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A few studies suggest that particulate matter (PM) exposure might play a role in bronchiolitis. However, available data are mostly focused on the risk of hospitalization and come from retrospective studies that provided conflicting results. This prospective study investigated the association between PM (PMThis prospective cohort study was conducted between November 2019 and February 2020 at the pediatric emergency department of the Fondazione IRCCS Ca' Ospedale Maggiore Policlinico, Milan, Italy. Infants1 year of age with bronchiolitis were eligible. The bronchiolitis severity score was assessed in each infant and a nasal swab was collected to detect respiratory viruses. The daily PMA positive association between the PMThis study shows for the first time a direct association between PM

Sustainable transport frameworks are gaining attention within national and international transportation policies, given the key role that decarbonisation plays in making urban environments people-friendly. Within this context, several shared services and micro-mobility options are being developed, especially as first/last mile facilities, further increasing public transport coverage levels. We present an overview of the environmental impacts of different transport modes and compare them from different perspectives, namely, CO2 emission levels, total costs (also including the user generalised cost) and service life of vehicles involved. The proposed methodology is applied to an urban context, using real trip data and showing the main findings under real conditions.

Although many studies have been carried out on pedestrian crossing safety, comprehensive research evaluating vehicle–pedestrian conflict in areas with zebra crossing (AWZCs) versus areas without zebra crossing (AWOZCs) is still neglected. In the present study, through a naturalistic driving study (NDS), drivers’ behavior was recorded in AWZCs and AWOZCs. Vehicle–pedestrian conflicts were evaluated by examining the evasive maneuver behavior of drivers and pedestrians based on surrogate measures of safety (SMoS). The severity of conflicts was categorized by a K-means clustering method into three specific groups based on the critical thresholds of SMoS. The evasive maneuvers performed by pedestrians and drivers were classified into three levels: normal, slight, and serious. In conflicts resulting in normal and serious maneuvers, drivers would attempt to prevent collisions by changing the speed and direction of the vehicle. Moreover, a pedestrian at the slight level of conflict was the determinative factor in reducing the possibility of collisions by performing actions such as returning to the curb of the street or increasing walking speed. Also, the results showed that pedestrians were more likely to cross with a smaller margin of safety in AWOZCs than in AWZCs. This study explains how both pedestrians and drivers play a crucial role in preventing collisions during different levels of conflict. Given this finding, conducting future research to evaluate the interaction between drivers and pedestrians may lead to establishing a basic framework for designing an algorithm to detect the possibility of a pedestrian collision.

Background: A few studies suggest that particulate matter (PM) exposure might play a role in bronchiolitis. However, available data are mostly focused on the risk of hospi-talization and come from retrospective studies that provided conflicting results. This prospective study investigated the association between PM (PM2.5 and PM10) expo-sure and the severity of bronchiolitis.Methods: This prospective cohort study was conducted between November 2019 and February 2020 at the pediatric emergency department of the Fondazione IRCCS Ca′ Ospedale Maggiore Policlinico, Milan, Italy. Infants

Recently, traffic management strategies, leveraging the Macroscopic Fundamental Diagram (MFD) paradigm, have gained a lot of interest since they allow to enhance urban network performance in term of efficiency and energy saving. Within this framework, this paper proposes a route guidance control system that, on the basis of traffic condition of each region, suggests to vehicles the best admissible route to follow. More specifically, a linear model is used to predict the evolution of traffic condition of each region over time on the basis of traffic data collected by loop detectors and, then, the MFD paradigm is exploited to estimate the time required to cross each region. In so doing, the control system computes the travel time of each admissible route and, in the end, suggests to each vehicle the one that minimize the travel time to reach a specific destination. Numerical simulations, carried out by exploiting SUMO microscopic traffic simulator and MATLAB, are performed to assess the effectiveness of the proposed control strategy in enhancing both the mobility and the environmental performances of the city network of Portici. The case without the control system is used as a base scenario. Results prove the effectiveness of the proposed routing control strategy in enhancing both mobility and environmental performance of the whole network w.r.t. the base scenario. Results prove the effectiveness of the proposed routing control strategy in enhancing both mobility and environmental performance of the whole network w.r.t. the base scenario.

Our cities are becoming increasingly smart thanks to information and communication technologies, sustainable solutions for human activities and innovative mobility frameworks. In this context, one of the most promising approaches is relying on the Internet of Things (IoT) which allows objects of everyday life to become computing devices exchanging useful data. In particular, the paper proposes to adopt such technologies for monitoring railway systems, thus obtaining information concerning, on one hand, service performance and, on the other, travellers’ behaviour. A numerical application has been performed in a real rail context, thus pointing out the feasibility of the proposed methodology.

For a driving simulator to be a valid tool for research, vehicle development, or driver training, it is crucial that it elicits similar driver behavior as the corresponding real vehicle. To assess such behavioral validity, the use of quantitative driver models has been suggested but not previously reported. Here, a task-general conceptual driver model is proposed, along with a taxonomy defining levels of behavioral validity. Based on these theoretical concepts, it is argued that driver models without explicit representations of sensory or neuromuscular dynamics should be sufficient for a model-based assessment of driving simulators in most contexts. As a task-specific example, two parsimonious driver steering models of this nature are developed and tested on a dataset of real and simulated driving in near-limit, low-friction circumstances, indicating a clear preference of one model over the other. By means of closed-loop simulations, it is demonstrated that the parameters of this preferred model can generally be accurately estimated from unperturbed driver steering data, using a simple, open-loop fitting method, as long as the vehicle positioning data are reliable. Some recurring patterns between the two studied tasks are noted in how the model’s parameters, fitted to human steering, are affected by the presence or absence of steering torques and motion cues in the simulator.

Modelling route choice decision making in Advanced Traveler information System (ATIS) contexts is still a crucial task. In particular, two main categories of variables can be identified in order to model travelers’ behaviors: the former may be defined as endogenous and are related to the experiment environment; the latter may be defined as exogenous (referring to the respondents involved in the experiment). This paper focuses on the analysis of exogenous variables. An experiment is carried out using a driving simulator, on a real route choice context (a sub-area of the urban network in the city of Naples, in the Campania Region) reproduced in a virtual reality. All data are analyzed by aggregate and statistical approaches to preliminarily investigate the correlations between some exogenous variables and the collected choices of drivers. Furthermore, collected observations have been modelled by applying the Structural Equation Model (SEM) approach to model the effect of information on switching behaviors.

The paper aims to develop an integrated modelling framework for urban network traffic control in the presence of connected and autonomous vehicles (CAVs). The framework is further composed of two sub models: the first of which focuses on the traffic control problem in the case of hybrid flow conditions (unequipped vehicles and connected vehicles) and the second aims to control the automated vehicles in terms of speed optimisation. The traffic control strategy drew on the hybrid combination between the centralised approach based on a multi-objective optimisation and a link metering based on a single control function; whilst with reference to the speed guidance, the GLOSA (Green Light Optimal Speed Advisory) procedure was considered. Furthermore, the presence of connected vehicles has also been considered to support the estimation procedure of location and speed of unequipped vehicles. In terms of traffic flow modelling the microscopic approach has been applied. The proposed framework was applied by considering a simple real network (in the city centre of Naples, in the Southern of Italy) that was composed by one origin–destination pair and two alternative paths. The network layout is characterised by one diversion node and two alternative paths connecting the same origin - destination pair; three scenarios were tested: the first was only based on a centralised traffic control procedure, the second on speed guidance optimisation and the third was based on the combination of both sub-models. Finally, the framework effectiveness was analised in terms of within-day dynamics with respect to the travel times and queue length performance indices.

This paper focuses on the presentation of an integrated framework based on two advanced strategies, aimed at mitigating the effect of traffic congestion in terms of performance and environmental impact. In particular, the paper investigates the &ldquo, operational benefits&rdquo, that can be derived from the combination of traffic control (TC) and route guidance (RG) strategies. The framework is based on two modules and integrates a within-day traffic control method and a day-to-day behavioral route choice model. The former module consists of an enhanced traffic control model that can be applied to design traffic signal decision variables, suitable for real-time optimization. The latter designs the information consistently with predictive user reactions to the information itself. The proposed framework is implemented to a highly congested sub-network in the city center of Naples (Italy) and different scenarios are tested and compared. The &ldquo, do nothing&rdquo, scenario (current, DN) and the &ldquo, modeled compliance&rdquo, (MC) scenario, in which travelers&rsquo, reaction to the information (i.e., compliance) is explicitly represented. In order to evaluate the effectiveness of the proposed strategy and the modeling framework, the following analyses are carried out: (i) Network performance analysis, (ii) system convergence and stability analysis, as well as the compliance evolution over time, (iii) and emissions and fuel consumption impact analysis.

The development of connected and automated driving functions involves that the interaction of autonomous/ automated vehicles with the surrounding environment will increase. Accordingly, there is a necessity for an improvement in the usage of traditional tools of the automotive development process. This is a critical problem since the classic development process used in the automotive field uses a very simplified driver model and the traffic environment, while nowadays it should contemplate a realistic representation of these elements. To overcome this issue, the authors proposed an integrated simulation environment, based on the co-simulation of Matlab/Simulink environment with simulation of urban mobility, which allows for a realistic model of vehicle dynamic, control logics, driver behaviour and traffic conditions. Simulation tests have been performed to prove the reasoning for such a tool, and to show the capabilities of the instrument. By using the proposed platform, vehicles may be modelled with a higher level of details (with respect to microscopic simulators), while the autonomous/automated driving functions can be tested in realistic traffic scenarios where the features of the road traffic environment can be varied to verify in a realistic way the level of robustness of the on-board implemented functions.

The development of Intelligent Transportation Systems (ITS) aims to improve the performance of transport systems in terms of efficiency, safety and energy saving. Among these services, the Variable Speed Limit (VSL) systems are widely used to mitigate the problem of traffic congestion by changing the road speed limits in response to traffic, incidents, and/or weather conditions. This paper investigates the effects of a VSL system on a freeway road segment of the city of Naples. The control strategy leverages volume, occupancy and average speed data, provided by loop detectors located along the road, for adapting dynamically the road speed limit. Numerical simulations were carried out by exploiting VISSIM microscopic traffic simulator, which embedded the control strategy, for two different scenarios: with and without the VSL system. Results disclose the effectiveness of the VSL control strategy in improving road efficiency by reducing speed variance and increasing both average speed and volume of vehicles. Moreover, the VSL system has also proved to have environmental benefits, since it allows reducing fuel consumption.

The paper presents some exploratory experiments for defining a Macroscopic Fundamental Diagram starting from data collected in some specific sensor network layouts, that is by just monitoring the cordon of a study area. Variables defined in the original proposition of the MFD where here re-defined by just considering the number of vehicles estimated to be present in the study area (N) by means of this layout. We found that in some cases a strong correlation among defined variables can be found, and also similar patterns in the depicted MFD are evidenced. Findings of the paper are limited, given the limited amount of simulation performed, and also considering the limited number of factors varied in the simulations; as expected, results seem to be strongly affected by the traffic demand. Apart that, the approach is worth to be investigated, because this kind of layout is becoming very common in some urban contexts (e.g. in Italy).

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); (ii) rarely considered (electric engine); (iii) 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.

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.

Intersections, and more traffic signals, introduce stops along trip increasing travel time, stress, fuel consumption and emissions. To confine the stop-and-go phenomenon due to traffic signals several researchers focused on the use of actuated traffic lights. In last years the digital transformation of cities give us lot of information could be used for several purposes, one of them is the implementation of cooperativeintelligent transportation systems (C-ITS). The paper introduces a specific C-ITS named Green Light Optimal Speed Advisory (GLOSA) that use V2I communication to calculate an optimal speed for the vehicle to pass the intersection avoiding idle time. Two simulations scenarios have been built to study the benefit of the service. The results demonstrate that even using a simple control algorithm with the aim to avoid the stop&go we observe a reduction of fuel consumption and emissions in the region of 5 to 12%

With the spread of automation in driving, vehicles will have to interact more with the surrounding environment, and consequently the automotive development process will have to contemplate a realistic representation of these elements. This is a critical issue, since in the classic development process used in the automotive field, driver model and traffic environment are represented in a very simplified way. This paper overcomes this criticality through the development of an Integrated Simulation Environment, at the Model in the Loop (MIL) level, which allows the integrated representation of the vehicle dynamic and its automation, of the driver and the traffic conditions. The tool is based on integrated simulation of MATLAB / Simulink with the commercial software CarRealTime and with SUMO, an open source microscopic simulator. A series of tests have been performed to prove the need for such a tool, and to show the potential of the instrument. The implemented system allows vehicle, represented with a high level of details, to be tested in realistic traffic scenarios, in which agents and characteristic variables of traffic flow can be varied in order to verify realistically the level of robustness of on-board automation functions.

The paper focuses on the evaluation of the combined effect of Traffic Signal Control Strategy (TSC) and Variable Message Sign (VMS). With reference to the TSC a dynamic selection strategy based on macroscopic flow variables was considered for off-line traffic signal plans design. The combination of two ITS solutions, TSC and VMS, was tested through microscopic approach by SUMO traffic simulator which allows to directly reproduce the pollutant emissions and fuel consumptions.

Many application fields in transportation engineering can benefit from an accurate modelling of car-following behavior. In particular, in recent years, an increased importance is assigned to embed behavioral abilities in ADAS (Advanced Driving Assistance Systems) and in driving automation solutions. However, accurate development of car-following models needs for accounting of the drivers’ heterogeneity, which can be easily observed in car-following data. This paper contributes to analyze different sources of heterogeneity with particular focus on three factors: the dispersion over-time of the behavior of a single driver; the heterogeneous behaviors of different drivers; and the possible bias introduced by some over-simplification of the modelling framework, with particular reference to the type of leading vehicle. Our analyses are based on the observation of car-following trajectories collected in a large experiment involving one hundred drivers. Observed behaviors have been interpreted by means of several car-following models proposed in past. The comparison of the values of the parameters identified for the models (versus observed data) is adopted for the analyses. Moreover, directly observed variables (car-following speed and spacing) are adopted to complement and confirm the analyses. Results show that the greater among the sources of dispersion is the across-driver heterogeneity and that by taking into account such an inherent drivers’ dispersion of car-following behaviors it is possible to better identify also the effect of the modelling oversimplifications induced by not considering the type of leading vehicle.

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.

Reduction of the environmental impact of cars represents one of the biggest transport industry challenges. Beyond more efficient engines, a promising approach is to use eco-driving technologies that help drivers achieve lower fuel consumption and emission levels. In this study, a real-time microscopic fuel consumption model was developed. It was designed to be integrated into simulation platforms for the design and testing of Advanced Driving Assistance Systems (ADAS), aimed at keeping the vehicle within the environmentally friendly driving zone and hence reducing harmful exhaust gases. To allow integration in platforms employed at early stages of ADAS development and testing, the model was kept very simple and dependent on a few easily computable variables. To show the feasibility of the identification of the model (and to validate it), a large experiment involving more than 100 drivers and about 8000 km of driving was carried out using an instrumented vehicle. An instantaneous model was identified based on vehicle speed, acceleration level and gas pedal excursion, applicable in an extra-urban traffic context. Both instantaneous and aggregate validation was performed and the model was shown to estimate vehicle fuel consumption consistently with in-field instantaneous measurements. Very accurate estimations were also shown for the aggregate consumption of each driving session.

The main idea of the present work is to define the domain in which it is possible to adopt very simple models of vehicle dynamics for applications in the testing of Advanced Driver Assistance Systems (ADAS) in lieu of complex models. The aim is to reduce the computational burden, and consequently the computing time. In particular, in the paper, the performances of a very simple model of vehicle dynamics, the Single Track with linear tires, have been compared with those of a complex and complete model, with non-linear tires, included in a commercial software (IPG CarMaker). For sake of shortness, the comparison has been carried out focusing on the lateral dynamical behaviour, and consequently the testing of a Lane Keeping Assistant (LKA) system has been carried out. Of course both the vehicle dynamic models, and the ADAS system have been integrated in a common simulation environment (Simulink), and tested in the standard traffic scenarios defined in EuroNCAP test protocols.

Connected and Automated Vehicles (CAVs) are likely to become an integral part of the traffic stream within the next few years. Their presence is expected to greatly modify mobility behaviours, travel demands and habits, traffic flow characteristics, traffic safety and related external impacts. Tools and methodologies are needed to evaluate the effects of CAVs on traffic streams, as well as the impact on traffic externalities. This is particularly relevant under mixed traffic conditions, where human-driven vehicles and CAVs will interact. Understanding technological aspects (e.g. communication protocols, control algorithms, etc.) is crucial for analysing the impact of CAVs, but the modification induced in human driving behaviours by the presence of CAVs is also of paramount importance. For this reason, the definition of appropriate CAV investigations methods and tools represents a key (and open) issue. One of the most promising approaches for assessing the impact of CAVs is operator in the loop simulators, since having a real driver involved in the simulation represents an advantageous approach. However, the behaviour of the driver in the simulator must be validated and this paper discusses the results of some experiments concerning car-following behaviour. These experiments have included both driving simulators and an instrumented vehicle, and have observed the behaviours of a large sample of drivers, in similar conditions, in different experimental environments. Similarities and differences in driver behaviour will be presented and discussed with respect to the observation of one important quantity of car-following, the maintained spacing.

In order to address the Signal Setting Design at urban level two main approaches may be pursued: the coordination and the synchronisation approaches depending on the steps considered for the optimisation of decision variables (two steps vs. one step). Furthermore, in terms of objective functions mono-criterion or multi-criteria may be adopted. In this paper the coordination approach is implemented considering the multi-criteria optimisation at single junctions and mono-criterion optimisation at network level whereas the synchronisation is implemented considering the mono-criterion optimisation. The main purpose of the paper is the evaluation of the performances of two strategies not only considering indicators such as the total delay, the queue length etc. but also considering other indicators such as the emissions and the fuel consumption. The methodological framework is composed by three stages: (i) the decision variables (green timings and offsets) computation through optimisation methods; (ii) the implementation of optimal signal settings in a microscopic traffic flow simulator (“Simulation of Urban MObility”-SUMO); (iii) the estimation of emissions and fuel consumption indicators.

Studies of driving behaviour are of great help for different tasks in transportation engineering. These include data collection both for statistical analysis and for identification of driving models and estimation of modelling parameters (calibration). The data and models may be applied to different areas: i) road safety analysis; ii) microscopic models for traffic simulation, forecast and control; iii) control logics aimed at ADAS (Advanced Driving Assistance Systems). In this paper we present a large survey based on the naturalistic (on-the-road) observation of driving behaviour with a view to obtaining microscopic data for single vehicles on long road segments and for long time periods. Data are collected by means of an instrumented vehicle (IV), equipped with GPS, radar, cameras and other sensors. The behaviour of more than 100 drivers was observed by using the IV in active mode, that is by observing the kinematics imposed on the vehicle by the driver, as well as the kinematics with respect to neighbouring vehicles. Sensors were also mounted backwards on the IV, allowing the behaviour of the driver behind to be observed in passive mode. As the vehicle behind changes, the next is observed and within a short period of time the behaviour of several drivers can be examined, without the observed driver being aware. The paper presents the experiment by describing the road context, aims and experimental procedure. Statistics and initial insights are also presented based on the large amount of data collected (more than 8000km of observed trajectories and 120hours of driving in active mode). As an example of how to use the data directly, apart from calibration of driving behaviour models, indexes based on aggregate measures of safety are computed, presented and discussed.

Several studies have developed operating speed prediction models. Most of the models are based on spot speed data, collected by radar guns, pavement sensors, and similar mechanisms. Unfortunately, these data collection methods force the users to assume some invalid assumptions in driver behavior modeling: constant operating speed throughout horizontal curves and occurrence of acceleration and deceleration only on tangents. In this study, an instrumented vehicle with GPS continuous speed tracking was used to analyze driver behavior in terms of speed choice and deceleration or acceleration performance and to develop operating speed prediction models. The data used in the study were from a field experiment conducted in Italy on the rural motorway A16 (Naples–Avellino). Models were developed to predict operating speed in curves and tangents, deceleration and acceleration rates to be used in the operating speed profiles, starting and ending points of constant operating speed in a curve, 85th percentile of the deceleration and acceleration rates of individual drivers, and 85th percentile of the individual drivers’ maximum speed reduction in the tangent-to-curve transition. The study results showed that (a) the drivers’ speed was not constant along curves, (b) the individual drivers’ maximum speed reduction was greater than the operating speed difference in the tangent-to-curve transition, and (c) the deceleration and acceleration rates experienced by individual drivers were greater than the deceleration and acceleration rates used to draw operating speed profiles.

Cooperative-Intelligent Transportation Systems (C-ITSs) aim to connect vehicles, both with one another and with road infrastructures, so as to increase traffic safety and efficiency. This paper focuses on the European framework for supporting the development of Cooperative, Connected, and Automated Mobility, and aims to shed light on the current state of testing and deployment activities in the field at the start of 2019. This may be considered particularly timely given that the year 2019 was identified as the starting date for the deployment of mature services, and the Community legislation is currently paying great attention to the matter. In order to present a concise (but comprehensive) picture, we consulted and analysed the most diverse sources comprising more than 2000 pages.

The research presented here is framed in the area of the design of Advanced Driving Assistance Systems (ADAS), carried out in the so-called automotive V-Cycle. In particular we concentrated our effort at the Model In the Loop (MIL) level. Indeed we developed an additional component for the fuel consumption evaluation at the MIL stage. The developed component has been based on the combination of a simplified model of the vehicle dynamics, and a fuel consumption model calibrated in previous experiments. The developed module is used for tuning the parameters of an Ecological fully Adaptive Cruise Control System.

In this paper a car-following model is formulated as a time-continuous dynamic process, depending on two parameters and two inputs. One of these inputs is the follower’s desired equilibrium spacing, assumed to exist and to be known. Another input is the speed of the lead vehicle. Given the formulation of the model, the contribution of these two inputs is separable from an analytical point of view. The proposed model is simple enough (whereas not being simplistic) to support real-time applications in the field of advanced driving assistance systems. Starting from the equilibrium spacing, it is possible to estimate the parameters of the model, allowing for a full identification procedure. The modeling framework was prevalidated against observed data from two different data sets, collected by means of two instrumented vehicles in independent experiments, carried out in Italy and the United Kingdom. The validation proved that the proposed car-following model gives good results not only around the desired equilibrium spacing but also in general car-following conditions. The experimental data sets are discussed in terms of parameter values as well as performance of the dynamic process against observed data.

Observation of vehicles kinematics is an important task for many applications in ITS (Intelligent Transportation Systems). It is at the base of both theoretical analyses and application developments, especially in case of positioning and tracing/tracking of vehicles, car-following analyses and models, navigation and other ATIS (Advanced Traveller Information Systems), ACC (Adaptive Cruise Control) systems, CAS and CWS (Collision Avoidance Systems and Collision Warning Systems) and other ADAS (Advanced Driving Assistance Systems). Modern technologies supply low-cost devices able to collect time series of kinematic and positioning data with medium to very high frequency. Even more data can be (almost continually) collected if vehicle-to-vehicle (V2 V) communications come true. However, some of the ITS applications (as well as car-following models, on which many ADAS and ACC are based) require highly accurate measures or, at least, smooth profiles of collected data. Unfortunately, even relatively high-cost devices can collect biased data because of many technical reasons and often this bias could lead to unrealistic kinematics, incorrect absolute positioning and/or inconsistencies between vehicles (e.g. negative spacing). As a consequence, data need filtering in most of the ITS applications. To this aim proper algorithms are required and several sensors and sources of data possibly integrated in order to obtain the maximum quality at the minimal cost. This work addresses the previous issues by developing a specific Kalman smoothing approach. The approach is developed in order to deal with car-following conditions but is conceived to take into account also navigation issues. The performances are analysed with respect to real-world car-following data, voluntarily biased for evaluation purposes. Assessment is carried out with reference to different mixtures of sensors and different sensors accuracies.