Abstract
Traffic accidents pose a significant challenge in modern society, leading to substantial human loss and economic damage. Therefore, accurate forecasting of such accidents holds a paramount importance in road safety status evaluation. However, models in many studies often prioritize individual factors like accuracy, stability, or anti-interference ability, rather than considering them comprehensively. Toward this end, this study presents a novel traffic accident forecasting model, known as the Gaussian radial Deep Belief Net - Gaussian Process Regression (GrDBN-GPR). This model integrates feature engineering and predictive algorithms to capture the intricate relationships among various traffic factors. This model comprises two key components: firstly, the GrDBN uses the Gaussian-Bernoulli Restricted Boltzmann Machine (GBRBM) and the Gaussian activation functions to extract valuable features more effectively and stably. This feature extraction mechanism enhances the ability to uncover meaningful patterns within the data. Secondly, the GPR can achieve stable predictions based on the extracted informative features achieved by GrDBN. Finally, this model is applied to evaluate the road safety status of Highway 401 in Ontario, Canada, using a set of collision data collected for over eight years. In comparison to six commonly used benchmark models, the predictive accuracy, stability, and resistance to interference of the proposed model are evaluated.
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Acknowledgement
This research is supported by National Natural Science Foundation of China under grant no. 62103177, Shandong Provincial Natural Science Foundation for Youth of China under grant no. ZR2023QF097 and the National Science and Engineering Research Council of Canada (NSERC) under grant no. 651247. The authors would also like to thank the Ministry of Transportation Ontario Canada for technical support.
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Pan, G., Wu, X., Fu, L., Zhang, A., Xiao, Q. (2024). Traffic Accident Forecasting Based on a GrDBN-GPR Model with Integrated Road Features. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1965. Springer, Singapore. https://doi.org/10.1007/978-981-99-8145-8_15
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