Matthew Muresan

Cycling in Toronto, Ontario, Canada

This research investigated the route choice behavior of cyclists in Toronto, Ontario, Canada, with data collected from a smartphone application deployed to many cyclists in the city. For the study, 4,556 cyclists registered and logged more than 30,000 commuting trips over 9 months. In addition to the time-stamped, second-by-second GPS readings on each trip, information on age, gender, and rider history was collected on a voluntary basis. Multinomial logit route choice models were estimated for the commuting cycling trips. The results revealed the critical importance of cycling facilities (e.g., bike lanes, cycling paths and trails) on cyclists’ route choice decisions, and provided valuable information for use in Toronto’s ongoing bicycle network planning.

Continual retiming of traffic signals using big travel time data

With the advances of new sensor technologies and the prospect of crowd-sourced big location data, it has become feasible to obtain vehicle travel times on a large temporal and spatial scale. This study presents a novel method to make use of this new rich travel time data source to re-optimize traffic signals on a continual basis without requiring turning movement counts. In this method, the traffic state — degree of saturation is first estimated for each movement based on the observed travel time distribution and the signal control parameters are then optimized accordingly. This signal retiming method is evaluated under a wide range of simulated scenarios varying by penetration rates, measurement errors, vehicle arrival patterns, and traffic demand, showing comparable results to the typical traffic count-based approach.