Developing an Active Traffic Management System for I-70 in Colorado

Details

• Creators:
  Abdel-Aty, Mohamed ; Ahmed, Mohamed M. ; Yu, Rongjie ; Qi, Shi
• Corporate Creators:
  University of Central Florida. Center for Advanced Transportation Systems Simulation
• Corporate Contributors:
  Colorado. Dept. of Transportation. Research Branch ; United States. Federal Highway Administration
• Subject/TRT Terms:
  • Speed limits
  • Automatic vehicle identification
  • Remote sensing
  • Microwave detectors
  • Sensors
  • Data mining
  • Crashes
  • Real time information
  • Mathematical prediction
  • Mathematical models
  More +
• Publication/ Report Number:
  CDOT-2012-9
• Resource Type:
  Tech Report
• Geographical Coverage:
  Colorado
• Edition:
  Final.
• Corporate Publisher:
  Colorado. DTD Applied Research and Innovation Branch
• Abstract:
  The Colorado DOT is at the forefront of developing an Active Traffic Management (ATM) system that not only ; considers operation aspects, but also integrates safety measures. In this research, data collected from Automatic ; Vehicle Identification (AVI), Remote Traffic Microwave Sensors (RTMS) and Real-Time weather data were utilized ; to incorporate safety within the ATM system. Preliminary investigation of crashes along 20-miles of I- 70 revealed ; that the mountainous terrain and adverse weather during the winter season may increase crash likelihood. A traditional ; automatic incident detection system is a reactive approach to mitigating the effects of crashes without attempting to ; avoid primary incidents. To reduce the risk of primary incidents, a more proactive approach that identifies locations ; where a crash is more likely to happen in real-time can be implemented. ; The results from the research study suggest that there is a clear demand to incorporate real-time weather conditions ; and roadway geometric characteristics within the development of the ATM system. Remote Traffic Microwave ; Sensors, AVI, weather data, and road geometry information were collected and utilized to develop a real-time risk ; assessment system. Data Mining (DM) techniques were also used to reveal important data relationships and improve ; prediction accuracy. Based on the data and DM techniques, models were tested and their performances were ; compared. Results show that the Full Model which incorporates AVI, RTMS data, weather data, and geometric ; information outperforms other models by identifying about 89% of crash cases in the validation dataset with only ; 6.5% false positive.
  More +
• Format:
  PDF
• Funding:
  92.11
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/25061/dot_25061_DS1.pdf
• File Type:
  [PDF - 1.91 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:5ae515e4ee4d57ec9fdffa0f1018ef13bc16ed371824cc8863a124f33d6a222a1e12504527e4651d2b83317806b379aa162ffee0088a6cf321575f2b498b27e9