Data-Driven Injury Severity Prediction by Integrating Clustering Analysis and Deep Neural Network Model

With the rapid development of intelligent transportation systems, injury severity prediction has been paid more attention. This study intended to predict the injury severity with massive data provided. To achieve this goal, the data set was firstly collected from Traffic Accident Database maintained by Nevada Department of Transportation from 2015 to 2017, and then Getis-Ord Gi* was selected as the hot spot analysis. Based on the hot spot analysis, deep neural network was considered to predict the injury severity. By training the data set and comparing the results with deep neural network, the results showed beneficial performance from the goodness-of-fit. Findings revealed that the proposed model can be considered as an alternative to predict injury severity. The results may provide potential insights for reducing the injury severity.

• Record URL:
  • https://doi.org/10.1061/9780784484265.152
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784484265
• Supplemental Notes:
  • © 2022 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Xiao, Daiquan
  • Qian, Cheng
  • Xu, Xuecai
  • Ma, Changxi
• Conference:
  • 22nd COTA International Conference of Transportation Professionals
  • Location: Changsha Hunan Province, China
  • Date: 2022-7-8 to 2022-7-11
• Publication Date: 2022

Language

• English

Media Info

• Media Type: Web
• Pagination: pp 1618-1629
• Monograph Title: CICTP 2022: Intelligent, Green, and Connected Transportation

Subject/Index Terms

• TRT Terms: Cluster analysis; Crash data; High risk locations; Injury severity; Neural networks; Predictive models
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01864131
• Record Type: Publication
• ISBN: 9780784484265
• Files: TRIS, ASCE
• Created Date: Nov 17 2022 10:15AM