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In modern taxi networks, large amounts of taxi occupancy status and location data are collected from networked in-vehicle sensors in realtime. They provide knowledge of system models on passenger demand and mobility patterns for efficient taxi dispatch and coordination strategies. Such approaches face new challenges: how to deal with uncertainties of predicted customer demand while fulfilling the system’s performance requirements, including minimizing taxis’ total idle mileage and maintaining service fairness across the whole city; how to formulate a computationally tractable problem. To address this problem, we develop a data-driven robust taxi dispatch framework to consider spatial-temporally correlated demand uncertainties. The robust vehicle dispatch problem we formulate is concave in the uncertain demand and convex in the decision variables. Uncertainty sets of random demand vectors are constructed from data based on theories in hypothesis testing, and provide a desired probabilistic guarantee level for the performance of robust taxi dispatch solutions. We prove equivalent computationally tractable forms of the robust dispatch problem using the minimax theorem and strong duality. Evaluations on four years of taxi trip data for New York City show that by selecting a probabilistic guarantee level at 75%, the average demand–supply ratio error is reduced by 31.7%, and the average total idle driving distance is reduced by 10.13% or about 20 million miles annually, compared with nonrobust dispatch solutions. [ABSTRACT FROM AUTHOR]

Traditional taxi systems in metropolitan areas often suffer from inefficiencies due to uncoordinated actions as system capacity and customer demand change. With the pervasive deployment of networked sensors in modern vehicles, large amounts of information regarding customer demand and system status can be collected in real time. This information provides opportunities to perform various types of control and coordination for large-scale intelligent transportation systems. In this paper, we present a receding horizon control (RHC) framework to dispatch taxis, which incorporates highly spatiotemporally correlated demand/supply models and real-time Global Positioning System (GPS) location and occupancy information. The objectives include 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 data 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 city during one experimental time slot by 45%. Moreover, our RHC framework is compatible with a wide variety of predictive models and optimization problem formulations. This compatibility property allows us to solve robust optimization problems with corresponding demand uncertainty models that provide disruptive event information. [ABSTRACT FROM AUTHOR]

Numerous studies focused on brain reorganization after stroke from aspects of task-related brain activity and resting-state brain networks. However, studies focusing on the longitudinal reorganization of task-state brain networks were scarce. In this study, functional magnetic resonance imaging data were collected from twelve stroke patients during blocked finger-tapping task at four post-stroke time points (less than 10 days, around 2 weeks, 1 month and 3 months), respectively. The dynamic changes and prognostic value of the network parameters (i.e., topological parameters, functional connectivity and nodal parameters) in task-state motor execution networks were thoroughly evaluated. We found that the topological configuration (clustering coefficient and characteristic path length) of task-state motor execution networks underwent significant shift during stroke recovery. Especially, we found the topological configuration of task-state motor execution networks at the early recovery stage were capable of predicting the motor function restoration during sub-acute phase. In addition, we found increasing functional connectivity between ipsilesional cerebellum and motor cortices in task-state motor execution networks. In general, this study demonstrated the reorganization and prognostic value of task-state brain network after stroke, which provides new insights into understanding the brain reorganization and rehabilitation after stroke. [ABSTRACT FROM PUBLISHER]