This group consists of Kuwahara laboratory and Inoue laboratory.

  Kuwahara laboratory deals with both theoretical and empirical researches based on traffic Big Data in order to realize better urban mobility system by resolving congestion, environmental and safety problems. The main features of our approaches are (1) “dynamic analysis” that can reveal essential nature of traffic phenomena, and (2) “data fusion analysis” of various sensing data that are usually scattered and are not utilized sufficiently. Specifically, we develop data fusion techniques utilizing conventional traffic detector and advanced sensing data such as prove vehicle data. We then apply them to the following applications: traffic signal control, transportation demand management, advanced mobility management using ITS. We also develop traffic simulation model that can evaluate impacts of the managements. Moreover, we recently start to develop monitoring methods for traffic and logistics after disasters, and design/evaluation methods for evacuation and anti-disaster planning.

  Inoue laboratory develops methods that analyze and share spatio-temporal data. In recent years, we have gained significant access to various data, on national and local ones from the past and the present. These data would play an important role for the citizens to develop consensus and make decisions upon envisioning regional development. This laboratory aims to nurture an information friendly environment for the citizens to be part of planning better future, though developing methods that enable easy access of spatio-temporal data.