Building AI and Machine Learning Technologies for Enhancing Transportation Station Area Safety in San Jose, CA

Criminal activities often cluster around transportation hubs like transit stations. While accurate crime prediction tools enhance crime prevention, mobility, and transportation equity, few research integrates historical crime data, transportation networks, and hub locations using artificial intelligence (AI). This project develops a machine learning algorithm with the implementation of the software package in Python, leveraging a multi-layered geo-statistical model to predict crimes within transportation systems, enhancing safety and increasing ridership. Unlike past tools focusing solely on historical crime or land use data, this tool combines transportation network insights with hub locations taking advantage of rigorous statistical model. This software package enables local jurisdictions to allocate resources more effectively, plan interventions, and strengthen public safety.

Language

• English

Project

• Status: Active
• Funding: $150000
• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Howard University

  2400 6th Street, NW
  Washington, DC  United States  20059
• Performing Organizations:

  San Jose State University

  1 Washington Sq
  San Jose, California  United States  95192
• Start Date: 20231101
• Expected Completion Date: 20241031
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Artificial intelligence; Crimes involving transportation; Forecasting; Machine learning; Passenger terminals; Transit safety
• Geographic Terms: San Jose (California)
• Subject Areas: Passenger Transportation; Planning and Forecasting; Public Transportation; Safety and Human Factors; Terminals and Facilities;

Filing Info

• Accession Number: 01931101
• Record Type: Research project
• Source Agency: Research and Education in Promoting Safety (REPS) University Transportation Center
• Files: UTC, RIP
• Created Date: Sep 17 2024 4:27PM