Traffic incident detection based on a global trajectory spatiotemporal map

Liang, Haoxiang; Song, Huansheng; Xu, Yong; Sun, Shijie; Wang, Yingxuan; Zhang, Zhaoyang

doi:10.1007/s40747-021-00602-8

Cited by 10 publications

(3 citation statements)

References 36 publications

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“…Vehicle data is supplied via the cloud to a data-mining engine that predicts engine stress using MLP-ANN algorithms and uses k-means clustering analysis to create a driver KPI score [116]. This work's follow-up research aims to more thoroughly analyze traffic monitoring video data and to offer helpful traffic travel advisory services [10]. With a software system, the circumstances of an accident can be determined by identifying overlapping images in real-time video streaming [129].…”

Section: Recent Trends Research Directions and Lessons Learnedmentioning

confidence: 99%

A Review of Artificial Intelligence and Machine Learning for Incident Detectors in Road Transport Systems

Olugbade

Ojo²,

Imoize

et al. 2022

MCA

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Road transport is the most prone to accidents, resulting in significant fatalities and injuries. It also faces a plethora of never-ending problems, such as the frequent loss of lives and valuables during an accident. Appropriate actions need to be taken to address these problems, such as the establishment of an automatic incident detection system using artificial intelligence and machine learning. This article explores the overview of artificial intelligence and machine learning in facilitating automatic incident detector systems to decrease road accidents. The study examines the critical problems and potential remedies for reducing road traffic accidents and the application of artificial intelligence and machine learning in road transportation systems. More, new, and emerging trends that reduce frequent accidents in the transportation sector are discussed extensively. Specifically, the study organized the following sub-topics: an incident detector with machine learning and artificial intelligence and road management with machine learning and artificial intelligence. Additionally, safety is the primary concern of road transport; the internet of vehicles and vehicle ad hoc networks, including the use of wireless communication technologies such as 5G wireless networks and the use of machine learning and artificial intelligence for road transportation systems planning, are elaborated. Key findings from the review indicate that route optimization, cargo volume forecasting, predictive fleet maintenance, real-time vehicle tracking, and traffic management are critical to safeguarding road transportation systems. Finally, the paper summarizes the challenges facing the application of artificial intelligence in road transport systems, highlights the research trends, identifies the unresolved questions, and highlights the essential research takeaways. The work can serve as reference material for road transport system planning and management.

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Section: Recent Trends Research Directions and Lessons Learnedmentioning

confidence: 99%

A Review of Artificial Intelligence and Machine Learning for Incident Detectors in Road Transport Systems

Olugbade

Ojo²,

Imoize

et al. 2022

MCA

View full text Add to dashboard Cite

show abstract

“…This provides a decision-making basis for traffic control personnel to use when dealing with abnormal traffic conditions, which helps to avoid law enforcement disputes effectively. Health [3].…”

Section: Introductionmentioning

confidence: 99%

Traffic Video-Based Parking and Abandoned Object Event Detection

Zubair

ALI

NAEEM

et al. 2022

Proceedings of MOL2NET'22, Conference on Molecular, Biomedical &Amp; Computational Sciences and Engineering, 8th Ed. - MOL2NET:

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To avoid road accidents, abandoned and parked things must be detected promptly and appropriately. To avoid road accidents, abandoned and parked things must be detected promptly and appropriately. Most detection algorithms employ 2D image features like target type position and backdrop model accuracy. Environment affects these algorithms, and target types are unreliable. This study proposes a 3D target-based parked and abandoned item recognition method to discriminate target types. State evolution discovers abnormalities first.Second, it splits the parked and abandoned item area using eight-neighborhood seed-filling and two-way monitoring of the initial anomalous zone. Finally, it distinguishes parked and abandoned objects using three ways. The first technique calculates feature height by comparing projection velocities. The height separates

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“…e proposed method also distinguishes nonrecurrent traffic congestion caused by incidents from recurrent congestion. Liang et al [18] used surveillance video stream to detect traffic objects and proposed an algorithm to detect traffic incidents based on a spatiotemporal map of vehicle trajectories. According to the vehicle trajectory and vehicle position in each frame, the vehicle is re-identified across frames to associate the same vehicle between different frames and a global spatiotemporal map of the trajectory was generated under the current road segment.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Incident-Responsive Signal Control Strategy under Partially Connected Vehicle Environment

Chandan

Seco

Silva

2022

Journal of Advanced Transportation

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The performance of the traffic system can drastically drop when nonrecurrent congestion caused by incidents occurs. Early detection and clearing of traffic incidents will enable the mitigation of the congestion and early restoration of normal traffic conditions. The research in this paper utilized the vehicle information from the recent technological advancement in transportation systems, connected vehicles (CV), and loop-detector information for nonconnected vehicles (NCVs) and developed a novel algorithm to (1) control traffic signals for normal traffic conditions in the absence of incidents, (2) detect traffic incidents using CV/NCV information, and (3) control traffic signals during the occurrence and dissipation of incidents. All the 3 strategies were integrated into one algorithm, which runs as per the real-time traffic conditions, in the presence or absence of incidents. Space-mean speeds of the vehicles on nonincident lanes and throughput maximization criteria were taken as the indicators for the activation of specific signal timings directed at the incident-affected approach. Diverse incident scenarios were tested on a four-legged isolated intersection using the VISSIM simulation tool. Incident detection results showed a higher detection rate and lower mean detection time at higher CV penetration and higher traffic volumes, and at the incident locations nearer to the stop-line. The proposed incident-responsive signal control strategy at 40% and higher CV penetration showed better performance over EPICS adaptive signal control solution, in reducing average travel time delay and the average number of stops per vehicle.

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Traffic incident detection based on a global trajectory spatiotemporal map

Cited by 10 publications

References 36 publications

A Review of Artificial Intelligence and Machine Learning for Incident Detectors in Road Transport Systems

A Review of Artificial Intelligence and Machine Learning for Incident Detectors in Road Transport Systems

Traffic Video-Based Parking and Abandoned Object Event Detection

Real-Time Incident-Responsive Signal Control Strategy under Partially Connected Vehicle Environment

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