This seminar brings together researchers working in areas contributing to Computational Transportation Science (CTS). CTS is an emerging discipline that combines computer science and engineering with the modeling, planning, social, and economic aspects of transportation. The discipline studies how to improve the safety, mobility, and sustainability of transportation systems by taking advantage of information technologies and ubiquitous computing. In this seminar we focus on the social computing aspect of CTS. Focusing on social computing was influenced by the many recent developments in social networks and crowd-sourcing for transportation as well as the integration of persuasive technologies, behavioral economics in social computing. The researchers and practitioners will review the developments in this direction, discuss issues and solutions, and plan joint publications on CTS from the social computing perspective.

The following topics will be discussed at the seminar:

Managing Competition and Collaboration among Travelers.
Travelers in a traffic network often need to make decisions about activities such as routing and parking. With the advent of location based services, wireless communication devices, and car navigation systems with real-time traffic, travelers now have the information to help them make these decisions. However, the models and tools needed to take advantage of this information are lacking. For example, the route guidance methods that are used by car navigation systems are based on choosing a shortest path. But the shortest path could be sub-optimal if a large number of vehicles choose the same path, leading to a “herding” effect. This in turn will backfire, i.e. lead to a longer travel, because as the number of vehicles on a route increases, so will the travel time on the route. Similarly, a bus, a train, or an available parking slot that seem attractive to a traveler may become much less desirable if many travelers make the same choice. In other words, the pervasiveness of real-time travel information renders the existing tools inadequate, and the decision-making unsupported.

Crowd-sourcing.
In a crowd-sourcing system, services are provided by the users themselves rather than by a business or organization. The transportation information that may be crowd-sourced includes real-time traffic information of road segments, information about car accidents and available parking slots, ride sharing opportunities, and so on. Crowd-sourcing may be implemented using either the client/server model, or the peer-to-peer model, or a combination of the two. In the transportation environment, peers can be highly mobile and the peer-to-peer communication is subjected to disconnections when a short-range wireless technology is used. In this case, the peer-to-peer crowd-sourcing introduces special challenges to incentive mechanisms. Many issues remain to be solved, among them transactional/atomicity issues in pricing schemes, reputation management in non-pricing schemes, game theoretic schemes, privacy issues, and exploiting social networks like Twitter of Facebook, where it is assumed that friends share trustworthy information.

Behavioral Economics and Persuasive Technologies.
Behavioral economics uses social, cognitive and emotional factors in understanding the economic decisions of individuals and institutions performing economic functions. Behavioral economics has made inroads in transportation in the areas of survey design, prospect theory, and attitudinal variables. Further infusion into transportation will lead to significant benefits in terms of increased ability to both predict and influence behavior. Computational transportation science can contribute to more sustainable transport by transferring findings in behavioral economics to transportation, with a focus on personalized information and social influences to change behavior.

• Francesco Ciari (ETH Zürich, CH) [dblp]
• Caitlin Doyle Cottrill (University of Aberdeen, GB) [dblp]
• Sergio Di Martino (Volkswagen AG - Wolfsburg, DE) [dblp]
• Jan Fabian Ehmke (FU Berlin, DE) [dblp]
• Rob Fitzpatrick (NICTA - Eveleigh, AU)
• Stefan Funke (Universität Stuttgart, DE) [dblp]
• Glenn Geers (Data61 / NICTA - Sydney, AU) [dblp]
• Sergio Ilarri (University of Zaragoza, ES) [dblp]
• Benjamin Kickhöfer (TU Berlin, DE) [dblp]
• Franziska Klügl (University of Örebro, SE) [dblp]
• Alexandros Labrinidis (University of Pittsburgh, US) [dblp]
• Patrick Laube (ZHAW - Wädenswil, CH) [dblp]
• Steve Liang (University of Calgary, CA) [dblp]
• Thomas Liebig (TU Dortmund, DE) [dblp]
• Seng Wai Loke (Latrobe Univ. - Bundoora, AU) [dblp]
• Dirk Christian Mattfeld (TU Braunschweig, DE) [dblp]
• Harvey J. Miller (University of Utah, US) [dblp]
• Walied Othman (Universität Zürich, CH) [dblp]
• Nicole Ronald (The University of Melbourne, AU) [dblp]
• Peter Sanders (KIT - Karlsruher Institut für Technologie, DE) [dblp]
• Monika Sester (Leibniz Universität Hannover, DE) [dblp]
• Sabine Storandt (Universität Freiburg, DE) [dblp]
• Sabine Timpf (Universität Augsburg, DE) [dblp]
• Stephan Winter (The University of Melbourne, AU) [dblp]
• Ouri E. Wolfson (University of Illinois - Chicago, US) [dblp]
• Bo Xu (Argonne National Laboratory, US) [dblp]
• Yang Yue (Shenzhen University - Guangdong, CN) [dblp]