Macro-Level Accident Modeling in Novi Sad: A Spatial Regression Approach

In this study, a macroscopic analysis was conducted in order to identify the factors which have an effect on traffic accidents in traffic analysis zones. The factors that impact accidents vary according to the characteristics of the observed area, which in turn leads to a discrepancy between research and practice. The total number of accidents was observed in this paper, along with the number of motorized and non-motorized mode accidents within a three-year period in the city of Novi Sad. The models used for this analysis were spatial predictive models comprised of the classical predictive space model, spatial lag model and spatial error model. The spatial lag model showed the best performances concerning the total number of accidents and number of motorized mode accidents, whereas the spatial error model was prominent within the number of non-motorized mode accidents. The results found that increasing Daily Vehicle-Kilometers Traveled, parking spaces, 5-legged intersections and signalized intersections increased all types of accidents. The other demographic, traffic, road and environment characteristics showed that they had a different effect on the observed types of accidents. The results of this research can be beneficial to researchers who deal with traffic engineering, space planning as well as making decisions with the aim of preparing countermeasures necessary for road safety improvement in the analyzed area.

• Record URL:
  • https://doi.org/10.1016/j.aap.2019.105259
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0001457519306190
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Supplemental Notes:
  • © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Pljakić, Miloš
  • Jovanović, Dragan
  • Matović, Boško
  • Mićić, Spasoje
• Publication Date: 2019-11

Language

• English

Media Info

• Media Type: Web
• Features: Figures; Maps; References; Tables;
• Serial:
  • Accident Analysis & Prevention
  • Volume: 132
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0001-4575
  • Serial URL: http://www.sciencedirect.com/science/journal/00014575

Subject/Index Terms

• TRT Terms: Crash causes; Crash characteristics; Crash data; Macroscopic traffic flow; Mathematical models; Traffic analysis zones
• Geographic Terms: Novi Sad (Serbia)
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01716991
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 18 2019 9:16AM