The modelling complexity of freight transport consists of several factors that influence the travel choices, the needs of the many companies involved, and the different points of view regarding the definition of the model (from the point of view of the goods or the vehicle, and therefore of the hauliers). Additionally, freight transport consists of several phases (e.g., loading and unloading phases, break and rest periods, and travel times), which are hard to clearly define, especially due to the presence of rest regulations and the different typologies of transport carried out (for instance, accompanied or not accompanied). To deal with such issues, the implementation of valid simulation models requires an extensive and detailed validation phase supported by the capabilities of Big Data, such as Floating Car Data (FCD), which have become exponentially important and largely utilised in recent years. FCD are signals generated by On Board Units (OBUs) usually built on vehicles with a mass greater than 3.5 tons, or on private or commercial vehicles in exchange for insurance price discounts. The OBU sends the recording to a data-processing centre, via a mobile network, at a fixed sampling rate, or variable based on speed, or on other variables that characterise the motion, such as acceleration, steering curvature, and the state of the vehicle’s ignition as either on or off. The potentiality of FCD increases together with the use of OBUs, with implications not only for user information, security, and functionality, but also for planning. According to [
1], the use of a geo-localised signal can be integrated into traditional approaches for the construction of transport models; in fact, FCD are commonly used to observe historical mobility patterns and, when integrated with traditional approaches, it allows to simplify, and make more effective, the construction and calibration of a transport model. Although the literature provides numerous studies mainly focusing on the analysis of geo-localised signals generated by vehicles in the field of private and public transport [
2,
3,
4], there are only a few studies that address the possible applications of these signals for performance evaluations and the monitoring of freight transport. The structure of the FCD allows for investigations in relation to time and space, the single displacement of each vehicle, which, with the low cost of this type of information, are its strength [
5]. Numerous studies have been carried out for light vehicles: the zoning phase and the selection of the infrastructures of the graph [
1], and the monitoring of both runoffs along infrastructures presenting a greater sensitivity to traffic anomalies for accidental events and for the estimation of travel times, which can be defined deterministically or stochastically, based on the history of the speeds held by the probe vehicles [
6]. Recently, due to sustainability and cost challenges, many studies are emerging to evaluate, model, and simulate freight interventions. For example, [
7] aims to optimise delivery routes in urban areas by exploiting FCD to determine the travel times on the network at different times of the day and, in addition, to use them to identify the optimal route according to heuristic procedures. Other applications rely on the signal produced by on-board detectors to assess the performance of the tracking system for freight transport units moving along intermodal transport chains, specifically from the access port to the egress port. According to [
8], it has been found necessary to monitor the cargo units during the navigation and to evaluate the logistics performance offered, recording the boarding time from the port operators and a wireless infrastructure for data exchange and transmission, in order to gain an insight into intermodal chains. In fact, the tracking system is particularly efficient on the road side, while, on the sea-side, the signals are of poor quality or totally absent because they are shielded by the thick walls of the vessels. However, the processing of FCD concerning heavy vehicles is a very complex operation. In fact, few studies refer to the definition of freight vehicle trips. As mentioned at the beginning of this paragraph, the difficulty of data management lies in the combination and distinction of the different phases of the trip and in the multiple points of view that might be adopted. Then, the poor use of FCD may be attributed to either the difficulty of arranging, manipulating, and managing such information due to the conceptual or operational issues, or the lack of interest of the various operators in sharing data and information. The definition of trips represents the main challenge, since the subdivision of the sequence of GPS points in a sequence of trips requires specific criteria [
9]. No single methodology can be found in the literature, as this is strongly related to the amount and type of information in the database. [
10] detects the areas in which trucks stop considering the concentration of records, and link these with the surveys provided by the drivers. Otherwise, it is possible to observe the use of purely temporal criteria [
11] without foundation [
12], or the combination of these with spatial criteria, matching the origin and destination of the trips to points of interest [
13].
Usually, the traditional approach for validation is well-established in the literature and is based on the use of surveys [
14]. Ref. [
15] performs interviews with several trucking companies operating in Italy, in order to verify whether the actual behaviour of trucking companies agrees with their model and also to ascertain the main aspects with which trucking companies are concerned in relation to their modal choice. Unlike these forms of research, the current study proposes an innovative validation method for a freight transport simulation model in a multimodal context [
16]. This model supports the decisions of transportation planners by assessing the impacts of policies in promoting more sustainable solutions for freight transport. This model simulates the mode choice between sea-road and road-only transport, focusing on the transport operators’ points of view. The simulation tool provides two set of solutions, distinguishing between travel-time (T*) and travel-cost (C*) minimisations. Then, the model returns the potential demand in terms of Origin–Destination (OD) pairs identifying paths with regard to travel-time and travel-cost solutions for each mode of transportation. The model refers to all heavy vehicles moving different commodities in the Italian context. The evolution of the validation phase with the adoption of Big Data for statistical information consists also in the development of in-depth analyses; FCD might constitute a valid element to validate the results obtained, refine the definition of the model, or possibly entirely modify the approach to the problem studied. By comparing the model results with the information obtained from the FCD signals produced by the vehicles, it can be determined that a real validation problem is faced when returning the structured data analysis methodology and the main results. Indeed, this study intends to demonstrate the possibilities of both quantitatively extrapolating key information in the complex field of combined sea-road transport, such as the sailing times, service schedules, and port dwell times, and obtaining detailed information through an in-depth analysis by tracking each vehicle during travel by each mode of transportation, such as the origin and destination of the trips, and the travel and rest times. The validation phase highlights the importance to consider the chain trips in freight simulation model. For example, the hauliers can start the travel with different hours of driving influencing the modal choice.