Spatio-temporal Analysis and Machine Learning for Traffic Accidents Prediction

Aldogom, Diena; Aburaed, Nour; Al-Saad, Mina; Mansoori, Saeed Al

doi:10.1109/icspis48135.2019.9045892

Cited by 8 publications

(5 citation statements)

References 21 publications

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“…Farhangi et al [10] Accident-risk modeling and mapping Lee et al [25] Accident severity prediction Mestri et al [26] Identification of accident-prone locations Al-dogom et al [21] Spatio-temporal analysis for accidents prediction Fan et al [27] Identification of accident black spots and analyzing their characteristics…”

Section: Paper Machine Learning Applicationmentioning

confidence: 99%

“…Researchers often combine GIS with other analysis methods. Machine learning is one of these methods utilized in road-safety assessments in various GISbased research [21], due to its popularity as a robust and data-driven family of prediction tools [23].…”

Section: Paper Machine Learning Applicationmentioning

confidence: 99%

“…Accident analysis is also a field in which machine learning has been successful [20]. Machine learning methods are practical in predicting accident occurrence and identify nonlinear relationships between affective factors and RTA risk better than traditional statistical algorithms [21,22]. These methods can work with high degrees of freedom, without needing traditional hypotheses, and are more flexible to outliers [23].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Tree-Based Machine Learning Algorithms for Accident Risk Mapping Caused by Driver Lack of Alertness at a National Scale

et al. 2021

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Drivers’ lack of alertness is one of the main reasons for fatal road traffic accidents (RTA) in Iran. Accident-risk mapping with machine learning algorithms in the geographic information system (GIS) platform is a suitable approach for investigating the occurrence risk of these accidents by analyzing the role of effective factors. This approach helps to identify the high-risk areas even in unnoticed and remote places and prioritizes accident-prone locations. This paper aimed to evaluate tuned machine learning algorithms of bagged decision trees (BDTs), extra trees (ETs), and random forest (RF) in accident-risk mapping caused by drivers’ lack of alertness (due to drowsiness, fatigue, and reduced attention) at a national scale of Iran roads. Accident points and eight effective criteria, namely distance to the city, distance to the gas station, land use/cover, road structure, road type, time of day, traffic direction, and slope, were applied in modeling, using GIS. The time factor was utilized to represent drivers’ varied alertness levels. The accident dataset included 4399 RTA records from March 2017 to March 2019. The performance of all models was cross-validated with five-folds and tree metrics of mean absolute error, mean squared error, and area under the curve of the receiver operating characteristic (ROC-AUC). The results of cross-validation showed that BDT and RF performance with an AUC of 0.846 were slightly more accurate than ET with an AUC of 0.827. The importance of modeling features was assessed by using the Gini index, and the results revealed that the road type, distance to the city, distance to the gas station, slope, and time of day were the most important, while land use/cover, traffic direction, and road structure were the least important. The proposed approach can be improved by applying the traffic volume in modeling and helps decision-makers take necessary actions by identifying important factors on road safety.

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Section: Paper Machine Learning Applicationmentioning

confidence: 99%

Section: Paper Machine Learning Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Tree-Based Machine Learning Algorithms for Accident Risk Mapping Caused by Driver Lack of Alertness at a National Scale

et al. 2021

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“…Among them, machine/deep learning based approaches are dominant. To provide some examples, [22] used a SVM based analysis which takes only numerical input and does a binary classification. Attention based works are demonstrated by [23] which looks for the relevant data points to predict the traffic incidents.…”

Section: A Literature On Accident Predictionmentioning

confidence: 99%

ARIS: A Real Time Edge Computed Accident Risk Inference System

Ovi

Dey

Roy

et al. 2021

2021 IEEE International Conference on Smart Computing (SMARTCOMP)

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To deploy an intelligent transport system in urban environment, an effective and real-time accident risk prediction method is required that can help maintain road safety, provide adequate level of medical assistance and transport in case of an emergency. Reducing traffic accidents is an important problem for increasing public safety, so accident analysis and prediction have been a subject of extensive research in recent time. Even if a traffic hazard occurs, a readily deployable structure with an accurate prediction of accident can contribute to better management of rescue resources. But the significant shortcomings of current studies are the use of small-scale datasets with minimal scope, being based on extensive data sets, and not being applicable for real-time purposes. To overcome these challenges, we propose ARIS: a system for real-time traffic accident prediction built on a traffic accident dataset named 'US-Accidents' which covers 49 states of United States, collected from February 2016 to June 2020. Our approach is based on a deep neural network model that utilizes a variety of data characteristics, such as time-sensitive weather data, textual information, and discerning factors. We have tested ARIS against multiple baselines through a comprehensive series of experiments across several major cities of USA, and we have noticed significant improvement during inference especially in detecting accident classes. Additionally, to make our model edge-implementable we have compressed our model using a joint technique of magnitude-based weight pruning and model quantization. We have also demonstrated the inference results along with power consumption profiling after deploying the model on a resource constrained environment that consists of Intel Neural Compute Stick 2 (NCS2) with Raspberry Pi 4B (RPi4). Our investigation and observations indicate major improvements to predict unusual traffic accident event even after model compression and deployment. We have managed to reduce the model size and inference time by ≈ 6x, and ≈ 70 % respectively with insignificant drop in performance. Furthermore, to better understand the importance of each individual type of variables used in our analysis, we have showcased a comprehensive ablation study.

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“…In recent years, deep learning methods have gained popularity as powerful techniques for extracting information from big data and have demonstrated their efficiency in several applications, typically in prediction tasks [17][18][19][20][21]. Therefore, much research has been conducted on forecasting road traffic accidents and predicting injury severity in urban areas using numerous types of data [22][23][24][25][26][27][28][29][30]. For example, the authors in [22] proposed a traffic accident casualty prediction model using neural networks and data mining techniques.…”

Section: Introductionmentioning

confidence: 99%

From Prediction to Prevention: Leveraging Deep Learning in Traffic Accident Prediction Systems

Jin,

Noh

2023

Electronics

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We propose a novel system leveraging deep learning-based methods to predict urban traffic accidents and estimate their severity. The major challenge is the data imbalance problem in traffic accident prediction. The problem is caused by numerous zero values in the dataset due to the rarity of traffic accidents. To address the issue, we propose a grid-clustered feature map with the ideas of grids and cells. To predict the occurrence of accidents in the grid, we introduce an accident detector that combines the power of a Convolutional Neural Network (CNN) with a Deep Neural Network (DNN). Then, hierarchical DNNs are supposed to be an accident risk classifier to estimate the risk of each cell in the accident-occurrence grid. The proposed system can effectively reduce instances with no traffic accidents. Furthermore, we introduce the concept of the Accident Risk Index (ARI) to better represent the severity of risk at each cell. Also, we consider all the explanatory variables, such as dangerous driving behaviors, traffic mobility, and safety facility information, that can be related to traffic accidents. To improve the prediction accuracy, we further take into consideration all the explanatory variables, such as dangerous driving behaviors, traffic mobility, and safety facility information, that can be related to traffic accidents. In the experiment, we highlight the benefits of our method for urban traffic accident management by significantly improving model performance compared to the baselines. The feasibility and applicability of the proposed system are validated in the data of Daejeon City, Republic of Korea. The proposed prediction system can dynamically advise and recommend commuters, traffic management systems, and city planners on alternatives, optimizations, and interventions.

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Spatio-temporal Analysis and Machine Learning for Traffic Accidents Prediction

Cited by 8 publications

References 21 publications

Evaluation of Tree-Based Machine Learning Algorithms for Accident Risk Mapping Caused by Driver Lack of Alertness at a National Scale

Evaluation of Tree-Based Machine Learning Algorithms for Accident Risk Mapping Caused by Driver Lack of Alertness at a National Scale

ARIS: A Real Time Edge Computed Accident Risk Inference System

From Prediction to Prevention: Leveraging Deep Learning in Traffic Accident Prediction Systems

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