{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/122100"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/122100","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Advances in data-driven models for transportation","abstract":"With the rising popularity of ride-sharing and alternative modes of transportation, there has been a renewed interest in transit planning to improve service quality and stem declining ridership. However, it often takes months of manual planning for operators to redesign and reschedule services in response to changing needs. To this end, we provide four models of transportation planning that are based on data and driven by optimization. A key aspect is the ability to provide certificates of optimality, while being practical in generating high-quality solutions in a short amount of time. 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This is achieved by replacing independence assumptions in probabilistic models and capturing the interactions of services in overlapping regions. In transit planning, we provide a unified framework that allows us to optimize frequencies and prices jointly in transit networks for minimizing total waiting time.","abstract_html":"With the rising popularity of ride-sharing and alternative modes of transportation, there has been a renewed interest in transit planning to improve service quality and stem declining ridership. However, it often takes months of manual planning for operators to redesign and reschedule services in response to changing needs. To this end, we provide four models of transportation planning that are based on data and driven by optimization. A key aspect is the ability to provide certificates of optimality, while being practical in generating high-quality solutions in a short amount of time. 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