Assessing Pedestrian Safety on Roads Through Machine Learning Approaches for State Highways in Washington State

Details

• Creators:
  Wang, Yinhai ; Sun, Wei ; Ricord, Sam ; Souza, Cesar Maia de
• Corporate Creators:
  University of Washington ; University of Alaska Fairbanks. Center for Safety Equity in Transportation (CSET)
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Safety
  • Data
  • Traffic lanes
  • Traffic safety
  • Pedestrian safety
  • Crashes
  • Crash severity
  • Machine learning
  • Statistical analysis
  • Classification
  More +
• Publication/ Report Number:
  INE/CSET 24.09
• Resource Type:
  Tech Report
• Geographical Coverage:
  Washington ; United States
• Edition:
  Final report, Sep 2019 - Sep 2022
• Corporate Publisher:
  University of Alaska Fairbanks. Center for Safety Equity in Transportation (CSET)
• Abstract:
  The report presents a unique contrast for several Machine Learning approaches aiming at understanding pedestrian fatal collisions. Four classification techniques are applied to assess how roadway features mainly correlate to pedestrian fatal crashes: Logistic Regression, Nearest Neighbor Classification, Decision Tree, and Random Forest Classifier. The data used in this project was collected from the Highway Safety Information System (HSIS) database, which provides both collision data for the entire state of Washington and roadway characteristics for all state highways. Each of the four modeling approaches was implemented using K-fold cross-validation, a process that allows choosing the best parameters for the model. Their results were evaluated and then compared in terms of accuracy score and confusion matrices for the testing data set. It was found that the Decision tree had consistent results and the best performance among all models, showing how the distinct predictors relate to each other to predict fatal pedestrian collisions.
• Format:
  PDF
• Funding:
  69A34520501020620
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/76521/dot_76521_DS1.pdf
• File Type:
  [PDF - 1.32 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:7943caabcbc5a33aeb5a0212bb93097554212aec018b84c5bdd1a07f21753e0e07e41afce0b03f6debdd166c66edc86e1ca2a3a46e3393f6797383a7e2497186