Short-term Traffic Flow Prediction in Urban Areas Using Neural Networks

• Main Authors: Zheng-Wei Ye, 葉政威
• Other Authors: Chen-Kang Huang
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/rh5un7

碩士 === 國立臺灣大學 === 生物產業機電工程學研究所 === 105 === In the past few years as the business industries develop, the phenomenon of urbanization has become more and more popular. With the increase of human populations, the density of vehicles in the city is also increased. As people rely more on motor vehicles, the traffic flow is often backed-up, causing a great deal in transportation costs and longer travel time on the roads. With vehicles having to travel longer than before, the air quality in the city is worsening due to the exhaust gases and heat produced by the vehicles. As a result, to provide better living qualities, it is an important topic to make improvements on traffic congestion prediction, transportation management, and advancement in avoiding traffic back-ups. There are many models of traffic flow prediction being proposed for different circumstances, such as highways, roundabouts, and general in-town roads. However, most of these models are established by traditional statistic analysis, but some researches have suggested that these models are too shallow to fulfill the complication of transportation network in life. Due to this cause, in recent years, more and more studies are introducing the new technology of computation, such as machine learning and deep learning, and have a higher accuracy. As a result, this study proposed a traffic flow prediction model based on long short-term memory neural network (LSTM NN). The historical time series of traffic flow is adopted as input to predict the traffic flow in next times step. The data of this model is derived from the traffic monitoring system in Taipei City that established by Taipei City Traffic Engineering Office. The data obtained by five vehicle detectors is adopted and spited into three parts, such as training data, validation data, and testing data. The training data is adopted to adjust the weights and the bias of network, and the validation data is used to adjust the structure of network. Then, the testing data would be thrown in the model and output the predicted traffic flow. Finally, a wavelet neural network is implemented and adopted to compare the performance of proposed LSTM NN model. The results show that the RMSE of LSTM NN model with five detectors ranges from 6.20 to 10.22 and the MAPE ranges from 7.13% to 11.14%. Compared to the results of wavelet NN, the RMSE decreases by 2 to 3, and the MAPE decreases by 2% to 5%.