnormal incident has occurred ( 14 , 15 ). Therefore, it is the core of the traffic incident detection algorithm to extract traffic flow pattern information hidden in massive traffic data sets.…”

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confidence: 99%

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Graph Spatiotemporal Pattern Learning Network for Real-Time Road Network Traffic Abnormal Incident Detection

Li,

Ma,

Wang

et al. 2023

Transportation Research Record: Journal of the Transportation R

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To improve the efficiency of detecting abnormal traffic incidents on the road network and reduce the false alarm rate, a real-time traffic anomaly detection framework based on a graph spatiotemporal pattern learning (GSTPL) network is proposed. In this framework, a traffic pattern search algorithm based on a fluctuation similarity measure is designed to screen traffic flow data with the same traffic pattern, and a traffic pattern graph tuple is constructed as the input of the network model to avoid the sample imbalance problem and the effect of single-sample randomness for traffic pattern learning. Then the GSTPL network is designed to extract, unsupervised, the traffic spatiotemporal pattern features and make a reasonable prediction of future traffic parameters as the basis for anomaly evaluation. To further restrain the effect of random fluctuations in traffic flow parameters, an abnormal state evaluation method is designed to calculate the anomaly state likelihood by prediction error distribution learning. The overall detection framework realizes stable prediction of network key node traffic parameters by using spatiotemporal pattern features to construct the traffic pattern graph tuple, and gives incident evaluation results in real time by combination with the detection data. The experiment uses I90 and I405 highway traffic data in Seattle, WA, from 2015. Through comparative analysis, the proposed incident detection method based on GSTPL has a higher detection rate and lower false alarm rate, can adaptively learn dynamic changes of the traffic pattern, and has strong adaptability and stability to different traffic environments.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Graph Spatiotemporal Pattern Learning Network for Real-Time Road Network Traffic Abnormal Incident Detection

Li,

Ma,

Wang

et al. 2023

Transportation Research Record: Journal of the Transportation R

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“…Algorithms for Automatic Incident Detection (AIDA) have undergone extensive research and have been classified in numerous systematic reviews (e.g. [24,25,26]). One such review categorizes these algorithms based on their data processing and methods used into four categories: comparative, statistical, artificial intelligence-based and video -image processing algorithms [27].…”

Section: Incident Detection Task Using Machine Learningmentioning

confidence: 99%

An AutoML-based approach for automatic traffic incident detection in smart cities

Gkioka,

Dominguez,

Mentzas

2024

IDT

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In the realm of modern urban mobility, automatic incident detection is a critical element of intelligent transportation systems (ITS), since the ability to promptly identify unexpected events allows for quick implementation of preventive measures and efficient response to the situations as they arise. With the growing availability of traffic data, Machine Learning (ML) has become a vital tool for enhancing traditional incident detection methods. Automated machine-learning (AutoML) techniques present a promising solution by streamlining the machine-learning process; however the application of AutoML for incident detection has not been widely explored in scientific research In this paper, we propose and apply an AutoML-based methodology for traffic incident detection and compare it with state-ofthe-art ML approaches. Our approach integrates data preprocessing with AutoML, and uses Tree-based Pipeline Optimization Tool (TPOT) to refine the process from raw data to prediction. We have tested the efficiency of our approach in two major European cities, Athens and Antwerp. Finally, we present the limitations of our work and outline recommendations for application of AutoML in the incident detection task and potentially in other domains.

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“…Compared with video anomaly detection [45], [26], [46], [47], [48], vision-based traffic accident perception shows the most inadequate research mainly due to the collection difficulty of accident video data. Most relating to this work, various surveys on traffic accident situations concentrate on traffic accident recognition or collision avoidance from specific occasions (e.g., intersection, urban scene), road participants (e.g., vehicles-centric and pedestrian-centric), and applications (e.g., surveillance safety [38], [39] and autonomous driving [43], [44]). However, the survey for vision-based traffic accident detection is few-explored.…”

Section: Distinction From Other Reviewsmentioning

confidence: 99%

Traffic Accident Detection via Self-Supervised Consistency Learning in Driving Scenarios

Fang

Qiao²,

Bai³

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Traffic accident detection and anticipation is an obstinate road safety problem and painstaking efforts have been devoted. With the rapid growth of video data, Vision-based Traffic Accident Detection and Anticipation (named Vision-TAD and Vision-TAA) become the last one-mile problem for safe driving and surveillance safety. However, the long-tailed, unbalanced, highly dynamic, complex, and uncertain properties of traffic accidents form the Out-of-Distribution (OOD) feature for Vision-TAD and Vision-TAA. Current AI development may focus on these OOD but important problems. What has been done for Vision-TAD and Vision-TAA? What direction we should focus on in the future for this problem? A comprehensive survey is important. We present the first survey on Vision-TAD in the deep learning era and the first-ever survey for Vision-TAA. The pros and cons of each research prototype are discussed in detail during the investigation. In addition, we also provide a critical review of 31 publicly available benchmarks and related evaluation metrics. Through this survey, we want to spawn new insights and open possible trends for Vision-TAD and Vision-TAA tasks.

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Evolution and Future of Urban Road Incident Detection Algorithms

Cited by 10 publications

References 21 publications

Graph Spatiotemporal Pattern Learning Network for Real-Time Road Network Traffic Abnormal Incident Detection

Graph Spatiotemporal Pattern Learning Network for Real-Time Road Network Traffic Abnormal Incident Detection

An AutoML-based approach for automatic traffic incident detection in smart cities

Traffic Accident Detection via Self-Supervised Consistency Learning in Driving Scenarios

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