Crash Prediction Model for Freeway Segment Considering Time Correlation
ID:1950 View Protection:ATTENDEE Updated Time:2021-12-03 14:43:21 Hits:227 Poster Presentation

Start Time:2021-12-17 08:56(Asia/Shanghai)

Duration:1min

Session:P2 Poster2021 » P2T4Track 4 Transportation Behavior, Safety and Security

No files

Abstract
Freeways play an important role in transportation system. But crashes occur frequently on freeways. Crash prediction models for freeway can evaluate road safety status and analyze safety influencing factors. Based on 9987 crashes occurred on Ningbo-Taizhou-Wenzhou Freeway from 2016 to 2019, this paper develops a Random Effect Negative Binomial (RENB) model, which introduces a random effect term into a Negative Binomial (NB) model to explain time correlation of the crash data among different years. In this paper, the NB model and the RENB model are compared in terms of goodness of fit and prediction accuracy. Results show that factors influencing crashes are annual average daily traffic volume, length of research unit, number of lanes and road alignment; the goodness of fit of the NB model and the RENB model is consistent, while the NB model has better prediction accuracy.
Keywords
CICTP
Speaker
Jia Li
Beijing University of Technology

Submission Author
Jia Li Beijing University of Technology
Submit Comment
Verify Code Change Another
All Comments
Important Date
  • Conference Date

    Dec 17

    2021

    to

    Dec 20

    2021

  • Dec 16 2021

    Contribution Submission Deadline

  • Dec 24 2021

    Registration deadline

Sponsored By
Chinese Overseas Transportation Association
Chang'an University
Contact Information