INTRODUCTION
Cities and Regions nowadays face complex challenges to meet objectives regarding their socio-economic development. The main objective of municipalities, especially at times of shrinking financial and material resources, is to improve the quality of life of their citizens. In order to achieve higher levels of citizens’ satisfaction, local governments are called to coordinate different actors involved in innovation processes. Coordination will improve public services in terms of efficiency, effectiveness and sustainability.
In the last few years the concept of “smart cities” has been introduced through different experiences at international level. Among the characteristics a smart city (or territory) should have, Giffinger et al. (2007) identify smart mobility. Transportation can be therefore considered as a driver that policy makers should use in order to improve the attractiveness of a particular urban area by providing a better and ‘smarter’ service.
The following links provide the basic idea of what a smart city is and how to move toward it.
Nowadays ICT provides decision makers with a huge set of data coming from non - traditional sources such as 2.0 platforms (the so called ‘big data’). How can they use them?
Without a proper frame this huge amount of data can be misleading. Therefore, a need of using new planning tools able at processing these information emerges.
" Our project aims at improving the ability to plan and manage new transportation systems for increasing the ‘smartness’ of a particular territory, which is the ‘Region Metropolitana Norte’ (RMN), periphery of the City of Buenos Aires. "
A smart territory, is an attractive area for people to live in and for business to invest in. For a peripheral area, increasing its smartness through reforming its transportation system, not only means improving people’s ability to move easier to and from the city center, but, more importantly, it means a ‘change in centrality’. Changing centrality to a particular area (e.g. RMN) will result in a less dependence from the city center. People will not need to go to the City of Buenos Aires for their businesses because they will be able to work, shop, and do leisure activities where they live. We can measure ‘smartness’ as the quality of living. This not only depends on economic variables, but also on social and environmental ones (Litman 2012). That is the reason why we built a set of economic, social and environmental indicators that may help decision makers in assessing better and equilibrated policies.
COMPLEX SYSTEMS
Urban Transportation systems operate in complex environments due to the interconnected influence of several factors, such as technical, social, economical and environmental ones. If we take an action to improve them in a certain direction, we may experience some unexpected ‘side effect’. These counterintuitive behaviors make particularly difficult for policy maker to act coherently with their aims.
This complexity is enhanced by the presence of different stakeholders (Federal Government, Province, Municipalities) having different aims and values in respect to the territory object of analysis.
In order to tackle complexity, together with addressing better smartness, some international experiences (Portland, Shangai, etc.) used System Dynamics modelling (SD). System Dynamics could be defined as “a perspective and set of conceptual tools that enable us to understand the structure and dynamics of complex systems” (Sterman 2000).
In 2010 the city of Portland, in Oregon, developed a System Dynamics model for the whole city. The model was aimed at better understanding the city system in order to design policies able to achieve fixed targets. The models showed its usefulness not only by helping at thinking with a long term perspective, but also by giving policy makers the possibility to negotiate shared understanding and meaning about the arising problems.
In our project we developed a System Dynamics model for the RMN (Province of Buenos Aires). The aims of our model concern three interconnected areas. Firstly, the model allows one to explore different solutions that can be applied in order to improve transportation system; secondly, it may help in ranking investment priorities in case of limited funds; lastly, by acknowledging actors participating to transportation service governance about the outcomes of possible decisions, the model may help in generating consensus among the different governance levels by aligning their aims.