Big data without Big Brother: emerging issues in smart transport in Milton Keynes

Valdez Juarez, Alan-Miguel and Potter, Stephen (2014). Big data without Big Brother: emerging issues in smart transport in Milton Keynes. In: Digital Economy All-Hands Conference 2014, 3-5 Dec 2014, Imperial College, London.


MK:Smart is a £16m smart city initiative taking place in Milton Keynes between 2014 and 2016. The project involves the deployment of infrastructure for sensing and managing “big data” relevant to city systems, with experts from industry and academia developing applications in several domains (e.g., energy, water, transport). This paper has been written from the perspective of the transport work package, but most of the issues discussed here are relevant to other work packages (e.g., energy, water) and to other smart city projects.

To explore issues around big data systems for transport, a specific application is being developed. This is the “MotionMap”, a city-wide transport information service that will continuously sense and describe the real-time movements of people and vehicles across the city. Transport applications will make use of this data to enable smarter spontaneous transport decisions and to permit the development of new products and services such as cyclist way-finding or trip sharing by car users.

The MotionMap pilot is designed to provide a vehicle for learning that goes beyond technical development, contributing to an understanding of three inter related pillars: Technology, economy and society. In that spirit, this paper will not discuss the technical implementation of the MotionMap. Instead, it explores the sociotechnical considerations that should be taken into account when designing such a large scale urban sensing system. The project will be evaluated on the basis of its contributions to economic development of the city, and to positive transport-related behaviors. Smart living and smart economy targets will be pursued by linking reduced environmental impact and better access into inclusive processes of value creation, capture and distribution.

The sociotechnical focus of this position paper involves the exploration of smart city systems as enablers for active, participatory co-creation of value by citizens. This approach is motivated by eminently practical strategic concerns, but also by an emerging vision of smart cities from recent literature.

On the practical side, our approach acknowledges that MotionMap is a pilot running until the end of 2016. After that date, the endgame for the transport work package implies having a coherent system on the ground, with a maturing network of stakeholders who find it valuable and that have the resources and the inclination to carry it forward. The approach in this paper is expected to be conductive to the formation of such a network. Additionally, this approach is consistent with recommendations in recent thinking about smart cities which makes a distinction between “smart city systems” and “smart communities” [18]. The potential benefits offered by smart city systems can be realized only if the systems are embraced and nurtured by the community to the extent that they facilitate widespread changes in everyday practices. [13][19]

Current smart city practice, however, is not always consistent with this approach. Smart city management often follows what has been called the “triple helix” model of university-industry-government innovation [1] [8] [10]).

The triple helix is certainly an improvement over the predominant top-down approach to policy and to smart cities, which is based on a “command-and-control” logic which makes use of regulations to enforce top-down decisions by governmental actors [21][25]. However, early discussions that took place in the context of the project’s Citizen Engagement Workshops revealed that citizens do not entirely trust systems managed through “triple helix” collaborations. Inclusion of industry in smart city programmes is seen as partially positive, as it reduces the potential for “Big Brother” scenarios in which the bureaucratic apparatus becomes omniscient and all-controlling through the use of large urban sensing systems. However, there is a perceived risk of industry acting as a “Little Brother”, in the form of private-sector information collectors with little respect for individual privacy and with an interest in marketizing public services[7][11][13][22]. The triple helix model fails to address these misgivings as it gives citizens little say on how value is created, captured and distributed. The general feeling is that citizens bear most of the cost of smart technologies through sacrifices in convenience and privacy, while the gains are mostly captured by industrial and governmental actors [5]. Observations from the MK:Smart citizen workshops suggest that unequal distributions of the gains of smart city systems can produce a feeling of “being taken advantage of”, counterproductive to the development of a community. The link between smart city systems and smart communities requires bottom-up approaches with strong local ownership and buy-in [19]. Citizens and businesses are to be empowered to create public value themselves through city data [19]. This represents a key challenge for MK:Smart, and smart city developments in general.

For purposes of discussion with MK:Smart stakeholders we describe our approach towards smart cities as the “Wikipedia approach”, emphasizing the importance of openness, empowerment and active co-creation. There is a growing body of literature on the impact that Wikipedia has had on economic models and conceptions of empowerment and bottom-up value creation [3][20][23]. Familiarity with this body of literature is not necessary to make sense of the “Wikipedia” label, however. Academics, businesspeople and laypeople alike are familiar with the collaborative online encyclopedia, so “Wikipedia” can be used as shorthand for a model based on open co-creation of value. Citizens have predominantly positive reaction when we encapsulate the issue as the “Wikipedia vs. Big Brother approach towards smart cities” in the context of the Citizen Engagement Workshops. In the case of MotionMap, the “Wikipedia” model implies that, rather than providing a service, we will provide open transport data and a publicly available platform for citizens and businesses to develop their own applications on. This model also implies that citizens will take an active role in providing transport data to the system, as opposed to having it “harvested” from them through passive or opportunistic mechanisms[14][15].

By opening our data and giving citizens/ users an active role in the provision of data and the creation of services, MotionMap gains increased legitimacy. By giving users the power to contribute towards the processes of value creation and distribution, we expect that actual and perceived value of the system will be increased. However, this approach also introduces challenges of its own. Particularly, we need mechanisms to encourage the high level of citizen participation that is required. Given that the full benefits of a collaborative system cannot be achieved unless a certain critical mass is achieved, we need a niche-building strategy so we can escape the chicken and egg problem[12][16].

Inclusiveness is a major concern for our niche-building strategy. Innovation literature suggests that benefits of technical innovations are not distributed evenly [24][26]. For example, innovations like Electric Vehicles or smartphones are initially available predominantly to the wealthy and only later become available to mainstream markets and eventually to marginalized users. In the case of MotionMap, it is important to explore how it can provide value not only to already empowered car users, but also to public transport users, pedestrians and cyclists. We must have a well defined strategy for bringing the benefits to marginalized users rather than relying on unspecified trickle-down effects.

Having identified the challenges and the strategic approach to be pursued in the design of MotionMap, the next obvious step in this positioning exercise is to identify existing smart transport systems that, faced with similar challenges, have pursued a similar approach. The software developers in our team made a preliminary study of other location-aware smart transport applications, which included smart transport systems such as Google Maps, Apple Maps, City Mapper, Roadify, Journey Pro Connect, UK Transport Search, Axa Rescue, Parkopedia, Uber, and Strave. Due to restrictions of space, this discussion will concentrate on one of them: Waze. Waze is a commercial smart transport system with an estimated user base of 50 million users, which contribute data relative to their vehicular activity through the GPS system of their smartphones. In return, they benefit from the collective intelligence of other users so they can identify traffic jams and road hazards in real time and re-route around them [9][17]. Waze users do not pay for the service, but are monetized through location-sensitive advertising delivered through the app (It must be noted, however, that this same technology has been used by other organizations in support of business models that are not driven by marketing. The case of INRIX is particularly interesting as they use a very similar real-time sensing system to aggregate user-contributed traffic data, but they monetize through a very different model based on the provision of Data as a Service)[2][4][6].

In addition to the pragmatic benefit delivered by the Waze application, there is a “ludic benefit”, as Waze merges GPS data with game-like content to foster user engagement. This gaming layer was crucial in the early days of the organization, when they did not have enough access to enough user-contributed data as to be of practical value. Waze’s strategy for niche building has been defined as “Build->Play->Deliver Value->Achieve Critical Mass”[9]. This recognizes that early versions of the system must be designed as “play spaces” to allow experimentation and develop momentum[12].

In brief, the non-technical goals for the “MotionMap” urban sensing system, developed after a review of emerging issues in the literature and in MK:Smart citizen engagement workshops, are:

• MotionMap must have a measurable positive impact on the sustainable economic development of the city, partly by reducing time and productivity lost in traffic but mainly by creating new business opportunities in smart transport.

• Through the provision of user information, MotionMap should encourage positive changes in transport-related behaviour that increase the sustainability of the transport system while improving the quality of life of the users.

• Secure a sound revenue stream, so that services can remain available after 2016. The new sources of revenue can be public or private, but there must be a sound economic case for continued funding of MotionMap.

• While participation of government, academia and industry is central to the success of MotionMap, we must go beyond the triple helix in order to avoid “Big Brother” and “Little Brother” scenarios. We will pursue the “Wikipedia model”, meaning that we will not provide a centrally-planed service. Instead, we intend to link our urban sensing system to an open data hub.

• Active contribution of data by citizens will be we must identify models for the creation, capture and fair distribution of value which are compelling and inclusive.

In all smart city urban sensing systems there is a real danger of technical solutions migrating towards Big Brother centralization. It is possible to have Big Data without Big Brother, but a wiki approach is not one existing players find easy.


[1] Allwinkle, S. and Cruickshank, P. Creating Smart-er Cities: An Overview. Journal of Urban Technology, 18(2): 1-16

[2] Athow, D. A closer look at INRIX, the world's largest traffic intelligence network., 2014. Retrieved August 12, 2014:

[3] Barbrook, R., & Cameron, A. The Californian ideology. Science as Culture, 6(1), 44-72.

[4] Barker, A., Cahn, R. C., Chapman, C. H., Downs, O. B., & Stoppler, W. Detecting anomalous road traffic conditions. U.S. Patent No. 7,899,611. Washington, DC: U.S. Patent and Trademark Office, 2011 .

[5] Chan, E., Harmon, R. R., & Demirkan, H. Privacy and Value Co-creation for IT-Enabled Service Systems: Cui Bono?. In System Science (HICSS), (Hawaii, USA,2012), IEEE, 1573-1582.

[6] Chapman, C. H., & Downs, O. B. Assessing road traffic flow conditions using data obtained from mobile data sources. U.S. Patent No. 7,831,380. Washington, DC: U.S. Patent and Trademark Office, 2010.

[7] Cruickshanks, S., & Waterson, B. Will Privacy Concerns Associated with Future Transport Systems Restrict the Public's Freedom of Movement?. Procedia-Social and Behavioral Sciences, 48, 941-950.

[8] Deakin, M., & Leydesdorff, L. The triple helix model of smart cities: a neo-evolutionary perspective. in Creating Smart-er cities, Routledge, Abingdon, 2013, 53-64.

[9] Eisnor, D. Game Mechanics and Location Based Services: Crossing the LBS Chasm, Where 2.0 Conference, 2011. Retrieved Jul 14, 2014:

[10] Etzkowitz, H. The triple helix: university-industry-government innovation in action. Routledge, London, 2010.

[11] Glancy, D. J. Privacy on the Open Road. Ohio NUL Rev., 30, 295-376.

[12] Hoogma, R., Kemp, R., Schot, J., and Truffer, B. Experimenting for Sustainable Transport: The Approach of Strategic Niche Management. Routledge: London, 2002.

[13] Kitchin, R. The real-time city? Big data and smart urbanism. GeoJournal, 79(1), 1-14.

[14] Lane, N. D., Eisenman, S. B., Musolesi, M., Miluzzo, E., & Campbell, A. T. Urban sensing systems: opportunistic or participatory? In Proceedings of the 9th workshop on Mobile computing systems and applications (Napa, USA, 2008).ACM, 11-16.

[15] Lim, H. B., Fu, C., Nasir, A., Srirangarajan, S., Wang, B., Wong, K. J., & Soong, B. H. An integrated framework for vehicular and urban sensing. In GLOBECOM Workshops. (Honolulu, USA, 2009) IEEE, 1-6

[16] Moore, Geoffrey A. Crossing the Chasm: Marketing and Selling Disruptive Products to Mainstream Customers. New York: HarperBusiness Essentials, 2002

[17] Olson, P. Q&A With Waze CEO Noam Bardin,, 2013. Retrieved Jul 27, 2014:

[18] PAS 180: Smart City Terminology, BSI, 2014. Retrieved July 12,2014:

[19] PAS 181: Smart city framework-Guide to establishing strategies for smart cities and communities, BSI, 2014.Retrieved July 12,2014:

[20] Raymond, E. The cathedral and the bazaar. Knowledge, Technology & Policy, 12(3), 23-49.

[21] Ritzer, G. The McDonaldization of society 5. Pine Forge Press, Los Angeles, 2008

[22] Schulman, M. Little brother is watching you. Business and Society Review, 100(1), 65-69.

[23] Tapscott D, Williams A D. Wikinomics: How Mass Collaboration Changes Everything. Atlantic Books, London, 1999

[24] Velaga, N. R., Beecroft, M., Nelson, J. D., Corsar, D., & Edwards, P. Transport poverty meets the digital divide: accessibility and connectivity in rural communities. Journal of Transport Geography, 21, 102-112.

[25] Walker, G. H., Stanton, N. A., Salmon, P. M., & Jenkins, D. P. A review of sociotechnical systems theory: a classic concept for new command and control paradigms. Theoretical Issues in Ergonomics Science 9.6. 479-499.

[26] Warschauer, M. Technology and social inclusion: Rethinking the digital divide. MIT press, Cambridge, 2004.

Viewing alternatives

No digital document available to download for this item

Item Actions