Beyond sharing: cultivating cooperative transportation systems through geographic information science
Introduction
Transportation systems are facing unprecedented challenges in the 21st century. In some parts of the world, such as the United States and Europe, increasing travel demand combined with aging infrastructure, flat or declining investment, and physical, environmental and social constraints on infrastructure expansion are leading to saturated and strained transportation systems (TRB Executive Committee, 2009). Other parts of the world, such as China and India, are experiencing rapid rates of urbanization and especially motorization as new wealth creates increasing demand for personal mobility (Purcher et al., 2007). In addition to these rising pressures, our current methods for generating mobility are inequitable (particularly for excluded populations such as the poor and aged), lead to public health problems, are not necessarily conducive to well-functioning communities and impose substantial environmental costs at scales ranging from toxic airsheds to global climate change (Black, 2010, Morency et al., 2011, Roorda et al., 2010, Steg and Gifford, 2005).
These challenges cannot be addressed using the technologies and processes that created the current problematic systems. We must extract not only more capacity but also new capabilities from our transportation infrastructure. By itself, wresting greater efficiency from transportation systems is not sufficient. In fact, making a system more efficient can induce more resource consumption due to demand elasticity; an effect often referred as Jevons’ Paradox after its 19th century discoverer, English economist William Stanley Jevons. Consequently, efficiency increases must often be accompanied by constraints such as quotas and rationing (Alcott, 2005). Similarly, Braess’ Paradox in transportation science shows that adding capacity to a congested network can reduce overall performance if travelers attempt to minimize their individual travel times and ignore the effects of their decisions on others (Braess, 1968, Murchland, 1970; Pas and Principio, 1997). Experience with the classic “predict and provide” paradigm in transportation planning illustrates these effects: it is difficult to solve transportation problems only by building infrastructure (Banister and Button, 1992, Noland and Lem, 2002, Owens, 1995).
There are opportunities for extracting new capabilities at the convergence of transportation and information technologies. Sensor technologies, embedded in vehicles and infrastructure, carried by travelers or remotely positioned, and connected via wireless communication, are generating massive amounts of fine-grained data about transportation systems and their dynamics. Geographic Information Science (GISci) and Geographic Information Systems (GIS) provide theory and methods for managing, exploring, analyzing and sharing georeferenced spatio-temporal transportation data and information. Social media can allow more meaningful interactions among travelers, managers and stakeholders. The potential for a data-driven revolution in planning, management and use of transportation systems is inspiring some to call for a new interdisciplinary field, computational transportation science, encompassing transportation, computer and geographic information sciences (Winter et al., 2011). In addition, private sector companies such as IBM envision a smarter planet through by making systems more instrumented, intelligent and interconnected (www.ibm.com/smarterplanet).
Big Data has arrived in transportation and will get much bigger soon. To what end should we direct these streams of data and computations in order to resolve or mitigate the challenges facing transportation systems in the 21st century? Similar to the Jevons and Braess paradoxes, flooding transportation systems with data and information may be counterproductive: these are types of additional capacity that can have a negative effect on system performance if travelers use that information to make selfish choices (Unnikrishnan and Waller, 2009).
Transportation is a classic social dilemma where individually rational behavior (being mobile) leads to collectively irrational outcomes such as congestion, accidents, environmental degradation, social exclusion and loss of community. One method for resolving collective action dilemmas is through cooperation (Kollock, 1998). Cooperation means compromising to work together since some may be worse-off (lower benefits, higher costs or both), at least in the short term. Cooperation does not necessarily require altruism: pro-social behavior can be altruistically motivated (intended to benefit other people based on empathy and understanding) or egoistically motivated (helping someone to ultimately benefit oneself; Avineri, 2009, Bierhoff, 2001). Regardless of the underlying motivations, the results are transportation systems with better collective outcomes (Levinson, 2005).
There is a growing body of theoretical and empirical evidence regarding cooperation and pro-social behaviors, much of it from the perspective of evolutionary and experimental game theory. Although classic results from game theory regarding cooperation are well-recognized, there are recent breakthroughs in the biological and social sciences regarding cooperative behavior. It is more common and powerful in both the biological and social worlds than previously recognized (De Waal, 2009, Rifkin, 2009). Time, space and networks play a central role in facilitating cooperation and collaboration (Nowak, 2006, Nowak and Highfield, 2011).
This paper discusses the role of sensed transportation, geographic information science and social media to cultivate cooperative transportation systems. Cooperative transportation requires some individuals to make sacrifices for the greater good. This can be as easy as sharing information and resources. But it also includes more difficult behaviors such as collaboration or collective action that require shared credit and responsibility (Shirky, 2008). Cooperative transportation systems are integrated technological and human systems where participants share information and resources, and collaborate on solving accessibility (mobility and communication) problems at all scales from local and operational (“How do we get to work today?”) to regional and strategic (“What do we want our transportation systems and communities to look like in twenty years?”).
Nascent examples of cooperative transportation systems have been around for a long time and have been growing in recent years. Proto-examples include spontaneous carpooling to take advantage of high occupancy vehicle lanes in urban areas such as Washington, DC (Mote and Whitestone, 2011). First generation examples include crowdsourced navigation through social media such as Waze (www.waze.com), community car sharing programs such as Zipcar (www.zipcar.com) and bike sharing programs such as those available in cities such as Washington, DC, Paris and Melbourne, Australia. These programs involve third party ownership of vehicles by private sector companies, often under contract with local governments. A more recent development is the rise of direct peer-to-peer vehicle and ride sharing systems such as Getaround (www.getaround.com) and Avego (www.avego.com). These developments are part of a larger trend sometimes referred to as collaborative consumption or the sharing economy. The idea is to convert goods into services through an economy built on access rather than ownership of vehicles, tools, homes, workspaces, garden plots and other forms of capital (Garthwaite, 2012, Shirky, 2008). While these trends are promising, they are only the beginning. A mature cooperative transportation system would extend capabilities for sharing, cooperation and collaboration across a wider range of spatial and temporal scales (from real-time route choice to long-term decisions such as home and business locations and automobile ownership), across multiple transportation modes (facilitating seamless integration of mobility services) and integrate transportation and non-transportation solutions to accessibility problems (such as substituting virtual access for physical access and activity coordination that minimizes aggregate mobility).
Facilitating greater cooperation and collaboration in visioning and strategic planning can also help reduce the contentious atmosphere that has become seemingly endemic to transportation planning. The potential for conflict has increased with wider recognition of the complexity of transportation problems and the need to address difficult issues such as social justice and sustainability (Weiner, 2008). Consequently, there has been a call for a more inclusive planning process that goes beyond the community only providing input on strategic goals and commenting on predetermined alternatives (Wilson et al., 2003). This has encouraged the development of technologies such as collaborative decision support systems (Jankowski et al., 1997) and community-based planning efforts such as Envision Utah that facilitate engagement and dialogue among a wide range of community citizens and stakeholders (www.envisionutah.org). A challenge is to leverage the coming torrent of data from transportation systems and cities – much of it fine-grained and near real-time – to extend collaborative transportation decision-making across a wider range of scales and contexts.
Geographic information science and technologies have a key role to play in facilitating cooperative transportation. First, and perhaps most superficially (but nonetheless important) is that transportation systems are geographic, and the streams of data that emerge will be referenced in geo-space and time. How do we leverage these data to generate the information and knowledge necessary to facilitate cooperative behavior? This requires integrating and expanding functionalities we currently see in digital earth, location-based services (LBS), social media and collaborative spatial decision support systems, and connecting this environment to the torrent of data that will be flowing from transportation systems. Given the volume of data expected, the geospatial infrastructure and management issues are formidable, as are needs for effective and scalable geographic knowledge discovery, spatial analysis and geovisualization techniques.
Beyond helping to resolve the technological and infrastructural challenges faced by processing massive spatio-temporal data from sensed transportation systems, geographic information science can contribute to the social processes that facilitate cooperative transportation systems. There are fundamental questions surrounding the nature of cooperative behavior – how to encourage cooperative behavior, how many people need to cooperative to achieve desirable outcomes, and what objectives are best served by cooperative behavior versus competition. Recent scientific insights on the role of time, space and networks in cooperative behavior suggest properties that can be leveraged through GIScience and tools for making sense of spatio-temporal data and supporting collaborative spatial decision-making.
How do we leverage Big Data to facilitate more cooperative transportation systems? More than two decades ago, computer scientist David Gerlernter presciently anticipated the ability to converge massive real-time data streams and information technologies to develop virtual environments for managing complex real-world systems. Mirror Worlds are virtual environments that parallel the real world to facilitate its understanding and engagement through tools for interpreting massive data streams and shared decision-making at varying temporal and spatial scales (Gelernter, 1991).
The next section of this paper provides background on the unprecedented challenges and opportunities facing transportation systems in the 21st century. Section 3 addresses the issue of whether transportation systems can be cooperative and provides a sketch of a cooperative transportation system. Section 4 asks a deeper question: will humans cooperate if given the appropriate information and tools? Empirical and theoretical evidence suggests that cooperative behavior is natural and facilitated by time, space, networks and groups. Section 5 considers the question of avoiding bad collective outcomes in transportation. There are bottom-up, self-organized methods for managing common resources; these can help with cooperative behavior over longer time frames and broader spatial scales. This section also identifies some fundamental challenges. Section 6 describes a mirror world – the decision environment for leveraging sensed environments and real-time data streams for understanding and engagement in the real world. Section 7 concludes the paper by outlining a GIScience-centric research agenda.
Section snippets
Challenges
To date, transportation systems have been very effective at a key transportation objective: generating mobility. People travel and interact with others at levels that would have seemed magical to our ancestors (Bannister and Stead, 2004, Bertolini and Dijst, 2003; Hanson, 2004, Nelson and Niles, 2000; Schafer and Victor, 1997). However, trends are not encouraging for maintaining this level of mobility into 21st century. Pressures on transportation systems are intensifying as urbanization and
Can transportation be cooperative?
Transportation is often competitive: people compete with other travelers for space, position and velocity to minimize effort and time required to reach a destination (subject to other considerations, such as safety, comfort and reliability). Creating more efficient transportation through smarter systems may be a false victory if we simply create more competitive transportation systems, as suggested by the Jevons and Braess paradoxes.
This tension between competition and cooperative mobility is
Will people cooperate?
How we use the technologies and data described above to encourage cooperative behavior in the nominally competitive environment of transportation. There is a body of theory and evidence in the social sciences and evolutionary biology that suggests: (i) cooperative behavior in biology and society is not as uncommon as previously thought; (ii) there are simple but fundamental conditions that will facilitate cooperative behavior. These conditions related to the capability to form neighborhoods and
Avoiding a transportation tragedy of the commons
As Couclelis (2000) argues, sustainable accessibility is a necessary precondition for sustainable transportation. ICTs facilitate complex travel patterns and space–time activity fragmentation, but they do not offer (as yet) a competitive alternative to mobility for many types of activity participation. Couclelis (2000) uses the PD as a framework to show how ICTs can exacerbate collective action problems in transportation by encouraging defection (mobility) rather than cooperative behavior
Mirror worlds
Real-world systems such as hospitals and cities are enormously complex and difficult to understand given traditional ways of collecting and sharing information and making decisions. Two decades ago in his book Mirror Worlds, computer scientist David Gerlernter described a virtual environment that presciently anticipated the ability to fuse massive real-time data streams and information technologies into a common environment to facilitate understanding and engagement in complex real-world
Conclusion: a research agenda
Transportation systems are facing unprecedented challenges in the 21st century. These challenges cannot be addressed using the technologies and processes that created the current problematic systems. Rather than simply increasing the efficiency of these systems, we must extract new capabilities from our transportation infrastructure to avoid collective failures such as gridlocked cities, exclusion, bad health, environmental degradation and loss of community. This paper discusses the role of
Acknowledgements
This paper derives from a set of invited lectures at several venues, including the Fleming Lecture in Transportation Geography, Association of American Geographers annual meeting, Washington, DC (17 April 2011); STGIS 2011 – International Conference on Spatial Thinking and Geographic Information Sciences, Tokyo, Japan (16 September 2011), and; Frontiers in Transportation International Workshop, Niagara-on-the-Lake, Ontario, Canada (20 October 2011). Thank you to the participants at these
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