Challenges and Results in City-Scale Sensing

Lisa Amini, Eric Bouillet, Francesco Calabrese, Luca Gasparini, Olivier Verscheure Smarter Cities Technology Centre

IBM Research Dublin, Ireland

Abstract— The dramatically increasing rate of urbanization is driving demand for more intelligent urban and environmental systems. City leaders and service providers are looking to base decisions on data, and to so, are deploying sensors extensively. However, even the most advanced cities are having difficulties assimilating and analyzing data collected from these sensors. In this talk, I will describe research we are conducting into creating analytics, optimizations, and systems for sustainable energy, constrained resources (e.g., municipal water management), transportation, and the underlying city fabric that assimilates and shares data and models for these domains.

I. INTRODUCTION According to United Nations reports [1], 70 million people (roughly 10 times New York City’s population) are moving into cities annually, and in the next decade Asian cities can expect to add 100,000 people per day. This dramatic demographic shift is causing unprecedented strain on urban infrastructures. City leaders are deploying vast numbers of sensors to gather insights needed to plan, monitor, manage and scale services. Transportation is an excellent example where GPS, loop detectors, axel counters, parking occupancy, CCTV, integrated public transport card readers, and a variety of other sensors are providing a wealth of data. Similar transformations are happening in water and energy contexts.

In this talk, I will describe the research we are conducting

in IBM Research’s new Smarter Cities lab in Dublin. I will discuss how city requirements map to research challenges in machine learning, optimization, control, visualization, and semantic analysis, how we are tackling the challenges, and some of our results.

II. MOTIVATION Recent studies [2] have shown that although typical cities

have deployed sensors capable of providing them near real-time data, only the most advanced are beginning to reach the stage of actionable insights and intelligent control from those investments. The most highlighted challenges are in data integration and analytics, especially across modalities.

III. RESULTS In IBM Research’s Smarter Cities Technology Centre, we are collaborating with universities, city officials, service providers, and other labs to develop methodologies, analytics, and systems to address challenges in water, energy, and transportation. For example, we are creating a system and analytics [3] to continuously ingest high volumes of observation data coming from a variety of road sensor and automated vehicle location tracking systems.

Fig. 1: Road network for Dublin, Ireland indicating most recently received vehicle GPS signals.

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Figure 1 illustrates a road network of over 700K segments and 5K GPS-equipped vehicles being used as a test-bed. Figure 2 illustrates how the system extracts the spatiotemporally-detailed view of road traffic conditions (Figure 3) in near real-time and exercises models built on historical data to predict future conditions across all of the road segments, stops, and vehicles several minutes to hours in advance. Research challenges include how to extract accurate information when the data is noisy and sparse; how to build good predictive models with insufficient historical data; and how to leverage information gained from uncertain data for reliable decisions and robust control.

As another example, we are analyzing digital traces from pervasive technologies to characterize how people move, interact, and utilize services in urban environments. In collaboration with MIT and AT&T [4], we analyzed an anonymized database of over 3 billion mobile phone calls to identify and understand communities.

Figure 4 illustrates that, while communities often follow state lines, some states merge or split up, and cities have evolved beyond political boundaries. This approach belongs to an emerging field that has been recently termed “computational social sciences” — i.e., the ability to address social-science research questions such as defining regions in space using huge datasets that have emerged over the past decades as a result of digitalization.

In addition to research challenges in analyzing digital traces from pervasive technologies, we are investigating how to represent these findings as models that can be used for optimized planning and control, and how to use these models for robust optimization and resilient control These challenges are especially difficult due to the inherent uncertainties of urban, environmental, and social models.

Fig. 4: Identification of communities based on mobile phone data, resulting from collaborative study between MIT Sensable Cities lab, AT&T, and IBM Research-Ireland.

Fig. 2: Application flow for analyzing transportation data from Figure 1 into the information in Figure 3.

Fig. 3: System assimilates data into citywide view continuously computing state for all vehicles, stops, and segments.

IV. CONCLUDING REMARKS In working with cities and their data, we have learned that

sensor technologies are creating a broad diversity of data that is continuously generated and contains highly valuable information for managing services and resources. However, there is a significant gap between the vision of what can be achieved with such data and the technology and understanding available today to achieve such visions.

The research challenges in this space are enormous. In addition to those discussed above, significant work is required to enable ongoing characterization of how people interact with one another, their environment, and urban infrastructures; algorithms that can assimilate multiple modalities of data for analytic results at the appropriate time scale; urban scale content management and learning systems that can proactively explore and manage potential data sources; and robust

optimization and control systems that take into account the inherent uncertainty in social and environmental systems.

REFERENCES

[1] United Nations. “World Urbanization Prospects: The 2009 Revision. http://www.un.org/esa/population/unpop.htm

[2] J Houghton, J Reiners, C Lim, “Intelligent Transportation: How Cities can Improve Mobility”, IBM Institute of Business Value, June 2009.

[3] L Gasparini, E Bouillet, F Calabrese, O Verscheure, B O\'Brien, M O\'Donnell, “System and Analytics for Continuously Assessing Transport Systems from Sparse and Noisy Observations: Case Study in Dublin.” IEEE International Conference on Intelligent Transportation Systems, 2011.

[4] “The Connected States of America.” http://senseable.mit.edu/csa/