{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T23:13:07Z","timestamp":1771629187059,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,24]],"date-time":"2025-08-24T00:00:00Z","timestamp":1755993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2023YFE0106800"],"award-info":[{"award-number":["2023YFE0106800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["22YJC630109"],"award-info":[{"award-number":["22YJC630109"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2024ITSKF02"],"award-info":[{"award-number":["2024ITSKF02"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Humanity and Social Science Youth Foundation of the Ministry of Education of China","award":["2023YFE0106800"],"award-info":[{"award-number":["2023YFE0106800"]}]},{"name":"Humanity and Social Science Youth Foundation of the Ministry of Education of China","award":["22YJC630109"],"award-info":[{"award-number":["22YJC630109"]}]},{"name":"Humanity and Social Science Youth Foundation of the Ministry of Education of China","award":["2024ITSKF02"],"award-info":[{"award-number":["2024ITSKF02"]}]},{"name":"Open Research Fund of Intelligent Transportation System Research Center, Southeast University","award":["2023YFE0106800"],"award-info":[{"award-number":["2023YFE0106800"]}]},{"name":"Open Research Fund of Intelligent Transportation System Research Center, Southeast University","award":["22YJC630109"],"award-info":[{"award-number":["22YJC630109"]}]},{"name":"Open Research Fund of Intelligent Transportation System Research Center, Southeast University","award":["2024ITSKF02"],"award-info":[{"award-number":["2024ITSKF02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Understanding how congestion forms and propagates over space and time is essential for improving the operational efficiency of urban traffic systems. Recent developments in causal emergence theory indicate that the causal structures underlying dynamic models are scale-dependent. Most existing studies on traffic congestion evolution focus on a single, fixed scale, which risks overlooking clearer causal patterns at other scales and thus limiting predictive power and practical applicability. To address this, we develop a multiscale congestion evolution modeling framework grounded in causal emergence theory. Within this framework we build dynamical models at multiple spatiotemporal scales using dynamic Bayesian networks (DBNs) and quantify the causal strength of these models using effective information (EI) and singular value decomposition (SVD)-based diagnostics. Using road networks from three central Kunshan regions, we validate the proposed framework across 24 spatiotemporal scales and five demand scenarios. Across all three regions and the tested scales, we observe evidence of causal emergence in congestion evolution dynamics. When results are pooled across regions and scenarios, models built at the 10 min\/150 m scale exhibit stronger and more coherent causal structure than models at other scales. These findings demonstrate that the proposed framework can identify and help build dynamical models of congestion evolution at appropriate spatiotemporal scales, thereby supporting the development of proactive traffic management and effective resilience enhancement strategies for urban transport systems.<\/jats:p>","DOI":"10.3390\/systems13090732","type":"journal-article","created":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T00:09:53Z","timestamp":1756080593000},"page":"732","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Understanding Congestion Evolution in Urban Traffic Systems Across Multiple Spatiotemporal Scales: A Causal Emergence Perspective"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6251-9930","authenticated-orcid":false,"given":"Jishun","family":"Ou","sequence":"first","affiliation":[{"name":"College of Civil Engineering and Transportation, Yangzhou University, Yangzhou 225127, China"},{"name":"Intelligent Transportation System Research Center, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1022-3837","authenticated-orcid":false,"given":"Jingyuan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Civil Engineering and Transportation, Yangzhou University, Yangzhou 225127, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9590-501X","authenticated-orcid":false,"given":"Weihua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Intelligent Transportation System Research Center, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengxiang","family":"Yue","sequence":"additional","affiliation":[{"name":"College of Civil Engineering and Transportation, Yangzhou University, Yangzhou 225127, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinghui","family":"Nie","sequence":"additional","affiliation":[{"name":"College of Civil Engineering and Transportation, Yangzhou University, Yangzhou 225127, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1049\/iet-its.2017.0355","article-title":"Systematic clustering method to identify and characterise spatiotemporal congestion on freeway corridors","volume":"12","author":"Ou","year":"2018","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"10793","DOI":"10.1038\/ncomms10793","article-title":"Understanding congested travel in urban areas","volume":"7","author":"Lima","year":"2016","journal-title":"Nat. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1126\/science.1245200","article-title":"The Hidden Geometry of Complex, Network-Driven Contagion Phenomena","volume":"342","author":"Helbing","year":"2013","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1142\/S0129183109014126","article-title":"Capacity assignment model to defense cascading failures","volume":"20","author":"Wu","year":"2009","journal-title":"Int. J. Mod. Phys. C"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/S0967-070X(98)00006-7","article-title":"The conceptual structure of traffic jams","volume":"5","author":"Wright","year":"1998","journal-title":"Transp. Policy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"220894","DOI":"10.1098\/rsos.220894","article-title":"A link model approach to identify congestion hotspots","volume":"9","author":"Bassolas","year":"2022","journal-title":"R. Soc. Open Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1142\/S0217984904008031","article-title":"Simulation of traffic congestion with SIR model","volume":"18","author":"Wu","year":"2004","journal-title":"Mod. Phys. Lett. B"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1038\/s41467-020-15353-2","article-title":"A simple contagion process describes spreading of traffic jams in urban networks","volume":"11","author":"Saberi","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1073\/pnas.1419185112","article-title":"Percolation transition in dynamical traffic network with evolving critical bottlenecks","volume":"112","author":"Li","year":"2015","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1073\/pnas.1801545116","article-title":"Switch between critical percolation modes in city traffic dynamics","volume":"116","author":"Zeng","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1038\/s41467-021-21483-y","article-title":"Percolation of heterogeneous flows uncovers the bottlenecks of infrastructure networks","volume":"12","author":"Hamedmoghadam","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"10094","DOI":"10.1038\/ncomms10094","article-title":"Spatio-temporal propagation of cascading overload failures in spatially embedded networks","volume":"7","author":"Zhao","year":"2016","journal-title":"Nat. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"104017","DOI":"10.1016\/j.trc.2023.104017","article-title":"Reliability of the traffic network against cascading failures with individuals acting independently or collectively","volume":"147","author":"Duan","year":"2023","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bellocchi, L., and Geroliminis, N. (2020). Unraveling reaction-diffusion-like dynamics in urban congestion propagation: Insights from a large-scale road network. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-61486-1"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"127668","DOI":"10.1016\/j.physa.2022.127668","article-title":"Modeling congestion considering sequential coupling applications: A network-cell-based method","volume":"604","author":"Zhang","year":"2022","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1016\/j.ins.2016.06.033","article-title":"Mining urban recurrent congestion evolution patterns from GPS-equipped vehicle mobility data","volume":"373","author":"An","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/S0377-2217(96)00299-8","article-title":"Diagnosis and treatment of congestion in central urban areas","volume":"104","author":"Roberg","year":"1998","journal-title":"Eur. J. Oper. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1007\/s11432-008-0038-9","article-title":"Urban traffic congestion propagation and bottleneck identification","volume":"51","author":"Long","year":"2008","journal-title":"Sci. China Ser. F Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"164","DOI":"10.48130\/DTS-2023-0013","article-title":"Overview of machine learning-based traffic flow prediction","volume":"2","author":"Xing","year":"2023","journal-title":"Digit. Transp. Saf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"115","DOI":"10.48130\/dts-0024-0011","article-title":"An evolutionary game theory-based machine learning framework for predicting mandatory lane change decision","volume":"3","author":"Xu","year":"2024","journal-title":"Digit. Transp. Saf."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Abbas, H.W., Sajid, Z., and Dao, U. (2024). Assessing the Impact of Risk Factors on Vaccination Uptake Policy Decisions Using a Bayesian Network (BN) Approach. Systems, 12.","DOI":"10.3390\/systems12050167"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"69481","DOI":"10.1109\/ACCESS.2018.2881039","article-title":"Discovering urban traffic congestion propagation patterns with taxi trajectory data","volume":"6","author":"Chen","year":"2018","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Di, X., Xiao, Y., Zhu, C., Deng, Y., Zhao, Q., and Rao, W. (2019, January 10\u201313). Traffic congestion prediction by spatiotemporal propagation patterns. Proceedings of the 2019 20th IEEE International Conference on Mobile Data Management (MDM), Hong Kong, China.","DOI":"10.1109\/MDM.2019.00-45"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s12065-019-00332-4","article-title":"Mining traffic congestion propagation patterns based on spatio-temporal co-location patterns","volume":"13","author":"Yang","year":"2020","journal-title":"Evol. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"103526","DOI":"10.1016\/j.trc.2021.103526","article-title":"Traffic congestion propagation inference using dynamic Bayesian graph convolution network","volume":"135","author":"Luan","year":"2022","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"126439","DOI":"10.1016\/j.physa.2021.126439","article-title":"Recurrence analysis of urban traffic congestion index on multi-scale","volume":"585","author":"Wu","year":"2022","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yuan, B., Zhang, J., Lyu, A., Wu, J., Wang, Z., Yang, M., Liu, K., Mou, M., and Cui, P. (2024). Emergence and causality in complex systems: A survey of causal emergence and related quantitative studies. Entropy, 26.","DOI":"10.3390\/e26020108"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"19790","DOI":"10.1073\/pnas.1314922110","article-title":"Quantifying causal emergence shows that macro can beat micro","volume":"110","author":"Hoel","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Marshall, W., Albantakis, L., and Tononi, G. (2018). Black-boxing and cause-effect power. PLoS Comput. Biol., 14.","DOI":"10.1371\/journal.pcbi.1006114"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"8932526","DOI":"10.1155\/2020\/8932526","article-title":"The emergence of informative higher scales in complex networks","volume":"2020","author":"Klein","year":"2020","journal-title":"Complexity"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Leung, A., and Tsuchiya, N. (2022). Emergence of integrated information at macro timescales in real neural recordings. Entropy, 24.","DOI":"10.1101\/2022.03.07.483390"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"nwae279","DOI":"10.1093\/nsr\/nwae279","article-title":"Finding emergence in data by maximizing effective information","volume":"12","author":"Yang","year":"2025","journal-title":"Natl. Sci. Rev."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1605","DOI":"10.1038\/s41467-025-56034-2","article-title":"Coarse-graining network flow through statistical physics and machine learning","volume":"16","author":"Zhang","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1038\/s44260-025-00028-0","article-title":"Dynamical reversibility and a new theory of causal emergence based on SVD","volume":"2","author":"Zhang","year":"2025","journal-title":"npj Complex."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Fu, F., Wang, D., Sun, M., Xie, R., and Cai, Z. (2024). Urban traffic flow prediction based on bayesian deep learning considering optimal aggregation time interval. Sustainability, 16.","DOI":"10.3390\/su16051818"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Urbanik, T., Tanaka, A., Lozner, B., Lindstrom, E., Lee, K., Quayle, S., Beaird, S., Tsoi, S., Ryus, P., and Gettman, D. (2015). Signal Timing Manual, Transportation Research Board.","DOI":"10.17226\/22097"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sun, S., Zhang, C., and Zhang, Y. (2005). Traffic flow forecasting using a spatio-temporal bayesian network predictor. Artificial Neural Networks: Formal Models and Their Applications\u2014ICANN 2005, Proceedings of the 15th International Conference, Warsaw, Poland, 11\u201315 September 2005, Springer.","DOI":"10.1007\/11550907_43"},{"key":"ref_38","unstructured":"Lomax, T., Turner, S., Shunk, G., Levinson, H., Pratt, R., Bay, P., and Douglas, G. (1997). NCHRP Report 398: Quantifying Congestion, Transportation Research Board, National Academy Press."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"8673","DOI":"10.1073\/pnas.1814982116","article-title":"Scale-free resilience of real traffic jams","volume":"116","author":"Zhang","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1111\/mice.12484","article-title":"A data-driven approach to determining freeway incident impact areas with fuzzy and graph theory-based clustering","volume":"35","author":"Ou","year":"2020","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"103922","DOI":"10.1016\/j.trc.2022.103922","article-title":"Percolation-based dynamic perimeter control for mitigating congestion propagation in urban road networks","volume":"145","author":"Hamedmoghadam","year":"2022","journal-title":"Transp. Res. Part C Emerg. Technol."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/9\/732\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:35:06Z","timestamp":1760034906000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/9\/732"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,24]]},"references-count":41,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["systems13090732"],"URL":"https:\/\/doi.org\/10.3390\/systems13090732","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,24]]}}}