Spatiotemporal correlation modelling for machine learning-based traffic state predictions: state-of-the-art and beyond

Cui, Haipeng; Meng, Qiang; Teng, Teck-Hou; Yang, Xin

doi:10.1080/01441647.2023.2171151

Cited by 7 publications

(2 citation statements)

References 94 publications

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“…In 2023 Cui et al conduct a critical review of spatiotemporal correlation modelling approaches in machine learning based ML-based traffic state prediction in their research paper titled "Spatiotemporal Correlation Modelling for Machine Learning Based Traffic State Predictions Stateof-the-Art and Beyond". This study contributes to advancing the understanding of spatiotemporal correlation modelling [24].…”

Section: VIImentioning

confidence: 83%

Artificial Intelligence Applications in Road Traffic Forecasting: A Review of Current Research

Khairi,

Abbas,

Sharif

et al. 2023

2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

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Artificial intelligence (AI) is revolutionising global businesses and permanently changing industries forever; it has become an integral part of our daily lives, and the potential of this technology goes beyond home or personal use, as artificial intelligence continues to make great traction in transforming the future of humanity. The use of AI in traffic forecasting in particular and transportation planning in general, has gained attention in recent years due to its flexibility and ability to solve complex problems. However, research has shown that there is a gap in AI applications for longer terms forecasts, as most research focuses on real-time data analysis or within shorty or very short term where the vehicles are already presented within the road network. This research paper reviews the current state of AI applications in traffic forecasting, highlighting the challenges and opportunities.

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Section: VIImentioning

confidence: 83%

Artificial Intelligence Applications in Road Traffic Forecasting: A Review of Current Research

Khairi,

Abbas,

Sharif

et al. 2023

2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

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“…According to the literature, three types of input including volume, time of the day, week, month, or year, and speed have been used to predict travel time [54]. However, this information needs to be extracted from the raw data collected from detectors.…”

Section: Feature Extractionmentioning

confidence: 99%

Travel time prediction for an intelligent transportation system based on a data-driven feature selection method considering temporal correlation

Kandiri,

Ghiasi,

Teixeira

et al. 2024

Preprint

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Travel-time prediction holds significant importance in Intelligent Transportation Systems (ITS), providing essential information for tasks such as accident detection and congestion control. While data-driven methods are commonly used for travel-time prediction, the accuracy of predictions heavily relies on the selection of appropriate features. In this study, a two-stage methodology for travel time prediction is introduced, comprising a novel feature selection method called OA2DD with two layers of optimization and a layer of data-driven predictive methods. In the first stage (offline process), the optimal set of features and architecture for the machine learning model is selected using interconnected optimization algorithms. In the second stage (real-time process), travel time prediction is performed using new data from unseen parts of the dataset. The method is applied to a case study involving the M50 motorway in Dublin. Additionally, several wrapper feature selection methods are employed to assess and validate its performance. Results show that the proposed method has a better convergence curve and reduces the number of selected features by up to half, which reduces the computational cost of prediction process up to 56%. Moreover, employing the selected features from the OA2DD method leads to a reduction in predication error by up to 29% compared to the full set of features and the other feature selection methods.

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EEMD-MST-Resnet: a hybrid deep learning approach for predicting passenger flow in urban transportation hubs

Chen,

Wei

2024

Neural Comput & Applic

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Spatiotemporal correlation modelling for machine learning-based traffic state predictions: state-of-the-art and beyond

Cited by 7 publications

References 94 publications

Artificial Intelligence Applications in Road Traffic Forecasting: A Review of Current Research

Artificial Intelligence Applications in Road Traffic Forecasting: A Review of Current Research

Travel time prediction for an intelligent transportation system based on a data-driven feature selection method considering temporal correlation

EEMD-MST-Resnet: a hybrid deep learning approach for predicting passenger flow in urban transportation hubs

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