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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: Conclusion: Trajectories through BSM (CV) and vBSM

Operational applications using CV data and solving penetration rate

[This slide contains four images: (1) a photo of a multilane road, (2) a photo from the driver’s seat of a car looking out to a multilane road, (3) a photo of traffic signals pointing out radar detection systems installed on the pole, and (4) a diagram showing the relationship between computer equipment, trays, and iPhones.]

Slide 2: Project #2 ‐ Proactive Traffic Incident Management

HERE traffic data

ALGO incident data

CARE crash data

Data mining → Identification & Prediction

  • Identify incidents before reported
  • Predict risk of incidents before happening
  • Estimating impacts of incidents for proactive traffic management

[This slide contains six images: (1) a photo of three fire trucks, (2) an image of a road and lines of binary code running along the road, (3) the logo for HERE, (4) the logo of Algo, (5) the logo for CARE, and (6) a word cloud showing the “data,” “mining,” and “learning” as the largest words.]

Slide 3: Project Objective

To develop a set of data-driven tools to support proactive traffic operations and incident management on Alabama interstates and principal arterials.

  • A tool to detect the incidents/crashes based on the traffic flow dynamics
  • A tool to predict the risk of an incident or crash according to the traffic flow dynamics
  • A tool to estimate the influence of an incident/crash on traffic flows

[This slide contains one diagram: (1) a flowchart titled “Empirical Analysis and Modeling” showing the relationship between the empirical analysis and modeling, real‐time forecasting tools, and implementation for the future.]

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