Short-Term Traffic Flow Forecasting for Urban Roads Using Data-Driven Feature Selection Strategy and Bias-Corrected Random Forests

Ou, Jishun; Xia, Jingxin; Wu, Yao; Rao, Wenming

doi:10.3141/2645-17

Cited by 38 publications

(22 citation statements)

References 40 publications

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“…Statistical methodologies apply hypothesis testing routines (i.e. Student's test) for identifying significant features; neural networks allow estimation of elasticities of input components [40]; and random forests include permutation importance heuristics [41]. Using these metrics, researchers can refine the feature set of the forecasting model.…”

Section: Class 3: Wrapper Feature Selection Methodsmentioning

confidence: 99%

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

Pavlyuk

2019

Eur. Transp. Res. Rev.

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A spatiotemporal approach that simultaneously utilises both spatial and temporal relationships is gaining scientific interest in the field of traffic flow forecasting. Accurate identification of the spatiotemporal structure (dependencies amongst traffic flows in space and time) plays a critical role in modern traffic forecasting methodologies, and recent developments of data-driven feature selection and extraction methods allow the identification of complex relationships. This paper systematically reviews studies that apply feature selection and extraction methods for spatiotemporal traffic forecasting. The reviewed bibliographic database includes 211 publications and covers the period from early 1984 to March 2018. A synthesis of bibliographic sources clarifies the advantages and disadvantages of different feature selection and extraction methods for learning the spatiotemporal structure and discovers trends in their applications. We conclude that there is a clear need for development of comprehensive guidelines for selecting appropriate spatiotemporal feature selection and extraction methods for urban traffic forecasting.

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Section: Class 3: Wrapper Feature Selection Methodsmentioning

confidence: 99%

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

Pavlyuk

2019

Eur. Transp. Res. Rev.

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“…There are interactions among these three variables of volume, speed, and density in traffic engineering theory (Roess et al., ; Ou et al., ). Figure illustrates the general form of these relationships.…”

Section: Methodsmentioning

confidence: 99%

Short‐Term Traffic Speed Forecasting Based on Attention Convolutional Neural Network for Arterials

Liu

Wang

Zhu

2018

Computer aided Civil Eng

140

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As an important part of the intelligent transportation system (ITS), short-term traffic prediction has become a hot research topic in the field of traffic engineering. In recent years, with the emergence of rich traffic data and the development of deep learning technologies, neural networks have been widely used in short-term traffic forecasting. Among them, the Recurrent Neural Networks (RNN), especially the Long Short-Term Memory network (LSTM) shows the excellent ability of time-series tasks. To improve the prediction accuracy of the LSTM, some research uses the spatial-temporal matrix or Convolutional Neural Network (CNN) to extract the spatial features of the data for the LSTM network to use. In this article, we propose an attention CNN to predict traffic speed. The model uses three-dimensional data matrices constructed by traffic flow, speed, and occupancy. The spatial-temporal features extraction and the attention models are all performed by the convolution unit. Experiments on traffic data at 15-minute intervals show that the proposed algorithm has considerable advantages in predicting tasks compared to other commonly used algorithms, and the proposed algorithm has an improvement effect for cases with missing data. At the same time, by visualizing the weights generated by the attention model, we can see the influence of different spatial-temporal data on the forecasting task. C 2018 Computer-Aided Civil and Infrastructure Engineering.

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“…Several researchers have noticed this problem, and tried to solve it by adopting some prior feature selection strategies [15,16]. Similarly, we presented a short-term traffic flow forecasting model using a data-driven feature selection strategy and bias-corrected random forests [24], which shows an excellent forecasting performance and good interpretability.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For the multi-regime model, the identified regimes are treated as the most important features, and will be used in the modeling. In addition, the temporal correlations of the forecasted traffic flow measure and the interactive correlations of the multiple traffic flow measures are also two significant factors that can be used to improve the forecasting accuracy [14,24,45]. With this in mind, the time-lagged and interactive features of the traffic flow are also added to the representative feature pool.…”

Section: Feature Constructionmentioning

confidence: 99%

Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning

Xia

Wang³

et al. 2020

Applied Sciences

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Short-term traffic flow forecasting is crucial for proactive traffic management and control. One key issue associated with the task is how to properly define and capture the temporal patterns of traffic flow. A feasible solution is to design a multi-regime strategy. In this paper, an effective approach to forecasting short-term traffic flow based on multi-regime modeling and ensemble learning is presented. First, to properly capture the different patterns of traffic flow dynamics, a regime identification model based on probabilistic modeling was developed. Each identified regime represents a specific traffic phase, and was used as the representative feature for the forecasting modeling. Second, a forecasting model built on an ensemble learning strategy was developed, which integrates the forecasts of multiple regression trees. The traffic flow data over 5-min intervals collected from four I-80 freeway segments, in California, USA, was used to evaluate the proposed approach. The experimental results show that the identified regimes are able to well explain the different traffic phases, and play an important role in forecasting. Furthermore, the developed forecasting model outperformed four typical models in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) on three traffic flow measures.

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Short-Term Traffic Flow Forecasting for Urban Roads Using Data-Driven Feature Selection Strategy and Bias-Corrected Random Forests

Cited by 38 publications

References 40 publications

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

Short‐Term Traffic Speed Forecasting Based on Attention Convolutional Neural Network for Arterials

Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning

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