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 Big Data Innovation Team conducts practical analyses of transportation data to be able to more easily measure the impact and benefits of policies and solutions. The Team partners with researchers and will focus on providing excellence in the communication and visualization of findings. The team’s code is Open Source and available in repositories prefixed by “bdit” on the City of Toronto’s GitHub Page.
The Innovation Team will begin by building on some current work being conducted by Transportation Services, including:
The Big Data Innovation Team, in partnership with Municipal Licensing and Standards, prepared an analysis of the impacts of Vehicles for Hire and Private Transportation Companies on the City of Toronto’s Transportation Network. The executive summary of this analysis can be found here as an attachment to the staff report.
This initiative running from May 31st until July 26th calls on civic innovators, transit users, data scientists, designers, urban and transportation aficionados, citizens, academics and advocates to answer the question:
How might we use data, design and technology to make all Toronto road users, especially seniors, newcomers and school children, safer immediately, and enable predictive and high priority interventions in the future?
The Challenge is an 8-week competition where participants will work in teams or independently to develop innovative and data driven solutions to make Toronto’s streets safer for everyone today and into the future.
The teams with the most promising solutions will be awarded cash prizes and the opportunity to receive coaching and training at Civic Hall Toronto to further develop their idea side by side with City of Toronto staff. You can find more details at the Vision Zero Challenge Website.
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:
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.
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.
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.
The invite-only Big Transportation Data for Big Cities Conference brought together city transportation officials, technical staff, academics, and public sector and industry leaders from 18 big cities across North America to commence dialogue on the practical and actionable use of urban transportation data. The conference provided an opportunity for
Visit the conference page to see who attended and view the presentations.
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.
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
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.
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.