Abstract Manager

In the age of big data, vast amounts of information are continuously collected by municipalities, public agencies, and private entities. Too often, these datasets are used for a single purpose and then archived, despite their potential for broader applications. While each dataset may be partial, incomplete, or outdated, taken together they can provide a strong foundation for mobility analysis without requiring large new data collection efforts. This opportunity guided the City of Laval—Quebec’s third-largest municipality and Canada’s thirteenth—to explore whether a comprehensive mobility diagnostic could be achieved solely with existing and open data.

The project began with a systematic review of available sources. Municipal datasets included traffic counts, collision records, land use information, aerial imagery, and traffic signal programming. Open data sources such as OpenStreetMap and GTFS feeds from public transit providers were also incorporated. Additional datasets from provincial or federal agencies, as well as subscription-based products providing congestion or travel time information, were considered when relevant.

Based on these inputs, structural networks were identified for all modes of transportation. For private vehicles and active modes, traffic counts and the location of trip generators were key indicators. For public transit, the existing structural network was defined by current routes, validated by cross-referencing service coverage with the accessibility of trip generators.

The diagnostic process then evaluated the robustness and performance of these networks. Key questions included: Are structural networks accident-prone? Do they align with current and planned land uses? Is the roadway footprint sufficient for all modes, or should reallocation strategies such as road diets be considered? Are certain corridors oversaturated while others remain underutilized? Are there critical gaps in the networks for active or transit modes? For public transit and active transportation, additional analyses focused on the relationship between infrastructure supply and observed demand.

The results demonstrated that a multimodal mobility diagnostic can be conducted effectively using only existing and open data sources. The methodology produced a complete picture of Laval’s mobility system, addressing safety, accessibility, capacity, and land use integration. Importantly, the approach is reproducible in other municipalities, adaptable to varying levels of data availability, and relatively low-cost compared to conventional large-scale data collection campaigns.

This study highlights the untapped potential of open and existing data for mobility planning. By strategically combining available datasets, municipalities can gain actionable insights into their transportation networks while reducing reliance on expensive new surveys.