Logisteed, Ltd.

Hitachi Transport System, Ltd.

Daichi Ojiro

Takeshi Tanaka

Hiroyuki Kuriyama

Shunsuke Minusa

Kiminori Sato

Koji Ara

Akira Yonekawa

Junichi Hirayama

Yoshihito Shimazu

A computer generates an accident risk definition model to estimate a probability of hazard occurrence as an accident risk by inputting first in-vehicle sensor data collected in the past and hazard occurrence data having information on hazard occurrence from the first in-vehicle sensor data preset therein, generates accident risk estimation data by inputting second in-vehicle sensor data collected in the past to the accident risk definition model and estimating the probability of the hazard occurrence, generates an accident risk prediction model to predict the accident risk after a predetermined time by inputting first biological index data corresponding to the second in-vehicle sensor data and the accident risk estimation data, calculates second biological index data from second biological sensor data by acquiring the second biological sensor data of a driver, and predicts the accident risk after the predetermined time by inputting second biological index data to the accident risk prediction model.

Operation support method, operation support system, and operation support server

A system for generating guidance information for a driver includes a processor and a storage device. The storage device stores guidance history of the driver. The processor generates a feature value based on match/mismatch of guidance types abstracted from the guidance history. The processor determines a guidance type for the driver on the basis of the feature value. The processor generates information for issuing guidance of the determined guidance type to the driver according to driver monitoring information.

System for generating guidance information

A logistics prediction system includes an arithmetic device that executes a predetermined process, a storage device connected to the arithmetic device, a shipment information input section to which past shipment information is input, a constraint input section to which a calculation resource and a calculation time period are input as constraints for prediction, an integration prediction database holding shipment trends of products, a prediction method selector that selects a prediction method to be used to predict a shipment volume for each of the products from among multiple prediction methods so that the constraints input to the constraint input section are satisfied, multiple prediction method sections that predict shipment volumes using the prediction methods, an integrator that integrates the results of the prediction by the prediction method sections for each of the products, and a shipment prediction output section that outputs the result of the integration by the integrator.

Logistics prediction system and prediction method

Growing community of inventors

Tokyo, Japan

Daichi Ojiro