Traffic Accident Detection Research Articles

Real-Time Freeway Crash Detection Framework using Connected Vehicle Waypoint Data

Accurate and timely detection of traffic accidents is crucial for transportation agencies seeking swift responses and effective traffic management, particularly on freeways where crash severity and traffic flow disruptions are amplified. While several methods have been developed using cameras or infrastructure-mounted sensors, these approaches may face challenges in geographical scalability. To address this issue, a novel approach has been developed that leverages real-time connected vehicle trajectories at a market penetration rate of 4%–9%. The method involves extracting five key features from individual journeys that sense the speed and acceleration fluctuations. The results demonstrate promising outcomes, achieving an overall accuracy of 63.5% with 4.1% false detections and 33.4% non-detections. This accuracy surpasses the speed-alone model using the same data by 24%. Furthermore, higher traffic volumes lead to even greater accuracy, exceeding 80% at level of service D and 90% at level of service E. The developed algorithm also exhibits precision in crash detection, with a mean latency of only 2.5 min after the actual incidents. The entire processing time may additionally add up to 1 min. The robustness of the model is tested for a single day on one of the interstates, providing promising results. This study highlights the potential of connected vehicle data in enhancing crash prediction methods and underlines its value for the safety and efficiency of future transportation systems.

TADS: a novel dataset for road traffic accident detection from a surveillance perspective

TADS: a novel dataset for road traffic accident detection from a surveillance perspective

Car Accident Detection and Alert System

Due to the high death and property damage rates from traffic accidents, road safety is a serious problem. This study suggests a novel traffic accident detection and warning system in response to this difficulty, with the goals of enhancing prompt response and minimizing damage. This system is made up of many different parts, such as an energy sensor, accelerometers, Arduino microcontrollers, GPS modules, and GSM modules. Combining these characteristics allows the system to differentiate between minor and severe occurrences, recognize and categorize collisions according to impact severity, and detect overturned vehicles. The system notifies designated projects in the precise accident zone of any serious accidents detected. The process includes developing algorithms, designing hardware, and implementing the system. Thorough testing is then done to assess the system's dependability and performance. The outcomes demonstrate how well the suggested approach works for precise accident detection and quick emergency response. This research uses technology to lessen the effects of traffic accidents and save lives, which advances road safety legislation.

Vision-Based Traffic Accident Detection and Anticipation: A Survey

Vision-Based Traffic Accident Detection and Anticipation: A Survey

IoT Based Traffic Congestion Management and Accident Detection System

This review proposes a traffic congestion management and accident detection system to reduce congestion at junctions and to provide emergency assistance during accidents. The proposed system employs advanced computer vision and image processing techniques like You Only Look Once (YOLO) to monitor and analyze real-time traffic conditions and accidents. The pivotal feature of this system lies in its adaptive decision-making capability, automatically adjusting traffic signal timings based on observed density patterns and updating and reporting about the congestions and accidents for which Internet of Things (IOT) technologies are used. Nowadays many junctions and roads are equipped with surveillance cameras connected to traffic management systems. Therefore, techniques like YOLO can be viable tools for automatic accident detection and traffic control.

Cross-Modality Interaction-Based Traffic Accident Classification

Traffic accidents on the road lead to serious personal and material damage. Furthermore, preventing secondary accidents caused by traffic accidents is crucial. As various technologies for detecting traffic accidents in videos using deep learning are being researched, this paper proposes a method to classify accident videos based on a video highlight detection network. To utilize video highlight detection for traffic accident classification, we generate information using the existing traffic accident videos. Moreover, we introduce the Car Crash Highlights Dataset (CCHD). This dataset contains a variety of weather conditions, such as snow, rain, and clear skies, as well as multiple types of traffic accidents. We compare and analyze the performance of various video highlight detection networks in traffic accident detection, thereby presenting an efficient video feature extraction method according to the accident and the optimal video highlight detection network. For the first time, we have applied video highlight detection networks to the task of traffic accident classification. In the task, the most superior video highlight detection network achieves a classification performance of up to 79.26% when using video, audio, and text as inputs, compared to using video and text alone. Moreover, we elaborated the analysis of our approach in the aspects of cross-modality interaction, self-attention and cross-attention, feature extraction, and negative loss.

Review of machine learning in traffic accident detection

With the development and wide application of New Energy Vehicles (NEVs) in the past decade, the chip and power supply method for Machine Learning (ML) intervention provides a good hardware premise, while the 5G technology under the auspices of large-scale rapid data processing is also a prerequisite for the development of Auto Driving, moreover the current traffic accident prediction algorithms have a more critical role in the development of the field of Auto Driving. In this paper, I will consider these aspects as shown below. On the one hand, Anticipation of Traffic Accidents (ATAs) can be well used in todays Autonomous Driving has not yet been popularized, the existing warning information into the corresponding automated driving intervention signals to achieve the possibility of improving the safety of automated driving to increase the possibility of future use of automated driving. On the other hand, the use of ML in the ATAs can be better in the complex traffic environment in a timely manner for the accident warning to achieve a certain degree of reduction in the rate of traffic accidents, in order to optimize the traffic and reduce the economic loss of people. At the same time, in this review, we will propose the combination of data from in-vehicle cameras and road surveillance cameras to analyze the current development of autonomous driving.

Detection of traffic accidents using artificial intelligence

Detection of traffic accidents using artificial intelligence

Evaluation of Traffic Accident Detection by Simulating and Modeling the Vehicle Network Using SUMO and OMNeT++ Simulators

Evaluation of Traffic Accident Detection by Simulating and Modeling the Vehicle Network Using SUMO and OMNeT++ Simulators

Smart City Transportation: Deep Learning Ensemble Approach for Traffic Accident Detection

Smart City Transportation: Deep Learning Ensemble Approach for Traffic Accident Detection

Spatiotemporal features of traffic help reduce automatic accident detection time

Quick and reliable automatic detection of traffic accidents is of paramount importance to save human lives in transportation systems. However, automatically detecting when accidents occur has proven challenging, and minimizing the time to detect accidents (TTDA) by using traditional features in machine learning (ML) classifiers has plateaued. We hypothesize that accidents affect traffic farther from the accident location than previously reported. Therefore, leveraging traffic signatures from neighboring sensors that are adjacent to accidents should help improve their detection. We confirm this hypothesis by using verified ground-truth accident data, traffic data from radar detection system sensors, and light and weather conditions and show that we can minimize the TTDA while maximizing classification performance by considering spatiotemporal features of traffic. Specifically, we compare the performance of different ML classifiers (i.e, logistic regression, random forest, and XGBoost) when controlling for different numbers of neighboring sensors and TTDA horizons. We use data from interstates 75 and 24 in the metropolitan area that surrounds Chattanooga, TN. Our results show that the XGBoost classifier produces the best results by detecting accidents as quickly as 1.0 min after their occurrence with an area under the receiver operating characteristic curve of up to 83% and an average precision of up to 49%. We describe limitations, open challenges, and how the proposed framework can be used for quicker operational accident detection.

Deep Learning-Based Consistent Object Detection in Distance on Highly Perspective Region

CCTVs are commonly used for traffic monitoring and accident detection, but their images suffer from severe perspective distortion causing object size reduction with distance. This issue is exacerbated in tunnel CCTVs, positioned low due to space constraints, leading to challenging object detection, especially for distant small objects, due to perspective effects. To address this, this study proposes a solution involving a region of interest setup and an inverse perspective transformation technique. The transformed images, achieved through this technique, enlarge distant objects, maintaining object detection performance and appearance velocity across distances. To validate this, artificial CCTV images were generated in a virtual tunnel environment, creating original and transformed image datasets under identical conditions. Comparisons were made between the appearance velocity and object size of individual vehicles and for deep learning model performance with multiple moving vehicles. The evaluation was conducted across four distance intervals (50 m to 200 m) from the tunnel CCTV location. The results reveal that the model using original images experiences a significant decline in object detection performance beyond 100 m, while the transformed image-based model maintains a consistent performance up to the distance of 200 m.

A Novel Approach for Car Accident Detection and Analysis Using AIML Techniques

Abstract: In the context of escalating concerns regarding road safety, this study introduces an automated traffic accident detection and analysis framework that utilizes surveillance footage. The approach synergistically combines the Motion Interaction Field (MIF) model and YOLO v5 algorithm to achieve accurate crash detection and precise vehicle classification. The primary objective is to develop an autonomous system for identifying and evaluating traffic events captured by security cameras. The MIF model extracts valuable insights from interactions among moving objects within images and videos, tailored explicitly for detecting damaged vehicles. Localization of damaged vehicles is achieved using the YOLO v5 algorithm, complemented by perspective transformation to enable vertical image representation. The unbiased finite impulse response (UFIR) technique, notable for its insensitivity to statistical noise data, effectively determines vehicle motion. Furthermore, this work delves into an in-depth analysis of car crashes, estimating impact speed and point of collision from an aerial perspective. This work encompasses meticulous accident clip analysis, precise car direction determination, and error identification. The outcomes underscore the potential of the proposed framework to revolutionize accident detection and analysis, contributing to heightened road safety and streamlined traffic management strategies.

Traffic Accident Detection Using Background Subtraction and CNN Encoder–Transformer Decoder in Video Frames

Artificial intelligence plays a significant role in traffic-accident detection. Traffic accidents involve a cascade of inadvertent events, making traditional detection approaches challenging. For instance, Convolutional Neural Network (CNN)-based approaches cannot analyze temporal relationships among objects, and Recurrent Neural Network (RNN)-based approaches suffer from low processing speeds and cannot detect traffic accidents simultaneously across multiple frames. Furthermore, these networks dismiss background interference in input video frames. This paper proposes a framework that begins by subtracting the background based on You Only Look Once (YOLOv5), which adaptively reduces background interference when detecting objects. Subsequently, the CNN encoder and Transformer decoder are combined into an end-to-end model to extract the spatial and temporal features between different time points, allowing for a parallel analysis between input video frames. The proposed framework was evaluated on the Car Crash Dataset through a series of comparison and ablation experiments. Our framework was benchmarked against three accident-detection models to evaluate its effectiveness, and the proposed framework demonstrated a superior accuracy of approximately 96%. The results of the ablation experiments indicate that when background subtraction was not incorporated into the proposed framework, the values of all evaluation indicators decreased by approximately 3%.

Optimal Deployment of Sensors along Freeway Corridors for Traffic Accident Detection

Sensor deployment is important to the detection performance of traffic accidents on freeways. However, the temporally and spatially varying effects of accident risks are seldom considered in sensor deployment, which can induce a decrease in accident detection accuracy and detection timeliness. To address this problem, this paper proposes an optimal deployment method of sensors according to the temporal-spatial effect of accidents on traffic flow. Considering the uncertainty of traffic accidents, the temporal-spatial distribution of accident risk is estimated according to historical accident data. Then the kinematic wave model and Van Aerder model are introduced to analyze the temporal-spatial effect of accidents on traffic flow. To obtain the optimal locations of sensors, the sensor deployment problem is converted into a coverage problem on accident risk in time and space, and an optimal deployment model of sensors is built by considering the temporal-spatial effect of accident risk, the coverage rate on accident risk, the detection timeliness of the accident, and deployment cost. The particle swarm optimization algorithm is introduced to solve it. A case study is carried out to validate the proposed method using consistency, sensitivity, reliability, and comparison experiments. The results show that the proposed method is effective and reliable under different traffic volumes, and it outperforms the uniform spacing method in traffic accident detection.

Real-Time Vehicle Accident Recognition from Traffic Video Surveillance using YOLOV8 and OpenCV

The automatic detection of traffic accidents is a significant topic in traffic monitoring systems. It can reduce irresponsible driving behavior, improve emergency response, improve traffic management, and encourage safer driving practices. Computer vision can be a promising technique for automatic accident detection because it provides a reliable, automated, and speedy accident detection system that can improve emergency response times and ultimately save lives. This paper proposed an ensemble model that uses the YOLOv8 approach for efficient and precise event detection. The model framework's robustness is evaluated using YouTube video sequences with various lighting circumstances. The proposed model has been trained using the open-source dataset Crash Car Detection Dataset, and its produced precision, recall, and mAP are 93.8% and 98%, 96.1%, respectively, which is a significant improvement above the prior precision, recall, and mAP figures of 91.3%, 87.6%, and 93.8%. The effectiveness of the proposed approach in real-time traffic surveillance applications is proved by experimental results using actual traffic video data.

Accident Detection and Flow Prediction for Connected and Automated Transport Systems

Effective accident detection and traffic flow forecasting are of great importance for quick respond, impact elimination and intelligent control of the traffic flow consisting of autonomous vehicles. This paper proposes a traffic accident detection method for connected and automated transport systems by conducting a grid-based parameter extracting and SVC-based traffic state classification. Allowing for the dynamic spread of traffic flow over time, from upstream to downstream and from accident lanes to other lanes, a spatiotemporal Markov model is established to predict the evolution of traffic flow after accident by introducing the grid as state detection unit and fitting the spatiotemporal evolution with the parameter space mean speed to match the need of both detection accuracy and monitoring scope. Compared with actual accident data, the validation results indicate that the proposed methods present a good performance in accident detection with the accident detection rate as 87.72% and a higher precision rate than both SVM (support vector machine) and ANN (artificial neural network) models in traffic flow prediction. With the active traffic accident identification and dynamic traffic flow prediction, it is beneficial to shorten detection time, reduce possible impacts of traffic accidents and carbon emissions from congestion. The methods can be implied to traffic state recognition and traffic flow prediction, which is one of the significant sections of connected and automated transport systems, and serve as references for accident handling and urban traffic management.

Traffic Accident Detection Method Using Trajectory Tracking and Influence Maps

With the development of artificial intelligence, techniques such as machine learning, object detection, and trajectory tracking have been applied to various traffic fields to detect accidents and analyze their causes. However, detecting traffic accidents using closed-circuit television (CCTV) as an emerging subject in machine learning remains challenging because of complex traffic environments and limited vision. Traditional research has limitations in deducing the trajectories of accident-related objects and extracting the spatiotemporal relationships among objects. This paper proposes a traffic accident detection method that helps to determine whether each frame shows accidents by generating and considering object trajectories using influence maps and a convolutional neural network (CNN). The influence maps with spatiotemporal relationships were enhanced to improve the detection of traffic accidents. A CNN is utilized to extract latent representations from the influence maps produced by object trajectories. Car Accident Detection and Prediction (CADP) was utilized in the experiments to train our model, which achieved a traffic accident detection accuracy of approximately 95%. Thus, the proposed method attained remarkable results in terms of performance improvement compared to methods that only rely on CNN-based detection.

BGCP-based traffic data imputation and accident detection applications for the national trunk highway

Facing the currently large quantity of intelligent transportation data, missing ones is often inevitable. Some previous works have shown the advantages of tensor decomposition-based approaches in solving multi-dimensional data imputation problems. However, a research gap still exists in examining the effect of applying these methods on imputation performance and their application to accident detection. Thus, referring to a two-month spatiotemporal traffic speed dataset, collected on the national trunk highway in Shandong, China, this paper employs the Bayesian Gaussian CANDECOMP/PARAFAC (BGCP) to impute missing speed data in different missing rates and missing scenarios. Moreover, the dataset is built while considering both the temporal and the road functions. Applying the generated results of data imputation in accident detection is also of the main targets of this work. Thus, while combining multiple sources of data, such as traffic operation status and weather, eXtreme Gradient Boosting (XGBoost) is deployed to build accident detection models. The generated results show that the BGCP model can produce accurate imputations even under temporally correlated data corruption. Added to that, it is also suggested that, when there are continuous periods of missing speed data (missing rate greater than 10%), pre-processing of data imputation is imperative to maintain the accuracy of accident detection. Thus, the objective of this work is to provide insights into traffic management and academics when performing spatiotemporal data imputation tasks.

Machine learning based IoT system for secure traffic management and accident detection in smart cities.

In smart cities, the fast increase in automobiles has caused congestion, pollution, and disruptions in the transportation of commodities. Each year, there are more fatalities and cases of permanent impairment due to everyday road accidents. To control traffic congestion, provide secure data transmission also detecting accidents the IoT-based Traffic Management System is used. To identify, gather, and send data, autonomous cars, and intelligent gadgets are equipped with an IoT-based ITM system with a group of sensors. The transport system is being improved via machine learning. In this work, an Adaptive Traffic Management system (ATM) with an accident alert sound system (AALS) is used for managing traffic congestion and detecting the accident. For secure traffic data transmission Secure Early Traffic-Related EveNt Detection (SEE-TREND) is used. The design makes use of several scenarios to address every potential problem with the transportation system. The suggested ATM model continuously modifies the timing of traffic signals based on the volume of traffic and anticipated movements from neighboring junctions. By progressively allowing cars to pass green lights, it considerably reduces traveling time. It also relieves traffic congestion by creating a seamless transition. The results of the trial show that the suggested ATM system fared noticeably better than the traditional traffic-management method and will be a leader in transportation planning for smart-city-based transportation systems. The suggested ATM-ALTREND solution provides secure traffic data transmission that decreases traffic jams and vehicle wait times, lowers accident rates, and enhances the entire travel experience.