Held in conjunction with IEEE ICDM 2026

Urban Intelligence and
Multimodal Mobility Mining
UIM3 2026

A workshop bringing together the data mining, urban computing, and intelligent transportation communities to advance how we model, predict, and govern movement in cities.

FormatHalf-day workshop
VenueTo be confirmed — see ICDM 2026 conference site
ProceedingsIEEE ICDM Workshop Proceedings (ICDMW) & IEEE Xplore

Why urban mobility, why now

Urban systems are generating unprecedented volumes of heterogeneous data from multimodal transportation networks, smart cards, GPS and probe vehicles, mobile phones, social sensing, administrative records, freight operations, public service platforms, infrastructure sensors, and contextual sources such as weather, land use, and events.

These data create major opportunities for the data mining community to advance mobility pattern recognition, urban behavior understanding, public service optimization, policy evaluation, safety monitoring, and resource allocation. At the same time, urban intelligence and mobility applications raise core ICDM challenges, including spatiotemporal mining, graph learning, multimodal data fusion, privacy-preserving analytics, cross-domain knowledge transfer, uncertainty quantification, interactive analytics, and robust evaluation.

UIM3 2026 brings together researchers and practitioners working on AI and data mining methods for urban intelligence and multimodal mobility systems, with particular interest in mobility pattern mining, population synthesis, urban sensing, privacy-aware learning, graph and trajectory analytics, simulation-ready data mining, and deployable decision-support pipelines for mobility and public services.

What we're looking for

UIM3 2026 invites original research papers on data mining and artificial intelligence for urban intelligence and multimodal mobility systems. We welcome methodological, application-driven, and interdisciplinary contributions that advance the understanding, modeling, prediction, and optimization of urban and mobility systems using modern data mining tools.

Accepted papers will be included in the IEEE ICDM workshop proceedings, and the program may include invited talks and a panel discussion on future directions in urban intelligence and multimodal mobility mining.

Topics of interest

Topics include, but are not limited to:

Mobility pattern recognition and behavioral mining in multimodal systems
Population synthesis, activity-based modeling, and demand generation
Urban sensing and data management for cities and transportation systems
Spatiotemporal mining, trajectory analytics, and graph learning for urban and mobility data
Multimodal data fusion from smart cards, probe/GPS, mobile, administrative, shared mobility, freight, and infrastructure sensing data
Urban computing and AI for mobility estimation, prediction, and optimization
Privacy-preserving computing, federated learning, fairness, robustness, and trustworthy urban analytics
Foundation models and large language models for urban intelligence and mobility systems
Simulation-integrated learning, digital twins, calibration, and scenario analysis
Causal and policy-aware urban and mobility mining for planning, governance, and operations
Anomaly detection, public safety discovery, and resilience analysis in urban systems
Interactive visual analytics for policymakers, planners, and domain experts
Domain adaptation, transfer learning, and cross-city generalization
Benchmarking, artifact evaluation, and reproducible urban and mobility data mining

Important dates

Paper submission deadlineAugust 20, 2026
Notification of acceptanceTBD
Camera-ready deadlineOctober 5, 2026
Workshop dateTBD (half-day, in conjunction with ICDM 2026)

All deadlines are 11:59 PM AoE (Anywhere on Earth) unless stated otherwise. Papers not received by the submission deadline, or camera-ready files not received by October 5, may be excluded from the ICDM workshop proceedings. Remaining dates will be posted here as soon as they are confirmed.

Submission guidelines

Manuscripts must be submitted electronically as a PDF, formatted according to the IEEE Computer Society proceedings template (two-column format). Submissions must be original and not under review or accepted elsewhere. Following ICDM convention, papers are reviewed under a triple-blind policy — please omit author names and affiliations from the submitted manuscript.

All accepted papers will appear in the IEEE ICDM 2026 Workshop Proceedings (ICDMW) and be indexed in IEEE Xplore. At least one author of each accepted paper is expected to register and present at the workshop.

Submit your paper

Submissions are handled through the ICDM 2026 CyberChair portal. Please select "UIM3 2026 Workshop" as the track when submitting.

Submit via CyberChair →

Invited speakers

Tentative — to be confirmed upon workshop acceptance

We plan to invite two to three speakers from the communities of urban computing, mobility data mining, digital government, and AI for transportation systems, selected to reflect both methodological depth and application relevance, and to promote diversity across geography, seniority, and research perspective.

Prof. Alexandre Alahi
EPFL
Prof. Patrick Gallinari
Sorbonne University

Organizers

Mostafa Ameli
GRETTIA, Université Gustave Eiffel, France · Affiliated Researcher, UC Berkeley
Research at the intersection of operations research, machine learning, and transportation systems, with a focus on mathematical and computational tools for smarter and more resilient mobility systems.
Pinghui Wang
School of Cyber Science and Engineering, Xi'an Jiaotong University
Research interests include network security, traffic measurement, graph data mining, and large-scale data analytics, with a strong focus on intelligent urban systems and privacy-aware computing.
Lijun Sun
Department of Civil Engineering, McGill University, Canada · William Dawson Scholar
Research centers on urban computing and smart transportation, including mobility sensing, machine learning, and simulation for efficient, resilient, and sustainable transportation systems.
Francisco Camara Pereira
Department of Technology, Management and Economics, Technical University of Denmark (DTU)
Leads the Intelligent Transport Systems group at DTU. Research focuses on the methodological combination of machine learning and transport research, including climate adaptation, demand modeling, traffic prediction, data collection, simulation metamodeling, and anomaly detection.
Yanhua Li
Computer Science Department & Data Science Program, Worcester Polytechnic Institute
Research focuses on urban intelligence, spatial-temporal data management and mining, and data-driven cyber-physical systems.
Xuan Sharon Di
Department of Civil Engineering and Engineering Mechanics, Columbia University
Research focuses on AI in transportation systems, shared mobility, autonomous systems, and smart-city applications.
Latifa Oukhellou
GRETTIA, Université Gustave Eiffel, France · Director, GRETTIA Laboratory
Research Director with a focus on data mining, machine learning, information fusion, and urban computing, particularly applications to intelligent transportation systems and the development of smart, resilient, and sustainable cities.

Tentative program committee

The program committee will include researchers with expertise in data mining, spatiotemporal analytics, graph learning, urban computing, transportation systems, privacy-preserving learning, visualization, and simulation-based urban modeling, formed with attention to diversity in geography, seniority, gender, and sector. A preliminary committee will be drawn from academia, research institutes, and industry working across:

A detailed PC list will be posted here after workshop approval and before the CFP is circulated.

Contact

For questions about scope, submissions, or logistics, please contact any of the organizers above, or reach out to the workshop chairs directly: