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Online-learning type of traveling time prediction model in expressway

机译:高速公路旅行时间预测模型的在线学习类型

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There are many requirements for predicting traveling times on express ways. It is difficult to predict traveling time with high precision when traffic flows change dynamically, such as at the beginning and end of traffic jams. It is therefore important to develop a simulation model using time-series data traffic counters. The model must cope with secular changes of traffic-flow characteristics such as construction of new expressways or environment condition changes. This paper proposes a new travel time prediction system which has a model learning function using time-series data processing. The new system is a mixed structure type neural network based travel time prediction system. It has a modeling function and a model learning function using field data processing on a time series basis. The proposed system has already been tested on an actual expressway, and satisfactory results were achieved.
机译:在快递方式预测旅行时间有许多要求。当交通流量动态变化时,难以预测高精度的行进时间,例如在交通拥堵的开始和结束时。因此,使用时间序列数据流量计数器开发仿真模型是重要的。该模型必须应对交通流量特性的世俗变化,例如新的高速公路或环境条件的构建变化。本文提出了一种新的旅行时间预测系统,其使用时间序列数据处理具有模型学习功能。新系统是混合结构型神经网络的行程预测系统。它具有使用现场数据处理的建模功能和模型学习功能。所提出的系统已经在实际高速公路上进行了测试,实现了令人满意的结果。

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