Deep learning for short-term traffic flow prediction

Polson, Nicholas G.; Sokolov, Vadim

doi:10.1016/j.trc.2017.02.024

Cited by 791 publications

(333 citation statements)

References 58 publications

(48 reference statements)

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“…With the successful application of Artificial Intelligence (AI) in various fields, many researchers use AI (Deep learning is one branch of the AI family) to solve traffic problems, such as travel mode choice predication [41], short-term traffic flow prediction [42] and destination forecast of bus passengers [29,43].…”

Section: Deep Learning Modelmentioning

confidence: 99%

Smart Card Data Mining of Public Transport Destination: A Literature Review

Sun

Jing

et al. 2018

Information

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Smart card data is increasingly used to investigate passenger behavior and the demand characteristics of public transport. The destination estimation of public transport is one of the major concerns for the implementation of smart card data. In recent years, numerous studies concerning destination estimation have been carried out-most automatic fare collection (AFC) systems only record boarding information but not passenger alighting information. This study provides a comprehensive review of the practice of using smart card data for destination estimation. The results show that the land use factor is not discussed in more than three quarters of papers and sensitivity analysis is not applied in two thirds of papers. In addition, the results are not validated in half the relevant studies. In the future, more research should be done to improve the current model, such as considering additional factors or making sensitivity analysis of parameters as well as validating the results with multi-source data and new methods.

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Section: Deep Learning Modelmentioning

confidence: 99%

Smart Card Data Mining of Public Transport Destination: A Literature Review

Sun

Jing

et al. 2018

Information

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“…Ma, Tao, Wang, Yu, and Wang () used long short‐term memory (LSTM) neural network to predict traffic speed based on data collected by microwave sensors. Polson and Sokolov () combined a linear model with a sequence of tanh layers to forecast traffic flow.…”

Section: Literature Reviewmentioning

confidence: 99%

A spatio‐temporal ensemble method for large‐scale traffic state prediction

Liu

et al. 2019

Computer aided Civil Eng

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How to effectively ensemble multiple models while leveraging the spatio‐temporal information is a challenging but practical problem. However, there is no existing ensemble method explicitly designed for spatio‐temporal data. In this paper, a fully convolutional model based on semantic segmentation technology is proposed, termed as spatio‐temporal ensemble net. The proposed method is suitable for grid‐based spatio‐temporal prediction in dense urban areas. Experiments demonstrate that through spatio‐temporal ensemble net, multiple traffic state prediction base models can be combined to improve the prediction accuracy.

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“…In another paper, the problem of traffic forecasting at peak hour and after an accident is approached using a generic deep learning framework based on long short term memory units [15]. Polson and Sokolov used a deep neural network to predict traffic flow, demonstrating that the deep neural network is capable of giving precise short-term prediction at the sharp traffic flow regime [16]. Lv et al used a stacked autoencoder model to learn features that capture the nonlinear spatial and temporal correlations from the traffic data, then forwarded these features to the output layer to predict the traffic flow [17].…”

Section: Related Workmentioning

confidence: 99%