DSSs for Top-Down Planning and Management
This section presents two DSSs developed by the authors to support two stakeholder categories (representing public and private sectors) involved in a top-down approach to sustainable urban mobility planning and management. The typical top-down approach starts from the perspective of the decision maker that looks at the big picture of the whole urban mobility system, focusing on its most relevant problems in order to identify the details that truly matter. The identification of these details enables the definition of possible policies, plans, and infrastructure and technological solutions to optimize the mobility system and to promote the reduction of negative economic, environmental, and social impacts resulting from urban transport.
A Simulation-Based DSS for Electric Urban Mobility
The recent political debate on the design and implementation of effective measures to address important global issues, such as fossil fuel depletion, energy security, and global warming, is causing urban decision makers to ask for new forms of mobility.
Due to its main characteristics and the recent technological developments in the automotive sector, electric mobility has been advertised as the green and sustainable answer to the mobility of the future (Dg CONNECT 2012). It also represents a real example of the synergy between ICT and the transport and energy sectors, which is one of the main goals of the European Commission within its European Innovation Partnership on Smart Cities and Communities (DG CONNECT 2012). Furthermore, the governments of developed countries all over the world (e.g., the United States, France, China, Japan, and Germany) and the European Commission have set up ambitious targets for the introduction and diffusion of electric vehicles (EVs) on their roads to meet sustainable development goals by 2020 (Wirgesa et al. 2012).
However, the identification and assessment of the potential impact of electric mobility on the urban system is crucial, since its analysis needs to take into consideration a variety of factors that are linked together. The history of EVs, starting in the mid-nineteenth century, reveals that EVs have been considered a promising technology at repeated time intervals until today (EFTE 2009; Chan 2007; Sovacool and Hirsh 2008). According to Dijk et al. (2013), electric mobility is now at the center of a technological turning point of the automotive sector, benefiting also from various technological developments outside the automotive sector and within the social context of car mobility. Through a deep analysis based on a sociotechnical transition perspective, the authors found that the development of vehicle engine technology is not driven by single factors but is influenced by multiple changes of the whole sociotechnical urban mobility framework. The term sociotechnical takes into account not only technological and engineering factors, but also cultural, social, political, and economic aspects (Sovacool and Hirsh 2008).
We designed a tailored DSS in order to understand, encourage, and optimize the diffusion of electric mobility as a new sustainable mobility paradigm. The basic techniques of this DSS include an accurate estimation of the diffusion of EVs, using a modeling approach that takes into account all the enablers and inhibitors to the adoption of the new technology and a careful simulation to assess the response of two main elements of the system: the complex urban mobility system, in terms of real road-network maps and traffic data; and the energy distribution network, considering its real topology and the charging infrastructure. The DSS analyzes the problem through the integration of three tiers of modeling and simulation, implemented as separate modules (see Figure 14.1). The first tier analyzes socioeconomic data to estimate the diffusion of EVs in the target market over time. The output of this tier is used as input by the second tier, which consists of a microscopic traffic simulator to assess the likely effects of real mobility patterns on the state of charge of EVs. The third tier analyzes the impact of electric mobility on the energy distribution network through the charging infrastructure.