ISSN 2186-7437

NII Shonan Meeting Report

No. 2012-12Social Issues in Computational
Transportation Science
Monika Sester
Ouri Wolfson
Glenn Geers
Masaaki Tanizaki
Stephan WinterDecember 17–20, 2012

National Institute of Informatics
2-1-2 Hitotsubashi, Chiyoda-Ku, Tokyo, Japan

Social Issues in Computational Transportation
ScienceOrganizers:
Monika Sester (University of Hannover, Germany)
Ouri Wolfson (University of Illinois at Chicago, USA)
Glenn Geers (NICTA, Australia)
Masaaki Tanizaki (Hitachi Ltd., Japan)
Stephan Winter (The University of Melbourne, Australia)
December 17–20, 2012

Acknowledgements
The organizers wish to thank Hiroki Ohashi, whose notes of the discussions went
into this report.

1

Overview of the Meeting

This meeting was bringing together researchers working in areas contributing
to Computational Transportation Science (CTS). CTS is an emerging disciplinethat 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 bytaking advantage of information technologies and ubiquitous computing. In
this seminar we focused on the social computing aspect of CTS.
Focusing on social computing was inﬂuenced by the many recent developments in social networks and crowdsourcing for transportation as well as theintegration of persuasive technologies, behavioral economics in social computing. The researchers and practitioners from industry reviewed the developmentin this direction, discussed issues and solutions, and planned joint publications
on CTS from the social computing perspective.
Social computing and information processing taps into the wisdom of crowds,
and relies on the (ubiquitous) connectedness and communication ability of themembers forming the society. Provided such infrastructure, cooperation interms of computing and information processing becomes feasible and forms new
research questions. While social computing is making its way into many disciplines, it is not obvious how real-time social interaction between mobile andstationary individuals (people, vehicles, goods, and infrastructure) can improvetransportation. From a state where every individual is acting autonomously inisolation, or with minimal (visual) interaction with their environment, it is quite1

a paradigm shift to think of transportation as an interconnected, communicating and cooperating complex system. Such a paradigm shift brings up researchquestions in multiple dimensions including, but not limited to: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 havethe information to help them make these decisions. However, the models andtools 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 theroute. 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.
The inappropriateness of existing methods results from competition among
travelers with real-time information. But the travelers also cooperate sincemuch of the travel information is provided via some sort of crowdsourcing, aphenomenon that should be encouraged and incentivized because it increases
the eﬃciency of the overall system.
Crowdsourcing. In a crowdsourcing system, services are provided by theusers themselves rather than by a business or organization. The transportation
information that may be crowdsourced includes real-time traﬃc information ofroad segments, information about car accidents and available parking slots, ridesharing opportunities, and so on. Crowdsourcing 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 andthe peer-to-peer communication is subjected to disconnections when a shortrange wireless technology is used. In this case, the peer-to-peer crowdsourcing
introduces special challenges to incentive mechanisms. Many issues remain tobe 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 intotransportation will lead to signiﬁcant beneﬁts in terms of increased ability toboth predict and inﬂuence behavior. Computational transportation science cancontribute to more sustainable transport by transferring ﬁndings in behavioral

2

economics to transportation, with a focus on personalized information and social
inﬂuences to change behavior.

2Participants

Caitlin Cottrill, Singapore-MIT Alliance for Research and Technology, Singapore
Thierry Delot, INRIA Lille and University of Valenciennes, France
Stefan Funke, Universität Stuttgart, Germany
Glenn Geers, NICTA, Australia
Shunsuke Kamijo, University of Tokyo, Japan
Steve Liang, University of Calgary, Canada
Dirk Christian Mattfeld, Universität Braunschweig, Germany
Harvey Miller, University of Utah, USA
Hiroki Ohashi, Central Research Laboratory, Hitachi, Ltd. Japan
Dennis Schieferdecker, Karlsruher Institut für Technologie, Germany
Monika Sester, Institut für Kartographie und Geoinformatik, Leibniz Universität Hannover, Germany
Masaaki Tanizaki, Hitachi, Ltd., Kyoto University, Japan
Piyushimita (Vonu) Thakuriah, University of Illinois at Chicago, USA
S. Travis Waller, University of New South Wales, Australia
Stephan Winter, The University of Melbourne, Australia
Ouri Wolfson, The University of Illinois at Chicago, USA

Figure 1: The participants.

3

Overview of Talks

Note: presentation slides are available from http://www.nii.ac.jp/shonan/
seminar022/.

3

Collaborative Mobility: Using Geographic Information Science to Cultivate Cooperative Transportation
Harvey Miller, University of Utah
Transportation systems are facing unprecedented challenges in the 21st century. Increasing the eﬃciency of transportation systems alone will not solvethese problems and may exacerbate them. Instead, we must extract new transportation capabilities related to more cooperative decision-making across a wide
range of time horizons, spatial scales and decision contexts. This talk discusses
the role of sensed transportation, geographic information science and social media to cultivate transportation systems where participants share, cooperate andact collectively to solve operational, tactical and strategic mobility and accessibility problems. This talk also provides a vision of the future by imaging aseamless multimodal transportation system combined with a virtual environment where data streams are fused, interpreted and made available with tools
for human engagement and shared decision making. This talk concludes byoutlining a research agenda.
Discussion:• As people have begun to realize that the data are invaluable, they started
to conceal them. Access to data is a critical problem.
• We need some open data initiative to accelerate our research.
• Is mechanism design a way to design persuasive services moving people
from pursuing individuals’ goals to pursuing systems’ goals? – Vickrey
auction is a good example, ending typically an epsilon above system optimum.

Current Issues of Urban Japanese Transport Systems
Masaaki Tanizaki, Hitachi Ltd., Japan
Regarding the current transportation situation in Japan, there are two typical diﬀerent situations and issues in urban/local area individually. In order toapply IT technology for two diﬀerent areas, it is important to develop technologies based on standardized speciﬁcation.
Firstly, regarding the transportation situation in the urban city area, it has
been equipped with public transportation networks such as train, subway andbus. These services provide congested train schedules, for example, train comesat ﬁve minutes interval by major train company operation. Additionally, sometrain and subway companies have shared their tracks with each other for passenger convenience by connecting their train services. On the other hand, weakness
for accident and emergency is an issue, because inﬂuence of an emergent event
may spread among railway companies which share the tracks. In such case, it
is important for passengers to get information quickly, however, broadcasting
information by train company is too slow. Therefore, we propose to develop
a gathering and broadcasting information system which utilizes two kinds ofresources: One is SNS information such as twitter, which is quick but has low
reliability, and the other is public information by railway companies, which has

4high reliability but slow. How to control the tradeoﬀ relationship between two
diﬀerent resources is the key.
Secondly, regarding transportation situations in local areas, public transportation facility has not been supported. Consequently, the current issue isthat many people extremely depend on their cars. The other issue is that local government needs high maintenance cost for social infrastructure such as
road/water/gas/electricity because of city area expansion and low population
density. In order to solve these issues, local government in Toyama has started tobuild public transport systems by expanding an extensive and user-friendly lightrail transit network, and also to intensify the urban functions such as private
residences and commercial facilities. As an expected goal with these activities,
the distances citizens have to travel are shortened and CO2 emission decrease
because of modal shift from their cars to public transportation services. In orderto achieve this goal, there are future subject as follows; monitoring method ofpeople movement (car, bus, LRT, and so on), incentive design for activation ofmodal shift.
Finally, there is another discussion for interoperability of transportation
technique. In order to promote utilization of technique among urban/local area,
it is important to consider mutual utilization of techniques based on standardized speciﬁcation of data model. ISO, CEN, OGC have published data model
standardized speciﬁcation based on each target ﬁeld as follows:• Road networks:– ISO 14825:2011 ITS Geographic Data Files(GDF5.0)– ISO17267:2009 ITS Navigation Systems API– ISO 24099:2011 Navigation data delivery structures and protocols• Public transportation networks:– CEN/TC278 IFOPT (Identiﬁcation of Fixed Objects in Public Transport)• Indoor networks:– OGC Indoor GML/CityGML
These data model should be a basis of consideration in application, simulationand system design.
Discussion:• Data models and standardized speciﬁcations are needed to integrate various kinds of information.
• Marginal cost pricing, congestion pricing...
• Utilizing user generated content is another good approach, for example,
Twitter data.
• Reliability of the information is key.

5

Cooperation versus competition in vehicular networks
Thierry Delot, Université de Valenciennes, France
In the last decade, a number of wireless and small-sized devices (e.g., PDAs,
smartphones, sensors, etc.) with increasing computing capabilities have appeared in the market at very aﬀordable costs. Some of these equipments havestarted to be embedded in modern cars in the form of on-board computers,
navigation devices or even multimedia centers. Today, thanks to wireless networks, two vehicles nearby (within communication range of each other) canshare relevant information about various events (e.g., an emergency braking, atraﬃc congestion, a driver exhibiting risky behaviour, etc.) or resources (e.g., aparking space released, an available charging station for electric vehicles, etc.).
All these trends are motivating a great amount of research to try to develop
suitable data management strategies for vehicular networks. For instance, exchanging dynamic data (i.e., data whose relevance can change very quickly)
in a vehicular network has been widely studied these last years. This constitutes the building blocks for new data gathering techniques, assuming thatdata sources/producers are numerous (e.g., sensors embedded in the car or deployed along the roads, crowdsourcers, or even classical data sources such as
web services accessible through mobile telephony networks. In this talk, we will
focus on the data management techniques to facilitate cooperation or manage
competition between drivers.
Discussion:• The duration of events (or the value / validity of information over time)is an issue. How long does the inﬂuence of an accident last?
• Can V2X communication be integrated?
• Issues with penetration rate of vehicles, reliability of information createdby users, and involving those for whom the information is not relevant(incentives?).

Integration of information and optimization models for routing in city logistics
Dirk C. Mattfeld, with contributions from Jan F. Ehmke, Technical University
Braunschweig, Germany
As urban congestion continues to be an ever increasing problem, routing inthese settings has become an important area of operations research. This monograph provides cutting-edge research, utilizing the recent advances in technology, to quantify the value of dynamic, time-dependent information for advanced
vehicle routing in city logistics. The methodology of traﬃc data collection isenhanced by GPS based data collection, resulting in a comprehensive number oftravel time records. Data Mining is also applied to derive dynamic information
models as required by time-dependent optimization. Finally, well-known approaches of vehicle routing are adapted in order to handle dynamic information
models.

6Competition in Intelligent Transportation
Ouri Wolfson, University of Illinois, Chicago, USA
The proliferation of mobile devices, location-based services and embedded
wireless sensors has given rise to applications that seek to improve the eﬃciencyof the transportation system. In this talk we show that many of these applications have to consider the competition among travelers, and demonstrate how
to use game theory in order to do so. Parking is used as an example of a large
class of competitive applications in Intelligent Transportation.

IT Challenges toward the Solution of Traﬃc Problems in
Emerging Countries
Hiroki Ohashi, Hitachi Ltd, Japan
In emerging countries, traﬃc congestion has become one of the social problems as their rapid urbanization. The ﬁrst step for solving this problem is tounderstand the current traﬃc situation. In order to understand the current
traﬃc situation, developed countries use road side sensors or taxi probe system. However, it is diﬃcult to use these methods in emerging countries dueto the following two reasons. Firstly, the road side sensors require too muchinitial investment to widely spread in emerging countries. Secondly, since themajor mode of transportation in emerging countries is motorbike, the data collected by taxi are not enough. To overcome these diﬃculties, we promote aprobe system using smartphones. Since smartphones are usually equipped withGPS receivers, we can understand the position of the smartphones using the
GPS data. The technical challenge to realize the smartphone probe is to automatically understand the modes of transportation of the smartphone holders,
because they usually use multiple modes of transportation in their daily life.
This study proposes the novel method of classifying the modes of transportation of the smartphone users based on a generative model of the vibration during
drive. We have conducted an experiment to evaluate our method in an emerging
country. As a result, we achieved over 80% precision and over 50% recall, which
are accurate enough to realize the probe system using smartphones.
Discussion:• Incentive to let people to install the data collection application: payment
models are not sustainable, and privacy problem is an issue.
• Accuracy of the GPS data can be problem, even if it doesn’t show inthe test data. The required accuracy depends on the application; some
applications may also expect map-matching.

Innovative Methodologies for Large-scale Stochastic Dynamic Transportation Network Systems
S. Travis Waller, University of New South Wales, Australia
The history and current state of transport network modelling for planning
is discussed. In particular, major theoretical concepts and deployed applications are covered. Further, research contributions covering recent advances in7

stochastic adaptive routing algorithms and transport network equilibrium withrecourse are presented (which have potential future applications for ITS planning).
Discussion:• Thinking about dynamic equilibrium is interesting.
• Behavioral issues: Some people always look for the best way, other people
prefer a priori ﬁxed cost.

The Algorithmics View on Route Planning
Stefan Funke, University of Stuttgart, and Dennis Schieferdecker, KIT, Germany
We give an overview of recent speed-up techniques for the classical shortest
path problem that have been developed in the algorithmics community overthe last few years. These techniques allow, after a preprocessing phase, theanswering of shortest path queries several magnitudes faster than straightforward Dijkstra (including its tuned A∗ or bidirectional variants) and can also
be extended to more complicated settings such as time-dependent edge costs,
constrained shortest path etc.
Contraction Hierarchies are the basis of many advanced route planning techniques. This includes, among others, time-dependent shortest path computation, ﬁnding meaningful alternative routes to the shortest path and batchedshortest path computation. Time-dependent Contraction Hierarchies allow toanswer both, earliest-arrival queries and proﬁle queries, when link costs arevariable in time. Computation takes in the order of milliseconds and additional
memory overhead is tiny. Recent results even make it possible to use combinations of time-dependent and time-invariant costs.
Finding good alternative routes to a shortest path quickly was an open
problem until recently. Now, Contraction Hierarchies based methods identify
multiple alternative routes in less than a millisecond. These routes are modelled
as a concatenation of two shortest paths, i.e. the route is described by a source,
target and via node. To ensure the meaningfulness of the route, three criteria
have to be fulﬁlled: stretch, overlap and local optimality.
Batched shortest-path computation encompasses all problems in which anaive solution has to compute subproblems multiple times. By identifying thesetasks, computing them only once and storing this preprocessed data in a cacheeﬃcient manner, queries can be speed up by orders of magnitude. Contraction
Hierarchies are useful in this context as their small search spaces are directly
reﬂected in a reduced of data that has to be preprocessed and stored. Some of the
problems that fall into this category are many-to-many searches (i.e. distancematrices), point-of-interest searches and advanced ride-sharing methods.
Following, there is a small selection of literature with regards to advanced
route planning techniques. Note that we restricted the list to publications thatwere shown during the presentation:
Time-dependent Algorithms• Batz et al.: Time-dependent Contraction Hierarchies (ALENEX 2009)8• Batz et al.: Time-dependent Contraction Hierarchies and Approximation(SEA 2010)• Batz, Sanders: Time-dependent Route Planning with Generalized Objective Functions (ESA 2012)
Alternative Routes• Abraham et al.: Alternative Routes in Road Networks (SEA 2010)• Bader et al.: Alternative Route Graphs in Road Networks (TAPAS 2011)• Luxen, Schieferdecker: Candidate Sets for Alternative Routes in Road
Networks (SEA 2012)
Batched Shortest Path Algorithms• Geisberger et al.: Faster Detour Computation for Ride Sharing (ATMOS
2010)• Geisberger, Sanders: Engineering Time-Dependent Many-to-Many Shortest Paths Computation (ATMOS 2010)• Geisberger: Advanced Route Planning in Transportation Networks (PhD
Thesis 2011)
Discussion:• Dijkstra on large (nationwide) networks cannot be solved on today’s smartphones.
• For ride sharing systems can the driver be informed about the cost to picksomeone up at some location, and about the beneﬁt he/she can get bydoing so?
• Can POI (Points Of Interest) be integrated in the routing algorithm?
• Adding multiple preferences / criteria: theoretically NP hard problem,
but practically solvable in reasonable time.

Transportation in a Connected and Ubiquitous Sensing Environment: Challenges and Opportunities
Piyushimita (Vonu) Thakuriah, University of Illinois at Chicago, USA (currently
University of Glasgow, UK)
The presentation focused on challenges and opportunities for the transportation sector from Big Data and Open Data initiatives. Mobility intelligence may
be considerably enhanced by connecting transportation information horizontallyto environment, energy, health and other sectors by means of ongoing smart
city-type initiatives; however, connecting dynamic streams of information posesigniﬁcant challenges. Real-time transportation information can also be linked
vertically to data within the transportation sector, such as synthetic outputs

9from transportation planning and operational models, as well as administrative, survey and census data, therefore opening up opportunities for for a broad
range of near-term decision-making; these strategies are being attempted by the
emerging area of urban informatics.
An example of challenges that result with more information integration wasgiven by considering the case of linking real-time transportation (speed) information to real-time weather information and by highlighting the spatial andtemporal inaccuracies that can occur and resulting propagation of errors. Under
bad weather conditions, the percent improvement in speed forecasts obtainedby machine learning due to real-time weather information varies signiﬁcantly
across diﬀerent weather conditions, time periods or traﬃc congestion levels andlocations. Results from simulations also showed that the accuracy of the weather
forecasts can matter considerably in making speed forecasts, such that if weather
forecasts (particularly precipitation forecasts) are degraded to below a threshold, the resultant speed forecasts are worse oﬀ than not having the forecasted
weather information at all. The presentation drew implications for sensor fusion(where to measure, how to temporally integrate, which sensors to integrate at
one point and so on).
An example of opportunities that arise was given by linking information
gleaned by real-time bus arrival information eﬀects to a range of small-area
urban indicators (constructed from models and other data) that describe thesocial, economic, built environment, housing and economic characteristics ofcommunities within cities. Near-term decision-making capabilities enabled bythis type of vertical linkage of information–and subsequent spatial clustering—
include spatially-targeted market identiﬁcation strategies for Location-Based
Services and transit operations and management, among others capabilities.

GeoSensor Web Lab: Connect, share and use world-wide
sensors
Steve Liang, University of Calgary, Canada
In recent years, large-scale sensor arrays and the vast data sets they produce world-wide are being utilized, shared and published by a rising number of
researchers on an ever-increasing frequency. The increased amount of available
data is being driven by sensor motes which are monitoring changes in everythingfrom climate to water to transportation systems. With the rapidly increasing
number of large-scale sensor network deployments, the vision of a World-WideSensor Web (WSW) is becoming a reality. Similar to the World-Wide Web(WWW), which acts essentially as a “World-Wide Computer”, the Sensor Web
can be considered as a “World-Wide Sensor” or a “cyberinfrastructure” that instruments and monitors the physical world at temporal and spatial scales thatare currently impossible. This short talk presented the vision, the potential impact, and the state-of-the-art of the Sensor Web. It also presented TraﬃcPulse,a participatory mobile urban sensor web that harnesses voluntary use of smart
phones as a cost-eﬀective, ready-made and pervasive sensor web that allows us
to query, model, understand, and visualize the city’s mobility information in
real-time.

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Figure 2: The excursion to Kamakura.

4

Summary of Discussions

Besides the talks of participants, a series of sessions was reserved for group
discussions.

4.1

Session I

The ﬁrst section focused on topic collection and topic selection. We identiﬁedthese themes (Figure 3), in this order as being of most common interest:
1. Mechanism design: A way to address cooperative versus competitive transport?
2. Crowdsourcing
3. Benchmarking datasets and information infrastructure for computationaltransportation science
4. Non-real time decision support systems
5. Role of computational transportation science for diﬀerent stakeholders(allocated to concluding session)

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Figure 3: The collected topics of the ﬁrst session.

4.2

Session II

Two breakout groups were formed to search for proposals on Topic #1: Mechanism design. The following plenum shared and discussed the breakout groupresults.
Group 1 considered road pricing as one of the solutions: changing the pricedynamically according to the traﬃc situation. However, how to quantify thecongestion or other factors for pricing remains an important problem. Easier torealize would be to show people some options for their travel, including multiple
modes, and indicating the diﬀerent costs, emissions and other factors. Known
choices may help to balance traﬃc. It had been acknowledged that car drivers
might not care on emissions. The minimum solution would be a seamless multimodal transportation service.
Group 2 added to these thoughts. People behave habitually because of lackof information about transport options, and because of low reliability of suchinformation where available. Furthermore, the incentives to change behavior
may be insigniﬁcant (see drivers’ lack of care about the emissions). So, why not
giving up all behavioral modelling and simple take tolls for every road? Pricing
can be adapted over time to reach system optimum of traﬃc load.

4.3

Session III

Two breakout groups were formed to search for proposals on Topic #2: Crowdsourcing. The following plenum shared and discussed the breakout group results.
Group 1 reported three discussed topics: applications of crowdsourcing data,
incentivizing crowdsourcing, and types of crowdsourcing data, especially via
DSRC or Google data. Incentivizing was seen possible by mechanisms suchas (a) receiving in return aggregated data (information)—note that people dosubmit data to services if the services are good enough (e.g., Google Maps),

12(b) receiving virtual/real currency (such like airmiles), (c) earning reputation
in a social community, (d) transportation safety (transmitting data contributes
to your safety), (e) games and fun. Group 1 identiﬁed a need for research on
reliability of crowdsourced information.
Group 2 identiﬁed a number of research questions (but no single proposal).
How many users are needed for sampling particular phenomena? Are cyclists
a target group that is more willing to contribute crowdsourced data: more
likely tech-savvy, and suﬀering from lack of information? If, in a world of
ubiquitous sensing (Internet of Things), everything will be sensed anyway, isn’t
the true challenge getting access to this data and integrate this data rather thanmotivating the crowd for sensing? Since reliability is an issue, could people (thecrowd) be presented sensor readings for veriﬁcation, in the style of captcha?
Could crowdsourced sensing be integrated in participatory planning and crowd
engagement, to motivate people? If data mining and knowledge discovery isapplied on natural language sources (social media) then placenames need to beresolved that may not be known in databases, and that may not be known bypeople living elsewhere (no ‘captcha’ style veriﬁcation)—human intelligence stillrequired, but local knowledge is involved.

4.4

Session IV

This session was reserved for matchmaking between participants for discussing
ideas in areas of common interest.

4.5

Session V: Next steps

The concluding session was dedicated to identify tangible outcomes, by that way
addressing Topics #3 and #5 from above in passing. The session also reviewed
the objectives and expected results set at the beginning:• Discovering social computing in CTS;
• Growing a community in CTS;
• Seeking interaction and collaboration with scientists and practitioners
from ITS.
We observe a transformative force on transportation science by Big Data,
the Internet of Things, and social computing. The challenges are of computational nature, and thus, many disciplines push into transportation science suchas computing, artiﬁcial intelligence, economics, or geographic information science. One challenge for a growing CTS community is bridging between these
disciplines and transportation science, planning and engineering. Harvey Miller
and Vonu Thakuriah have founded a TRB subcommittee on Computational
Transportation and Society (reﬂecting in the name an emphasis on the applications of a CTS), which is an important step in this direction given the natureof the TRB. They call for broader support from the CTS community.
A Sixth International Workshop on Computational Transportation Science(2013) is on the horizon. We discussed the advantages of continuity (attaching it
again to ACM SIGSPATIAL) and of ﬂexibility (showing presence at other conferences, e.g., INFORMS). Depending on enthusiastic organizers there could be

13

Figure 4: The to-do list of the concluding session.
multiple such workshops. However, we also discussed how to attract people andpapers in an environment of competitive conferences and research performanceframeworks. Linking the calls to selected issues of journals should help.
There was, however, no support of an own journal or an own conference due
to lack of critical mass. Instead there was a sense that growing the community
is more a long-term goal that should be pursued with patience, supported by amultitude of small eﬀorts such as having workshops, promoting everywhere theterm ‘computational transportation science’ (keyword lists, CVs, etc.), selectedissues in related journals, or an edited book shaping the term. Travis Waller
oﬀered to organize a special issue in Computer-Aided Civil and Infrastructure
Engineering. A book proposal will be developed by Harvey Miller and Vonu
Thakuriah in January or February 2013. The book should be a solid review ofthe diﬀerent contributing areas to CTS, such that it can be used as a textbook
for a graduate seminar on CTS. It may contain some perspective papers as well.
The community could also consider to deﬁne it through a (long-term) grandchallenge or some (short-term) benchmarking or application challenges. In thiscontext Monika Sester and Dirk Mattfeld pointed to a new transportation research infrastructure that could be deployed to collect benchmarking datasets:
the Application Platform for Intelligent Mobility (AIM), http://www.dlr.de/fs/en/desktopdefault.aspx/tabid-6422/10597_read-23684/. With this meeting starting out to discuss the role of social computing within CTS, a follow-up
meeting in Dagstuhl, December 2013, could focus on such a benchmarking exercise, either data collection or data integration and knowledge discovery.

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