Temporal stability of associations between crash characteristics: A multiple correspondence analysis

Understanding the associations between crash characteristics facilitates the development of traffic safety policies for improving traffic safety. This study investigates the temporal stability of associations between crash characteristics at different temporal levels using multiple correspondence analysis (MCA). For each date in 2020, crash data from the previous week, month, season, half year, one year, two years, three years, and four years are collected respectively as eight temporal levels. MCA plots and chi-square distance analysis are used to assess the temporal stability of associations between crash characteristics across dates in 2020 with data from various temporal levels. The key findings of this study demonstrate that associations between crash characteristics at lower temporal levels show notable and potential cyclical variations across dates, while more stable and long-term trend of associations between crash characteristics may be identified as the temporal level increases, especially at the two-year level and higher temporal levels at which temporal stability may be expected. The study contributes to the literature by presenting a challenge for traffic analysts in that both temporally stable and unstable associations between crash characteristics may be observed at any point in time when different temporal levels are considered as study periods. Therefore, it may serve as a foundation for future research and practical works to identify traffic safety issues and optimal policies as well as facilitate the interpretation of statistical modeling in the presence of temporally unstable data.

• Record URL:
  • https://doi.org/10.1016/j.aap.2022.106590
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0001457522000264
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Supplemental Notes:
  • © 2022 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Hsu, Tien-Pen
  • Wu, Yuan-Wei
  • Chen, Albert Y
• Publication Date: 2022-4

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 106590
• Serial:
  • Accident Analysis & Prevention
  • Volume: 168
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0001-4575
  • Serial URL: http://www.sciencedirect.com/science/journal/00014575

Subject/Index Terms

• TRT Terms: Crash characteristics; Crash data; Data quality; Time of crashes; Traffic characteristics
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01841044
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 29 2022 11:05AM