Nec Corporation

Konstantinos Gkiotsalitis

Nitin Maslekar

Francesco Alesiani

Yuki Kamiya

Oded Cats

Zongxiang Wu

Raymond Leong

In a first aspect, a method for optimizing efficiency of a transport provider is provided, comprising: receiving, by a processor, a first departure time of a vehicle, which is administered by the transport provider, at a first location; receiving, by the processor, a second departure time of the vehicle at a second location which is located after the first location; determining, by the processor, a difference between the first departure time and the second departure time; and updating, by the processer, a current schedule to provide an updated schedule in response to the determination of the difference, the updated schedule indicating an updated estimated arrival time of the vehicle at a location after the second location.

Method and apparatus for optimizing efficiency of a transport provider

A method for automatically allocating a plurality of available vehicles to a plurality of original service lines and virtual service lines of a transportation network includes receiving the virtual lines from a virtual line generator; approximating a constrained fleet allocation problem with an unconstrained fleet allocation problem that utilizes penalty terms to penalize violation of constraints; performing a multi-start sequential genetic search using a first population to identify a first solution; generating, using the first solution, a second population; performing a second multi-start genetic search using the second population to identify a second solution; and dispatching vehicles to different routes and lines according to the second solution.

Method for route optimization for demand responsive transportation

A method for robust machine learning of a target system includes determining a disturbance budget and defining an optimization problem based on the disturbance budget. Input data is provided iteratively to the target system. The input data at a current iteration is an integrated solution of a previous iteration. A disturbance is generated iteratively. The disturbance at the current iteration is generated according to the disturbance budget and the input data at the current iteration. An optimal solution of the target system is generated iteratively. The optimal solution of the target system at the current iteration is generated based on the disturbance and the input data at the current iteration. The input data at the current iteration is integrated with the optimal solution of the target system at the current iteration to obtain an integrated solution of the current iteration.

Method for robust control of a machine learning system and robust control system

A method for providing configuration information for a system having a plurality of vehicles includes assigning, for each vehicle, a first number of degrees of freedom (DOF); presetting one or more system parameters representing a performance of at least part of the system (SP); successively optimizing each respective vehicle at each station by selecting a DOF for each vehicle from a second number of DOF for the respective vehicle and computing a global SP; assigning each vehicle of each station a DOF resulting from a first system configuration (SC); evaluating the global SP for the first SC; identifying one or more stations having a negative impact (NIS) on the global SP on the first SC; and successively optimizing, for the one or more NIS, each vehicle locally at each respective NIS with regard to the SP with a number of DOF greater than said second number of DOF.

Method for providing configuration information for a system comprising a plurality of moving objects

A method for providing a demand-responsive transportation system includes receiving mobility trace data of collaborative individuals. Clusters of individuals are generated and mobility-activity models for the clusters are defined. Non-collaborative individuals are assigned to the clusters using a combinatorial optimization problem. An Origin-Destination (OD) demand is determined from the clusters. Non-collaborative individuals are re-allocated to different ones of the clusters using an approximation function that learns from an observed OD and the mobility trace data. The mobility-activity models are trained based on the re-allocation of the non-collaborative individuals to different ones of the clusters. An OD database (OD-DB) is maintained to be queried with a geographic location and time so as to receive information from the OD-DB about the current OD demand for the geographic location and time. Control actions are issued to vehicles in a fleet of the transportation system based thereon.

Method and system for providing demand-responsive dispatching of a fleet of transportation vehicles, and a mobility-activity processing module for providing a mobility trace database

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Frankfurt-am-Main, Germany

Konstantinos Gkiotsalitis