计算机科学
人工神经网络
深度学习
人工智能
数据挖掘
基质(化学分析)
过程(计算)
循环神经网络
趋同(经济学)
机器学习
材料科学
经济
复合材料
经济增长
操作系统
作者
T. Pamuła,Renata Żochowska
标识
DOI:10.1016/j.engappai.2022.105550
摘要
In this article, we propose a new method for OD (Origin–Destination) matrix prediction based on traffic data using deep learning. The input values of the developed model were determined based on data on the structure of the road network, origin and destination points of trips, as well as data on traffic intensity on road network sections recorded by video-sensing devices. The advantage of the method is that the complex process of data acquisition and processing is not required for the estimation and prediction of the matrix. Historical data and the iterative method of estimating a prior OD matrix were used only to generate training sequences for the neural network. The proposed method using deep learning neural networks with the long short-term memory (LSTM) or autoencoders layers (DLNa — deep learning networks with autoencoders) is characterized by relatively high accuracy and resistance to temporary missing data from several measurement points located in the urban road network. The case study was conducted for a network of a medium-sized city in Poland. The results show (average MAPE = 7.18% (LSTM), 6.80% (DLNa)) that the proposed method can have a practical implementation in real-time dynamic traffic assignment (DTA) systems for ITS applications. The proposed method of short-term forecasting the OD matrix does not require questionnaire research or detailed information on spatial development. Therefore, it is not as expensive and time-consuming as the methods based on these data.
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