Abstract Manager

Most jurisdictions implement traffic calming on a request basis, with residents needing to submit forms or gather signatures to initiate a traffic calming project. While this method has its merits, the equity implications are problematic – not all communities have the same access to time, knowledge, and wherewithal to even know these programs exist, let alone apply for them. 

To address this, the City of Vancouver developed a prioritization framework to identify neighbourhoods that would most benefit from traffic calming. Using collision data, demographic data, and the locations of civic amenities like libraries, schools and community centres, the City created an initial framework to narrow down the candidate neighbourhoods for area-wide traffic calming projects. This framework was then validated with traffic data from a Big Data platform that uses cellphone location data to understand how people move through the city. The results were layered over the City’s equity map and maps of other city projects, to identify opportunities for co-benefits. 

In this presentation, we will share our process for developing the Neighbourhood Traffic Management Selection Framework, including data sources and initial results, with the goal of empowering other jurisdictions to take a more data-driven and equity focused approach to prioritizing projects.