Abstract
Traffic accident management as an approach to improve public security and reduce economic losses has received public attention for a long time, among which traffic accidents post-impact prediction (TAPIP) is one of the most important procedures. However, existing systems and methodologies for TAPIP are insufficient for addressing the problem. The drawbacks include ignoring the recovery process after clearance and failing to make comprehensive prediction in both time and space domain. To this end, we build a 3-stage TAPIP model on highways, using the technology of spiking neural networks (SNNs) and convolutional neural networks (CNNs). By dividing the accident lifetime into two phases, i.e., clean-up phase and recovery phase, the model extracts characteristics in each phase and achieves prediction of spatial-temporal post-impact variables (e.g., clean-up time, recovery time, and accumulative queue length). The framework takes advantage of SNNs to efficiently capture accident spatial-temporal features and CNNs to precisely represent the traffic environment. Integrated with an adaptation and updating mechanism, the whole system works autonomously in an online manner that continues to self-improve during usage. By testing with a new dataset CASTA pertaining to California statewide traffic accidents on highways collected in four years, we prove that the proposed model achieves higher prediction accuracy than other methods (e.g., KNN, shockwave theory, and ANNs). This work is the introduction of SNNs in the traffic accident prediction domain and also a complete description of post-impact in the whole accident lifetime.
Highlights
People’s living standards have increased all over the world, leading to an increase in the ownership of private vehicles [1]
According to statistical data released by World Health Organization in 2004, road traffic accidents are among the main causes of deaths and injuries all over the world, leading to 1.2 million deaths and 50 million injuries each year [2]
Mean-shift clustering and artificial neural networks (ANNs) classifier ranks first in the comparing methods, but still needs more running time and higher MAPE and RMSE. e other three benchmarks achieves unsatisfied prediction accuracy due to the lacks of combination of clustering and classification. e results prove that clustering the records into classes and using the centroid value to present the predicted value is better than direct prediction model when the data are dispersive distributed, as well as the suitability of applying spiking neural networks (SNNs) in capturing spatial and temporal features
Summary
People’s living standards have increased all over the world, leading to an increase in the ownership of private vehicles [1]. While private vehicles have improved people’s traveling experience, they have contributed to several traffic problems, where traffic safety is one of the main concerns. According to statistical data released by World Health Organization in 2004, road traffic accidents are among the main causes of deaths and injuries all over the world, leading to 1.2 million deaths and 50 million injuries each year [2]. In 1988, the total extra travel time and fuel consumption caused by traffic congestion, both regular and occasional, in 50 major cities in the United States was estimated at 35 billion U.S dollars [4]
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