Taxi Dispatch With Real-Time Sensing Data in Metropolitan Areas: A Receding Horizon Control Approach


Ancient taxi systems in metropolitan areas usually suffer from inefficiencies thanks to uncoordinated actions as system capability and customer demand modification. With the pervasive deployment of networked sensors in fashionable vehicles, massive amounts of data regarding customer demand and system status will be collected in real time. This info provides opportunities to perform numerous types of management and coordination for massive-scale intelligent transportation systems. During this paper, we present a receding horizon control (RHC) framework to dispatch taxis, which incorporates highly spatiotemporally correlated demand/provide models and real-time World Positioning System (GPS) location and occupancy info. The objectives embrace matching spatiotemporal ratio between demand and supply for service quality with minimum current and anticipated future taxi idle driving distance. Extensive trace-driven analysis with a information set containing taxi operational records in San Francisco, CA, USA, shows that our solution reduces the average total idle distance by 52%, and reduces the supply demand ratio error across the town during one experimental time slot by 45%. Moreover, our RHC framework is compatible with a large choice of predictive models and optimization downside formulations. This compatibility property permits us to solve sturdy optimization issues with corresponding demand uncertainty models that offer disruptive event info.

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