Improving Metropolitan-Scale Transportation Systems
Traditionally, the design of urban transit services has been based on limited sampling data collected through surveys and censuses, which are often dated and incomplete. The theory and practice of transit services has also typically focused on isolated individual transposition modes. These two limitations result in unsatisfactory passenger experiences, such as unnecessary detours and prolonged travel delays. Fortunately, a new opportunity to address these limitations has arisen in recent years: the latest expansion of urban information infrastructure enables these researchers to model behaviors of urban transportation systems with massive multi-modal online feeds and to apply effective local and global cyber-control. This work will transform how transportation systems are optimized because, in addition to macro-level historical statistics, the availability of massive micro-level trip information will make it possible to apply fine-grained real-time control to handle rapid changes in dynamic urban environments, and aggregated information from multi-modal transit allows more effective inter-transit coordination.
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