Reference number : GC-2017-Q3-00624, amendment number : , agreement type : G, recipient legal name : Shalaby, Amer (University of Toronto), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Transit vehicle bunching is a common and well-known operational problem for transit agencies. It has negative_x000D_
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 by_x000D_
developing corrective actions to mitigate the effects of bunching once it occurs. Most of these studies were_x000D_
developed for exploratory purposes. It is rare to find studies that focused on streetcar bunching or developed_x000D_
predictive analytics of bunching for real-time applications. In response to this gap, this research aims at_x000D_
developing predictive models of the time to bunching occurrence along bus and streetcar lines, enriching the_x000D_
knowledge and practice in this critical area of transit management. The ability to correctly predict the time to_x000D_
bunching in real-time would allow transit agencies to take pre-emptive actions towards preventing bunching_x000D_
occurrence or at least mitigating its effects. This can only result in improving the service efficiency and rider_x000D_
satisfaction. This research will use Toronto's streetcar and bus systems as a case study, providing ready-to-use_x000D_
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 in_x000D_
Canada and across the globe., program purpose french : Transit vehicle bunching is a common and well-known operational problem for transit agencies. It has negative_x000D_
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 by_x000D_
developing corrective actions to mitigate the effects of bunching once it occurs. Most of these studies were_x000D_
developed for exploratory purposes. It is rare to find studies that focused on streetcar bunching or developed_x000D_
predictive analytics of bunching for real-time applications. In response to this gap, this research aims at_x000D_
developing predictive models of the time to bunching occurrence along bus and streetcar lines, enriching the_x000D_
knowledge and practice in this critical area of transit management. The ability to correctly predict the time to_x000D_
bunching in real-time would allow transit agencies to take pre-emptive actions towards preventing bunching_x000D_
occurrence or at least mitigating its effects. This can only result in improving the service efficiency and rider_x000D_
satisfaction. This research will use Toronto's streetcar and bus systems as a case study, providing ready-to-use_x000D_
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 in_x000D_
Canada and across the globe., agreement title english : Predictive Analytics of Bus and Streetcar Bunching Occurrence Time for Real-Time Applications, agreement title french : Predictive Analytics of Bus and Streetcar Bunching Occurrence Time for Real-Time Applications, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-11-08, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada