Big Data Innovation

The Big Data Innovation team was created in 2015 with the mission of leveraging emerging transportation datasets together with existing City data to develop a new understanding of transportation issues across all modes in the City. The focus of the Big Data Innovation Team will be on conducting practical analyses of transportation data to be able to more easily measure the impact and benefits of policies and solutions. The team will be partnering with researchers and will focus on providing excellence in the communication and visualization of findings.

The Innovation Team will begin by building on some current work being conducted by Transportation Services, including:

  • Partnering with McMaster University to analyze historical travel data on city expressways and streets
  • Working with the TTC to closely analyze surface transit data to identify operational improvements to further improve streetcar service
  • Partnering with the Cycling Unit of Transportation Services to release a report evaluating cycling travel patterns based on data collected from the City of Toronto Cycling App
  • Developing a Big Data Strategy and Work Plan for Transportation Services to determine ways to make this type of information available to map out how the team will proceed, and
  • Vetting products and services that might be useful in assisting the City in better decision making and investments.

TrafficJam Hackathon October 2nd-4th, 2015

Transportation Services partnered with Evergreen CityWorks to host TrafficJam , a 48-hour traffic data Hackathon at the Evergreen Brickworks from October 2nd to 4th, 2015. At TrafficJam, 135 participants collaborated with traffic analysts, government officials and data collectors to offer insight and solutions toward better understanding and management of transportation issues. New ideas were brainstormed, a range of large datasets were analyzed and tools were built to provide for better decision making. At the TrafficJam Expo the most promising entries were recognized and awarded.

The winning submissions were:

1) TrafficJam Tacos

Using TTC vehicle GPS data as regular traffic probes of road conditions, team TrafficJam Tacos mapped congestion and resulting slow speeds, as well as unreliable areas with highly variable speeds. Based on this insight commuters can make more informed route choices, and planners can target unreliable areas. This baseline opens up numerous possibilities, such as predicting the impact of construction and street events in terms of both geographical reach and duration

2) RueView

Using the Westbound Gardiner Expressway as a proof of concept, the RueView team’s TrafficJam submission forecasts traffic congestion to predict rush-hour periods. RueView can be utilized in various ways, from as simple as avoiding rush-hour to empowering Torontonians to make better transit choices.

3) Walkr

The team visualized pedestrian infrastructure and volumes layered together with social media activity to highlight geographical and behavioural hot spots and cold spots. With this, the city has visibility into social/behavioural trends in order to improve, sustain and create new infrastructure. The end-state would include a consumer wayfinding interface that encourages exploring and feedback on municipal efforts to make Toronto more walkable.

Transportation Services will continue to work with teams to further transportation management analytics. More information on the hackathon can be found at

Ongoing Projects

ExpandToronto Road System Performance: Stretching the State of Knowledge

The City of Toronto commissioned a study from the McMaster Institute for Transport & Logistics (MITL) that used INRIX GPS probe data to analyze historical traffic data on City expressways and arterials.  The study looked at three time periods (August-December 2011, July – December 2013 and January- December 2014).

The study is the City's first foray into making practical use of this type of probe vehicle data. Much of the work done here is on the cutting edge, and through this study the City of Toronto and MITL are developing methodologies that will evolve and improve over time, allowing the City to track and monitor congestion levels across the City, year by year.  The City recognizes that the methodology may not be perfect or final, but it is a strong first step in what will be an on-going process. While there are varying opinions on the how congestion is defined, what is most useful is that a strong academically supported methodology has been developed that can measure changes in congestion over time. 

The overall study was conducted in three parts:

Congested Days: The first phase identified that the single most congested days occurred on days during which there was snow or rain. While this is in many ways expected, these results illustrate the role of weather in travel conditions and demonstrate the utility of these approaches when analyzing Big Data for performance monitoring.

City Congestion Trends: The second phase estimated changes in traffic congestion over the three year period from 2011 to 2014 by looking at annual, monthly, daily and hourly variations in performance metrics, including speed, delay, and unreliability. The study found that congestion did materially grow from 2011 to 2014, but the growth was uneven and congestion was in fact lower in 2013.

Corridor Report Cards: The final phase included a set of corridor report cards for 36 corridors across the City.  Corridor report cards provided comparable snapshots of changes in performance between 2011 and 2014, hourly speed profiles for typical days of the week, and measures of unreliability.   Results identified uneven changes in congestion over time among City roadways and expressways.


Summary Presentation


Congested Days in Toronto

Congestion Trends in the City of Toronto (2011-2014)

Report Cards:

McMaster Institute for Transportation & Logistics (MITL) Report Card Memo

City of Toronto - Highways

City of Toronto - Major Arterials

MTO - Highways

ExpandToronto Cycling App

The Toronto Cycling App was developed by Brisk Synergies for the City of Toronto. The App will allow cyclists to record their cycling routes and provide this data to the City. This data will be used to inform the development of the new Cycling Plan as well as to assist in the ongoing monitoring of cycling patterns over time as cycling infrastructure is improved and expanded. More information about the App can be found at the Cycling Page.

The Big Data Innovation will be working together with the cycling unit to release a report that evaluates cycling travel patterns based on data collected for the Toronto Cycling App.

ExpandReview of King Streetcar Operations

The City of Toronto and the TTC are studying ways to improve the flow of traffic on King Street with the intent of providing better and more reliable transit service. As part of this initiative, the City of Toronto commissioned a study from local consultant Steve Munro to examine transit vehicle behaviour from September 2013 to June 2014 and the effect of various factors on travel times and reliability.

The analysis was structured around the following themes and questions, looking at the King streetcar route between Jarvis and Roncesvalles:

  • What was the impact of the imposition of higher fines for various offences effective January 23, 2014
  • What was the impact of the extension of prohibited stopping/parking hours for various segments of King Street between Bathurst and Jarvis effective March 3, 2014
  • What was the impact of the restriction of capacity on the Gardiner Expressway for construction effective April 28, 2014
  • When and where is variability the greatest along the routes and throughout the day?


Some of the major findings include:

  • The Gardiner Expressway construction had by far the largest impact on travel times, creating several minutes of delays in Parkdale, with the effects noticed not just during peak periods, but during midday and occasionally evening periods.
  • The post-extended clearance period saw an across-the-board improvement in travel times on the entire route
  • The imposition of higher fines did not have a measurable impact on route travel times and variability.