Short-Term Traffic Speed Prediction Method for Urban Road Sections Based on Wavelet Transform and Gated Recurrent Unit

Fu, Xin; Luo, Wei; Xu, Chengyao; Zhao, Xiaoxuan

doi:10.1155/2020/3697625

Cited by 19 publications

(13 citation statements)

References 38 publications

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“…e K-nearest neighbor method using an ISM search engine is used to construct historical traffic flow time similar to current traffic flow, and support vector regression is used to estimate the short term [17]. Based on the real traffic data, the influence of the water flow and the flow of water on the planned road were determined and the prediction accuracy of the KNN-SVR model was analyzed.…”

Section: Literature Reviewmentioning

confidence: 99%

Highway Traffic Flow Prediction Algorithm Based on Multiscale Transformation and Convolutional Networks

Luo

Wang

Ming

2022

Journal of Control Science and Engineering

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In order to solve the problem that the traditional long-term high-speed traffic forecasting algorithm is affected by the approximation ability of the function and easy to fall into the local mass value, we wrote a multivariate-based highway traffic forecasting algorithm scaling and convolutional networks. Because the feedforward wavelet neural network algorithm predicts the short-term traffic flow in different areas, it is necessary to examine the ability to predict the difference between different models. From the standard feedforward wavelet neural network algorithm using global optimization capabilities, we improve the wolf pack algorithm, improve the search accuracy of the algorithm, get the best solution of the estimated value of the work according to the search results when completing the research objectives, and get the ability to predict the work of the model. Feedforward neural network algorithm: we develop and obtain the best short-term high-speed traffic forecast values. The results are as follows: after using the author’s algorithm, the processing time increases by 1.5 seconds, but the average percentage of errors decreases by more than 50%, in fact the error and the root mean square error decreased by about 30%, and the smoothing coefficient increased by about 1%. The prediction of the author’s algorithm for short-term high-speed traffic is better than the wavelet neural network prediction algorithm, and the prediction accuracy and stability of the author’s algorithm are higher.

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Section: Literature Reviewmentioning

confidence: 99%

Highway Traffic Flow Prediction Algorithm Based on Multiscale Transformation and Convolutional Networks

Luo

Wang

Ming

2022

Journal of Control Science and Engineering

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“…For example, Vlahogianni [52] combined some different univariate and multivariate algorithms for short-term forecasting models of travel speed. In a similar study, a combination of W+GRU+ARMA was used to accurately forecast and analyze traffic speed [55]. Also, traffic flow forecasting in graph-based hybrid models and CNN neural networks have been investigated in refs.…”

Section: Hybrid Forecasting Modelsmentioning

confidence: 99%

A hybrid deep and machine learning model for short‐term traffic volume forecasting of adjacent intersections

Mirzahossein

Gholampour

Sajadi

et al. 2022

IET Intelligent Trans Sys

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Despite complex fluctuations, missing data, and maintenance costs of detectors, traffic volume forecasting at intersections is still a challenge. Moreover, most existing forecasting methods consider an isolated intersection instead of multiple adjacent ones. By accurately forecasting the volume of short-term traffic, a low-cost method can be provided to solve the problems of congestion, delay, and breakdown of detectors in the road transport system. This paper outlines a novel hybrid method based on deep learning to estimate short-term traffic volume at three adjacent intersections. The gated recurrent unit (GRU) and long short-term memory (LSTM) bilayer network with wavelet transform (WL) noise reduction algorithm (WL+GRU-LSTM) are used to analyze raw traffic volume data. The WL+GRU-LSTM is constructed by comparing different machine learning and deep learning methods. A comparative study was used to choose the model's network structure, training technique, and optimizer type. To prove the model's accuracy and resilience, it was compared with the leading short-term traffic forecasting approaches. Experimental results confirm that the WL+GRU-LSTM model can forecast complex traffic volume fluctuations in different approaches of intersections with an accuracy of over 94%. It also shows better results compared to current methods. The proposed model could replace intermediate loop detectors.

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“…The need for transportation continues to rise as a result of socio-economic progress and increasing civilization. [11]. Some of the critical aspects involved in traffic theory design and analysis should be included below.…”

Section: Traffic Stream Characteristicsmentioning

confidence: 99%

A Review of Studying the Relationship of Traffic Stream Characteristics and Noise Intensity with Flexible Pavement Surface Condition

Merza

Al-Jumaili

2022

IOP Conf. Ser.: Earth Environ. Sci.

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In general, roads are a very important infrastructure to facilitate people’s access to their social and economic activities, so knowing the things that affect the efficiency of these roads is very important and how to maintain them. One of the aims of this research is to shed light on previous studies that showed the relationship between traffic characteristics and noise intensity on the condition of the breakable Pavement. The results of previous studies show that flexible Pavement has 19 potential failures and how to address them. There is a relationship between the characteristics of traffic flow and failures that occur in flexible paving. It is also possible to calculate the road condition by knowing the types of faults on the road using several roads or by using specific formulas to calculate the road condition index. There are relationships between the road condition index, the vehicles’ speed, and the models that link Noise to speed. And that most of the relationships that were used to calculate the elastic pavement condition index and its relationship to the movement characteristics are statistical relationships using ANOVA, recreation, and R2

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Short-Term Traffic Speed Prediction Method for Urban Road Sections Based on Wavelet Transform and Gated Recurrent Unit

Cited by 19 publications

References 38 publications

Highway Traffic Flow Prediction Algorithm Based on Multiscale Transformation and Convolutional Networks

Highway Traffic Flow Prediction Algorithm Based on Multiscale Transformation and Convolutional Networks

A hybrid deep and machine learning model for short‐term traffic volume forecasting of adjacent intersections

A Review of Studying the Relationship of Traffic Stream Characteristics and Noise Intensity with Flexible Pavement Surface Condition

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