Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019).

Transit vehicle bunching is a common and well-known operational problem for transit agencies. It has negativex000D
impacts on service quality and efficiency as well as on users' perception. In both research and practice,x000D
investigation efforts have mainly focused on bus bunching by examining the impacts of a range of factors or byx000D
developing corrective actions to mitigate the effects of bunching once it occurs. Most of these studies werex000D
developed for exploratory purposes. It is rare to find studies that focused on streetcar bunching or developedx000D
predictive analytics of bunching for real-time applications. In response to this gap, this research aims atx000D
developing predictive models of the time to bunching occurrence along bus and streetcar lines, enriching thex000D
knowledge and practice in this critical area of transit management. The ability to correctly predict the time tox000D
bunching in real-time would allow transit agencies to take pre-emptive actions towards preventing bunchingx000D
occurrence or at least mitigating its effects. This can only result in improving the service efficiency and riderx000D
satisfaction. This research will use Toronto's streetcar and bus systems as a case study, providing ready-to-usex000D
output that will have a direct impact on the reliability and efficiency of the surface network in Toronto.x000D
However, the research output is expected to be generic enough for transfer to other agencies elsewhere inx000D
Canada and across the globe.