| Budget Amount *help |
¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 1991: ¥400,000 (Direct Cost: ¥400,000)
Fiscal Year 1990: ¥1,100,000 (Direct Cost: ¥1,100,000)
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| Research Abstract |
This study is mainly concerned with developing of a self-organizing traffic control system using neural network models. To realize a self-organizing traffic management system, neural network models were introduced. First, a multilayered neural network model, consisting of both a training process and an optimization process, was applied to an optimal traffic control problem of a single intersection. It was shown that in the training process a few iterations of the training operation by the backpropagation method was able to build up a steady input-output relationship. In the optimization process a stepwise method that combined the Cauchy method with a feedback method was able to produce a good approximated sequence of control variables. Compared to results by the feedback method alone, this stepwise method was able to avoid entrapment into local minimums and reach a global minimum promptly.
Second, to deal with the rapid increase of synaptic weights of neural networks for roads that cons
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ist of several intersections, a multiple split model, in which only neurons that were related to an intersection were connected to each other, was proposed. This multiple split model improve d not only the computation time but also the estimation precision for untrained patterns.
Third, to deal with the optimization of offsetsand the variation of traffic situations, a complicated neural network, which has three input sources, one for splits, one for offsets and the other for inflow traffic volumes, was proposed. This model was classified into four types by how the neural networks for those input sources were connected.
Finally an interconnected neural network model was applied to a general minimum cost flow problem that was closely associated with a route guidance system in dynamic traffic management systems. To obtain the optimal solution the Hopfield model was used. That is, synaptic weights and input biases of the neural system were formulated based on the assumption that the total traveling time corresponds to the total energy of the neural network system. Considered were constraints on link flow where each link flow does not exceed the link capacity, and where the average traveling time depends on link flow. It was found that by specifying proper weight factors of the objective function, the Hopfield algorithm was able to give approximated solutions that were in good agreement with analytical ones. Less
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