# Smart City for Sustainable Development: Applied Processes from SUMP to MaaS at European Level
## 4 The European Smart City Applied Process at the Sectorial Level
### Transport System Models (TSM) and Environmental Impact Functions  
As synthetically presented in Section 4.1, emerging ICTs enrich the modelling capabilities of TSM. They expand information and data about transport systems and their sub-components. This has theoretical implications on TSM, in particular for modelling transport supply and demand. In relation to transport supply modelling, it is necessary to evolve the classical approaches that represent the system from a static condition (i.e., without explicitly considering the temporal evolution of performances) to a dynamic approach by considering the temporal lags (day-to-day t and within day \(\tau\)) introduced by [PERSON] [13]. The MaaS paradigm imposes the adoption of a schedule-based approach to represent the temporal connections inside the same transport mode or modes supplied by different operators (e.g., urban and extra-urban buses), as well as to forecast travel times. In relation to demand modelling, information and data driving from emerging ICT make the evolutions of classical approaches towards dynamic processes possible. The dynamic approach is needed for considering that ICT expand the user's knowledge on the whole transport network in the previous days before the day t at the time \(\tau\). This implies the specification of two models:  
* A utility update model for considering the different users' perceptions of travel options and the relative utilities, in relation to current and previous knowledge and the information derived from ICT;
* A choice update model for considering the influence of information on the travel choice mechanism in a generic instant (\(\tau\)) of the generic day (t). In the following, according to the general formulations reported in [13], there is the specification of updated utilities, with the availability of big data (BD) and Internet of Things (IoT) technologies. The utilities of the generic path k at the generic day t and instant \(\tau\), \(\
u_{\text{k}}[\text{t},\tau]\), can be expressed with the following formulation:  
\[\
u_{\text{k}}[\text{t},\tau]=\varv(\
u_{\text{k}}^{\text{BD}}[\text{t}-1, \tau],g_{\text{k}}^{\text{IoT}}[\text{t},\tau-1]) \tag{1}\]  
where \(\
u_{\text{k}}^{\text{BD}}[\text{t}-1,\tau]\) is the travel utility at the day t \(-\) 1, calculated and stored with the support of BD; \(g_{\text{k}}^{\text{IoT}}[\text{t},\tau-1]\) is k path cost experienced at the time \(\tau-1\) of day t, supported by IoT. The formulations referred to apply, recursively, to all days with similar characteristics, such as all working days. In this way, the generic day (t) can be any working day of any week. TSM provide simulations of the transport system in relation to current and planned configurations defined for achieving sustainability goals [47]. Simulations produce data and information for the ex-ante evaluation of internal effects produced by decisions in the transport system. However, the verification of sustainability goals requires researcher to obtain an estimation of the effects produced in the external environment (externalities). For supporting quantitative ex ante evaluations, the outputs produced by TSM are used in specific impact functions, or EIF. EIF relate transport flows with the specific externalities (e.g., road accidents, environmental emissions,... ), by using physical and functional parameters. A specification of EIF regards each link of the transport network, and it can assume the following form:  
\[\mathbf{e^{*}}=e(\mathbf{f},\mathbf{\lambda})=e(\mathbf{\Delta\times h},\mathbf{ \lambda}) \tag{2}\]  
where:  
\(\mathbf{e^{*}}\) is the vector of the impact values;  
\(e\) is the vector of the impact functions;  
\(\mathbf{f}\) is the vector of link flows;  
\(\mathbf{h}\) is the vector of path flows;  
\(\mathbf{\Delta}\) is the link-path incidence matrix;  
\(\mathbf{\lambda}\) is the set of physical and functional parameters. In the literature, there are works reporting the calibration of the \(\mathbf{\lambda}\) parameters ([48]).