Citation

Fan Z, Liu C, Cai D, Yue S. Safety Sci. 2019; 118: 607-616.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.ssci.2019.05.039

PMID

unavailable

Abstract

With the rapid development of economy and urbanization, the number of urban motor vehicles keeps increasing. Urban travel is more convenient, but the traffic safety problems are increasingly prominent. Traffic accident data include not only time and place, but also people, roads, vehicles and the surrounding environment. Traffic accident black spot is the spatial location of traffic accident concentrated distribution. Most of the traditional traffic accident black spot identification only considers time and space factors, ignoring other factors. Based on the traffic accident data of Suzhou Industrial Park, this paper makes a fusion analysis of the multi-source influencing factors involved in traffic accident black spot. According to the structured association characteristics of urban traffic accident big data, a support vector machine method based on maximizing the classification interval is used to train the complex model and optimal learning of accident black spots in the study area. The accuracy of black spot identification is improved. At the same time, aiming at the rapid growth of traffic accident multi-source data, a black point identification algorithm based on deep neural network is proposed. The deep neural network of relevant data category information is established to verify the model's ability to identify accident black spots. A feature-based black spot identification method based on depth neural network is proposed. Furthermore, a dynamic adaptive machine learning architecture is built.

Language: en

Keywords

Black spot identification; Deep neural network; Machine-learning; Support vector machine