{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T17:41:41Z","timestamp":1776966101650,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:00:00Z","timestamp":1776297600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62263019"],"award-info":[{"award-number":["62263019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62263019"],"award-info":[{"award-number":["62263019"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In response to the current severe traffic congestion issues, highly reliable traffic flow prediction serves as a fundamental prerequisite for optimizing municipal road networks and mitigating systemic vehicular congestion. Aiming to elevate the precision of short-term traffic flow prediction, this paper first addresses the low precision of the Dung Beetle Optimizer (DBO) algorithm by introducing an exponential adaptive weight in the way of position update for the ball-rolling dung beetle, along with incorporating a Cauchy\u2013Gaussian mutation strategy. We propose the Multi-strategy improved Dung Beetle Optimizer (MDBO), which is validated using eight benchmark test functions, demonstrating that MDBO outperforms common optimization algorithms in solution accuracy. Secondly, we adopt a combined prediction model, Traffic Flow Temporal-Spatio Network (TFTSNet), which constructs spatial feature modules and temporal feature modules in parallel fusion. Finally, we achieve short-term traffic flow prediction by optimizing the TFTSNet combined prediction model using MDBO. The experiment evaluated model performance using publicly available traffic flow datasets. The results demonstrate that, compared to other state-of-the-art models, the proposed joint prediction model based on MDBO-optimized TFTSNet achieves substantial enhancements in both prediction precision and generalization capability. Root mean square error (RMSE) decreased by 8.7\u201335.7%, mean absolute error (MAE) decreased by 6.6\u201340.0%, and R2 reached 0.975, showcasing robust predictive capabilities and engineering reference value.<\/jats:p>","DOI":"10.3390\/a19040314","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T14:05:36Z","timestamp":1776348336000},"page":"314","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Data-Driven Spatiotemporal Feature Fusion Method for Traffic Flow Prediction"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2552-5746","authenticated-orcid":false,"given":"Long","family":"Li","sequence":"first","affiliation":[{"name":"School of Automation and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiwen","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, China"},{"name":"National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou 730050, China"},{"name":"Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoxu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,16]]},"reference":[{"key":"ref_1","unstructured":"Sun, H., Zhang, C., and Ran, B. (2004, January 3\u20136). Interval prediction for traffic time series using local linear predictor. Proceedings of the 7th International IEEE Conference on Intelligent Transportation Systems, Washington, WA, USA."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0191-2615(84)90002-X","article-title":"Dynamic prediction of traffic volume through Kalman filtering theory","volume":"18","author":"Okutani","year":"1984","journal-title":"Transp. Res. Part B Meth."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s11704-011-1192-6","article-title":"Prediction of urban human mobility using large-scale taxi traces and its applications","volume":"6","author":"Li","year":"2012","journal-title":"Front. Comput. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1109\/TITS.2013.2247040","article-title":"Short-term traffic flow forecasting: An experimental comparison of time-series analysis and supervised learning","volume":"14","author":"Lippi","year":"2013","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.1109\/TITS.2013.2262376","article-title":"Predicting taxi\u2013passenger demand using streaming data","volume":"14","author":"Gama","year":"2013","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_6","first-page":"878","article-title":"A functional data analysis approach to traffic volume forecasting","volume":"19","author":"Guardiola","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"04014026","DOI":"10.1061\/(ASCE)TE.1943-5436.0000672","article-title":"Improved k-nn for short-term traffic forecasting using temporal and spatial information","volume":"140","author":"Wu","year":"2014","journal-title":"J. Transp. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.trc.2015.11.002","article-title":"A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting","volume":"62","author":"Cai","year":"2016","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.ins.2022.06.090","article-title":"Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient","volume":"608","author":"Lin","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tambouratzis, T., Chernikova, D., and P\u00e1zsit, I. (2013, January 16\u201319). A comparison of artificial neural network performance: The case of neutron\/gamma pulse shape discrimination. Proceedings of the 2013 IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), Singapore.","DOI":"10.1109\/CISDA.2013.6595432"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1109\/TITS.2013.2294934","article-title":"Intelligent trip modeling for the prediction of an origin\u2013destination traveling speed profile","volume":"15","author":"Park","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5276","DOI":"10.1109\/ACCESS.2017.2787696","article-title":"Interactive temporal recurrent convolution network for traffic prediction in data centers","volume":"6","author":"Cao","year":"2017","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"He, Y., Huang, P., Hong, W., Luo, Q., Li, L., and Tsui, K.-L. (2024). In-depth insights into the application of recurrent neural networks (RNNs) in traffic prediction: A comprehensive review. Algorithms, 17.","DOI":"10.3390\/a17090398"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.trc.2018.03.001","article-title":"A hybrid deep learning based traffic flow prediction method and its understanding","volume":"90","author":"Wu","year":"2018","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1049\/iet-its.2016.0208","article-title":"LSTM network: A deep learning approach for short-term traffic forecast","volume":"11","author":"Zhao","year":"2017","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4145353","DOI":"10.1155\/2019\/4145353","article-title":"Spatiotemporal traffic flow prediction with KNN and LSTM","volume":"2019","author":"Luo","year":"2019","journal-title":"J. Adv. Transp."},{"key":"ref_19","first-page":"354","article-title":"Long short term memory based traffic prediction using multi-source data","volume":"23","author":"Leinonen","year":"2025","journal-title":"Int. J. Intell. Transp. Syst. Res."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Fiorini, S., Ciavotta, M., and Maurino, A. (2022). Listening to the city, attentively: A spatio-temporal attention-boosted autoencoder for the short-term flow prediction problem. Algorithms, 15.","DOI":"10.3390\/a15100376"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111367","DOI":"10.1016\/j.engappai.2025.111367","article-title":"A Wavelet Disentanglement and topological semantic neural network for traffic flow forecasting","volume":"156","author":"Cui","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"130665","DOI":"10.1016\/j.physa.2025.130665","article-title":"Light attention-based neural networks for traffic flow prediction","volume":"673","author":"Li","year":"2025","journal-title":"Physica A"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"111208","DOI":"10.1016\/j.engappai.2025.111208","article-title":"Adaptive lightweight temporal convolutional network with context-aware downsampling strategy for traffic flow prediction","volume":"156","author":"Zhang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7305","DOI":"10.1007\/s11227-022-04959-6","article-title":"Dung beetle optimizer: A new meta-heuristic algorithm for global optimization","volume":"79","author":"Xue","year":"2023","journal-title":"J. Supercomput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"99","DOI":"10.54254\/2755-2721\/2025.22562","article-title":"Fusion of Adaptive t-Distribution Dung Beetle Optimizer Algorithm with Tissue P System","volume":"61","author":"Xu","year":"2025","journal-title":"Comput. Eng. Appl."},{"key":"ref_26","first-page":"11","article-title":"Power transformer vibration signal prediction based on IDBO-ARIMA","volume":"37","author":"Zhou","year":"2023","journal-title":"Electron. Meas. Instrum."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"17522","DOI":"10.1038\/s41598-025-02446-5","article-title":"A study of scheduling strategies for microgrids based on the non-dominated sorting dung beetle optimization algorithm","volume":"15","author":"Chen","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Puli, S., and Nulaka, S. (2025, January 6\u20137). Cauchy Gaussian mutation strategy based parrot optimizer for feature selection in intrusion detection system. Proceedings of the 2025 International Conference on Intelligent Computing and Knowledge Extraction (ICICKE), Bengaluru, India.","DOI":"10.1109\/ICICKE65317.2025.11136636"},{"key":"ref_29","first-page":"1688","article-title":"Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning","volume":"15","author":"Zhang","year":"2019","journal-title":"Transp. A Transp. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2377","DOI":"10.1109\/TIM.2019.2956332","article-title":"A new intelligent bearing fault diagnosis method using SDP representation and SE-CNN","volume":"69","author":"Wang","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"126660","DOI":"10.1016\/j.energy.2023.126660","article-title":"Robust framework based on hybrid deep learning approach for short term load forecasting of building electricity demand","volume":"268","author":"Sekhar","year":"2023","journal-title":"Energy"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lee, J.Y., and Dernoncourt, F. (2016). Sequential short-text classification with recurrent and convolutional neural networks. arXiv.","DOI":"10.18653\/v1\/N16-1062"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ling, G., Wang, Z., Shi, Y., Wang, J., Lu, Y., and Li, L. (2022). Membrane fouling prediction based on Tent-SSA-BP. Membranes, 12.","DOI":"10.3390\/membranes12070691"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v053.i04","article-title":"GA: A package for genetic algorithms in R","volume":"53","author":"Scrucca","year":"2013","journal-title":"J. Stat. Softw."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Akan, Y.Y., and Herrmann, J.M. (2023, January 15). Stability, Entropy and Performance in PSO. Proceedings of the Companion Conference on Genetic and Evolutionary Computation, New York, NY, USA.","DOI":"10.1145\/3583133.3590688"},{"key":"ref_36","first-page":"328","article-title":"Review of whale optimization algorithm","volume":"40","author":"Xu","year":"2023","journal-title":"Appl. Res. Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2713","DOI":"10.1007\/s00521-023-09202-8","article-title":"Review of the grey wolf optimization algorithm: Variants and applications","volume":"36","author":"Liu","year":"2024","journal-title":"Neural Comput. Appl."},{"key":"ref_38","first-page":"4710","article-title":"Partition fault location of active distribution network based on AC-DBO","volume":"25","author":"Zhao","year":"2025","journal-title":"J. Comput. Methods. Sci."},{"key":"ref_39","unstructured":"National Highways (2025, September 08). National Highways Open Data. Available online: https:\/\/webtris.highwaysengland.co.uk."},{"key":"ref_40","first-page":"3097","article-title":"Traffic Flow Prediction with Heterogenous Data Using a Hybrid CNN-LSTM Model","volume":"76","author":"Wang","year":"2023","journal-title":"CMC-Comput. Mater. Contin."},{"key":"ref_41","first-page":"276","article-title":"Logging curve prediction based on a CNN-GRU neural network","volume":"2","author":"Wang","year":"2022","journal-title":"Geophys. Prospect. Pet."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhuang, W., and Cao, Y. (2022). Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information. Appl. Sci., 12.","DOI":"10.3390\/app12178714"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"138372","DOI":"10.1109\/ACCESS.2024.3466527","article-title":"Research on ship traffic flow prediction using CNN-BiGRU and WOA with multi-objective optimization","volume":"12","author":"Xie","year":"2024","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Su, H., Peng, S., Mo, S., and Wu, K. (2022). Neural network-based hybrid forecasting models for time-varying passenger flow of intercity high-speed railways. Mathematics, 10.","DOI":"10.3390\/math10234554"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/4\/314\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T17:07:49Z","timestamp":1776964069000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/4\/314"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,16]]},"references-count":44,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["a19040314"],"URL":"https:\/\/doi.org\/10.3390\/a19040314","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,16]]}}}