{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T20:28:19Z","timestamp":1783110499815,"version":"3.54.6"},"reference-count":73,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"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":["52478030"],"award-info":[{"award-number":["52478030"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Advanced Engineering Informatics"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.aei.2026.104833","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T02:28:28Z","timestamp":1779416908000},"page":"104833","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PD","title":["Enhancing agent-based wayfinding simulation for transportation hub evaluation with a visual memory graph Neural network (VM-GNN)"],"prefix":"10.1016","volume":"74","author":[{"given":"Runyu","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyan","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siwei","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengyu","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.aei.2026.104833_b0005","doi-asserted-by":"crossref","first-page":"5677","DOI":"10.1038\/s41467-024-49722-y","article-title":"Human navigation strategies and their errors result from dynamic interactions of spatial uncertainties","volume":"15","author":"Kessler","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.aei.2026.104833_b0010","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.trb.2017.06.017","article-title":"Crowd behaviour and motion: Empirical methods","volume":"107","author":"Haghani","year":"2018","journal-title":"Transp. Res. B Methodol."},{"key":"10.1016\/j.aei.2026.104833_b0015","unstructured":"Feng, Y., Duives, D. C., Daamen, W., & Hoogendoorn, S. P. (2019). Pedestrian exit choice behavior during an evacuation-a comparison study between field and VR experiment."},{"key":"10.1016\/j.aei.2026.104833_b0020","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.proeng.2010.07.006","article-title":"Exit choice, (pre-) movement time and (pre-) evacuation behaviour in hotel fire evacuation\u2014Behavioural analysis and validation of the use of serious gaming in experimental research","volume":"3","author":"Kobes","year":"2010","journal-title":"Procedia Eng."},{"key":"10.1016\/j.aei.2026.104833_b0025","doi-asserted-by":"crossref","first-page":"286","DOI":"10.36680\/j.itcon.2021.016","article-title":"Virtual reality as a tool to investigate and predict occupant behaviour in the real world: the example of wayfinding","volume":"26","author":"Ewart","year":"2021","journal-title":"Itcon"},{"key":"10.1016\/j.aei.2026.104833_b0030","first-page":"43","article-title":"Social influence on evacuation behavior in real and virtual environments","volume":"3","author":"Kinateder","year":"2016","journal-title":"Front. Rob. AI"},{"key":"10.1016\/j.aei.2026.104833_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.ssci.2021.105158","article-title":"Using virtual reality to study pedestrian exit choice behaviour during evacuations","volume":"137","author":"Feng","year":"2021","journal-title":"Saf. Sci."},{"key":"10.1016\/j.aei.2026.104833_b0040","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.aei.2018.11.007","article-title":"Assessing the influence of repeated exposures and mental stress on human wayfinding performance in indoor environments using virtual reality technology","volume":"39","author":"Lin","year":"2019","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104833_b0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2021.101475","article-title":"Wayfinding behaviour in a multi-level building: a comparative study of HMD VR and Desktop VR","volume":"51","author":"Feng","year":"2022","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104833_b0050","article-title":"Crowdsourced virtual reality experimental approach in pedestrian and evacuation dynamics research: Part I. Process model and digital platform","volume":"65","author":"Chen","year":"2025","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104833_b0055","article-title":"Crowdsourced virtual reality experimental approach in pedestrian and evacuation dynamics research: Part II. Validation and new insights","volume":"65","author":"Chen","year":"2025","journal-title":"Adv. Eng. Inf."},{"issue":"1","key":"10.1016\/j.aei.2026.104833_b0060","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1038\/s41598-024-53420-6","article-title":"The role of strategic visibility in shaping wayfinding behavior in multilevel buildings","volume":"14","author":"Gath-Morad","year":"2024","journal-title":"Sci. Rep."},{"issue":"2","key":"10.1016\/j.aei.2026.104833_b0065","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/S0191-2615(03)00007-9","article-title":"Pedestrian route-choice and activity scheduling theory and models","volume":"38","author":"Hoogendoorn","year":"2004","journal-title":"Transp. Res. B Methodol."},{"issue":"8","key":"10.1016\/j.aei.2026.104833_b0070","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1016\/j.trb.2005.09.006","article-title":"Discrete choice models of pedestrian walking behavior","volume":"40","author":"Antonini","year":"2006","journal-title":"Transp. Res. B Methodol."},{"issue":"5\u20136","key":"10.1016\/j.aei.2026.104833_b0075","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1080\/23249935.2017.1309472","article-title":"A unified pedestrian routing model for graph-based wayfinding built on cognitive principles","volume":"14","author":"Kielar","year":"2018","journal-title":"Transportmetrica a: Transport Science"},{"key":"10.1016\/j.aei.2026.104833_b0080","doi-asserted-by":"crossref","unstructured":"Sun, C., de Vries, B., & Zhao, Q. (2008). Architectural cue model in evacuation simulation for underground space. In Pedestrian and Evacuation Dynamics 2008 (pp. 627-640).","DOI":"10.1007\/978-3-642-04504-2_58"},{"issue":"1","key":"10.1016\/j.aei.2026.104833_b0085","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1068\/b37024","article-title":"Width: an indispensable factor in selection of emergency exit door","volume":"40","author":"Sun","year":"2013","journal-title":"Environment and Planning b: Planning and Design"},{"key":"10.1016\/j.aei.2026.104833_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2024.104651","article-title":"A study of pedestrian wayfinding behavior based on desktop VR considering both spatial knowledge and visual information","volume":"163","author":"Dai","year":"2024","journal-title":"Transp. Res. Part C Emerging Technol."},{"key":"10.1016\/j.aei.2026.104833_b0095","unstructured":"Carpman, J. R., & Grant, M. A. (2002). Wayfinding: A broad view. In R. B. Bechtel & A. Churchman (Eds.), Handbook of environmental psychology (pp. 427-442). John Wiley & Sons."},{"key":"10.1016\/j.aei.2026.104833_b0100","first-page":"132","article-title":"Understanding spatial cognition for designing pedestrian wayfinding systems: Development of practical guidance. U.Porto","volume":"9","author":"Bomfim","year":"2023","journal-title":"J. Eng."},{"key":"10.1016\/j.aei.2026.104833_b0105","doi-asserted-by":"crossref","first-page":"49359","DOI":"10.1109\/ACCESS.2022.3172285","article-title":"Large event halls evacuation using an agent-based modeling approach","volume":"10","author":"Cotfas","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.aei.2026.104833_b0110","article-title":"Cognitively grounded floorplan optimization to nudge occupant route choices","author":"Dubey","year":"2022","journal-title":"SSRN"},{"issue":"3","key":"10.1016\/j.aei.2026.104833_b0115","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s10339-021-01012-x","article-title":"Landmarks in wayfinding: a review of the existing literature","volume":"22","author":"Yesiltepe","year":"2021","journal-title":"Cogn. Process."},{"key":"10.1016\/j.aei.2026.104833_b0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.tust.2025.106617","article-title":"Effects of pedestrians\u2019 visual search effectiveness and behavioral characteristics on the wayfinding performance at underground rail interchange stations: a field test study","volume":"162","author":"Peng","year":"2025","journal-title":"Tunn. Undergr. Space Technol."},{"issue":"4","key":"10.1016\/j.aei.2026.104833_b0125","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1016\/j.apergo.2012.12.002","article-title":"The influence of environmental features on route selection in an emergency situation","volume":"44","author":"Vilar","year":"2013","journal-title":"Appl. Ergon."},{"key":"10.1016\/j.aei.2026.104833_b0130","series-title":"19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007)","first-page":"36","article-title":"An intelligent cellular automaton model for crowd evacuation in fire spreading conditions","author":"Georgoudas","year":"2007"},{"issue":"5","key":"10.1016\/j.aei.2026.104833_b0135","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1177\/0013916590225001","article-title":"Finding the building in wayfinding","volume":"22","author":"Peponis","year":"1990","journal-title":"Environ. Behav."},{"issue":"1","key":"10.1016\/j.aei.2026.104833_b0140","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1177\/0013916502238867","article-title":"The secret is to follow your nose: Route path selection and angularity","volume":"35","author":"Dalton","year":"2003","journal-title":"Environ. Behav."},{"key":"10.1016\/j.aei.2026.104833_b0145","doi-asserted-by":"crossref","unstructured":"Calori, C., & Vanden-Eynden, D. (2015). Signage and wayfinding design: A complete guide to creating environmental graphic design systems. John Wiley & Sons.","DOI":"10.1002\/9781119174615"},{"issue":"19","key":"10.1016\/j.aei.2026.104833_b0150","doi-asserted-by":"crossref","first-page":"8968","DOI":"10.3390\/app14198968","article-title":"Hub traveler guidance signage evaluation via panoramic visualization using entropy weight method and TOPSIS","volume":"14","author":"Zhang","year":"2024","journal-title":"Appl. Sci."},{"key":"10.1016\/j.aei.2026.104833_b0155","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10489-014-0583-4","article-title":"A spatio-temporal probabilistic model of hazard-and crowd dynamics for evacuation planning in disasters","volume":"42","author":"Radianti","year":"2015","journal-title":"Appl. Intell."},{"key":"10.1016\/j.aei.2026.104833_b0160","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1007\/s00146-014-0557-4","article-title":"Simulating effects of signage, groups, and crowds on emergent evacuation patterns","volume":"30","author":"Chu","year":"2015","journal-title":"AI & Soc."},{"key":"10.1016\/j.aei.2026.104833_b0165","doi-asserted-by":"crossref","first-page":"79541","DOI":"10.1109\/ACCESS.2020.2986012","article-title":"A cellular-automaton agent-hybrid model for emergency evacuation of people in public places","volume":"8","author":"Chang","year":"2020","journal-title":"IEEE Access"},{"issue":"6","key":"10.1016\/j.aei.2026.104833_b0170","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1007\/s11633-013-0750-9","article-title":"Path planning in complex 3D environments using a probabilistic roadmap method","volume":"10","author":"Yan","year":"2013","journal-title":"Int. J. Autom. Comput."},{"issue":"7","key":"10.1016\/j.aei.2026.104833_b0175","doi-asserted-by":"crossref","first-page":"1384","DOI":"10.3390\/app9071384","article-title":"Integrating a path planner and an adaptive motion controller for navigation in dynamic environments","volume":"9","author":"Zeng","year":"2019","journal-title":"Appl. Sci."},{"key":"10.1016\/j.aei.2026.104833_b0180","doi-asserted-by":"crossref","unstructured":"Wong, C., Xia, B., Zou, Z., Wang, Y. and You, X. (2024). SocialCircle: Learning the angle-based social interaction representation for pedestrian trajectory prediction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 19005-19015). IEEE.","DOI":"10.1109\/CVPR52733.2024.01798"},{"key":"10.1016\/j.aei.2026.104833_b0185","doi-asserted-by":"crossref","unstructured":"Yang, R., Wang, W., & Gui, P. (2024). Predicting pedestrian trajectories in architectural spaces: A graph neural network approach. In A. Globa, J. Smith, J. C. Portilla, et al. (Eds.), Proceedings of the 29th CAADRIA, Volume 1 (pp. 251-260). CAADRIA.","DOI":"10.52842\/conf.caadria.2024.1.251"},{"key":"10.1016\/j.aei.2026.104833_b0190","doi-asserted-by":"crossref","unstructured":"Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., & Savarese, S. (2016). Social lstm: Human trajectory prediction in crowded spaces. InProceedings of the IEEE conference on computer vision and pattern recognition(pp. 961-971).","DOI":"10.1109\/CVPR.2016.110"},{"key":"10.1016\/j.aei.2026.104833_b0195","doi-asserted-by":"crossref","unstructured":"Mohamed, A., Qian, K., Elhoseiny, M., & Claudel, C. (2020). Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction. InProceedings of the IEEE\/CVF conference on computer vision and pattern recognition(pp. 14424-14432).","DOI":"10.1109\/CVPR42600.2020.01443"},{"issue":"1","key":"10.1016\/j.aei.2026.104833_b0200","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1111\/tops.12592","article-title":"Understanding differences in wayfinding strategies","volume":"15","author":"Hegarty","year":"2023","journal-title":"Top. Cogn. Sci."},{"key":"10.1016\/j.aei.2026.104833_b0205","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.tbs.2021.05.010","article-title":"A big data approach to understanding pedestrian route choice preferences: evidence from San Francisco","volume":"25","author":"Sevtsuk","year":"2021","journal-title":"Travel Behav. Soc."},{"key":"10.1016\/j.aei.2026.104833_b0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2022.101827","article-title":"Behavioral, data-driven, agent-based evacuation simulation for building safety design using machine learning and discrete choice models","volume":"55","author":"Zhu","year":"2023","journal-title":"Adv. Eng. Inf."},{"issue":"4","key":"10.1016\/j.aei.2026.104833_b0215","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1016\/j.aei.2012.03.006","article-title":"Generation and use of sparse navigation graphs for microscopic pedestrian simulation models","volume":"26","author":"Kneidl","year":"2012","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104833_b0220","article-title":"Strategy selection in a conflicting context during indoor wayfinding: Insights from direction and floor strategies","volume":"102711","author":"Nanahara","year":"2025","journal-title":"J. Environ. Psychol."},{"issue":"1","key":"10.1016\/j.aei.2026.104833_b0225","first-page":"42","article-title":"Uncertainty promotes information-seeking actions, but what information?","volume":"5","author":"Keller","year":"2020","journal-title":"Cognit. Res.: Princ. Implic."},{"issue":"8","key":"10.1016\/j.aei.2026.104833_b0230","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1037\/0003-066X.49.8.709","article-title":"Integration of the cognitive and the psychodynamic unconscious","volume":"49","author":"Epstein","year":"1994","journal-title":"Am. Psychol."},{"issue":"2011","key":"10.1016\/j.aei.2026.104833_b0235","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1146\/annurev-psych-120709-145346","article-title":"Heuristic decision making","volume":"62","author":"Gigerenzer","year":"2011","journal-title":"Annu. Rev. Psychol."},{"issue":"2","key":"10.1016\/j.aei.2026.104833_b0240","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1080\/13875860902906496","article-title":"Taxonomy of human wayfinding tasks: a knowledge-based approach","volume":"9","author":"Wiener","year":"2009","journal-title":"Spatial Cognition & Computation"},{"issue":"7","key":"10.1016\/j.aei.2026.104833_b0245","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1016\/j.trb.2002.10.001","article-title":"A learning-based transportation oriented simulation system","volume":"38","author":"Arentze","year":"2004","journal-title":"Transp. Res. B Methodol."},{"key":"10.1016\/j.aei.2026.104833_b0250","doi-asserted-by":"crossref","first-page":"63","DOI":"10.3389\/fncom.2020.00063","article-title":"The neuroscience of spatial navigation and the relationship to artificial intelligence","volume":"14","author":"Bermudez-Contreras","year":"2020","journal-title":"Front. Comput. Neurosci."},{"issue":"8","key":"10.1016\/j.aei.2026.104833_b0255","doi-asserted-by":"crossref","first-page":"3535","DOI":"10.1109\/TVCG.2022.3163794","article-title":"Cognitive path planning with spatial memory distortion","volume":"29","author":"Dubey","year":"2022","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"10.1016\/j.aei.2026.104833_b0260","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103651","article-title":"A targeted memory-based intervention model of construction workers\u2019 unsafe behavior in human-robot interactions","volume":"68","author":"Lin","year":"2025","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104833_b0265","unstructured":"El\u2013Ela, M. H. A. and Hamdi, A. (2024). Deep heuristic learning for real-time urban pathfinding. arXiv. https:\/\/arxiv.org\/abs\/2411.05044."},{"key":"10.1016\/j.aei.2026.104833_b0270","unstructured":"Li, H., Zhang, J., Yang, L., Qi, J., & Gao, Z. (2022). Graph-GAN: A spatial-temporal neural network for short-term passenger flow prediction in urban rail transit systems. arXiv e-prints, arXiv-2202."},{"key":"10.1016\/j.aei.2026.104833_b0275","doi-asserted-by":"crossref","DOI":"10.1016\/j.physa.2022.127959","article-title":"Multi-point short-term prediction of station passenger flow based on temporal multi-graph convolutional network","volume":"604","author":"Wang","year":"2022","journal-title":"Physica A"},{"key":"10.1016\/j.aei.2026.104833_b0280","doi-asserted-by":"crossref","unstructured":"Dai, Z., & Li, D. (2024, August). MDSE-SLSTM: A Mobility-Driven Based Deep Learning Framework for Passenger Flow Distribution Forecasting in Multimodal Transportation Hub. In International Conference on Traffic and Transportation Studies (pp. 294-304). Singapore: Springer Nature Singapore.","DOI":"10.1007\/978-981-97-9644-1_32"},{"issue":"7540","key":"10.1016\/j.aei.2026.104833_b0285","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"issue":"1","key":"10.1016\/j.aei.2026.104833_b0290","doi-asserted-by":"crossref","first-page":"11771","DOI":"10.1038\/s41598-020-68447-8","article-title":"Segregation dynamics with reinforcement learning and agent based modeling","volume":"10","author":"Sert","year":"2020","journal-title":"Sci. Rep."},{"key":"10.1016\/j.aei.2026.104833_b0295","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2022.104715","article-title":"Simulating travel paths of construction site workers via deep reinforcement learning considering their spatial cognition and wayfinding behavior","volume":"147","author":"Kim","year":"2023","journal-title":"Autom. Constr."},{"issue":"1","key":"10.1016\/j.aei.2026.104833_b0300","doi-asserted-by":"crossref","first-page":"13923","DOI":"10.1038\/s41598-022-18245-1","article-title":"A comparison of reinforcement learning models of human spatial navigation","volume":"12","author":"He","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.aei.2026.104833_b0305","article-title":"Imitating shortest paths in simulation enables effective navigation and manipulation in the real world","author":"Ehsani","year":"2023","journal-title":"CoRR"},{"key":"10.1016\/j.aei.2026.104833_b0310","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2020.101180","article-title":"A CNN-based personalized system for attention detection in wayfinding tasks","volume":"46","author":"Wang","year":"2020","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104833_b0315","doi-asserted-by":"crossref","DOI":"10.1016\/j.ssci.2023.106100","article-title":"Cognition-driven navigation assistive system for emergency indoor wayfinding (CogDNA): Proof of concept and evidence","volume":"162","author":"Zhou","year":"2023","journal-title":"Saf. Sci."},{"key":"10.1016\/j.aei.2026.104833_b0320","unstructured":"Min, E., Chen, R., Bian, Y., Xu, T., Zhao, K., Huang, W., ... & Rong, Y. (2022). Transformer for graphs: An overview from architecture perspective. arXiv preprint arXiv:2202.08455."},{"key":"10.1016\/j.aei.2026.104833_b0325","unstructured":"Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2018). How powerful are graph neural networks? arXiv. https:\/\/arxiv.org\/abs\/1810.00826."},{"key":"10.1016\/j.aei.2026.104833_b0330","doi-asserted-by":"crossref","unstructured":"Zou, M., Sun, C., & Chen, Y. (2024). Improving metro station navigation: Findings from an online virtual reality wayfinding experiment. In A. Globa, J. Smith, J. C. Portilla, et al. (Eds.), Proceedings of the 29th CAADRIA, Volume 3 (pp. 431-440). CAADRIA.","DOI":"10.52842\/conf.caadria.2024.3.431"},{"issue":"5","key":"10.1016\/j.aei.2026.104833_b0335","doi-asserted-by":"crossref","first-page":"4282","DOI":"10.1103\/PhysRevE.51.4282","article-title":"Social force model for pedestrian dynamics","volume":"51","author":"Helbing","year":"1995","journal-title":"Physical Review E"},{"key":"10.1016\/j.aei.2026.104833_b0340","doi-asserted-by":"crossref","unstructured":"Brogan, D. C., & Johnson, N. L. (2003, May). Realistic human walking paths. In Proceedings 11th IEEE International Workshop on Program Comprehension (pp. 94-101). IEEE.","DOI":"10.1109\/CASA.2003.1199309"},{"key":"10.1016\/j.aei.2026.104833_b0345","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.cag.2021.08.020","article-title":"Towards a human-like approach to path finding","volume":"102","author":"Rahmani","year":"2022","journal-title":"Computers & Graphics"},{"issue":"Suppl 1","key":"10.1016\/j.aei.2026.104833_b0350","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s10339-012-0482-8","article-title":"The language of landmarks: the role of background knowledge in indoor wayfinding","volume":"13","author":"Frankenstein","year":"2012","journal-title":"Cognitive Processing"},{"key":"10.1016\/j.aei.2026.104833_b0355","article-title":"Does people\u2019s behaviour in virtual reality differ from that in real?","volume":"1\u201325","author":"Qing","year":"2025","journal-title":"Behaviour & Information Technology"},{"key":"10.1016\/j.aei.2026.104833_b0360","doi-asserted-by":"crossref","DOI":"10.1016\/j.jobe.2024.109199","article-title":"Generating trajectory data without behavior modeling: an online virtual reality method in wayfinding performance evaluation for buildings","volume":"88","author":"Sun","year":"2024","journal-title":"Journal of Building Engineering"},{"issue":"1","key":"10.1016\/j.aei.2026.104833_b0365","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s44223-025-00086-3","article-title":"Optimized and cost-effective behavior studies via VR: a serious game of wayfinding at PVG","volume":"4","author":"Zou","year":"2025","journal-title":"Architectural Intelligence"}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626005252?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626005252?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T20:12:00Z","timestamp":1783109520000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1474034626005252"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":73,"alternative-id":["S1474034626005252"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2026.104833","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Enhancing agent-based wayfinding simulation for transportation hub evaluation with a visual memory graph Neural network (VM-GNN)","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2026.104833","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104833"}}