Applying Machine Learning Approaches to Analyze the Vulnerable Road-Users' Crashes at Statewide Traffic Analysis Zones

In this paper, the authors present machine learning techniques to analyze pedestrian and bicycle crash by developing macro-level crash prediction models. The authors collected the 2010–2012 Statewide Traffic Analysis Zone (STAZ) level crash data and developed rigorous machine learning approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To the authors’ knowledge, this is the first application of DTR models in the burgeoning macro-level traffic safety literature. The DTR models uncovered the most significant predictor variables for both response variables (pedestrian and bicycle crash counts) in terms of three broad categories: traffic, roadway, and socio-demographic characteristics. Additionally, spatial predictor variables of neighboring STAZs were considered along with the targeted STAZ in both DTR models. The DTR model considering spatial predictor variables (spatial DTR model) were compared without considering spatial predictor variables (aspatial DTR model) and the model comparison results discovered that the prediction accuracy of the spatial DTR model performed better than the aspatial DTR model. Finally, the current research effort contributed to the safety literature by applying some ensemble techniques (i.e. bagging, random forest, and gradient boosting) in order to improve the prediction accuracy of the DTR models (weak learner) for macro-level crash count. The study revealed that all the ensemble techniques performed slightly better than the DTR model and the gradient boosting technique outperformed other competing ensemble techniques in macro-level crash prediction models.

• Record URL:
  • https://doi.org/10.1016/j.jsr.2019.04.008
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0022437518304146
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/1800052
• Supplemental Notes:
  • Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Rahman, Md Sharikur
  • Abdel-Aty, Mohamed
  • Hasan, Samiul
  • Cai, Qing
• Publication Date: 2019-9

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 275-288
• Serial:
  • Journal of Safety Research
  • Volume: 70
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0022-4375
  • Serial URL: http://www.sciencedirect.com/science/journal/00224375

Subject/Index Terms

• TRT Terms: Bicycle crashes; Crash analysis; Machine learning; Mathematical prediction; Pedestrian vehicle interface; Traffic analysis zones; Traffic safety; Vulnerable road users
• Subject Areas: Data and Information Technology; Highways; Pedestrians and Bicyclists; Safety and Human Factors;

Filing Info

• Accession Number: 01711709
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 22 2019 7:58AM