Multimodal Planning in Toronto, Canada
Toronto, ON, Canada
Toronto used StreetLight InSight to obtain areawide travel behavior data for two multimodal planning studies. The data was used to forecast demand for planned infrastructure, model the impact of transit and active transportation on specific corridors, and find opportunities to shift vehicle trips to other modes.
Initial: 20 Thousand USD
General Fund/Existing Public Funds
Operational since 2016
The City of Toronto is planning vibrant, mixed-use neighborhoods around future transit infrastructure and an active, shared mobility network.
We focused on planning studies for three “Gateway Mobility Hubs” that will provide convenient, affordable, multimodal modes and improve connections to transit options in Toronto’s outskirts. In particular, the city needed areawide travel data to create accurate projections and to estimate demand for new infrastructure.Traditional data do not capture the sources, patterns, and dynamic nature of trips. However, these measurements are important for prioritizing the best tactics to transform auto-oriented networks into truly multimodal networks. To get the information required, Toronto planners used an on-demand big data analytics platform, StreetLight InSight®.
The StreetLight InSight platform provided the city with on-demand access to customize, real-world transportation analytics.
The analytics were derived from location data created by mobile devices and connected cars. Planners in Toronto used the platform to design and run their own regional and corridor-level origin-destination and traffic infiltration studies. They analyzed both personal vehicle and commercial truck travel behavior. Use of StreetLight InSight helped the city save time and money. The platform allowed them to quickly optimize and re-run analytics for developing scenarios until they had the most context-sensitive mobility solutions.They also benefited form a larger sample than surveys and more complete origin-destination information than sensors, without intensive human labor. They could also "go back in time" to collect pre-construction data. Planners could also easily calibrate StreetLight InSight analytics with local sensor data for trip distribution and model calibration. Note: costs listed are for all 3 studies.
Obtained data that was not available from any other data collection tool.
Built data-driven forecasts of future demand for transit.
Identified the highest potential locations for active transportation and transit infrastructure.
Quantified traffic from commercial trucks and personal vehicles from specific areas that used specific roadways.
It is one of the first uses of big data analytics for multimodal planning, and it is a great example of bringing transit and active transportation options beyond a city's downtown core.
Who Should Consider
Communities of all sizes that are exploring new potential transit and active transportation infrastructure and/or are building demand forecasts for new multimodal transportation options.
Last UpdatedJun 20th, 2018