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High-Speed Rail (HSR) is a transport mode that operates significantly faster than traditional services, using integrated and specialized rolling stock, and often dedicated tracks. These rapid transit services have profoundly impacted mobility habits on medium-long range journeys, and have also brought about social, economic, and environmental changes in the geographical areas involved. HSR has become a successful “brand”, including not only faster trains, but also a number of other on-board services that increase rail attractiveness (e.g. restaurants, wi-fi connection, free newspapers, cinema, business areas and relaxation lounges). Starting from these considerations, in this research we investigated the conjecture according to which a traveler, in choosing an HSR service to reach a destination (against a traditional rail service, or other competing transport modes), is not only influenced by its original peculiarity as a “faster train” service, but also takes into account the presence of all the “hedonic” characteristics of the service. That said, the literature in transportation modeling has yet to analyze the impact of hedonic quality on travelers’ behavior by quantifying whether and to what extent it increases their propensity to use HSR services. Aim of this research was twofold: i ) to quantify, for the first time in the literature, the hedonic value of an HSR service related to a domestic tourist trip, as compared to more conventional levels of service and attractiveness attributes; ii ) investigate the domestic tourist attractiveness of the main Italian cities, evaluating which attributes influence the perception of a city as a domestic tourist destination. The application case study consists in the supply of current rail services (HSR vs. traditional) between the 11 main Italian cities in terms of tourist destination. On the basis of an RP&SP survey, carried out among university students at national scale, a binomial logit model was specified with serial correlation in residuals, estimating whether or not the cities in the panel were perceived as possible destinations for a domestic tourist trips made by train. The estimation results show that city-specific attractiveness attributes (entertainment and restaurant rate; number of sites of interests; crime rate), and level of services variables (e.g. travel time and cost; HSR brand ) significantly influence the perception of a city as a possible destination for a domestic tourist trip by train. Furthermore, the average monetary value for the “ pure preference ” for the HSR brand (faster trains, high frequency, and on-board services) is about 22 Euros/trip, while the hedonic value relative only to on-board services is about 12 Euros/trip , underlining that the “faster train” is only one of the perceived attributes of HSR services. Overall, the willingness to pay for an HSR ticket is up to 40% greater than that for a traditional one. Furthermore, tourists are willing to spend 2.2 h more travelling on an HSR train to reach their destinations. The estimation results show that there is also a “ distance traveled effect ”; the pure preference for HSR services increases in value from 9 Euros/trip to 13 Euros/trip (+44%) for distances traveled greater than 400 km. If confirmed, these results allow the conclusion to be drawn that the “catchment area” of cities on HSR networks is larger than that served by traditional rail. [ABSTRACT FROM AUTHOR]

This paper analyses driving behaviour in car-following conditions, based on extensive individual vehicle data collected during experimental field surveys carried out in Italy and the UK. The aim is to contribute to identify simple evidence to be exploited in the ongoing process of driving assistance and automation which, in turn, would reduce rear-end crashes. In particular, identification of differences and similarities in observed car-following behaviours for different samples of drivers could justify common tuning, at a European or worldwide level, of a technological solution aimed at active safety, or, in the event of differences, could suggest the most critical aspects to be taken into account for localisation or customisation of driving assistance solutions. Without intending to be exhaustive, this paper moves one step in this direction. Indeed, driving behaviour and human errors are considered to be among the main crash contributory factors, and a promising approach for safety improvement is the progressive introduction of increasing levels of driving automation in next-generation vehicles, according to the active/preventive safety approach. However, the more advanced the system, the more complex will be the integration in the vehicle, and the interaction with the driver may sometimes become unproductive, or risky, should the driver be removed from the driving control loop. Thus, implementation of these systems will require the interaction of human driving logics with automation logics and then an enhanced ability in modelling drivers’ behaviour. This will allow both higher active-safety levels and higher user acceptance to be achieved, thus ensuring that the driver is always in the control loop, even if his/her role is limited to supervising the automatic logic. Currently, the driving mode most targeted by driving assistance systems is longitudinal driving. This is required in various driving conditions, among which car-following assumes key importance because of the huge number of rear-end crashes. The increased availability of lower-cost information and communication technologies (ICTs) has enhanced the possibility of collecting copious and reliable car-following individual vehicle data. In this work, data collected from three different experiments, two carried out in Italy and one in the UK, are analysed and compared. The experiments involved 146 drivers (105 Italian drivers and 41 UK drivers). Data were collected by two instrumented vehicles. Our analysis focused on inter-vehicular spacing in equilibrium car-following conditions. We observed that (i) the adopted equilibrium spacing can be fitted using lognormal distributions, (ii) the adopted equilibrium spacing increases with speed, and (iii) the dispersion between drivers increases with speed. In addition, according to different headway thresholds (up to 1 second) a significant number of potentially dangerous behaviours is observed. Three different car-following paradigms are also applied to each of the experiments, and modelling parameters are calibrated and compared to obtain indirect confirmation about the observed similarities and differences in driving behaviour. [ABSTRACT FROM AUTHOR]

The Action Point theory is one of the paradigms that can be applied to understand and reproduce car-following behaviour. Several different approaches to this theory have been proposed, some more simple and others more complex. In particular, the reference point in this field is still the paradigm from Wiedemann, which requires the identification of four action-point thresholds. In this paper we review Action Point theories in order to highlight similarities and differences and to ascertain whether all the thresholds proposed by Wiedemann actually bind the driving behaviour. Based on a large-scale experiment in which car-following data were collected, we identified all candidate action points assuming that the more complex (four-threshold) theory holds. Then we tested these points with respect to the large data set of available observations, in order to check whether actual actions are performed at the points. The results show that very often simpler approaches better match the observed data and that in order to explain car-following behaviour it is sufficient in most cases to refer to two thresholds. The results obtained by real-world observation were also tested in virtual environments (two different kinds of driving simulators) and were confirmed. [ABSTRACT FROM AUTHOR]

Highlights: [•] We investigate driving behaviours. [•] Based on a pilot survey, we confirm the Action Point (AP) theory, with some simplifications. [•] We propose a new characterisation of the AP theory in terms of opening and closing waves. [•] Based on the pilot survey, we identify a (linear) pattern for the distribution of action points. [ABSTRACT FROM AUTHOR]

Abstract: Adaptive Cruise Control systems have been developed and introduced into the consumer market for over a decade. Among these systems, fully-adaptive ones are required to adapt their behaviour not only to traffic conditions but also to drivers’ preferences and attitudes, as well as to the way such preferences change for the same driver in different driving sessions. This would ideally lead towards a system where an on-board electronic control unit can be asked by the driver to calibrate its own parameters while he/she manually drives for a few minutes (learning mode). After calibration, the control unit switches to the running mode where the learned driving style is applied. The learning mode can be activated by any driver of the car, for any driving session and each time he/she wishes to change the current driving behaviour of the cruise control system. The modelling framework which we propose to implement comprises four layers (sampler, profiler, tutor, performer). The sampler is responsible for human likeness and can be calibrated while in learning mode. Then, while in running mode, it works together with the other modelling layers to implement the logic. This paper presents the overall framework, with particular emphasis on the sampler and the profiler that are explained in full mathematical detail. Specification and calibration of the proposed framework are supported by the observed data, collected by means of an instrumented vehicle. The data are also used to assess the proposed framework, confirming human-like and fully-adaptive characteristics. [Copyright &y& Elsevier]

• Comprehensive and systematic safety assessment of Automated Driving Systems. • Assessment methodology to quantify the impact of situational parameters. • Employment of stochastic simulation and uncertainty modeling techniques. • Identification of unknown hazardous areas of operation and triggering conditions. • Comparison with traditional approaches. This paper aims to contribute to the comprehensive and systematic safety assessment of Automated Driving Systems (ADSs) by identifying unknown hazardous areas of operation. The current methodologies employed in this domain typically involve estimating the distributions of situational variables based on human-centered field test, crash databases, or expert knowledge of critical values. However, due to the lack of a-priori knowledge regarding the influential factors, their critical ranges, and their distributions, these approaches may not be entirely suitable for the assessment of emerging automated driving technologies. To deal with this challenging problem, here we propose a testing methodology incorporating realistic yet unobserved driving conditions, distinguished by numerous situational variables, so to encompass unknown unsafe conditions comprehensively. Our methodology utilizes stochastic simulation and uncertainty modeling techniques to account for the variability of realistic driving conditions and their impact on ADSs' performances. By doing so, we aim to identify unsafe operational regions and triggering conditions that can lead to hazardous behaviors, thus improving the development and safety of automated driving functions. For our purposes, the Latin Hypercube Sampling technique and the recently proposed PAWN density-based sensitivity analysis method are employed. We apply this methodology for the first time in the specific field of ADSs design and validation, using an exemplificative use case. We discuss and compare the results obtained from our approach with those obtained from a traditional approach. [ABSTRACT FROM AUTHOR]

The Green Light Optimal Speed Advisory (GLOSA) system is a Cooperative Intelligent Transportation System (C-ITS) that is supposed to reduce the energy consumption and travel time associated with a vehicle trip by providing an optimal speed profile to avoid unnecessary stops at an intersection, based on Traffic Light Signals (TLSs) information. It is expected that GLOSA solutions will be widely deployed on transportation networks, thanks to the rapid spread of increasingly effective and pervasive communication technologies. Thus, appropriate methodologies and tools should be adopted to test sustainability objectives and evaluate the effects of such systems on the performance of traffic networks. In this perspective, this paper proposes an enhanced testing approach of GLOSA services based on an Integrated Simulation Environment that combines custom models developed in Matlab/Simulink with the SUMO traffic simulation environment, allowing both the traffic environment and vehicle dynamics to be represented with a suitable level of detail. The proposed approach can be used to cover several aspects which, in the existing technical literature, are: (i) not considered (traffic signal phase condition); (i i) rarely considered (electric engine); (i i i) considered in a non-integrated way (traffic condition, TLS cycle duration, communication distance and minimum speed). Indeed, the considered factors pertain to different subsystems of the mobility environment (network and vehicles), and the proposed testing framework allows an equally detailed simulation of all, thus enhancing the accuracy of the results. To show the validity of the proposed approach, this study considers one controlled vehicle, equipped with a GLOSA system, travelling along a single route through a city centre including many TLSs. The simulation analysis deals with different levels of considered factors, assessed through Key Performance Indicators (KPIs) related to mobility and environmental indicators. Numerical results confirm that the proposed Integrated Simulation Environment can give valuable insights and suggestions to design a GLOSA system aiming to enhance both mobility and environmental performance. They also show that the factors in question affect system performance differently. • CAVs technologies are better tested by means of an integrated simulation environment. • GLOSA system functioning and effectiveness mainly affected by external factors. • Different engine types can affect GLOSA system functioning and effectiveness. • Micro-simulators should be carefully used for computing vehicles' consumption. [ABSTRACT FROM AUTHOR]