- Autonomous car and ride sharing : flexible road trains : (vision paper) : article in GIS '16 Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Article No. 10 - Agatz, Niels; Bazzan, Ana L. C.; Kutadinata, Ronny; Mattfeld, Dirk C.; Sester, Monika; Wolfson, Ouri E.; Winter, Stephan - New York : ACM, 2016. - 4 pp. - (Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems ; 24. 2016 : article).
- Benefit of Online Real-Time Data in the Braess Paradox with Anticipatory Routing : article in 2016 IEEE International Conference on Autonomic Computing (ICAC), 17-22 July 2016 - Varga, Laszlo Zsolt - Los Alamitos : IEEE, 2016. - pp. 245-250.
- Collaborative urban transportation : Recent advances in theory and practice : article - Cleophas, Catherine; Cottrill, Caitlin; Ehmke, Jan Fabian; Tierney, Kevin - Amsterdam : Elsevier, 2019. - pp. 801-816 - (European Journal of Operational Research ; 273. 2019, 3).
- Equilibrium with predictive routeing in the online version of the Braess paradox : article pp. 165-170 - Varga, Laszlo Zsolt - London : Institution of Engineering and Technology IET, 2017 - (IET Software ; 11. 2017, 4: article).
- How Good Is Predictive Routing in the Online Version of the Braess Paradox? : article in ECAI 2016 : 2 pp. - Varga, Laszlo Zsolt - Amsterdam : IOS Press, 2016. - 2 pp. - (ECAI 2016 : article).
Computational transportation science (CTS) is a new discipline that combines computer science and engineering with the modeling, planning, social, and economic aspects of transportation. It clearly goes beyond vehicular technology, addressing pedestrian and bike systems on hand-held devices and also deals with data issues such as, e.g., transportation data mining. The research agenda of CTS is structured into the following five directions: knowledge discovery, decentralized computing, social computing, applications, and societal issues. In this Dagstuhl Seminar, we plan to focus on decentralized computing and data challenges with regard to cooperative intelligent transport in urban areas.
In recent years, urban transportation networks have become more diverse, with a growing mix of public and private operators providing disaggregated services and information. The resulting multitude of transportation options includes non-traditional modes such as car and bike sharing, as well as established public transport and individual car options. In addition, the expectations of businesses for city logistics services have increased in an environment of crowded urban traffic infrastructure.
Cooperative intelligent transportation forms a shift in the basic paradigm of transportation management and thus poses many challenging questions especially from a computational perspective. The shift is made possible by advances in mobile technologies in combination with social networks. With the increased number of transportation options, a variety of data sources describing the experienced and/or the expected quality of the operations of individual transport services have become available. On the one hand, since cooperative intelligent urban transport is inherently multimodal, it is challenging to combine the available transportation options in a user-friendly manner, incorporating distributed quality of service information. On the transportation management side, however, the shift from centralized to decentralized information and service provision challenges currently reliable planning and realization of multimodal transportation, and methods of planning and management of intelligent urban transportation need to be extended.
The seminar aims at discussing how the above-mentioned data sources and services can be made available and used for intelligent planning at the level of travelers as well as in system-wide coordination of urban transportation. We assume that approaches from distributed computing, artificial intelligence, optimization, geographic information sciences, and traffic engineering among others will play a role in getting all involved parties to cooperate in providing and using relevant spatial and temporal information in a timely fashion. Data sources may differ in trustworthiness, impacting upon processes used for their aggregation to provide information for decentralized planning and control purposes. It is not clear how to evaluate reliability of information, how to derive reliable information for planning and control approaches, or how to adapt optimization methodologies to make urban transportation more cooperative and intelligent.
Topics to be discussed in the seminar include:
- collective travel data gathering and combination of these insuring high quality;
- modeling and transformation of data into individualized information services for cooperative transportation;
- potential use and problems of data/information for transport system management;
- providing traveler information and planning services while enabling cooperative transportation;
- efficient, sustainable and reliable organization of city logistics services;
- adapting current methodologies in planning and
- control of urban transport systems to cooperative intelligent transportation.
Following the history of two Dagstuhl seminars on Computational Issues in Transportation in 2010 and 2013, the organizers of this follow-up seminar concentrated on upcoming, data-driven challenges in the area of urban transport. In recent years, urban transportation networks have become more diverse, with a growing mix of public and private operators providing disaggregated services and information. The resulting multitude of transportation options includes non-traditional modes and services such as car and bike sharing in addition to established public transport and individual car options. So far, it is challenging to combine detailed operational data automatically arising from these services, since these data are generated both from service operation and from the users of services via crowdsourcing. The seminar aimed to discuss how data sources can be made available for individual planning and system-wide coordination of urban transportation using an approach from distributed computing, i.e., getting all involved parties to cooperate in providing relevant spatial and temporal information in a timely fashion. It was not clear how to derive reliable information for planning and control approaches, or how to adapt optimization methodologies to make urban transportation more cooperative and intelligent.
The aims of the seminar were to extend the existing network in disciplines such as Computational Traffic Science, Optimization, Autonomic Computing and Artificial Intelligence for discussing computational challenges in cooperative intelligent urban transportation, mesh communities by collecting suggestions for (partial) solutions for burning issues in urban transportation and discussing the prerequisites for merging into interdisciplinary approaches, document the state of the art and current computational challenges in cooperative intelligent transportation.
To this end, an interdisciplinary group from areas such as computer science, geography, applied optimization and traffic engineering met at Dagstuhl. The number of attendees was advantageous for group discussions, not too small for breakout groups but also not too large for meaningful discussions in the plenum.
We started on Sunday evening with a game ("Cards Against Urbanity -- special issue for this seminar") specifically designed for this event by Ms. Cottrill. The game was a great success as icebreaker and helped bringing together the participants with their various backgrounds. Monday was opened with a keynote by Vonu Thakuriah, who discussed examples, prospects and challenges of emerging forms of data in transportation research and applications. The participants introduced themselves, bringing a significant object describing their relationship with the seminar’s topic.
For the remaining seminar time, the participants were asked to contribute to the seminar’s content by one of the following options: they could give an overview talk of an emerging area (20 minutes), a research statement on what they have been working on in their particular area (5 minutes), and they were asked to come together in groups that were defined dynamically on Monday afternoon. The resulting abstracts can be found in this report. Based on the participants’ interests, groups discussing the topics of online simulation, pedestrian behavior, autonomous transportation, smart cities, and benchmark data emerged. On Wednesday afternoon, the participants went on a ‘field trip’ to the retail lab by DFKI in St. Wendel, where the future of retail can be explored hands-on. Since there was a significant interest in the provision of benchmark data for urban transport, there was a special session and group work devoted to this topic on Thursday afternoon. Friday morning was meant for collecting the results of the group work and collecting open challenges for future seminars.
Summarizing, the seminar identified computational challenges to cooperative intelligent urban transport, among others notably research on opportunistic groups in public transport (i.e., people sharing tickets and or trajectories in an ad-hoc fashion), freight pods attached to light rail (i.e., mixing of freight and passenger transportation), define a common language for sharing complex knowledge and real-time data in smart cities and creating benchmark datasets for different modelling purposes and at different scales. We think that the seminar was quite successful in extending the existing networks by bringing together researchers from many different disciplines relevant for the future of urban transport. Some of the groups are planning to write proposals for the appropriate EU calls coming out in October, while others have started to work on position papers describing the state of the art as well as resulting future challenges of the field.
- Niels Agatz (Erasmus University - Rotterdam, NL) [dblp]
- Ana Lucia Bazzan (Federal University of Rio Grande do Sul, BR) [dblp]
- Catherine Cleophas (RWTH Aachen, DE) [dblp]
- Caitlin Doyle Cottrill (University of Aberdeen, GB) [dblp]
- Sybil Derrible (University of Illinois - Chicago, US) [dblp]
- Ivana Dusparic (University College Dublin, IE) [dblp]
- Jan Fabian Ehmke (FU Berlin, DE) [dblp]
- Cecilia Gomes (New University of Lisbon, PT) [dblp]
- Benjamin Heydecker (University College London, GB) [dblp]
- Andreas Hotho (Universität Würzburg, DE) [dblp]
- Benjamin Kickhöfer (TU Berlin, DE) [dblp]
- Franziska Klügl (University of Örebro, SE) [dblp]
- Tobias Kretz (PTV AG - Karlsruhe, DE) [dblp]
- Ronny Kutadinata (The University of Melbourne, AU) [dblp]
- Marco Mamei (University of Modena, IT) [dblp]
- Dirk Christian Mattfeld (TU Braunschweig, DE) [dblp]
- Thomas Leo McCluskey (University of Huddersfield, GB) [dblp]
- Andrea Prati (University of Parma, IT) [dblp]
- Daniele Quercia (NOKIA Bell Labs - Cambridge, GB) [dblp]
- Jörg-Rüdiger Sack (Carleton University - Ottawa, CA) [dblp]
- Jörn Schlingensiepen (TH Ingolstadt, DE)
- Monika Sester (Leibniz Universität Hannover, DE) [dblp]
- Piyushimita Vonu Thakuriah (University of Glasgow, GB) [dblp]
- Kevin Tierney (Universität Paderborn, DE) [dblp]
- Sabine Timpf (Universität Augsburg, DE) [dblp]
- Eric van Berkum (University of Twente, NL) [dblp]
- Ronald Van Katwijk (TNO Telecom - Delft, NL) [dblp]
- Laszlo Zsolt Varga (Eötvös Lorand University - Budapest, HU) [dblp]
- Giuseppe Vizzari (University of Milano-Bicocca, IT) [dblp]
- Ouri E. Wolfson (University of Illinois - Chicago, US) [dblp]
- mobile computing
- modelling / simulation
- society / human-computer interaction
- Computational transportation science
- intelligent transportation systems
- cooperative computing
- crowd-sourcing of transportation data