{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T10:49:27Z","timestamp":1782989367181,"version":"3.54.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686707","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,2]]},"abstract":"<jats:p>Personalized pedestrian routing can be framed as an instance of AI-driven adaptive routing, where the system learns user preferences and adjusts route recommendations accordingly. However, modeling heterogeneous route-choice preferences between users and travel contexts remains challenging, as fully individualized models are difficult to scale. We propose a context-aware routing framework that captures this heterogeneity through a small number of interpretable subgroup models. We apply Exceptional model mining to identify subgroups of observations exhibiting statistically distinct decision patterns relative to a global preference model. Our approach enables prediction through shared subgroup-specific preference models rather than individual optimization. Using simulated user models for context-dependent route choice decisions, our experimental results show that our approach successfully identifies meaningful context-dependent preference groups, supporting scalable and interpretable personalized pedestrian routing.<\/jats:p>","DOI":"10.3233\/faia260539","type":"book-chapter","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T09:57:52Z","timestamp":1782986272000},"source":"Crossref","is-referenced-by-count":0,"title":["When Context Matters: Exceptional Model Mining for Pedestrian Route Choice"],"prefix":"10.3233","author":[{"given":"Paolo","family":"Speziali","sequence":"first","affiliation":[{"name":"Vrije Universiteit Brussel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehrdad","family":"Asadi","sequence":"additional","affiliation":[{"name":"Vrije Universiteit Brussel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diederik M.","family":"Roijers","sequence":"additional","affiliation":[{"name":"Vrije Universiteit Brussel"},{"name":"imec AI labs"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ann","family":"Now\u00e9","sequence":"additional","affiliation":[{"name":"Vrije Universiteit Brussel"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","HHAI 2026"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA260539","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T09:57:53Z","timestamp":1782986273000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA260539"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,2]]},"ISBN":["9781643686707"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia260539","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,2]]}}}