{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T14:27:50Z","timestamp":1782397670142,"version":"3.54.5"},"reference-count":30,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T00:00:00Z","timestamp":1745366400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Humanities and Social Sciences Fund of the Ministry of Education of China","award":["23YJA890038"],"award-info":[{"award-number":["23YJA890038"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The prediction of pre-hospital medical emergencies based on historical timing data holds significant potential for enhancing individual safety. In this study, we constructed ARIMA (Autoregressive Integrated Moving Average Model), Autoformer, and the structurally symmetric deep learning model LSTNFCL (Long-Short-Term Network with Conv2Former, CBAM (Convolutional Block Attention Module), and LSTM) using the time-period symmetric pre-hospital traffic accident medical emergency calls in Chengdu from 1 January 2022 to 31 December 2023. We systematically evaluated the prediction efficiency of the three models on pre-hospital traffic accident medical emergency call demand. The experiments show the following: The MAE (mean absolute error) of Autoformer is 0.849 and the RMSE (root mean square error) is 0.922, but its inference takes longer; LSTNFCL achieves competitive performance with an MAE of 1.681 and an RMSE of 3.301 under a lightweight architecture by integrating LSTM (Long Short-Term Memory), Conv2Former, and CBAM modules. It demonstrates notably superior computational efficiency and reasoning time compared to ARIMA. Furthermore, it surpasses Autoformer in terms of efficiency and reasoning time. Ablation experiments demonstrate that the Conv2Former module reduces the MSE (mean squared error) by 21%, while the CBAM module further optimizes the MAPE (mean absolute percentage error) to 0.891%. This study provides a prediction scheme that balances accuracy and real-time performance for pre-hospital emergency systems, and the results show that Autoformer is suitable for offline high-precision scenarios, while LSTNFCL is more suitable for real-time prediction needs with resource constraints. Focusing on the public safety, this paper offers an effective early prediction method for the pre-hospital emergency medical system.<\/jats:p>","DOI":"10.3390\/sym17050639","type":"journal-article","created":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T10:40:48Z","timestamp":1745404848000},"page":"639","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Performance Evaluation of ARIMA, Autoformer, and Symmetric LSTNFCL Models for Traffic Accident Emergency Prediction"],"prefix":"10.3390","volume":"17","author":[{"given":"Honglei","family":"Wei","sequence":"first","affiliation":[{"name":"School of Mathematics, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingbiao","family":"Song","sequence":"additional","affiliation":[{"name":"School of Mathematics, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chonghong","family":"Dan","sequence":"additional","affiliation":[{"name":"School of Mathematics, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhou","family":"He","sequence":"additional","affiliation":[{"name":"School of Mathematics, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maowu","family":"Pu","sequence":"additional","affiliation":[{"name":"School of Mathematics, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,23]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2023). Global Status Report on Road Safety 2023: Summary, World Health Organization."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.apenergy.2012.04.001","article-title":"Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction","volume":"98","author":"Liu","year":"2012","journal-title":"Appl. Energy"},{"key":"ref_3","first-page":"103","article-title":"Time-series analysis","volume":"423","author":"Broomhead","year":"1989","journal-title":"Proc. R. Soc. Lond. A Math. Phys. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"16453","DOI":"10.1007\/s11227-022-04503-6","article-title":"Mobility prediction for random walk mobility model using ARIMA in mobile ad hoc networks","volume":"78","author":"Theerthagiri","year":"2022","journal-title":"J. Supercomput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.egyr.2022.11.038","article-title":"Forecasting ethanol demand in India to meet future blending targets: A comparison of ARIMA and various regression models","volume":"9","author":"Dey","year":"2023","journal-title":"Energy Rep."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhao, D., Zhang, R., Zhang, H., and He, S. (2022). Prediction of global omicron pandemic using ARIMA, MLR, and Prophet models. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-23154-4"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yuan, Z., Zhou, X., and Yang, T. (2018, January 19\u201323). Hetero-convlstm: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, New York, NY, USA.","DOI":"10.1145\/3219819.3219922"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4206919","DOI":"10.1155\/2020\/4206919","article-title":"Traffic Accident Prediction Based on LSTM-GBRT Model","volume":"2020","author":"Zhang","year":"2020","journal-title":"J. Control Sci. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"106090","DOI":"10.1016\/j.aap.2021.106090","article-title":"A deep learning based traffic crash severity prediction framework","volume":"154","author":"Rahim","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"ref_10","unstructured":"Zaremba, W., Sutskever, I., and Vinyals, O. (2014). Recurrent neural network regularization. arXiv."},{"key":"ref_11","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_12","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv."},{"key":"ref_13","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lai, G., Chang, W.C., Yang, Y., and Liu, H. (2018, January 8\u201312). Modeling long- and short-term temporal patterns with deep neural networks. Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, New York, NY, USA.","DOI":"10.1145\/3209978.3210006"},{"key":"ref_15","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume":"34","author":"Wu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"128877","DOI":"10.1016\/j.physa.2023.128877","article-title":"Cycle-based signal timing with traffic flow prediction for dynamic environment","volume":"623","author":"Li","year":"2023","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5807","DOI":"10.1109\/JSTARS.2021.3084805","article-title":"MHA-Net: Multipath Hybrid Attention Network for building footprint extraction from high-resolution remote sensing imagery","volume":"14","author":"Cai","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1007\/s11227-024-06670-0","article-title":"An integrated static and dynamic graph fusion approach for traffic flow prediction","volume":"81","author":"Che","year":"2025","journal-title":"J. Supercomput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1007\/s12205-023-2457-y","article-title":"A hybrid framework combining LSTM NN and BNN for short-term traffic flow prediction and uncertainty quantification","volume":"28","author":"Wang","year":"2024","journal-title":"KSCE J. Civ. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Dong, J. (2025). Short-Term Prediction of Traffic Flow Based on the Comprehensive Cloud Model. Mathematics, 13.","DOI":"10.3390\/math13040658"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2220","DOI":"10.1109\/TVT.2024.3390997","article-title":"Adaptive spatio-temporal relation based transformer for traffic flow prediction","volume":"74","author":"Wang","year":"2024","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/s11227-024-06653-1","article-title":"Spatio-temporal graph neural network based on time series periodic feature fusion for traffic flow prediction","volume":"81","author":"Chen","year":"2025","journal-title":"J. Supercomput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"119140","DOI":"10.1016\/j.eswa.2022.119140","article-title":"What is the best RNN-cell structure to forecast each time series behavior?","volume":"215","author":"Khaldi","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3315","DOI":"10.1016\/j.egyr.2023.09.175","article-title":"Comparing Long Short-Term Memory (LSTM) and bidirectional LSTM deep neural networks for power consumption prediction","volume":"10","year":"2023","journal-title":"Energy Rep."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cahuantzi, R., Chen, X., and G\u00fcttel, S. (2023, January 13\u201314). A comparison of LSTM and GRU networks for learning symbolic sequences. Proceedings of the Science and Information Conference, London, UK.","DOI":"10.1007\/978-3-031-37963-5_53"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"9523230","DOI":"10.1155\/2023\/9523230","article-title":"Spatiotemporal Self-Attention-Based LSTNet for Multivariate Time Series Prediction","volume":"2023","author":"Wang","year":"2023","journal-title":"Int. J. Intell. Syst."},{"key":"ref_27","unstructured":"Zeng, A., Chen, M., Zhang, L., and Xu, Q. (2023, January 13\u201314). Are transformers effective for time series forecasting?. Proceedings of the AAAI Conference on Artificial Intelligence, Washington, DC, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_29","unstructured":"Hou, Q., Lu, C.Z., Cheng, M.M., and Feng, J. (2022). Conv2former: A simple transformer-style convnet for visual recognition. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/5\/639\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:20:21Z","timestamp":1760030421000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/5\/639"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,23]]},"references-count":30,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["sym17050639"],"URL":"https:\/\/doi.org\/10.3390\/sym17050639","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,23]]}}}