Exploring Transit Performance And Traffic Congestion in Downtown Toronto Using Big Data
thesisposted on 2021-05-25, 07:14 authored by Christopher Chun Kong Yuen
This exploratory research evaluates the linkages between roadway operations and mixed-traffic transit performance on three arterial corridors in Toronto- King Street, Queen Street, and Dundas Street. Using Inrix traffic speed probe data as well as GPS location data from the Toronto Transit Commission’s vehicles between January 2014 and June 2016, this research visualizes spatial and temporal trends in traffic congestion and transit headway regularity. Three regression models were estimated that indicate both traffic congestion and terminus departure times are statistically significant, but weak predictors of mixed-traffic transit reliability. These models leave most of the variability unexplained. The findings highlight opportunities and limitations for congestion management and transit scheduling as tools for improving headway reliability. They also illustrate the complexity of the relationships between transportation modes in downtown Toronto.