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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 describes NEC's latest IDR digital modulator and demodulator equipment, and the IDR digital modem terminal configuration with its associated 1:N redundancy switch‐over and monitor and control units, to comply with the current INTELSAT specification IESS‐308 (Rev. 4).

In urban street networks, traffic flow is seriously affected by traffic signals. A traffic signal is controlled by using three parameters, cycle, split and offset and these parameters should be determined based on traffic conditions. This paper discusses on the optimal signal control in small street networks. First, we apply genetic algorithm to the optimization of signal control parameters. In this approach, we use the microscopic traffic simulation for calculating fitness value. Secondly, we analyze the optimal signal control in small networks as comparing with the results of conventional approaches and prove effectiveness of our proposed approach. Further, we propose the practical method of setting signal control parameters based on traffic conditions. 784T. IEE Japan,No. 6, '95

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