Academic Journal
Cost-effective and scalable traffic forecasting: A graph-free multi-view MLP architecture.
| Τίτλος: | Cost-effective and scalable traffic forecasting: A graph-free multi-view MLP architecture. |
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| Συγγραφείς: | Jin G; National Innovative Institute of Defense Technology, Beijing, China. Electronic address: jinguangyin96@foxmail.com., Lai S; The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China. Electronic address: slai892@connect.hkust-gz.edu.cn., Hao X; Beijing Academy of Artificial Intelligence, Beijing, China. Electronic address: xshao@baai.ac.cn., Zhang M; Peking University, Beijing, China. Electronic address: mingtaozhang@pku.edu.cn., Zhou T; Hainan University, Haikou, China. Electronic address: teng.zhou@hainanu.edu.cn., Zhang J; Beijing Jiaotong University, Beijing, China. Electronic address: zhangjinlei@bjtu.edu.cn. |
| Πηγή: | Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2027 Jan; Vol. 205 (Pt B), pp. 109454. Date of Electronic Publication: 2026 Aug 05. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: New York : Pergamon Press, [c1988- |
| Ιατρικοί όροι (MeSH): | Multilayer Perceptrons* , Cost-Benefit Analysis*, Forecasting/methods ; Large Language Models ; Prediction Algorithms ; Predictive Learning Models ; Humans |
| Περίληψη: | Achieving accurate and scalable traffic prediction is a cornerstone of modern intelligent transportation systems. While the recent advent of Large Language Models (LLMs) has introduced remarkable zero-shot capabilities to urban computing, their immense parametric scale and exorbitant deployment costs fundamentally restrict their viability for real-time, large-scale traffic operations. Concurrently, mainstream spatio-temporal forecasting methods remain heavily tethered to explicit topological graph structures or computationally expensive attention mechanisms, which inherently introduce severe scalability bottlenecks. These limitations underscore a critical divergence in current research: an urgent necessity to develop highly efficient, lightweight predictive architectures capable of bridging the gap between theoretical accuracy and practical deployability.To address this deployment imperative, we propose the Spatio-Temporal Multi-view MLP Network (STMMN), a highly scalable, graph-free computational framework. Diverging from traditional graph-dependent paradigms, STMMN utilizes an adaptive spatial clustering multilayer perceptron (MLP) module to autonomously extract latent spatial homogeneity. This mechanism streamlines spatial dimensionality, effectively circumventing the rigidity of predefined topologies and drastically reducing learning complexity. Furthermore, we introduce a temporal multi-view aggregation module that seamlessly integrates distinct temporal branches, specifically capturing proximity, trend, and periodicity, to dynamically fuse multi-scale temporal dependencies without relying on exhaustive attention maps.Extensive experiments conducted on multiple real-world, large-scale datasets confirm that STMMN achieves state-of-the-art prediction accuracy while exhibiting exceptional computational efficiency. Ultimately, this research validates the immense practical value of lightweight, knowledge-aware models in overcoming the deployment bottlenecks of large-scale urban engineering operations. (Copyright © 2026 Elsevier Ltd. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Multi-view modeling; Multilayer perceptron; Spatio-temporal neural network; Traffic forecasting |
| Entry Date(s): | Date Created: 20260811 Date Completed: 20261014 Latest Revision: 20261014 |
| Update Code: | 20261014 |
| DOI: | 10.1016/j.neunet.2026.109454 |
| PMID: | 42580088 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1879-2782 |
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| DOI: | 10.1016/j.neunet.2026.109454 |