1. Introduction to Vehicle Trajectory Processing Tools

2. Vehicle Trajectories Contain a Rich Set of Performance and Demand Statistics – Mobility: Avg travel time, and avg speed (derived from travel time) – Reliability: End-to-end travel distribution, mean, standard deviation, percentile travel time, travel time buffer index – Demand temporal selection: vehicle trajectories from multiple days – Spatial Selection: Network, corridor-level, Subarea and selected link analysis

3. Processing GPS or Sensor Trajectory Data TomTom GPS Probe Data – Map matching algorithm to process trajectory data – New York Regional Network – About 3 weeks of data 3

4. Probe Data Coverage: 3D Visualization Height = volume of traffic probe data Color code = speed limit

5. Probe Data Coverage Height = volume of traffic probe data Color code = % of speed limit

6. Open-Source Vehicle Trajectory Processor: Process and compare both simulated and observed trajectory Data 6 Development Site: https://github.com/xzhou99 /dtalite_beta_test

7. Compare Simulated vs. Observed Travel Time (Mean and Variability) 7

8. Rich Visualization Tool for Subarea Reliability MOEs

9. New Enhancements – From DYNASMART trajectory format to Dynus-T format – From single day to multi-day analysis Multiple days of simulation in In VehTrajectory binary file – Incorporate multiple days of Inrix speed data – Next steps: Dynus-T simulated trajectory data Multiple day of sensor data (from Inrix or HERE?)