https://www.dagstuhl.de/22162

18. – 22. April 2022, Dagstuhl-Seminar 22162

Urban Mobility Analytics

Organisatoren

David Jonietz (HERE – Zürich, CH)
Monika Sester (Leibniz Universität Hannover, DE)
Kathleen Stewart (University of Maryland – College Park, US)
Stephan Winter (The University of Melbourne, AU)

Koordinatoren

Martin Tomko (University of Melbourne – Carlton, AU)

Auskunft zu diesem Dagstuhl-Seminar erteilen

Jutka Gasiorowski zu administrativen Fragen

Michael Gerke zu wissenschaftlichen Fragen

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Dokumente

Teilnehmerliste
Gemeinsame Dokumente
Dagstuhl-Seminar Wiki
Programm des Dagstuhl-Seminars [pdf]

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Motivation

The Dagstuhl Seminar aims to bring together researchers from academia and industry who work in complementary ways on urban mobility analytics such that they do not necessarily meet at the same conferences. Especially we aim to collide ideas and approaches from deep learning research – requiring large datasets – and reproducible research – requiring access to data.

Transportation in cities is undergoing unprecedented change – such as by vehicle technology towards autonomous driving (‘disruptive mobility’); massive real-time data and data analytics (smart cities, sensing cities, dashboards); sharing platforms and integration (mobility-as-a-service) and urban logistics (changing shopping patterns). All this happens in parallel with an increasing willingness to share mobility resources or change mobility behaviour. Critical to the success of transforming urban mobility is therefore information provided to planners, operators, and travellers.

The seminar will address recent trends that are shaping the information derived from urban mobility analytics.

  • A prominent one, not only in transportation research, is the rise of deep learning methods for massive data analytics. In the domain of urban mobility this massive data emerges from a range of sensor platforms, from infrastructure (CCTV, induction loops, people counters, WiFi, smart cards, air quality) to vehicles (GPS, vision, LiDAR, radar) and smartphones (GPS, location-based apps, accelerometer, gyroscope, magnetometer), in volume, heterogeneity, velocity and veracity a prime application domain for deep learning.
  • A second trend is the emerging digital divide between academia and industry and its challenges for reproducible research, a trend that has been compared to digital feudalism. While massive data on urban mobility is collected by industry and transport authorities, their access for academic research is limited by privacy concerns and also by commercial sensitivities. While reproducible research hinges on access to data, much of the urban mobility research and development is now done behind the closed doors of large transnational companies.

Related to both trends above is the buzzword of Digital Twins. Since both data and data analytics becomes more often available sufficiently close to real-time, the information derived is less and less consumed in human decision making but in the self-regulation of cyber-physical-social systems. These systems will propel future urban mobility by autonomously driving vehicles and mobility-as-a-service, however, their development and use involves many still-open research questions, such as reliability, trust and HCI.

In this context the seminar makes a deliberate effort to invite people from both sides of the digital divide (i.e., from academia and industry) to share their experiences, their approaches, and their challenges, and to explore more future collaboration.

We aim for a joint vision paper of the participants as much as new international collaborative research initiatives. The dynamics at the seminar will be fostered by a large traffic dataset sponsored by IARAI (derived from https://www.iarai.ac.at/traffic4cast/). This dataset will be available for the working groups to explore and test their ideas.

Motivation text license
  Creative Commons BY 4.0
  David Jonietz, Monika Sester, Kathleen Stewart, and Stephan Winter

Dagstuhl-Seminar Series

Classification

  • Artificial Intelligence
  • Machine Learning
  • Multiagent Systems

Keywords

  • Machine learning
  • Deep learning
  • Massive data analytics
  • Travel prediction

Dokumentation

In der Reihe Dagstuhl Reports werden alle Dagstuhl-Seminare und Dagstuhl-Perspektiven-Workshops dokumentiert. Die Organisatoren stellen zusammen mit dem Collector des Seminars einen Bericht zusammen, der die Beiträge der Autoren zusammenfasst und um eine Zusammenfassung ergänzt.

 

Download Übersichtsflyer (PDF).

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Publikationen

Es besteht weiterhin die Möglichkeit, eine umfassende Kollektion begutachteter Arbeiten in der Reihe Dagstuhl Follow-Ups zu publizieren.