Mobility as a Service (MaaS) is the new approach in transportation systems that allows users to use different transport services as a single option, by using digital platforms and with integrated design. In MaaS many actors can be identified: MaaS operators, MaaS companies, MaaS users, citizens, system manager/planner. In order to be able to design the system in an integrated way, it is necessary to identify comprehensive methodologies that make it possible to reach sustainability targets in a context where the decisions to be taken are shared between several operators and affect users and citizens. In this paper, the methods to be adopted for the design of an integrated transport service system have been studied. The main aim of this paper concerns the specification of transport system models for estimating the effects of decision-makers’ actions on MaaS. The consolidated design methodologies of transport networks have been extended in the context of the MaaS. The paper reports a methodology that can be used and describes the main models to be used, which derive from consolidated specifications in the field of transport systems engineering. The methodologies have to be integrated into Intelligent and Communication Technology systems to build the Intelligent Transport System in the MaaS environment.

This paper presents a procedure for the solution of the Vehicle Routing Problem (VRP) based on reliable link travel times. They are obtained as a combination of spatially disaggregated and aggregated data about simulated traffic conditions on an urban road network. The disaggregated data concern the congested link travel times, which are traditionally used as variable to be minimized in VRP. The novelty of this research article consists in the introduction of spatially aggregated data, which are estimated by means of the Network Fundamental Diagram (NFD). In a within-day dynamic context, they are a measure of reliability of travel times because they could anticipate the latter ones’ variation in the short term. The equation of reliable link travel times is composed of a congestion term, expressing the traditional congested link travel times (or generalized costs), and a reliability term, which depends on the fundamental diagram of the link and the NFD of the homogeneous cluster of adjacent links. The proposed procedure has been validated and applied for two real test cases. NFDs data are used in the proposed link travel time function to calculate reliable travel times. The reliable link travel times are used for the solution of VRP to obtain optimal routes of freight vehicles.

This paper attempts to integrate data from models, traditional surveys and big data in a situation of limited information. The goal is to increase the capacity of transport planners to analyze, forecast, and plan passenger mobility. (Big) data are a precious source of information and substantial effort is necessary to filter, integrate, and convert big data into travel demand estimates. Moreover, data analytics approaches without demand models are limited because they allow: (a) the analysis of historical and/or real-time transport system configurations, and (b) the forecasting of transport system configurations in ordinary conditions. Without the support of travel demand models, the mere use of (big) data does not allow the forecasting of mobility patterns. The paper attempts to support traditional methods of transport systems engineering with new data sources from ICTs. By combining traditional data and floating car data (FCD), the proposed framework allows the estimation of travel demand models (e.g., trip generation and destination). The proposed method can be applied in a specific case of an area where FCD are available, and other sources of information are not available. The results of an application of the proposed framework in a sub-regional area (Calabria, southern Italy) are presented.

Urban mobility is evolving today towards the concept of Mobility as a Service (MaaS). MaaS allows passengers to use different transport services as a single option, by using a digital platform. Therefore, according to the MaaS concept, the mobility needs of passengers are the central element of the transport service. The objective of this paper is to build an updated state-of-the-art of the main disaggregated and aggregated variables connected to travel demand in presence of MaaS. According to the above objective, this paper deals with methods and case studies to analyze passengers’ behaviour in the presence of MaaS. The methods described rely on the Transportation System Models (TSMs), in particular with the travel demand modelling component. The travel demand may be estimated by means of disaggregated, or sample, surveys (e.g., individual choices) and of aggregate surveys (e.g., characteristics of the area, traffic flows). The surveys are generally supported by Information Communication System (ICT) tools, such as: smartphones; smartcards; Global Position Systems (GPS); points of interest. The analysis of case studies allows to aggregate the existing scientific literature according to some criteria: the choice dimension of users (e.g., mode, bundle and path, or a combination of them); the characteristics of the survey (e.g., revealed preferences or stated preferences); the presence of behavioural theoretical background and of calibrated choice model(s).