Data Ecosystems, Transport, and Urban Transformation in Sao Paulo

-Notes on the ESRC urban modelling workshop

From 20 to 24 June 2016, I had the opportunity to participate in the urban modelling workshop organised by the ESRC Strategic Network: Data and Cities as Complex Adaptive Systems (DACAS). The workshop was held in the ICTP-South American Institute for Fundamental Research in the municipality of Sao Paulo, Brazil. The event brought together researchers across multi-disciplinary fields, all interested on how Data and Complex Adaptive Systems can be applied to describe and understand the underlying emergent behaviours in cities, and ultimately, plan for smarter cities: sustainable and resilient.

Data and urban challenges

On the opening day, Tomás Wissenbach from the Sao Paulo’s urban development agency talked about the challenges of urban transformation in Brazil and explained the recent efforts of Sao Paulo’s administration to collect all available datasets across the different governmental authorities regarding Sao Paulo’s population and infrastructure. This data fusion and processing endeavour culminated in an online interactive application (Figure 1) which anyone can access and download the datasets. In the second phase of the project, Wissenbach announced the possibility to collaborate in projects that can capture the urban transformation experienced in the city, and that can help the government to make informed decisions to plan for a better city.

Figure 1. Screenshot of interactive Map of Sao Paulo. Blue for the metro lines, red for the bicycle lanes, and in orange tones the population density
Figure 1. Screenshot of interactive Map of Sao Paulo. Blue for the metro lines, red for the bicycle lanes, and in orange tones the population density

Prof. Ana Bazzan, from the Institute of Informatics at Universidade Federal do Rio Grande do Sul (UFRGS), presented in her keynote presentations on her work in agents and multi-agent systems in traffic and transportation. (video here, slides here) The talk started with the rise of the cities, and the inherent transportation challenges within. Prof. Bazzan introduced then the idea of a data ecosystem triggered by people’s participatory sensing as the key to develop analytical applications to improve the transportation system. In a smart city, citizens interact directly with the system instead of just being passively receiving information. This change in the paradigm requires a human/agent- approach for the information, modelling and control challenges in which humans act as both targets and active subjects (i.e. sensors).

Putting all together: Data and Complex Adaptive Systems for Transportation Planning

My presentation on our research project Engaging Big Data supplemented the prior presentations quite nicely. This ongoing project conducted at the Future Cities Laboratory of the Singapore ETH Centre seeks to build up an agent-based simulation framework for transport planning using MATSim that can benefit from both urban mobility sensors (e.g. mobile phone and smart card data) and traditional data inputs (e.g. household travel survey and census information) (Figure 2). In the era of ubiquitous sensing and big data, the first challenge for developing the next generation of predictive, large-scale transport simulation models relies on designing a data mining pipeline that can fuse the knowledge from these different datasets in order to have an enriched and full explanation of the urban mobility dynamics. The second challenge aims in using this information to automate the parameters of a MATSim scenario, which would not only allow to significantly lower the efforts required for setting up simulation scenarios but would also lead to even more realistic results. This will ultimately serve as a platform to test the viability of policy and infrastructure decisions before they are implemented, and guide and inform the urban and transport planning process.

Figure 2. Big Data-driven MATSim
Figure 2. Big Data-driven MATSim

Witnessing Sao Paulo’s Mobility transformation

Besides the workshop, I took the opportunity to experience some of the results of the city of Sao Paulo’s recent pushes to improve the adoption of sustainable  transportation policies. Those initiatives primarily target the notorious traffic congestion the 21 million inhabitants of the metropolitan areas are suffering from. With the introduction of the ‘Bilhete Único’ in 2004, a smart card automatic fare collection system for the public transport, citizens are being incentivise to opt for public transport through standard fares regardless of distance or number of connections. The data on mobility patterns that this system generates every day would also be an ideal source for setting up Big Data driven urban transport simulation. In addition, Sao Paulo’s municipality has recently done major investments on bicycle infrastructure throughout the main avenues of the city, including the symbolic, Avenida Paulista. (Figure 3)

– although my colleagues at FCL who study how street design can support active mobility think that there is potential to make cyclists feel more comfortable and safe on this major arteria ;-).

Figure 3. Bicycle lane in Avenida Paulista
Figure 3. Bicycle lane in Avenida Paulista

Author: Cuauhtemoc Anda

PhD researcher at the ETH Future Cities Lab. Interested in data mining, probabilistic machine learning, urban computing, and transportation planning.

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