熊澤 宏之

In ad-hoc network such as inter-vehicle communication (IVC) system, safety applications that vehicles broadcast the information such as car velocity, position and so on periodically are considered. In these applications, if there are many vehicles broadcast data in a communication area, congestion incurs a problem decreasing communication reliability. We propose autonomous distributed transmit power control method to keep high communication reliability. In this method, each vehicle controls its transmit power using feed back control. Furthermore, we design a communication protocol to realize the proposed method, and we evaluate the effectiveness of proposed method using computer simulation.

ITS（Intelligent Transport Systems）とは，先進の情報通信技術を用いて道路·車·人が一体となったシステムを実現することにより，道路交通の安全性，輸送効率，快適性の向上などを目指したシステムである．このITS専用の路車間通信としてDSRC（Dedicated Short Range Communication）がある．本稿では，このDSRCの応用技術として，高速移動する車に対して車両位置に特化した情報提供を行うプッシュ型情報配信アプリケーションについて，そのアーキテクチャとプロトコルについて述べる．また，提案するプロトコルを実際のDSRC路側機と車載器に実装し，通信試験を行うことで，走行車両への通信量の検討と，様々な車載システムへの適用可能性を確認した結果について述べる．DSRC (Dedicated Short Range Communication) is an expected and potential wireless communication medium for ITS applications. Location based information push services for driving vehicle, such as safety information, road traffic conditions and so on, are key application of DSRC. In this paper, we have proposed architecture and the application protocol for these services. And we report its performance and availability for various services through experiments with real DSRC RSU (Road Side Unit) and OBU (On Board Unit).

We propose a general prediction method based on the efficient computation and online update of the Singular Value Decomposition (SVD) of historical data. The SVD is fundamental to many data modeling algorithms, but the traditional methods for computing it require large computational costs. By adopting a fast sequential SVD updating scheme, the tasks of prediction, imputation of missing values, and model updating can be performed very quickly. In this paper, an application of our method to route travel time prediction is described. Using real travel time data from short sections (links) on expressway, we evaluated prediction performance of travel time on longer section (route) including the links. Experimental comparisons with several statistical machine learning methods suggest that our linear prediction method can achieve similar prediction performance (prediction error) to other nonlinear methods at less computaional cost.

This paper presents an experimental comparison of several statistical machine learning methods for short-term prediction of travel times on road segments. The comparison includes linear regression, neural networks, regression trees, k-nearest neighbors, and locally-weighted regression, tested on the same historical data. In spite of the expected superiority of non-linear methods over Unear regression, the only non-linear method that could consistently outperform linear regression was locally-weighted regression. This suggests that novel iterative linear regression algorithms should be a preferred prediction methods for large-scale travel time prediction.