Deep traffic congestion prediction model based on road segment grouping

Tu, Yue; Lin, Shukuan; Qiao, Jianzhong; Liu, Bin

doi:10.1007/s10489-020-02152-x

Cited by 25 publications

(6 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [140], the authors developed a technique for constructing a tra c congestion index by extracting freestream speed and ow. The author proposed the Tra c Congestion Index (TCI), which can synthesize changes in tra c ow and speed data to assess tra c congestion, and discussed how it is generated.…”

Section: Techniques For Tra C Flow Predictionmentioning

confidence: 99%

Artificial Intelligence-Based Traffic Flow Prediction: A Comprehensive Review

Sayed

Abdelhamid

Hefny

2022

Preprint

View full text Add to dashboard Cite

The expansion of the Internet of Things has resulted in new creative solutions, such as smart cities, that have made our lives more productive, convenient, and intelligent. The core of smart cities is the Intelligent Transportation System (ITS) which has been integrated into several smart city applications that improve transportation and mobility. ITS aims to resolve traffic issues, especially traffic congestion. Recently, new traffic flow prediction models and frameworks have been rapidly developed in tandem with the introduction of Artificial Intelligence (AI) approaches to improve the accuracy of traffic flow prediction. Traffic forecasting is a crucial duty in the transportation industry. It can significantly affect the design of road constructions and projects in addition to its importance for route planning and traffic rules. Furthermore, traffic congestion is a critical issue in urban areas and overcrowded cities. Therefore, it must be accurately evaluated and forecasted. Hence, a reliable and efficient method for predicting traffic is essential. The main objectives of this study are: First, present a comprehensive review of the most popular machine learning and deep learning techniques applied in traffic prediction. Second, identifying inherent obstacles to applying machine learning and deep learning in the domain of traffic prediction.

show abstract

Section: Techniques For Tra C Flow Predictionmentioning

confidence: 99%

Artificial Intelligence-Based Traffic Flow Prediction: A Comprehensive Review

Sayed

Abdelhamid

Hefny

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Traffic efficiency is affiliated to a variety of factors, such as road type, road event, vehicle density, the average speed [15], [21], [22]. Lower traffic efficiency means that the roads with high congested levels.…”

Section: B Cognitive Model Of Traffic Situationmentioning

confidence: 99%

“…The authors in [21] propose a convolutional neural network (CNN) based supervised congestion prediction method on a statistical analysis framework. To better evaluate traffic congestion criteria for prediction, the authors in [22] propose a traffic congestion prediction model based on a roadway grouping algorithm by combining traffic data mining and CNN. In [23], the authors construct the congestion matrix of regional traffic networks to predict future congestion at all locations of the road network.…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Enabled Online Traffic Congestion Duration Prediction in Cognitive Internet of Vehicles

Chang

Liu

Sheng³

2022

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…After scattered or local traffic accidents, natural disasters, and other emergencies in the road network have a congestion impact on the expressway, this impact will spread to the regional road network and slow down the efficiency of emergency rescue. Therefore, there is an urgent need to improve the intelligent control and guidance ability of the expressway network, strengthen the close cooperation between people, vehicles, and roads, improve road traffic efficiency, and create an efficient, accurate, and real-time expressway operation system [1]. In recent years, with the continuous development of big data and artificial intelligence technology, traffic data collected by sensors has gradually improved, providing a certain data basis for the construction of machine learning models.…”

Section: Introductionmentioning

confidence: 99%

Combined Prediction of Short-Term Travel Time of Expressway Based on CEEMDAN Decomposition

Jia

Zhou²,

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Travel time is the basis for emergency intelligent control and guidance in expressway networks. To realize its accurate prediction and improve the expressway service level during emergencies, this study uses the combined model to predict the short-term travel time of expressway sections based on the expressway gantry data of Sichuan Province. First, the travel time series is extracted using a data matching algorithm, and the double standard deviation-cyclic elimination (2SD-CE) algorithm is used to clean the data. Then, combined with the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, the travel time subsequence was extracted, and the frequency of the subsequence was divided by Sample entropy (SampEn) algorithm. Based on this, bidirectional long short-term memory (BiLSTM), long short-term memory (LSTM), and vanilla recurrent neural network (vanilla RNN) models are used to construct prediction combination model 1 (CM1) under the condition of a single feature. Subsequently, the CEEMDAN and empirical mode decomposition (EMD) algorithms were combined with the LSTM algorithm to obtain the combination models (CM2 and CM3) without frequency division. The example calculation and analysis show that under different time granularities (5 min, 10 min, and 15 min) and different highway sections, the combined model can integrate the advantages of all prediction models and has higher prediction accuracy and stability, among which the prediction effect of CM1 can reduce the prediction value of root mean squared error (RMSE) by 18.8~26.4%, 0.8~41%, 4.1~13.3%.

show abstract

Deep traffic congestion prediction model based on road segment grouping

Cited by 25 publications

References 46 publications

Artificial Intelligence-Based Traffic Flow Prediction: A Comprehensive Review

Artificial Intelligence-Based Traffic Flow Prediction: A Comprehensive Review

Blockchain-Enabled Online Traffic Congestion Duration Prediction in Cognitive Internet of Vehicles

Combined Prediction of Short-Term Travel Time of Expressway Based on CEEMDAN Decomposition

Contact Info

Product

Resources

About