28-05-2025
There's a hidden pattern behind Toronto's awful congestion problem. We need to find it before we can fix it
Torontonians waste an average of 142 hours annually sitting in traffic, costing the region an estimated $6 billion in lost productivity each year.
Beyond the economic impact, this congestion contributes significantly to our carbon footprint and diminishes our quality of life.
Will building new lanes above and below Highway 401 solve our traffic congestion problems?
Is now the right time to close a lane on the Gardiner Expressway for construction?
Should we build more bike lanes or remove the ones already installed?
When the Eglinton Crosstown LRT finally opens, and the Ontario Line after that, will congestion improve?
Is congestion charging the answer?
Is building a tunnel under Highway 401 the best way to help reduce traffic congestion?
R.J. Johnston Toronto Star
To answer these questions, we must first recognize that transportation data is inherently spatial — it is geographical information about where and when movement occurs. Traditional data analysis struggles with these kinds of questions because it treats this rich spatial data as entries in a spreadsheet, stripping away critical real-world context.
The solution is clear: we need a geographic approach, a way of understanding and solving real-world problems by making sense of data through the lens of location. This approach is enabled by geographic information system (GIS) technology.
For example, Santa Clara County, which is just south of San Francisco, used GIS to analyze millions of vehicles turning movements per day and optimize signal timing, eliminating 18,000 unnecessary stops per day.
GIS technology has already been widely adopted in more than 20 industries and numerous government departments, from emergency services to urban planning.
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ARTICLE CONTINUES BELOW
While GIS has been successfully applied to many municipal issues, its full potential for addressing traffic congestion, specifically, is a significant opportunity for many cities, including Toronto.
Traffic congestion is fundamentally a supply and demand problem: too many vehicles competing for limited road space. Building more roads seems logical, but induced demand complicates this approach. There are countless residents who don't currently drive but would choose to if roads were less congested.
How many people?
What is the ideal number of new roads to build?
Traditional analyses have failed to provide clear answers.
GIS reveals otherwise invisible patterns by visualizing layers of data as maps, allowing planners to see, analyze and determine true correlations between cause and effect.
For example, GIS analysis in Barcelona revealed that 60 per cent of congestion occurred at just 15 per cent of intersections, leading to targeted improvements rather than broad, expensive solutions.
Toronto's transportation planners, city council and provincial authorities are already making significant strides through the recently updated Toronto Congestion Management Plan and various technology innovation pilots.
These forward-thinking initiatives demonstrate the city's commitment to addressing traffic challenges, but their impact remains uncertain. By further incorporating geographical approaches into transportation planning, the city can complement these existing efforts with additional evidence-driven insights.
GIS-based analysis would work alongside current programs to provide deeper spatial understanding of traffic patterns, helping to optimize infrastructure investments and enhance data-backed strategies. Without precise spatial analysis, we're essentially guessing.
