In most metropolitan areas, a diverse array of transportation modes is available to travelers, each serving specific travel needs and shaping distinctive usage patterns. This research focuses on analyzing these patterns across a wide spectrum of transportation options in the Greater Montreal Area. While traditional transport surveys are valuable for modeling individual behaviors during an average weekday, they fail to capture the variability in the use of different transportation modes over time.
This study addresses this gap by leveraging a diverse set of operational data from transportation systems and services. Clustering approaches are applied to identify typical daily and weekly usage patterns. Six relatively independent data streams are analyzed: vehicle road counts, bicycle counters, transit validation records for subway and bus, GPS data for taxis, BIXI bikesharing usage data, and Communauto carsharing data. These datasets collectively cover a common area in the central part of the Island of Montreal for the years 2019 to 2023.
The four years of continuous data enable an analysis of how usage patterns have evolved over time, offering insights into shifts in mobility trends. Additionally, this research highlights the methodological challenges of integrating multiple data streams to model usage at varying spatial and temporal resolutions.
