A deep-learning model for urban traffic flow prediction with traffic events mined from twitter

In this paper, a fusion deep learning model considering spatial–temporal correlation is proposed to solve the problem of urban road traffic flow prediction. Firstly, this paper holds that the traffic flow of a section in the urban road network not only depends on the fluctuation of its own time series, but is also related to the traffic flow of other sections in the whole region. Therefore, a traffic flow similarity measurement method based on wavelet decomposition and dynamic time warping is proposed to screen the sections which are similar to the traffic flow state of the target section. Secondly, in order to improve the prediction accuracy, the unstable time series are reconstructed into stationary time series by differential method. Finally, taking the extracted traffic flow data of a similar section as an independent variable and the traffic flow data of target section as dependent variable, we input the above variables into the proposed CNN-LSTM fusion deep learning model for traffic flow prediction. The results show that the proposed model has a higher accuracy and stability than the other benchmark models. The MAPE can reach 92.68%, 93.39%, 85.14%, and 76.14% at a time interval of 5 min, 15 min, 30 min, and 60 min, and the other evaluation indexes are also better than the rest of the benchmark models.

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Urban Traffic Flow Fore-casting based on Deep Learning Model

Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019) ◽

10.2991/icmeit-19.2019.113 ◽

2019 ◽

Author(s):

Shengdong Mou ◽

Zhengxian Xiong

Keyword(s):

Deep Learning ◽

Traffic Flow ◽

Learning Model ◽

Urban Traffic ◽

Deep Learning Model

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Capsules TCN Network for Urban Computing and Intelligence in Urban Traffic Prediction

Wireless Communications and Mobile Computing ◽

10.1155/2020/6896579 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Dazhou Li ◽

Chuan Lin ◽

Wei Gao ◽

Zeying Chen ◽

Zeshen Wang ◽

...

Keyword(s):

Deep Learning ◽

Traffic Flow ◽

Geographical Location ◽

Urban Traffic ◽

Basic Unit ◽

Urban Computing ◽

Convolutional Network ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Real World Datasets

Predicting urban traffic is of great importance to smart city systems and public security; however, it is a very challenging task because of several dynamic and complex factors, such as patterns of urban geographical location, weather, seasons, and holidays. To tackle these challenges, we are stimulated by the deep-learning method proposed to unlock the power of knowledge from urban computing and proposed a deep-learning model based on neural network, entitled Capsules TCN Network, to predict the traffic flow in local areas of the city at once. Capsules TCN Network employs a Capsules Network and Temporal Convolutional Network as the basic unit to learn the spatial dependence, time dependence, and external factors of traffic flow prediction. In specific, we consider some particular scenarios that require accurate traffic flow prediction (e.g., smart transportation, business circle analysis, and traffic flow assessment) and propose a GAN-based superresolution reconstruction model. Extensive experiments were conducted based on real-world datasets to demonstrate the superiority of Capsules TCN Network beyond several state-of-the-art methods. Compared with HA, ARIMA, RNN, and LSTM classic methods, respectively, the method proposed in the paper achieved better results in the experimental verification.

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Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method

Journal of Advanced Transportation ◽

10.1155/2017/6575947 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 33

Author(s):

Yuhan Jia ◽

Jianping Wu ◽

Ming Xu

Keyword(s):

Deep Learning ◽

Traffic Flow ◽

Short Term Memory ◽

Complex Model ◽

Urban Traffic ◽

Environmental Data ◽

Traffic Modeling ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

The Impact

Accurate traffic flow prediction is increasingly essential for successful traffic modeling, operation, and management. Traditional data driven traffic flow prediction approaches have largely assumed restrictive (shallow) model architectures and do not leverage the large amount of environmental data available. Inspired by deep learning methods with more complex model architectures and effective data mining capabilities, this paper introduces the deep belief network (DBN) and long short-term memory (LSTM) to predict urban traffic flow considering the impact of rainfall. The rainfall-integrated DBN and LSTM can learn the features of traffic flow under various rainfall scenarios. Experimental results indicate that, with the consideration of additional rainfall factor, the deep learning predictors have better accuracy than existing predictors and also yield improvements over the original deep learning models without rainfall input. Furthermore, the LSTM can outperform the DBN to capture the time series characteristics of traffic flow data.

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