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A Digital Twin for Traffic Monitoring & Proactive Incident Management
(May 11, 2021)

Presenter: Jun Liu, PhD
Presenter’s Org: College of Engineering, University of Alabama

T3 and T3e webinars are brought to you by the Intelligent Transportation Systems (ITS) Professional Capacity Building (PCB) Program of the U.S. Department of Transportation (USDOT)’s ITS Joint Program Office (JPO). References in this webinar to any specific commercial products, processes, or services, or the use of any trade, firm, or corporation name is for the information and convenience of the public, and does not constitute endorsement, recommendation, or favoring by the USDOT.


[The slides in this presentation contain the University of Alabama’s logo.]

Slide 1: Takeaways

  • Distance to the nearest ramp, AADT and number of through lanes play a critical role in the estimation of spatiotemporal impacts of traffic crashes
  • Random Forest model is associated with improved performance

[This slide contains a photograph of several overlapping multilane highways.]

Slide 2: Conclusion

  • Project #1 ‐ West Central Alabama ACTION Initiative (USDOT ATCMTD Grant)
    • A framework to develop a Digital Twin to monitor network‐wide traffic
    • A method to reconstruct complete vehicle routes in network
    • Implementation in SUMO for real‐time traffic simulation
    • Traffic signal optimization for connected vehicles
    • Future work:
      • Large network implementation of SUMO simulation
      • Network‐wide signal optimization and implementation
      • Simulation capturing traffic events (e.g., congestion and road closure)
      • Vehicle route reconstruction accuracy & installation of sensors
  • Project #2 ‐ Proactive Traffic Incident Management (ALDOT RAC Award)
    • Machine learning models to predict crash risk based on probe data
    • Machine learning models to estimate the spatiotemporal impacts of traffic crashes
    • Future work
      • Model validation
      • Testing more models
      • Expand the investigation to entire arterial and interstate network, and all incidents (non‐crash incidents)
      • Predictability of risk for different types of crashes or incidents

Data and Simulation and Machine Learning for Transportation Systems Management and Operations

Slide 3: Acknowledgements

[This slide contains a handwritten thank you message with a smiley face.]

Slide 4: Thank You! Questions?

Contact Information

Jun Liu, jliu@eng.ua.edu

Alex Hainen, ahainen@eng.ua.edu

[This slide contains a photo of the engineering quad at the University of Alabama.]

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