{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T10:06:24Z","timestamp":1784801184219,"version":"3.55.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:00:00Z","timestamp":1775088000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:00:00Z","timestamp":1775088000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Open Fund Project of Vehicle Measurement, Con-trol and Safety Key Laboratory of Sichuan Province","award":["QCCK2025-0012"],"award-info":[{"award-number":["QCCK2025-0012"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. ITS Res."],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1007\/s13177-026-00639-4","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T05:13:21Z","timestamp":1775106801000},"page":"1061-1078","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Spatiotemporal BiLSTM-Transformer Model with Multi-Scale Attention for Car-Following Prediction"],"prefix":"10.1007","volume":"24","author":[{"given":"Weiwei","family":"Huo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jintao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zehui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,2]]},"reference":[{"key":"639_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2024.109624","volume":"119","author":"K Jalil","year":"2024","unstructured":"Jalil, K., Xia, Y., Chen, Q., Zahid, M.N., Manzoor, T., Zhao, J.: Integrative review of data sciences for driving smart mobility in intelligent transportation systems. Computers and Electrical Engineering 119, 109624 (2024). https:\/\/doi.org\/10.1016\/j.compeleceng.2024.109624","journal-title":"Computers and Electrical Engineering"},{"issue":"8","key":"639_CR2","doi-asserted-by":"publisher","first-page":"12276","DOI":"10.1109\/TITS.2021.3098765","volume":"23","author":"B Groelke","year":"2021","unstructured":"Groelke, B., Earnhardt, C., Borek, J., Vermillion, C.: A predictive command governor-based adaptive cruise controller with collision avoidance for non-connected vehicle following. IEEE Trans. Intell. Transp. Syst. 23(8), 12276\u201312286 (2021). https:\/\/doi.org\/10.1109\/TITS.2021.3098765","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"639_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2022.106502","volume":"64","author":"X Wang","year":"2022","unstructured":"Wang, X., Zhang, X., Guo, F., Gu, Y., Zhu, X.: Effect of daily car-following behaviors on urban roadway rear-end crashes and near crashes: A naturalistic driving study. Accid. Anal. Prev. 64, 106502 (2022). https:\/\/doi.org\/10.1016\/j.aap.2022.106502","journal-title":"Accid. Anal. Prev."},{"key":"639_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2021.103168","volume":"128","author":"F Hart","year":"2021","unstructured":"Hart, F., Okhrin, O., Treiber, M.: Formulation and validation of a car-following model based on deep reinforcement learning. Transp. Res. Part C Emerg. Technol. 128, 103168 (2021). https:\/\/doi.org\/10.1016\/j.trc.2021.103168","journal-title":"Transp. Res. Part C Emerg. Technol."},{"issue":"6","key":"639_CR5","doi-asserted-by":"publisher","first-page":"6014","DOI":"10.1109\/TITS.2023.3245362","volume":"24","author":"D Song","year":"2023","unstructured":"Song, D., Zhu, B., Zhao, J., Han, J., Chen, Z.: Personalized car-following control based on a hybrid of reinforcement learning and supervised learning. IEEE Transactions on Intelligent Transportation Systems 24(6), 6014\u20136029 (2023). https:\/\/doi.org\/10.1109\/TITS.2023.3245362","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"1","key":"639_CR6","doi-asserted-by":"publisher","first-page":"314","DOI":"10.3390\/smartcities4010019","volume":"4","author":"HU Ahmed","year":"2021","unstructured":"Ahmed, H.U., Huang, Y., Lu, P.: A review of car-following models and modeling tools for human and autonomous-ready driving behaviors in microsimulation. Smart Cities 4(1), 314\u2013335 (2021). https:\/\/doi.org\/10.3390\/smartcities4010019","journal-title":"Smart Cities"},{"issue":"1","key":"639_CR7","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-023-02718-7","volume":"10","author":"X Chen","year":"2023","unstructured":"Chen, X., Zhu, M., Chen, K., Wang, P., Lu, H., Zhong, H., Han, X., Wang, X., Wang, Y.: FollowNet: A comprehensive benchmark for car-following behavior modeling. Sci. Data 10(1), 828 (2023). https:\/\/doi.org\/10.1038\/s41597-023-02718-7","journal-title":"Sci. Data"},{"key":"639_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2024.104486","volume":"159","author":"F Hart","year":"2024","unstructured":"Hart, F., Okhrin, O., Treiber, M.: Towards robust car-following based on deep reinforcement learning. Transportation Research Part C: Emerging Technologies 159, 104486 (2024). https:\/\/doi.org\/10.1016\/j.trc.2024.104486","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"639_CR9","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/3398862","volume":"2022","author":"L Xu","year":"2022","unstructured":"Xu, L., Ma, J., Wang, Y.: A car-following model considering the effect of following vehicles under the framework of physics-informed deep learning. Journal of Advanced Transportation 2022, 3398862 (2022). https:\/\/doi.org\/10.1155\/2022\/3398862","journal-title":"Journal of Advanced Transportation"},{"issue":"3","key":"639_CR10","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1063\/1.1721265","volume":"24","author":"LA Pipes","year":"1953","unstructured":"Pipes, L.A.: An operational analysis of traffic dynamics. J. Appl. Phys. 24(3), 274\u2013281 (1953). https:\/\/doi.org\/10.1063\/1.1721265","journal-title":"J. Appl. Phys."},{"issue":"2","key":"639_CR11","doi-asserted-by":"publisher","DOI":"10.3390\/electronics14020304","volume":"14","author":"Z Li","year":"2025","unstructured":"Li, Z., Wang, Z., Liu, Y.: A multi-regime car-following model capturing traffic breakdown. Electronics 14(2), 304 (2025). https:\/\/doi.org\/10.3390\/electronics14020304","journal-title":"Electronics"},{"key":"639_CR12","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1016\/j.ijtst.2023.02.003","volume":"12","author":"B Kim","year":"2023","unstructured":"Kim, B., Heaslip, K.P.: Identifying suitable car-following models to simulate automated vehicles on highways. Int. J. Transp. Sci. Technol. 12, 652\u2013664 (2023). https:\/\/doi.org\/10.1016\/j.ijtst.2023.02.003","journal-title":"Int. J. Transp. Sci. Technol."},{"issue":"12","key":"639_CR13","doi-asserted-by":"publisher","DOI":"10.3390\/su14127045","volume":"14","author":"D Qu","year":"2022","unstructured":"Qu, D., Wang, S., Liu, H., Meng, Y.: A car-following model based on trajectory data for connected and automated vehicles to predict trajectory of human-driven vehicles. Sustainability 14(12), 7045 (2022). https:\/\/doi.org\/10.3390\/su14127045","journal-title":"Sustainability"},{"key":"639_CR14","unstructured":"Kometani, E.: Dynamic behavior of traffic with a nonlinear spacing-speed relationship. In: Theory of Traffic Flow: Proceedings of the Symposium on the Theory of Traffic Flow, pp. 105\u2013119. General Motors, New York (1959)"},{"issue":"2","key":"639_CR15","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/0191-2615(81)90037-0","volume":"15","author":"PG Gipps","year":"1981","unstructured":"Gipps, P.G.: A behavioral car-following model for computer simulation. Transp. Res. Part. B Methodol. 15(2), 105\u2013111 (1981). https:\/\/doi.org\/10.1016\/0191-2615(81)90037-0","journal-title":"Transp. Res. Part. B Methodol."},{"key":"639_CR16","unstructured":"Michaels, R.: Perceptual factors in car-following. In: Proc. 2nd Int. Symp. Theory Traffic Flow, London, pp. 44\u201359 (1963)"},{"issue":"2","key":"639_CR17","doi-asserted-by":"publisher","first-page":"1035","DOI":"10.1103\/PhysRevE.51.1035","volume":"51","author":"M Bando","year":"1995","unstructured":"Bando, M., Hasebe, K., Nakayama, A., Shibata, A., Sugiyama, Y.: Dynamical model of traffic congestion and numerical simulation. Phys. Rev. E. 51(2), 1035\u20131042 (1995). https:\/\/doi.org\/10.1103\/PhysRevE.51.1035","journal-title":"Phys. Rev. E"},{"issue":"2","key":"639_CR18","doi-asserted-by":"publisher","first-page":"1805","DOI":"10.1103\/PhysRevE.62.1805","volume":"62","author":"M Treiber","year":"2000","unstructured":"Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Phys. Rev. E 62(2), 1805\u20131824 (2000). https:\/\/doi.org\/10.1103\/PhysRevE.62.1805","journal-title":"Phys. Rev. E"},{"key":"639_CR19","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.trc.2017.08.004","volume":"84","author":"M Zhou","year":"2017","unstructured":"Zhou, M., Qu, X., Li, X.: A recurrent neural network-based microscopic car-following model to predict traffic oscillation. Transp. Res. Part C Emerg. Technol. 84, 245\u2013264 (2017). https:\/\/doi.org\/10.1016\/j.trc.2017.08.004","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"639_CR20","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1016\/j.physa.2018.09.174","volume":"514","author":"X Wang","year":"2019","unstructured":"Wang, X., Jiang, R., Li, L., Lin, Y., Wang, F.: Long memory is important: A test study on deep-learning-based car-following model. Physica A 514, 786\u2013795 (2019). https:\/\/doi.org\/10.1016\/j.physa.2018.09.174","journal-title":"Physica A"},{"key":"639_CR21","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.trc.2018.07.004","volume":"95","author":"X Huang","year":"2018","unstructured":"Huang, X., Sun, J., Sun, J.: A car-following model considering asymmetric driving behavior based on long short-term memory neural networks. Transp. Res. Part C Emerg. Technol. 95, 346\u2013362 (2018). https:\/\/doi.org\/10.1016\/j.trc.2018.07.004","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"639_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2020.102785","volume":"120","author":"L Ma","year":"2020","unstructured":"Ma, L., Qu, S.: A sequence-to-sequence learning-based car-following model for multi-step predictions considering reaction delay. Transportation Research Part C: Emerging Technologies 120, 102785 (2020). https:\/\/doi.org\/10.1016\/j.trc.2020.102785","journal-title":"Transportation Research Part C: Emerging Technologies"},{"issue":"9","key":"639_CR23","doi-asserted-by":"publisher","first-page":"12203","DOI":"10.1109\/TITS.2024.3374200","volume":"25","author":"N Xu","year":"2024","unstructured":"Xu, N., Chen, C., Zhang, Y., Wang, J., Liu, Q., Guo, C.: A sequence-to-sequence car-following model for addressing driver reaction delay and cumulative error in multi-step prediction. IEEE Trans. Intell. Transp. Syst. 25(9), 12203\u201312214 (2024). https:\/\/doi.org\/10.1109\/TITS.2024.3374200","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"2","key":"639_CR24","doi-asserted-by":"publisher","DOI":"10.3390\/s23020660","volume":"23","author":"P Qin","year":"2023","unstructured":"Qin, P., Li, H., Li, Z., Guan, W., He, Y.: A CNN-LSTM car-following model considering generalization ability. Sensors 23(2), 660 (2023). https:\/\/doi.org\/10.3390\/s23020660","journal-title":"Sensors"},{"issue":"1","key":"639_CR25","doi-asserted-by":"publisher","first-page":"2816","DOI":"10.1109\/TASE.2021.3068765","volume":"19","author":"R Yan","year":"2021","unstructured":"Yan, R., Jiang, R., Jia, B., Huang, J., Yang, D.: Hybrid car-following strategy based on deep deterministic policy gradient and cooperative adaptive cruise control. IEEE Trans. Autom. Sci. Eng. 19(1), 2816\u20132824 (2021). https:\/\/doi.org\/10.1109\/TASE.2021.3068765","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"639_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2020.102662","volume":"117","author":"M Zhu","year":"2020","unstructured":"Zhu, M., Wang, Y., Pu, Z., Hu, J., Wang, X., Ke, R.: Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving. Transportation Research Part C: Emerging Technologies 117, 102662 (2020). https:\/\/doi.org\/10.1016\/j.trc.2020.102662","journal-title":"Transportation Research Part C: Emerging Technologies"},{"issue":"20","key":"639_CR27","doi-asserted-by":"publisher","DOI":"10.3390\/s22208055","volume":"22","author":"W Li","year":"2022","unstructured":"Li, W., Zhang, Y., Shi, X., Qiu, F.: A decision-making strategy for car following based on naturalistic driving data via deep reinforcement learning. Sensors 22(20), 8055 (2022). https:\/\/doi.org\/10.3390\/s22208055","journal-title":"Sensors"},{"key":"639_CR28","doi-asserted-by":"publisher","first-page":"23111","DOI":"10.1109\/ACCESS.2025.3535596","volume":"13","author":"KL Liu","year":"2025","unstructured":"Liu, K.L., Ma, J., Lai, E.-K.: Dynamic car-following model with jerk suppression for highway autonomous driving. IEEE Access 13, 23111\u201323119 (2025). https:\/\/doi.org\/10.1109\/ACCESS.2025.3535596","journal-title":"IEEE Access"},{"issue":"2","key":"639_CR29","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1109\/MITS.2023.3317081","volume":"16","author":"S Fang","year":"2024","unstructured":"Fang, S., Yang, L., Wang, W., Zhao, X., Xu, Z.: A dynamic transformation car-following model for the prediction of the traffic flow oscillation. IEEE Intell. Transp. Syst. Mag. 16(2), 174\u2013180 (2024). https:\/\/doi.org\/10.1109\/MITS.2023.3317081","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"issue":"2","key":"639_CR30","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1109\/MITS.2024.3354796","volume":"16","author":"D Xu","year":"2024","unstructured":"Xu, D., Gao, G., Qiu, Q.: A car-following model considering missing data based on TransGAN networks. IEEE Intell. Transp. Syst. Mag. 16(2), 174\u2013180 (2024). https:\/\/doi.org\/10.1109\/MITS.2024.3354796","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"issue":"12","key":"639_CR31","doi-asserted-by":"publisher","first-page":"15021","DOI":"10.1109\/TITS.2023.3234567","volume":"24","author":"T Yu","year":"2023","unstructured":"Yu, T., Zhao, X., Xie, Z.: A dual-driven theory-data framework for modeling car-following behavior in mixed traffic scenarios. IEEE Trans. Intell. Transp. Syst. 24(12), 15021\u201315033 (2023). https:\/\/doi.org\/10.1109\/TITS.2023.3234567","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"639_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2024.109901","volume":"122","author":"Q Li","year":"2025","unstructured":"Li, Q., Chen, Q., Wang, S., Wang, Q., Tu, J., Jafaripournimchahi, A.: A novel spatio-temporal attention mechanism model for car-following in autonomous driving. Computers and Electrical Engineering 122, 109901 (2025). https:\/\/doi.org\/10.1016\/j.compeleceng.2024.109901","journal-title":"Computers and Electrical Engineering"},{"issue":"11","key":"639_CR33","doi-asserted-by":"publisher","first-page":"14789","DOI":"10.1109\/TITS.2023.3221056","volume":"24","author":"Y Li","year":"2023","unstructured":"Li, Y., Liu, Z., Li, J.: Spatio-temporal attention-based end-to-end framework for vehicle behavior prediction. IEEE Trans. Intell. Transp. Syst. 24(11), 14789\u201314799 (2023). https:\/\/doi.org\/10.1109\/TITS.2023.3221056","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"10","key":"639_CR34","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","volume":"28","author":"K Greff","year":"2017","unstructured":"Greff, K., Srivastava, R.K., Koutn\u00edk, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: A search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222\u20132232 (2017). https:\/\/doi.org\/10.1109\/TNNLS.2016.2582924","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"639_CR35","doi-asserted-by":"publisher","unstructured":"Siami-Namini, S., Tavakoli, N., Namin, A.S.: The performance of LSTM and BiLSTM in forecasting time series. In: Proc. 17th IEEE Int. Conf. Mach. Learn. Appl. (ICMLA), pp. 1184\u20131190. IEEE, Los Alamitos (2019). https:\/\/doi.org\/10.1109\/ICMLA.2019.00204","DOI":"10.1109\/ICMLA.2019.00204"},{"issue":"1","key":"639_CR36","doi-asserted-by":"publisher","first-page":"251","DOI":"10.3390\/s25010251","volume":"25","author":"S Natha","year":"2025","unstructured":"Natha, S., Ahmed, F., Siraj, M., Lagari, M., Altamimi, M., Ali, A.: Deep BiLSTM attention model for spatial and temporal anomaly detection in video surveillance. Sensors. 25(1), 251 (2025). https:\/\/doi.org\/10.3390\/s25010251","journal-title":"Sensors"},{"key":"639_CR37","doi-asserted-by":"publisher","DOI":"10.21949\/1504477","author":"U.S. Department of Transportation","year":"2016","unstructured":"U.S. Department of Transportation: Next generation simulation (NGSIM) vehicle trajectories and supporting data. ITS DataHub. (2016). https:\/\/doi.org\/10.21949\/1504477","journal-title":"ITS DataHub"},{"key":"639_CR38","doi-asserted-by":"publisher","unstructured":"Krajewski, R., Bock, J., Kloeker, L., Eckstein, L.: The highD dataset: a drone dataset of naturalistic vehicle trajectories on German highways for validation of highly automated driving systems. In: Proc. 21st IEEE Int. Conf. Intell. Transp. Syst. (ITSC), pp. 2118\u20132125. IEEE, Maui (2018). https:\/\/doi.org\/10.1109\/ITSC.2018.8569552","DOI":"10.1109\/ITSC.2018.8569552"},{"key":"639_CR39","first-page":"018","volume":"812","author":"D Bezzina","year":"2014","unstructured":"Bezzina, D., Sayer, J.: Safety pilot model deployment: Test conductor team report. U.S. Department of Transportation. Rep. No DOT HS. 812, 018 (2014). https:\/\/data.transportation.gov\/Automobiles\/Safety-Pilot-Model-Deployment-Data\/a7qq-9vfe","journal-title":"Rep. No DOT HS"},{"key":"639_CR40","doi-asserted-by":"crossref","unstructured":"Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo,J., Zhou, Y., Chai, Y., Caine, B., Vasudevan, V., Han, W., Ngiam, J., Zhao, H., Timofeev,A., Ettinger, S., Krivokon, I., Gao, A., Joshi, A., \u2026 Anguelov, D.: Scalability in perception for autonomous driving: Waymo open dataset. In: Proc. IEEE\/CVF Conf. Comput.Vis. Pattern Recognit. (CVPR), pp. 2446\u20132454. IEEE, Seattle (2020). https:\/\/waymo.com\/open\/","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"639_CR41","unstructured":"Houston, J., Zuidhof, G., Bergamini, L., Ye, Y., Mensink, T., Sun, Y., Iglovikov, V., Ondruska, P.: One thousand and one hours: self-driving motion prediction dataset. In: Proc. Conf. Robot Learn. (CoRL), pp. 409\u2013418. PMLR, London (2021). https:\/\/woven.toyota\/en\/prediction-dataset"},{"key":"639_CR42","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.trf.2024.11.022","volume":"108","author":"Z Hussain","year":"2025","unstructured":"Hussain, Z., Sayed, S., Dias, C., Hussain, Q.: Empirical analysis of car-following behavior: Impacts of driver demographics, leading vehicle types, and speed limits on driver behavior and safety. Transp. Res. Part. F Traffic Psychol. Behav. 108, 188\u2013205 (2025). https:\/\/doi.org\/10.1016\/j.trf.2024.11.022","journal-title":"Transp. Res. Part. F Traffic Psychol. Behav."}],"container-title":["International Journal of Intelligent Transportation Systems Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13177-026-00639-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13177-026-00639-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13177-026-00639-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T09:48:02Z","timestamp":1784800082000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13177-026-00639-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,2]]},"references-count":42,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["639"],"URL":"https:\/\/doi.org\/10.1007\/s13177-026-00639-4","relation":{},"ISSN":["1348-8503","1868-8659"],"issn-type":[{"value":"1348-8503","type":"print"},{"value":"1868-8659","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,2]]},"assertion":[{"value":"22 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 January 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This manuscript represents core research and has not yet been published or submitted anywhere. All the data sources were cited properly. The authors have obtained all the ethical approvals about this paper. The authors declare to obey all the academic ethical standards. Informed Consent All the authors who made contributions to this paper are included and aware of the content of this paper. They also agree to submit this paper to International Journal of Intelligent Transportation Systems Research.","order":1,"name":"Ethics","label":"Ethical Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors disclosed no relevant relationships.","order":2,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}]}}