Applying AI to Data Sources to Improve Driver-Pedestrian Interactions at Intersections [supporting dataset]

Details

• Alternative Title:
  Applying AI to Data Sources to Improve Driver-Pedestrian Interactions at Intersections [R43]
• Creators:
  Chakraborty, Subhadeep ; Khattak, Asad ; Nordback, Krista ; Sanyal, Jibonananda
• Corporate Creators:
  University of Tennessee, Knoxville. Center for Transportation Research ; University of North Carolina at Chapel Hill ; Collaborative Sciences Center for Road Safety
• 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 ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  Artificial Intelligence Behavior Crashes Driver-pedestrian Edge Cases Drivers Driving Behavior Fatalities Highways Intersection Crashes Human Factors Intersections Machine Learning Nighttime Crashes Optimization Pedestrian Detectors Pedestrians Pedestrian Vehicle Interface Traffic Safety Traffic Signal Control Systems Traffic Signals

  More +
• Publication/ Report Number:
  CSCRS-R43
• DOI:
  https://doi.org/10.15139/S3/C6DHKY
• Resource Type:
  Dataset
• Geographical Coverage:
  United States
• Corporate Publisher:
  Collaborative Sciences Center for Road Safety
• Abstract:
  Tragically, roadway crossings sometimes act as killing fields, especially for pedestrian-vehicle collisions, with intersections accounting for 40% of transportation crashes in the US. This emphasizes the importance of effectively addressing pedestrian safety at intersections to mitigate such incidents. The first chapter of this report incorporates pedestrian safety into the optimization of traffic signals by collecting and linking data from traffic signals (cameras) and analyzing the behaviors of pedestrians and drivers at intersections using Artificial Intelligence techniques, i.e., a decentralized Dyna Q-Learning environment. The results indicate that AI agents may safely prioritize pedestrian service even with longer waiting times or reduce pedestrian delays at the expense of vehicle delay performance. The report’s second chapter explores rare pedestrian crashes at intersections, called “corner cases,” using Fatality Analysis Reporting System (FARS) data and applying text analytics and the K-means unsupervised learning approach. Such crashes are likely to be triggered by a combination of factors, including poor visibility, severe weather, impaired pedestrian or driver behaviors, and dark lighting conditions. The final chapter of the research investigates the determinants of nighttime pedestrian crash injury severity in pedestrian-involved crashes on intersections using the Random Forest algorithm and ordered logit models. The analysis results reveal that alcohol impairment, foggy weather, elderly pedestrians, a speed limit of 50-55 mph, and motorists not yielding to pedestrians are more likely to contribute to severe pedestrian injuries at intersections. The implications of the findings are discussed in each chapter.
  
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• Content Notes:
  National Transportation Library (NTL) Curation Note: As this dataset is preserved in a repository outside U.S. DOT control, as allowed by the U.S. DOT’s Public Access Plan (https://doi.org/10.21949/1503647) Section 7.4.2 Data, the NTL staff has performed NO additional curation actions on this dataset. This dataset has been curated to CoreTrustSeal's curation level "C. Initial Curation." To find out more information on CoreTrustSeal's curation levels, please consult their "Curation & Preservation Levels" CoreTrustSeal Discussion Paper" (https://doi.org/10.5281/zenodo.11476980). NTL staff last accessed this dataset at its repository URL on 2024-09-17. If, in the future, you have trouble accessing this dataset at the host repository, please email NTLDataCurator@dot.gov describing your problem. NTL staff will do its best to assist you at that time.
• Format:
  ZIP
• Funding:
  69A3551747113
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:83d4e87cdffd5b3177d978d9817cd27d42a1ab504d6dd54873e82f525aa5ea71b87a4080ce9fcc6dc59ed838d6d74e3def277fbef1178abc53e8318235a01dda
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/77567/dot_77567_DS1.json
• File Type:
  [JSON - 4.82 KB ]