A Period-Specific Combined Traffic Flow Prediction Based on Travel Speed Clustering

Feng, Bin; Xu, Jie; Lin, Yongjie; Li, Penghao

doi:10.1109/access.2020.2992657

Cited by 11 publications

(3 citation statements)

References 44 publications

(53 reference statements)

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“…Based on the collected real-world traffic data, VISSIM, traffic simulation software, was used for simulation analysis. To simulate road conditions during different periods of time, three different traffic states were modeled: peak, off-peak, and lowtraffic periods [38]. In order to facilitate data collection, coordinated signal control was implemented for the key intersection in the area.…”

Section: A Experimental Settingsmentioning

confidence: 99%

Improved Long Short-Term Memory-Based Periodic Traffic Volume Prediction Method

Chen

Guo

et al. 2023

IEEE Access

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In response to the problem of fixed time intervals for short-term traffic flow prediction, which fails to meet the requirements of traffic signal control based on traffic cycle signals, this paper proposes an improved long short-term memory-based method for periodic traffic volume prediction. The method presented in this study involves improvements to the Long Short-Term Memory (iLSTM) and Bidirectional Long Short-Term Memory (iBiLSTM) models, leading to the construction of the iBiLSTM-iLSTM -NN model. This model incorporates spatial data from surrounding intersections and employs data fitting techniques to establish the correlation between periodic queue length and traffic volume. Subsequently, a predictive model for periodic traffic volume is developed based on this correlation, enabling reliable forecasting of future traffic volumes within a given cycle. Additionally, actual intersection data is collected for simulation analysis. The results indicate that the prediction error of periodic traffic volume is influenced by different traffic flow characteristics such as peak, off-peak, and normal periods, as well as different inbound lanes. Different model parameters have a noticeable impact on the model's performance, with smaller batch sizes leading to more stable models. By comparing the performance of different prediction models using various error evaluation metrics, this study finds that the proposed model exhibits the most stable performance. The research findings can be applied to rapidly predict future traffic volumes for several periods based on the instantaneous queue length at the end of the red signal phase, providing reliable, accurate, and timely data for urban traffic signal control.

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Section: A Experimental Settingsmentioning

confidence: 99%

Improved Long Short-Term Memory-Based Periodic Traffic Volume Prediction Method

Chen

Guo

et al. 2023

IEEE Access

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“…Zhongshan is one of the pilot cities to implement ITS applications in China, which has the ability to automatically collect city-level traffic flow data at signalized intersections. The testbed is selected on Xingzhong Rd with two-way six motorized lanes, which is the busiest and most congested south-north corridors in the Zhongshan downtown area [39]. Northbound and southbound traffic flow were collected by loop detectors located several meters before the stopline at the signalized intersection between Zhongshan Rd.…”

Section: Case Studymentioning

confidence: 99%

Multi-Step Traffic Speed Prediction Based on Ensemble Learning on an Urban Road Network

Feng

Zhang

et al. 2021

Applied Sciences

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Short-term traffic speed prediction plays an important role in the field of Intelligent Transportation Systems (ITS). Usually, traffic speed forecasting can be divided into single-step-ahead and multi-step-ahead. Compared with the single-step method, multi-step prediction can provide more future traffic condition to road traffic participants for guidance decision-making. This paper proposes a multi-step traffic speed forecasting by using ensemble learning model with traffic speed detrending algorithm. Firstly, the correlation analysis is conducted to determine the representative features by considering the spatial and temporal characteristics of traffic speed. Then, the traffic speed time series is split into a trend set and a residual set via a detrending algorithm. Thirdly, a multi-step residual prediction with direct strategy is formulated by the ensemble learning model of stacking integrating support vector machine (SVM), CATBOOST, and K-nearest neighbor (KNN). Finally, the forecasting traffic speed can be reached by adding predicted residual part to the trend one. In tests that used field data from Zhongshan, China, the experimental results indicate that the proposed model outperforms the benchmark ones like SVM, CATBOOST, KNN, and BAGGING.

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“…To solve problems with the processing of textual information, various technologies and methods of artificial intelligence, and in particular machine learning (ML), are widely used. AI (artificial intelligence) is used in various areas, for example, in the field of automating the processes of pattern recognition [1], separating data into certain classes [2], adaptive control [3], clustering [4], forecasting [5] and others. One of the tasks of scientists in this field is the classification of the submitted text on certain topics.…”

Section: Introductionmentioning

confidence: 99%

Development of a thematic and neural network model for data learning

Аkanova

Ospanova

Sharipova

et al. 2022

EEJET

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Research in the field of semantic text analysis begins with the study of the structure of natural language. The Kazakh language is unique in that it belongs to agglutinative languages and requires careful study. The object of this study is the text in the Kazakh language. Existing approaches to the study of the semantic analysis of text in the Kazakh language do not consider text analysis using the methods of thematic modeling and learning of neural networks. The purpose of this study is to determine the quality of a topic model based on the LDA (Latent Dirichlet Allocation) method with Gibbs sampling, through neural network learning. The LDA model can determine the semantic probability of the keywords of a single document and give them a rating score. To build a neural network, one of the widely used LSTM architectures was used, which has proven itself well in working with NLP (Natural Language Processing). As a result of learning, it is possible to see to what extent the text was trained and how the semantic analysis of the text in the Kazakh language went. The system, developed on the basis of the LDA model and neural network learning, combines the detected keywords into separate topics. In general, the experimental results showed that the use of deep neural networks gives the expected results of the quality of the LDA model in the processing of the Kazakh language. The developed model of the neural network contributes to the assessment of the accuracy of the semantics of the used text in the Kazakh language. The results obtained can be applied in systems for processing text data, for example, when checking the compliance of the topic and content of the proposed texts (abstracts, term papers, theses, and other works).

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A Period-Specific Combined Traffic Flow Prediction Based on Travel Speed Clustering

Cited by 11 publications

References 44 publications

Improved Long Short-Term Memory-Based Periodic Traffic Volume Prediction Method

Improved Long Short-Term Memory-Based Periodic Traffic Volume Prediction Method

Multi-Step Traffic Speed Prediction Based on Ensemble Learning on an Urban Road Network

Development of a thematic and neural network model for data learning

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