Near Miss Metrics
Overview[edit | edit source]
Discuss difficulties of traditional gap studies.
Advances in processing technology - video and LIDAR
Allow for constant evaluation that gap studies do not
"Near-miss and near-collision investigations are fast considered to be an essential tool for effective risk management"[1].
Timeline[edit | edit source]
Discuss different timelines of the technology
Key Definitions[edit | edit source]
According to Occupational Safety and Health Administration (OHSA), near misses "describe incidents where no property was damaged and no personal injury sustained, but where, given a slight shift in time or position, damage and/or injury easily could have occurred."
Near-miss reporting systems are typically voluntary, non-punitive, confidential, proactive, and used as a tool for continuous improvement.[2]
Analysis of Implications[edit | edit source]
Safety[edit | edit source]
Fear of injury is cited as a barrier to cycling, and near misses (or non-injury incidents) can contribute to this fear. Examples of near-misses include "problematic passing maneuvers" of people cycling by people driving. Higher rates of near-misses for people cycling occur in morning peak hours. Slower cyclist experience a higher rate of near misses.[3]
Studying near misses provides "an opportunity to improve safety practices based on a condition, or an incident with a potential for more serious consequences."[2]
Today, near-miss data is captured by video and sensor data recorders. There is a need to identify and improve ways to process the data to identify near-miss events.[4]
Equity[edit | edit source]
Public Health[edit | edit source]
Benefits[edit | edit source]
Case Studies[edit | edit source]
SFMTA
GDOT
Other studies
"Players in the Field"[edit | edit source]
GDOT
SFMTA
Georgia Tech Research Institute
University of Toronto (need to double check)
References[edit | edit source]
- ↑ Why Near Collision and Near Miss Analysis Matters. [1]
- ↑ 2.0 2.1 Bureau of Transportation Statistics. Marine Board Spring Meeting. TRB presentation. 2014. [2]
- ↑ Aldred, R., & Crosweller, S. (2015). Investigating the rates and impacts of near misses and related incidents among UK cyclists. Journal of Transport & Health, 2(3), 379-393.[3]
- ↑ Yamamoto S., Kurashima T., Toda H. (2020) Identifying Near-Miss Traffic Incidents in Event Recorder Data. In: Lauw H., Wong RW., Ntoulas A., Lim EP., Ng SK., Pan S. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2020. Lecture Notes in Computer Science, vol 12085. Springer, Cham.[4]