Non-linear effects of the built environment on automobile-involved pedestrian crash frequency: A machine learning approach

Although a growing body of literature focuses on the relationship between the built environment and pedestrian crashes, limited evidence is provided about the relative importance of many built environment attributes by accounting for their mutual interaction effects and their non-linear effects on automobile-involved pedestrian crashes. This study adopts the approach of Multiple Additive Poisson Regression Trees (MAPRT) to fill such gaps using pedestrian collision data collected from Seattle, Washington. Traffic analysis zones are chosen as the analytical unit. The effects of various factors on pedestrian crash frequency investigated include characteristics the of road network, street elements, land use patterns, and traffic demand. Density and the degree of mixed land use have major effects on pedestrian crash frequency, accounting for approximately 66% of the effects in total. More importantly, some factors show clear non-linear relationships with pedestrian crash frequency, challenging the linearity assumption commonly used in existing studies which employ statistical models. With various accurately identified non-linear relationships between the built environment and pedestrian crashes, this study suggests local agencies to adopt geo-spatial differentiated policies to establish a safe walking environment. These findings, especially the effective ranges of the built environment, provide evidence to support for transport and land use planning, policy recommendations, and road safety programs.

• Record URL:
  • https://doi.org/10.1016/j.aap.2017.12.026
• Record URL:
  • https://www.sciencedirect.com/science/article/pii/S0001457517304724
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Ding, Chuan
  • Chen, Peng
  • 0000-0002-3201-7339
  • Jiao, Junfeng
• Publication Date: 2018-3

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 116-126
• Serial:
  • Accident Analysis & Prevention
  • Volume: 112
  • Publisher: Elsevier
  • ISSN: 0001-4575
  • Serial URL: http://www.sciencedirect.com/science/journal/00014575

Subject/Index Terms

• TRT Terms: Built environment; Crash causes; Crash rates; Machine learning; Pedestrian vehicle crashes; Traffic analysis zones; Traffic safety
• Geographic Terms: Seattle (Washington)
• Subject Areas: Highways; Pedestrians and Bicyclists; Safety and Human Factors;

Filing Info

• Accession Number: 01659787
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 12 2018 10:01AM