Tamer BatainehTransportation Engineer, EIT

Projects / Traffic Safety Analysis / LiDAR & Trajectory Analytics

Roadside LiDAR Safety Indicators at Signalized Intersections

Used roadside LiDAR vehicle trajectories at five high-crash Lubbock intersections to find which safety indicators best predict each type of collision.

5

high-crash signalized intersections

6 + 2

proximity and evasive-action safety indicators tested

3

collision types — same-direction, opposite-direction and angle

Problem

Crash records are slow to build up and often under-reported. Agencies need proactive measures that flag risky driving before crashes happen, and they need to know which measure fits which kind of collision.

My role

First author of the published study.

Approach

  1. Collected vehicle trajectories with a Velodyne VLP-32 roadside LiDAR (10 frames per second, 100-m range) mounted on a signal pole at five high-crash signalized intersections in Lubbock, over five weekday evening peaks in October 2022.
  2. Extracted vehicle trajectories from the 3D point clouds.
  3. Calculated six proximity indicators (TTC, PET, DRAC, MTTC, PSD, ACT) and two evasive-action indicators (maximum deceleration and yaw rate).
  4. Modeled crash probabilities with bivariate extreme value theory for same-direction, opposite-direction and angle collisions.

Results

MTTC and ACT worked best for same-direction conflicts, PET and ACT for angle conflicts, and PET for opposite-direction conflicts. Maximum deceleration generally outperformed yaw rate as the evasive-action indicator.

The method gives collision-type-specific thresholds for safety indicators that can guide targeted safety treatments.

Deliverables & links

Agency project

Crash Data Analysis Dashboards for Four TxDOT Districts

Interactive ArcGIS crash dashboards built from TxDOT CRIS data for the Lubbock, Amarillo, Fort Worth and Childress Districts, plus dashboards for the Lubbock MPO and the City of Lubbock.

4 TxDOT districts