New Project (TRCLC 15-7)

TRCLC 15-7: Integrated Crowdsourcing Platform to Investigate Non-Motorized Behavior and Risk Factors on Walking, Running and Cycling Routes

PI: Stephen Mattingly, University of Texas Arlington

Abstract

Most agencies and decision-makers rely on crash and crash severity (property damage only, injury or fatality) data to assess transportation safety; however, in the context of public health where perceptions of safety may influence the willingness to adopt active transportation modes (e.g. bicycling and walking), pedestrian-vehicle and other similar conflicts may represent a better performance measure for safety assessment. For transportation safety, a clear conflict occurs when two parties’ paths cross and one of the parties must undertake an evasive maneuver (e.g. change direction or stop) to avoid a crash. Other less severe conflicts where paths cross but no evasive maneuver occurs may also impact public perceptions of safety. Most existing literature on conflicts focuses on vehicle conflicts and intersections. While some research has investigated bicycle and pedestrian conflicts, most of this as focused on the intersection environment. In this project, we propose field testing a crowd-sourced data app to better understand the continuum of conflicts (bicycle/pedestrian, bicycle/vehicle, and pedestrian/vehicle) experienced by pedestrians and cyclists commuting to school; the study also tests the effectiveness the app and its associated crowd-sourced data collection. 

This study assesses the data quality of the crowd sourced data and compares it to more traditional data sources and data collection strategies and their related performance measures. Additionally, the research team investigates the process of developing crowd-sourced data and the community level factors impacting app and data collection success. If widely adopted, the app will enable communities to create their own data collection efforts to identify dangerous sites within their neighborhoods. Agencies will have a valuable data source at low-cost to help inform their decision making related to bicycle and pedestrian education, enforcement, infrastructure, programs and policies.