Osamu Masutani

Pheromone model: application to traffic congestion prediction

Social insects perform complex tasks without top-down style control, by sensing and depositing chemical markers called “pheromone”. We have examined applications of this pheromone paradigm towards intelligent transportation systems (ITS). Many of the current traffic management approaches require central processing with the usual risk for overload, bottlenecks and delays. Our work points towards a more decentralized approach that may overcome those risks. In this paper, a car is regarded as a social insect that deposits (electronic) pheromone on the road network. The pheromone represents density of traffic. We propose a method to predict traffic congestion of the immediate future through a pheromone mechanism without resorting to the use of a traffic control center. We evaluate our method using a simulation based on real-world traffic data and the results indicate applicability to prediction of immediate future traffic congestion. Furthermore, we describe the relationship between pheromone parameters and accuracy of prediction.

A sensing coverage analysis of a route control method for vehicular crowd sensing

General vehicles have much potential to contribute to city surveillance in a context of Smart City. Vehicular crowd sensing is essential for reasonable and sustainable city surveillance. We propose route control method to enhance sensing coverage of crowd sensing system. The method is composed of sensing demand-aware cost assignment and a cooperative path reservation. We performed a traffic simulation to evaluate the route control method. The result shows sensing coverage of vehicular crowd sensing can be significantly enhanced without much additional travel of sensing vehicle. Therefore total sensing ability can be increased without enhancing sensor ability or enhancing the number of sensing vehicles.

BEIRA: An Area-based User Interface for Map Services

This paper introduces BEIRA, an area-based map user interface for location-based contents. Recently, various web map services are widely used to search for location-based contents. However, browsing a large number of contents that are arranged on a map as points may be troublesome. We tackle this issue by using area-based representations instead of points. AOI (Area of Interest), which is core concept of BEIRA, is an arbitrary shaped area boundary with text summary information. With AOI, users can instantly grasp area characteristics without examining each point. AOI is deduced by performing geo-semantic co-clustering of location-based contents. Geo-semantic co-clustering takes both geographic and semantic features of contents into account. We confirm that the ratio of the geo-semantic blend is the key to deducing an appropriate boundary. We further propose and evaluate location-aware term weighting to obtain an informative summary.

BEIRA: a geo-semantic clustering method for area summary

This paper introduces a new map browser of location based contents (LBC) that summarizes area characteristics. Recently various web map services have been widely used to search web contents. As LBC increase, browsing a number of LBC which are viewed as POI (point of interest) on a geographical map becomes inefficient. We tackle this issue by using AOI (area of interest) instead of POI. With the AOI a user can instantly find area characteristics without viewing each content of POI. We assume that semantically homogeneous and geographically distinguishable areas are suitable for the AOI. The AOI is formed by geo-semantic clustering which is a co-clustering that takes into account both geographical and semantic aspects of POI information. By the experiment using real LBC on the web, we confirmed our method has potential to extract good AOI.