Abstract Manager

The road safety management process employed by jurisdictions, including the network screening used to identify priority locations, relies heavily on collision frequency as the primary object of study. Literature has widely reported that the nature of collisions can lead to certain risky sites being overlooked. Such limitations include: (1) collisions are rare events, (2) collisions are underreported, and (3) procedures that rely solely on collision frequency are inherently reactive.

With the advent of new technologies, additional sources of information are now available to enhance the traditional collision-based approach. The objective of this Transport Canada funded project is to integrate Connected Vehicle (CV) data into the jurisdictional road safety management process.

In this project, CV data refer to movement data (i.e., position and speed) and event data (i.e., instances of harsh braking and harsh acceleration). Specifically, the project used CV data to: (1) enhance the calibration of Safety Performance Functions (SPFs), (2) proactively complement network-wide assessments by identifying locations with low collision frequency but high CV event frequency, (3) support in-service road safety reviews with detailed information on CV events at specific locations, and (4) support network-wide monitoring of traffic speeds.

CV data were acquired for the entire province of Ontario and Alberta, with pilot cases developed for the City of Mississauga, City of Waterloo, and City of Lethbridge. Incorporating CV events as explanatory variables in SPF development resulted in improved models, showing better goodness-of-fit and accuracy metrics compared to traditional models based solely on traffic exposure and geometric characteristics. Additionally, in both jurisdictions, it was possible to identify locations with relatively low frequency of collisions but high frequency of CV events, allowing a proactive assessment of these sites by the municipal transportation authorities. Furthermore, speed visualization tools – allowing visualization of average speed, 85th percentile speeds, or differential speeds (i.e., 85th percentile minus speed limit) across different levels of time periods (e.g., weekdays AM peak or weekends off-peak) – enabled jurisdictions to gain a more complete understanding of operational speeds across the road network.