Smart City Gnosys
Smart city article details
| Title | Network-Level Traffic Flow Prediction: Functional Time Series Vs. Functional Neural Network Approach |
|---|---|
| ID_Doc | 39033 |
| Authors | Ma T.; Yao F.; Zhou Z. |
| Year | 2024 |
| Published | Annals of Applied Statistics, 18, 1 |
| DOI | http://dx.doi.org/10.1214/23-AOAS1795 |
| Abstract | Traffic state prediction is an essential component and an underlying backbone of intelligent transportation systems, especially in the context of smart city framework. Its significance is mainly twofold in modern transportation systems: supporting advanced traffic operations and management for highways and urban road networks to mitigate traffic congestion and enabling individual drivers with connected vehicles in the traffic system to dynamically optimize their routes to improve travel time. Traffic state prediction with interval-based pointwise methods at 15-minute or hourly intervals is common in traffic literature. However, because traffic dynamics are a continuous process over time, the discrete-time pointwise methods for traffic prediction at a fixed time interval hardly meet the advanced demands of continuous prediction in modern transportation systems. To close the gap, we propose functional approaches to intraday and day-by-day continuous-time prediction for traffic volume. This research focuses on network-level traffic flow predictions concurrently for all locations of interest. Two functional approaches are introduced, namely, the network-integrated functional time-series model and the functional neural network model. With functional approaches a 24-hour intraday traffic profile is modeled as a functional curve over time, and sequences of historical traffic curves are used to predict traffic curves for near future days in a row and multiple locations of interest. We also include the functional varying coefficient model, Sparse VAR and traditional AR models in the comparative study; empirical results show that the network-integrated functional time-series model outperforms other approaches in terms of the accuracy of predictions at network-scale. © Institute of Mathematical Statistics, 2024. |
| Author Keywords | functional neural network; functional principal component; functional time series; functional varying coefficient; Matrix-variate factor model; network-level traffic flow prediction |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
58566  | 0.892 | Ruther R.; Klos A.; Rosenbaum M.; Schiffmann W. | Traffic Flow Forecast Of Road Networks With Recurrent Neural Networks | Proceedings - 2021 International Symposium on Computer Science and Intelligent Controls, ISCSIC 2021 (2021) |
| 40920 | 0.885 | Abdullah S.M.; Periyasamy M.; Kamaludeen N.A.; Towfek S.K.; Marappan R.; Kidambi Raju S.; Alharbi A.H.; Khafaga D.S. | Optimizing Traffic Flow In Smart Cities: Soft Gru-Based Recurrent Neural Networks For Enhanced Congestion Prediction Using Deep Learning | Sustainability (Switzerland), 15, 7 (2023) |
| 5610 | 0.885 | Pulligilla M.K.; Vanmathi C. | A Traffic Flow Prediction Framework Based On Integrated Federated Learning And Recurrent Long Short-Term Networks | Peer-to-Peer Networking and Applications, 17, 6 (2024) |
| 39088 | 0.883 | Dkhar T.; Pandey C.; Francis S.; Sinha Roy D.; Kr Luhach A. | Neurosync: A Novel Neural Network Architecture For Time Series Forecasting Of Vehicle Traffic Data Over 5G And Beyond | International Journal of Communication Systems, 38, 6 (2025) |
| 37566 | 0.881 | Lei X.; Mei H.; Shi B.; Wei H. | Modeling Network-Level Traffic Flow Transitions On Sparse Data | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2022) |
| 58677 | 0.88 | Lian P.; Li Y.; Liu B.; Feng X. | Traffic Speed Prediction Using Multivariate Time Series Dynamic Graph Neural Network; [基于多元时间序列动态图神经网络的交通速度预测] | Journal of Geo-Information Science, 27, 3 (2025) |
| 934 | 0.88 | Bakir D.; Moussaid K.; Chiba Z.; Abghour N. | A Comprehensive Review Of Traffic Congestion Prediction Models: Machine Learning And Statistical Approaches | 2024 IEEE International Conference on Computing, ICOCO 2024 (2024) |
| 58652 | 0.875 | Swathi V.; Yerraboina S.; Mallikarjun G.; Jhansirani M. | Traffic Prediction For Intelligent Transportation System Using Machine Learning | 2022 2nd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2022 (2022) |
| 61010 | 0.875 | Almeida A.; Brás S.; Oliveira I.; Sargento S. | Vehicular Traffic Flow Prediction Using Deployed Traffic Counters In A City | Future Generation Computer Systems, 128 (2022) |
| 1395 | 0.874 | Tripathi A.N.; Sharma B. | A Deep Review: Techniques, Findings And Limitations Of Traffic Flow Prediction Using Machine Learning | Lecture Notes in Mechanical Engineering (2023) |