The rise and fall of transit ridership across Canada: understanding the determinants

Ehab Diab (Corresponding author), Dena Kasraian, Eric J. Miller, Amer Shalaby

Research output: Contribution to journalArticleAcademicpeer-review

17 Citations (Scopus)


Recently, there have been growing concerns about the negative impacts of rising automobile use and road congestion on personal mobility, safety, air quality and climate change. To address these issues, special attention has been given to improving and expanding transit services in order to attract new riders and retain existing ones. Despite these efforts, transit ridership across Canada has been slowing down and, in many cases, declining over the past few years. The goal of this study is to provide an empirical investigation of variables that affect variations in transit ridership among transit systems and over time. To achieve this goal, the study used a comprehensive longitudinal dataset of different factors collected from 103 transit agencies in Canada between 2002 and 2016. Two-stage least square (2SLS) models were developed for the purpose of the study. The results suggest that transit ridership (in terms of number of linked trips) in Canada is associated with several factors that differ according to transit agency size. These factors include built environment and socioeconomic aspects in addition to transit supply attributes. Additionally, ride-sharing systems (i.e., Uber service) have a mixed impact on ridership that varies according to the size of transit system. The presence of Uber within large transit agencies service areas is shown to have a small positive impact on ridership, while it is negatively associated with ridership for small transit agencies. This paper provides policy makers and planners with useful policy-relevant information related to the association between different factors and transit ridership.
Original languageEnglish
Pages (from-to)101-112
Number of pages12
JournalTransport Policy
Publication statusPublished - 1 Sept 2020


  • Longitudinal analysis
  • Public transit
  • Ridership
  • Service supply
  • Two-stage least square (2SLS) model


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