|NIPS2016 workshop on Machine Learning for Intelligent Transportation Systems, Barcelona, Spain, 9 December 2016|
We propose a new method to optimize public transport schedules by minimizing the waiting time during transfers. Using ticket validation data to construct a realistic scenario-based model of the waiting times, our goal is to design shifts of the current schedules that reduce the overall expected waiting time. For that we propose a parallel local search heuristic that exploits the structure of the problem to efficiently explore a large number of possible schedules. Compared to previous approaches, our algorithm should allow to both treat more transfers (bigger cities) and more scenarios (ensuring a better generalization). We provide promising preliminary results on transit data collected from Nancy, France.
You may choose which kind of cookies you allow when visiting this website. Click on "Save cookie settings" to apply your choice.
FunctionalThis website uses functional cookies which are required for the search function to work and to apply for jobs and internships.
AnalyticalOur website uses analytical cookies to make it possible to analyse our website and optimize its usability.
Social mediaOur website places social media cookies to show YouTube and Vimeo videos. Cookies placed by these sites may track your personal data.
This content is currently blocked. To view the content please either 'Accept social media cookies' or 'Accept all cookies'.
For more information on cookies see our privacy notice.