Safe-D: Safety through Disruption

Exploring Crowdsourced Monitoring Data for Safety

Abstract

This project encompasses four different activities to explore safety applications of emerging crowd-sourced data and datasets available from commercial aggregators. The first activity examines systems used to monitor and count pedestrian activity. Developing crash rates for these vulnerable users depends on knowing the volume of activity. Data from metropolitan planning organizations as well as commercial and community-based reports of bicycle and pedestrian activity will be assessed for accuracy by comparing them to manual counts. The second activity will examine waypoint travel pattern data from a commercial data aggregator to determine its applicability to safety analyses. The evaluation will assess the geographic and temporal distribution and representativeness of the sample as well as how roadways of different functional classes are represented. The third activity will examine data from a crowd-sourced traveler information app to determine if incident reports in the app correspond to official incident records. The task will develop a data catalogue identifying data attributes, coverage areas, resolution of the data, and timeliness of the data. Overall these three exploratory efforts will provide future research projects an understanding of what is available from these new big data sources and how they could be used to improve safety.

Project Highlights

  • We found promising correlations when comparing StreetLight Data bicyclist metrics to field-collected bicyclist counts.
  • We found the Miovision video system to have fairly accurate pedestrian counting ability.
  • We concluded that Waze traffic incident data was suitable to identify high-risk locations.

Final Report

TTI-Student-05 Final Report

EWD & T2 Products

Student Impact Statement (pdf): Multiple graduate and undergraduate students were funded under this project from TTI. This file contains a statement of the impact this project made on these students’ education and workforce development.

Code on Github (link): TTI Assistant Research Scientist Lingtao Wu, Ph.D., helped in developing a replicable framework for the spatial join, which is available as open source code on Github.

Presentations/Publications

Le, M. “Video Analytics for Counting Pedestrians.” To be presented at the Texas Trails & Active Transportation Conference, San Antonio, TX, March 25, 2020.

Wang, R., S. Das, and A. Mudgal. “Patterns of Origin Destination Distributions: Rules Mining using Massive GPS Trajectory Data.” Proceedings of UDS’20: First International Conference on Urban Data Science, January 20-21, Madras, India.

Li, X., B. Dadashova, S. Turner, and D. Goldberg. “Rethinking Highway Safety Analysis by Leveraging Crowdsourced Waze Data.” Presented at the 99th TRB Annual Meeting, Washington, DC, January 12-16, 2020.

Turner, S. “Making Sense of Emerging Data for Nonmotorized Transportation.” Presented at the 99th TRB Annual Meeting, Washington, DC, January 12-16, 2020.

Final Dataset

The final datasets for this project are located in the Safe-D Collection on the VTTI Dataverse; DOI: 10.15787/VTT1/OBV82F, DOI: 10.15787/VTT1/351GZJ, DOI: 10.15787/VTT1/81SKJW.

Research Investigators (PI*)

Shawn Turner (TTI-TAMU)*
Bahar Dadashova (TTI-TAMU)
Subasish Das (TTI-TAMU)
Greg Griffin (TTI-TAMU)

Project Information

Start Date: 2019-03-01
End Date: 2019-08-31
Status: Complete
Grant Number: 69A3551747115
Total Funding: $70,000
Source Organization: Safe-D National UTC
Project Number: TTI-Student-05

Safe-D Theme Areas

Big Data Analytics

Safe-D Application Areas

Planning for Safety

More Information

RiP URL
UTC Project Information Form

Sponsor Organization

Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC 20590 United States

Performing Organization

Texas A&M University
Texas A&M Transportation Institute
3135 TAMU
College Station, Texas 77843-3135
USA