{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T22:07:49Z","timestamp":1779142069802,"version":"3.51.4"},"reference-count":58,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52372322"],"award-info":[{"award-number":["52372322"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011710","name":"Shaanxi Province Department of Science and Technology","doi-asserted-by":"publisher","award":["2024KXJ-173"],"award-info":[{"award-number":["2024KXJ-173"]}],"id":[{"id":"10.13039\/501100011710","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008642","name":"Center for Health Design","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008642","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013804","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["300102225101"],"award-info":[{"award-number":["300102225101"]}],"id":[{"id":"10.13039\/501100013804","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2025M771633"],"award-info":[{"award-number":["2025M771633"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Accident Analysis &amp; Prevention"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.aap.2025.108379","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T08:04:47Z","timestamp":1767600287000},"page":"108379","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Uncertainty-aware spatiotemporal interaction learning for pre-conflict risk evolution with a risk-increase prior"],"prefix":"10.1016","volume":"228","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7303-9725","authenticated-orcid":false,"given":"Chenhao","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5994-2770","authenticated-orcid":false,"given":"Min","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiawei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhishun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8978-220X","authenticated-orcid":false,"given":"Shifeng","family":"Niu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7406-6831","authenticated-orcid":false,"given":"Dongdong","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.aap.2025.108379_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2023.107191","article-title":"Advances and applications of computer vision techniques in vehicle trajectory generation and surrogate traffic safety indicators","volume":"191","author":"Abdel-Aty","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2025.108379_b0010","article-title":"Assessing traffic conflict\/crash relationships with extreme value theory: recent developments and future directions for connected and autonomous vehicle and highway safety research","volume":"39","author":"Ali","year":"2023","journal-title":"Anal. Methods Accid. Res"},{"key":"10.1016\/j.aap.2025.108379_b0015","unstructured":"Allen, B.L., Shin, B.T., Cooper, P.J., 1978. Analysis of traffic conflicts and collisions (No. 0361\u20131981)."},{"key":"10.1016\/j.aap.2025.108379_b0020","article-title":"A systematic review of traffic conflict-based safety measures with a focus on application context","volume":"32","author":"Arun","year":"2021","journal-title":"Anal. Methods Accid. Res"},{"key":"10.1016\/j.aap.2025.108379_b0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2021.106016","article-title":"A systematic mapping review of surrogate safety assessment using traffic conflict techniques","volume":"153","author":"Arun","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2025.108379_b0030","series-title":"Proceedings of the 28th ACM International Conference on Multimedia","first-page":"2682","article-title":"Uncertainty-based traffic accident anticipation with spatio-temporal relational learning","author":"Bao","year":"2020"},{"key":"10.1016\/j.aap.2025.108379_b0035","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.ijtst.2023.07.001","article-title":"Evaluation of false alarm alarms in truck FCW based on calibration of RSS model under different driving scenarios","volume":"15","author":"Bao","year":"2024","journal-title":"Int. J. Transp. Sci. Technol."},{"key":"10.1016\/j.aap.2025.108379_b0040","first-page":"67","article-title":"Accident investigations: Multilinear events sequencing methods","volume":"7","author":"Benner","year":"1975","journal-title":"J. Saf. Res."},{"key":"10.1016\/j.aap.2025.108379_b0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2024.107640","article-title":"Evaluating the performance of traffic conflict measures in real-time crash risk prediction using pre-crash vehicle trajectories","volume":"203","author":"Chen","year":"2024","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2025.108379_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2025.108186","article-title":"The effect of data transformation on the severe event prediction in road traffic using extreme value theory","volume":"221","author":"Chen","year":"2025","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2025.108379_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2024.104981","article-title":"Emergency index (EI): a two-dimensional surrogate safety measure considering vehicles\u2019 interaction depth","volume":"171","author":"Cheng","year":"2025","journal-title":"Transp. Res. Part C Emerging Technol."},{"key":"10.1016\/j.aap.2025.108379_b0060","doi-asserted-by":"crossref","first-page":"4262","DOI":"10.1109\/TITS.2023.3234560","article-title":"A superposition assessment method of road crash risk and congestion risk: an empirical analysis","volume":"24","author":"Cheng","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.aap.2025.108379_b0065","article-title":"Traffic studies at T-junctions. 2. a conflict simulation record","volume":"17","author":"Cooper","year":"1976","journal-title":"Traffic Engineering & Control"},{"key":"10.1016\/j.aap.2025.108379_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2024.107684","article-title":"Attentional warnings caused by driver monitoring systems: how often do they appear and how well are they understood?","volume":"205","author":"Forster","year":"2024","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2025.108379_b0075","unstructured":"Gal, Y., Ghahramani, Z., 2016. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. Presented at the international conference on machine learning, PMLR, pp. 1050\u20131059."},{"key":"10.1016\/j.aap.2025.108379_b0080","doi-asserted-by":"crossref","unstructured":"Gettman, D., Head, L., 2003. Algorithms for surrogate measures of safety at intersections. In: Surrogate Safety Measures from Traffic Simulation Models. Federal Highway Administration, McLean, VA.","DOI":"10.3141\/1840-12"},{"key":"10.1016\/j.aap.2025.108379_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2023.107063","article-title":"Modeling driver\u2019s evasive behavior during safety\u2013critical lane changes: two-dimensional time-to-collision and deep reinforcement learning","volume":"186","author":"Guo","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2025.108379_b0090","doi-asserted-by":"crossref","unstructured":"Gupta, S., Pati, U.C., Parameswari, D.C., 2022. Traffic lane localization using clothoid road model, in: 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA). Presented at the 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA), IEEE, Gunupur, India, pp. 1\u20136. 10.1109\/ICCSEA54677.2022.9936164.","DOI":"10.1109\/ICCSEA54677.2022.9936164"},{"key":"10.1016\/j.aap.2025.108379_b0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2024.107766","article-title":"Enhancing mixed traffic safety assessment: a novel safety metric combined with a comprehensive behavioral modeling framework","volume":"208","author":"Hou","year":"2024","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2025.108379_b0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2025.107966","article-title":"A deep transfer learning approach for real-time traffic conflict prediction with trajectory data","volume":"214","author":"Hou","year":"2025","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2025.108379_b0105","series-title":"Presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018"},{"key":"10.1016\/j.aap.2025.108379_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2024.107558","article-title":"Assessing the collision risk of mixed lane-changing traffic in the urban inter-tunnel weaving section using extreme value theory","volume":"200","author":"Hu","year":"2024","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2025.108379_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2023.107203","article-title":"Spatio-temporal dynamic change mechanism analysis of traffic conflict risk based on trajectory data","volume":"191","author":"Hu","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2025.108379_b0120","doi-asserted-by":"crossref","unstructured":"Huang, T., 2024. Driver lane change intention prediction based on topological graph constructed by driver behaviors and traffic context for human-machine co-driving system. 10.1016\/j.trc.2024.104497.","DOI":"10.1016\/j.trc.2024.104497"},{"key":"10.1016\/j.aap.2025.108379_b0125","article-title":"A unified probabilistic approach to traffic conflict detection","volume":"45","author":"Jiao","year":"2025","journal-title":"Anal. Methods Accid. Res"},{"key":"10.1016\/j.aap.2025.108379_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.tust.2024.106216","article-title":"Collision causal discovery and real-time prediction of freeway tunnels: a novel dual-task approach","volume":"155","author":"Jin","year":"2025","journal-title":"Tunn. Undergr. Space Technol."},{"key":"10.1016\/j.aap.2025.108379_b0135","article-title":"Real-time crash risk prediction in freeway tunnels considering features interaction and unobserved heterogeneity: a two-stage deep learning modeling framework","volume":"40","author":"Jin","year":"2023","journal-title":"Anal. Methods Accid. Res"},{"key":"10.1016\/j.aap.2025.108379_b0140","unstructured":"Kendall, A., Gal, Y., 2017. What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems 30."},{"key":"10.1016\/j.aap.2025.108379_b0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2021.103428","article-title":"A risk field-based metric correlates with driver\u2019s perceived risk in manual and automated driving: a test-track study","volume":"133","author":"Kolekar","year":"2021","journal-title":"Transp. Res. Part C Emerging Technol."},{"key":"10.1016\/j.aap.2025.108379_b0150","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2024.104659","article-title":"Unravelling uncertainty in trajectory prediction using a non-parametric approach","volume":"163","author":"Li","year":"2024","journal-title":"Transp. Res. Part C Emerging Technol."},{"key":"10.1016\/j.aap.2025.108379_b0155","doi-asserted-by":"crossref","unstructured":"Li, Y., 2025. TrajPT: A trajectory data-based pre-trained transformer model for learning multi-vehicle interactions, doi: 10.1016\/j.trc.2025.105013.","DOI":"10.1016\/j.trc.2025.105013"},{"key":"10.1016\/j.aap.2025.108379_b0160","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.ress.2018.07.035","article-title":"Traffic accident modelling via self-exciting point processes","volume":"180","author":"Li","year":"2018","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aap.2025.108379_b0165","unstructured":"Lin, T., Jin, Z., Choi, S., Yeo, H., 2024. A real-time evaluation framework for pedestrian\u2019s potential risk at non-signalized intersections based on predicted post-encroachment time. arXiv preprint arXiv:2404.15635."},{"key":"10.1016\/j.aap.2025.108379_b0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2022.106755","article-title":"Learning the representation of surrogate safety measures to identify traffic conflict","volume":"174","author":"Lu","year":"2022","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2025.108379_b0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2025.105219","article-title":"A real-time conflict risk prediction modeling based on causal inference and graph-model: applied to multi-configuration expressway diversion zones","volume":"178","author":"Lyu","year":"2025","journal-title":"Transp. Res. Part C Emerging Technol."},{"key":"10.1016\/j.aap.2025.108379_b0180","first-page":"1","article-title":"Unobserved heterogeneity and the statistical analysis of highway accident data","volume":"11","author":"Mannering","year":"2016","journal-title":"Anal. Methods Accid. Res"},{"key":"10.1016\/j.aap.2025.108379_b0185","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/S0001-4575(00)00019-1","article-title":"Extended time-to-collision measures for road traffic safety assessment","volume":"33","author":"Minderhoud","year":"2001","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2025.108379_b0190","series-title":"Proceedings of the Computer Vision and Pattern Recognition Conference","first-page":"2583","article-title":"Nexar dashcam collision prediction dataset and challenge","author":"Moura","year":"2025"},{"key":"10.1016\/j.aap.2025.108379_b0195","unstructured":"Peterson, B.E., 1977. Proceedings: First workshop on traffic conflicts, oslo, 1977."},{"key":"10.1016\/j.aap.2025.108379_b0200","doi-asserted-by":"crossref","first-page":"6536","DOI":"10.1038\/s41598-024-57135-6","article-title":"Relationship between prediction accuracy and uncertainty in compound potency prediction using deep neural networks and control models","volume":"14","author":"Roth","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.aap.2025.108379_b0205","article-title":"A review of surrogate safety measures on road safety at unsignalized intersections in developing countries","volume":"195","author":"Sarkar","year":"2024","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2025.108379_b0210","doi-asserted-by":"crossref","unstructured":"Sengupta, A., Mondal, S., Das, A., Guler, S.I., 2023. A bayesian approach to quantifying uncertainties and improving generalizability in traffic prediction models. 10.48550\/arXiv.2307.05946.","DOI":"10.1016\/j.trc.2024.104585"},{"key":"10.1016\/j.aap.2025.108379_b0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2025.108216","article-title":"Safety space index (SSI): a two-dimensional metric for quantifying drivers\u2019 perceived risk","volume":"222","author":"Tang","year":"2025","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2025.108379_b0220","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I., 2017. Attention is all you need. Advances in neural information processing systems 30."},{"key":"10.1016\/j.aap.2025.108379_b0225","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2022.103655","article-title":"Anticipated collision time (ACT): a two-dimensional surrogate safety indicator for trajectory-based proactive safety assessment","volume":"139","author":"Venthuruthiyil","year":"2022","journal-title":"Transp. Res. Part C Emerging Technol."},{"key":"10.1016\/j.aap.2025.108379_b0230","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2024.104874","article-title":"A conflict risk graph approach to modeling spatio-temporal dynamics of intersection safety","volume":"169","author":"Wang","year":"2024","journal-title":"Transp. Res. Part C Emerging Technol."},{"key":"10.1016\/j.aap.2025.108379_b0235","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2024.104543","article-title":"Surrogate safety measures for traffic oscillations based on empirical vehicle trajectories prior to crashes","volume":"161","author":"Wang","year":"2024","journal-title":"Transp. Res. Part C Emerging Technol."},{"key":"10.1016\/j.aap.2025.108379_b0240","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2023.107268","article-title":"Cycle-level traffic conflict prediction at signalized intersections with LiDAR data and bayesian deep learning","volume":"192","author":"Wu","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2025.108379_b0245","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2024.107559","article-title":"Enhancing intersection safety in autonomous traffic: a grid-based approach with risk quantification","volume":"200","author":"Wu","year":"2024","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2025.108379_b0250","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2023.104470","article-title":"Geometric field model of driver\u2019s perceived risk for safe and human-like trajectory planning","volume":"159","author":"Xia","year":"2024","journal-title":"Transp. Res. Part C Emerging Technol."},{"key":"10.1016\/j.aap.2025.108379_b0255","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2024.107709","article-title":"Investigating the features of risky driving behaviors on expressway diverge area based on conflict and modeling analysis","volume":"206","author":"Xia","year":"2024","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2025.108379_b0260","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2024.107647","article-title":"Real-time driving risk prediction using a self-attention-based bidirectional long short-term memory network based on multi-source data","volume":"204","author":"Xie","year":"2024","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2025.108379_b0265","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2022.106934","article-title":"A novel approach for fine-grained traffic risk characterization and evaluation of urban road intersections","volume":"181","author":"Yang","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2025.108379_b0270","doi-asserted-by":"crossref","first-page":"10390","DOI":"10.1109\/JSEN.2025.3529877","article-title":"Priority sequential inference: improving safety for efficient autonomous highway driving using MARL","volume":"25","author":"Yang","year":"2025","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.aap.2025.108379_b0275","article-title":"Using traffic flow characteristics to predict real-time conflict risk: a novel method for trajectory data analysis","volume":"35","author":"Yuan","year":"2022","journal-title":"Anal. Methods Accid. Res"},{"key":"10.1016\/j.aap.2025.108379_b0280","unstructured":"Zhao, X., Chen, Y., Xu, S., Li, X., Wang, X., Li, Y., Huang, H., 2024. An open and comprehensive pipeline for unified object grounding and detection. 10.48550\/arXiv.2401.02361."},{"key":"10.1016\/j.aap.2025.108379_b0285","article-title":"Bayesian hierarchical modeling of traffic conflict extremes for crash estimation: a non-stationary peak over threshold approach","volume":"24","author":"Zheng","year":"2019","journal-title":"Anal. Methods Accid. Res"},{"key":"10.1016\/j.aap.2025.108379_b0290","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2023.107395","article-title":"Characteristics identification and evolution patterns analyses of road chain conflicts","volume":"195","author":"Zhong","year":"2024","journal-title":"Accid. Anal. Prev."}],"container-title":["Accident Analysis &amp; Prevention"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0001457525004671?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0001457525004671?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T21:49:17Z","timestamp":1773956957000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0001457525004671"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":58,"alternative-id":["S0001457525004671"],"URL":"https:\/\/doi.org\/10.1016\/j.aap.2025.108379","relation":{},"ISSN":["0001-4575"],"issn-type":[{"value":"0001-4575","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Uncertainty-aware spatiotemporal interaction learning for pre-conflict risk evolution with a risk-increase prior","name":"articletitle","label":"Article Title"},{"value":"Accident Analysis & Prevention","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aap.2025.108379","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"108379"}}