Time Series Classification Using Imbalanced Learning for Real-Time Safety Assessment

The probability of estimating a traffic collision happening in real-time primarily depends on comparing traffic conditions just before a collision with traffic conditions during normal operations. Most studies however utilize aggregated traffic data and are not concerned with the dynamic nature of collisions or the imbalance of safety databases which can lead to erroneous real-time predictions. In this study, this is overcome through the use of raw speed time series data of variant duration (i.e. 1-minute to 5-minute time series data) from a driving simulator experiment and the use of imbalanced learning techniques. Two classifiers are then employed to examine the proposed idea: (i) Random Forests (RFs) – an ensemble classifier and (ii) Neural Networks (NNs) – a popular classifier in the literature. These classifiers are tested on the original time series data, as well as on time-series treated with the imbalanced learning techniques of undersampling and its integration with oversampling. The main results demonstrate the viability of using raw speed time series data for real-time safety assessment and the superiority of time series with 4-minute duration in the classification results. Furthermore, RFs perform well even on 1-minute time series data while the classification results can be enhanced by up to 40% from imbalanced learning approaches. It is also demonstrated that the classification results outperform similar approaches in the literature. However, real-world traffic data and the use of more sophisticated classifiers (e.g. Deep Learning) are expected to provide more effective collision predictions.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications.
• Corporate Authors:

  Transportation Research Board

  ,    
• Authors:
  • Katrakazas, Christos
  • Antoniou, Constantinos
  • Yannis, George
• Conference:
  • Transportation Research Board 98th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2019-1-13 to 2019-1-17
• Date: 2019

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 15p

Subject/Index Terms

• TRT Terms: Data management; Highway safety; Mathematical prediction; Real time information; Safety analysis; Time series; Traffic crashes
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01697466
• Record Type: Publication
• Report/Paper Numbers: 19-04457
• Files: TRIS, TRB, ATRI
• Created Date: Mar 1 2019 3:50PM