Chapter
Jun 29, 2020
13th Asia Pacific Transportation Development Conference
Method of Highway Risk Assessment and Accident Quantity Prediction Based on Multi-Source Heterogeneous Data and Deep Neural Network
Authors: Xiaodan Zhang [email protected], Chengwei Huang [email protected], and Yongsheng Chen [email protected]Author Affiliations
Publication: Resilience and Sustainable Transportation Systems
ABSTRACT
In this paper, using the multi-source data, combined with deep neural network, an automatic assessment model for highway risk and accident number prediction model are established. First, based on the multi-source heterogeneous data collected by different monitoring facilities and sensors on the highway, the features are fused. Secondly, the shuffled frog leaping algorithm is used to optimize the features to reduce the dimension. Thirdly, we improve the traditional deep neural network by connecting the output of each layer of neural network to the last fully connected layer to obtain a fused vector. Finally, the effectiveness of the proposed algorithm is verified in the experimental results.
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ACKNOWLEDGMENTS
This work was supported by National Key R&D Program of China (2017YFC0840200)
REFERENCES
Hermans, E. Van den Bossehe, F., Wets, G. (2008). “Combining road safety information in a performance index”. Accident Analysis and Prevention, 40(4):1337~1344.
Hinton, G. E., Osindero, S, The, Y. W. (2006). “A Fast Learning Algorithm for Deep Belief Nets”. Neural Computation, 18(7):1527-1554.
Miaou, S. P., Song, J. J. (2005). “Bayesian ranking of sites for engineering safety improvements: Decision parameter, treatability concept, statistical criterion, and spatial dependence”. Accident Analysis and Prevention, 37(4):699~720.
Information & Authors
Information
Published In
Resilience and Sustainable Transportation Systems
Pages: 118 - 125
Editors: Fengxiang Qiao, Ph.D., Texas Southern University, Yong Bai, Ph.D., Marquette University, Pei-Sung Lin, Ph.D., University of South Florida, Steven I Jy Chien, Ph.D., New Jersey Institute of Technology, Yongping Zhang, Ph.D., California State Polytechnic University, and Lin Zhu, Ph.D., Shanghai University of Engineering Science
ISBN (Online): 978-0-7844-8290-2
Copyright
© 2020 American Society of Civil Engineers.
History
Published online: Jun 29, 2020
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Authors
Affiliations
Research Institute of Highway, Ministry of Transport, Beijing, China. E-mail: [email protected]
Sugon (Nanjing) Institute of Chinese Academy of Sciences Co. Ltd., Nanjing, China. E-mail: [email protected]
Research Institute of Highway, Ministry of Transport, Beijing, China. E-mail: [email protected]
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ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.