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This Special Issue collects selected contributions presented at the 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) held in Naples, Italy in 2017. MT-ITS 2017 brought together researchers and practitioners from across Europe and the rest of the world to discuss emerging trends and developments in modeling and technology for Intelligent Transportation Systems, to present state-of-the-art updates and to facilitate cross-fertilization between these two key areas of transportation systems. Consistently, papers published in the special issue encompass timely and emerging research topics in modeling, optimization and assessment of transportation systems, as well as relevant technological issues.

Recently the growing demand in railway transportation has raised the need for practitioners to improve the design of railway systems. This means to identify a configuration of the infrastructure components (e.g. number of rail tracks, type and layout of the signalling system, layout of station tracks) and the operational schedule (e.g. train headways, scheduled dwell times) that improves given measures of performance such as the level of capacity, the punctuality of the service and the energy saving.Planners and designers involved in this process have the hard task of determining sound design solutions in order to achieve certain levels of network performances in a cost-effective way, especially when investment funds are limited. To this aim, a sensitivity analysis can support early decisional phases in order to better understand dependencies between performances and design variables and drive the decisional process towards effective solutions.In this paper, the Sobol variance-based method is applied to this purpose. A practical application has been carried out for a mass transit line in the city of Naples. Such study has investigated how train delays and energy consumption are affected by variations in design variables relative to the operational plan, the signalling system and factors related to the layout of station platforms. Results highlight the ability of this analysis in explaining the effects of different design solutions from a statistical point of view and finding the most influential factors for a given performance. This aspect suggests to practitioners the usefulness of this approach in addressing decisions towards cost-effective interventions. �� 2013 Elsevier Ltd.

Point-to-point (P2P) speed enforcement is a relatively new approach to traffic law enforcement. Its technology allows vehicles whose average speed exceeds the speed limit over the controlled section to be fined. It therefore encourages compliance over distances longer than those where spot enforcement policies have been in place. In this paper, a procedure for consistently setting speed limits with such enforcement systems is proposed. Such a method has been applied to design the speed limits on two motorways in the district of Naples, Italy, where P2P enforcement systems became operational in 2009 and 2010. The speed limits, which were set using the Italian geometric design standard to assess vehicle stability and stopping sight distance, have been compared with those provided by using well-known international standards. The impact of the newly designed speed limits and of the P2P enforcement system on drivers’ speeding behaviour has been quantified for each highway section and vehicle type. In fact, accurate measurements of the average travel speeds of each vehicle crossing the enforced sections, before and after the activation of the system, were available. The migration from the old speed limits with spot speed enforcement to the new approach resulted in a notable increase in drivers’ compliance to the speed limits with a remarkable decrease in both the average of individual speeds and in their standard deviation. In addition, the analysis of 3 years of data shows that a gradual adaptation of drivers’ behaviour to the system took place. In particular, a decreasing compliance to the speed limits points to a non-optimal system management. Finally, the results of a revealed preference survey allowed us to make a behavioural interpretation regarding the significantly different impacts measured on the two motorways.

Fuel consumption or pollutant emissions can be assessed by coupling a microscopic traffic flow model with an instantaneous emission model. Traffic models are usually calibrated using goodness of fit indicators related to the traffic behavior. Thus, this paper investigates how such a calibration influences the accuracy of fuel consumption and NOx and PM estimations. Two traffic models are investigated: Newell and Gipps. It appears that the Gipps model provides the closest simulated trajectories when compared to real ones. Interestingly, a reverse ranking is observed for fuel consumption, NOx and PM emissions. For both models, the emissions of single vehicles are very sensitive to the calibration. This is confirmed by a global sensitivity analysis of the Gipps model that shows that non-optimal parameters significantly increase the variance of the outputs. Fortunately, this is no longer the case when emissions are calculated for a group of many vehicles. Indeed, the mean errors for platoons are close to 10% for the Gipps model and always lower than 4% for the Newell model. Another interesting property is that optimal parameters for each vehicle can be replaced by the mean values with no discrepancy for the Newell model and low discrepancies for the Gipps model when calculating the different emission outputs. Finally, this study presents preliminary results that show that multi-objective calibration methods are certainly the best direction for future works on the Gipps model. Indeed, the accuracy of vehicle emissions can be highly improved with negligible counterparts on the traffic model accuracy., JRC.F.8-Sustainable Transport

As models are simplifications of reality, the management of the uncertainty arising along the whole modelling process is a crucial and delicate operation, which primarily affects the credibility of model results. In the field of traffic simulation to tackle this issue, it is common practice to include the model uncertainty alongside the uncertainty in the parametric inputs. However, reducing the uncertainty in the modelling process through the indirect estimation of the model parameters is far from being simple. In this picture, a key role can be played by model sensitivity analysis. In the present work, in particular, the role of sensitivity analysis in the management of modelling uncertainties is firstly illustrated. Then, one of the most advanced techniques to perform sensitivity analysis is explained and applied to identify, in the specific context of application, which of the input factors of two car-following models can be fixed without appreciably affecting a specific output of interest. Results confirmed the relevance of sensitivity analysis in driving analysts??? activities for models??? comprehension, calibration and validation, namely, for their appropriate use.

In the field of traffic simulation, the calibration of uncertain inputs against real data is usually taken to cover the epistemic uncertainty regarding the un-modeled details of the phenomena and the aleatory not predicted by the models. For this reason, model parameters are usually indirectly derived by means of an optimization framework, which tries to maximize the fit between real and simulated measures of the traffic system. This is the case, for example, of the calibration of car-following models’ parameters against vehicle trajectory data. Only recently, it has been proven that the capability of the optimization framework to provide the parameters’ values that allow the car-following model reproducing real trajectories at its best is strictly connected to the setup of the optimization framework itself. This, in particular, entails the necessity to carefully choose an appropriate combination of optimization algorithm and measure of goodness of fit (GOF). In this study, the authors focus the attention on this latter issue. Specifically, it is claimed here that the commonly used GOFs are not able to capture the dynamics of the time-series which calibration is performed against. Therefore, a spectral analysis based approach to evaluate the overall performance of the simulation model in the objective function is proposed. The new measure of goodness of fit is tested in the calibration of the Intelligent Driver Model against synthetic trajectory data. Results confirm that the resulting optimization framework is always able to find the global optimum of the optimization problem.

Prediction of service availability in railway systems requires an increasing attention by designers and operators in order to satisfy acceptable service quality levels offered to passengers. For this reason it is necessary to reach high availability standards, relying on high-dependable system components or identifying effective operational strategies addressed to mitigate failure effects. To this purpose, in this paper an innovative architecture is proposed to simulate railway operation in order to conduct different kinds of analysis. This architecture encompasses a set of components considering, in an integrated way, several system features. Finally an application to a first case study demonstrates the impact on quality of service and service availability of different recovery strategies. Complexity of a railway system requires a heterogeneous working group composed of experts in transport and in computer science areas, with the support of industry.

Because speed has been recognized as the most important contributory factor in fatal road crashes, speed management has been widely implemented in several countries to improve road safety. The automated section speed enforcement system is increasingly being used as an effective way to tackle speeding, especially on motorways and in tunnels. However, the effects on traffic flow patterns of such systems remain controversial. An empirical analysis of traffic flow patterns before and after the introduction of such a system along a freeway section with recurrent congestion is presented. Gathered data consisted of point measurements at detectors and average travel speeds of each vehicle crossing the stretch. The main observed features were (a) a strong homogenization of individual speeds and of mean speeds among the lanes, (b) a reduction in the strength of the bottleneck, (c) the emergence of significant oscillations in time of traffic characteristics, and (d) a sensible reduction of travel times during the congestion pattern caused by the bottleneck moving downstream of the section. Empirical evidence suggests that driver compliance with speed limits is the key factor in analysis of such speed management systems and that their concurrent application with dynamic speed limit strategies should be thoroughly evaluated with a particular focus on this measure.

Actors of railway systems are more and more required to achieve certain levels of system performances and acceptable standards of service quality offered to passengers. To reach this aim, effects on both supply and demand-side induced by different solutions, need to be assessed within design and real-time operation phases, especially when failures occur during service. To this purpose, in this paper an integrated framework is introduced to simulate railway operation considering also interactions with passengers demand. A practical application to a MRT line has been realized, analyzing impacts of two recovery strategies for different failure scenarios. Results underline the necessity of assessing both service availability and quality of service to identify the most effective intervention solution, and highlight the relevance of the proposed framework to support decisional processes.

In this work a learning machine model is proposed in order to develop an Adaptive Cruise Control (ACC) system with human-like driving capabilities. The system is based on a neural network approach and is intended to assist the drivers in safe car-following conditions. The proposed approach allows for an extreme flexibility of the ACC that can be continuously trained by drivers in order to accommodate their actual driving preferences as these changes among drivers and over time. The model has been calibrated against accurate experimental data consisting in trajectories of vehicle platoons gathered on urban roads. Its performances have been compared with those of a conventional car-following model.

Parameter calibration of traffic microsimulation models usually takes the form of a simulation-based optimization problem, that is, an optimization in which every objective function evaluation calls for a simulation. It is recognized that such a problem is computationally intractable. Running time grows exponentially both in the number of parameters and in the digits accuracy. In addition, considerable computing time is required by each objective function evaluation. This means that only heuristic techniques can be applied. Accordingly, results of the application of the OptQuest/Multistart algorithm to the calibration of AIMSUN microsimulation model parameters on a freeway network are presented. Furthermore, it is claimed that the search for an effective solution to the calibration problem cannot be exhausted by the choice of the most efficient optimization algorithm. The use of available information concerning the phenomenon could allow calibration performance to be enhanced, for example, by reducing dimensions of the domain of feasible solutions. It is argued that this goal could be achieved by using information from the stationary counterpart of microscopic traffic-flow models that depict the aggregate variables of traffic flows as a function of drivers’ microscopic parameters. Because they have a closed analytical formulation, they are well suited for faster calibrations. Results show that values of parameters from stationary model-based calibrations are not far from the optimal ones. Thus the integration of the two approaches cannot be excluded but is worth investigating.

Difficulty in obtaining accurate car-following data has traditionally been regarded as a considerable drawback in understanding real phenomena and has affected the development and validation of traffic microsimulation models. Recent advancements in digital technology have opened up new horizons in the conduct of research in this field. Despite the high degrees of precision of these techniques, estimation of time series data of speeds and accelerations from positions with the required accuracy is still a demanding task. The core of the problem is filtering the noisy trajectory data for each vehicle without altering platoon data consistency; i.e., the speeds and accelerations of following vehicles must be estimated so that the resulting intervehicle spacings are equal to the real one. Otherwise, negative spacings can also easily occur. The task was achieved in this study by considering vehicles of a platoon as a sole dynamic system and reducing several estimation problems to a single consistent one. This process was accomplished by means of a nonstationary Kalman filter that used measurements and time-varying error information from differential Global Positioning System devices. The Kalman filter was fruitfully applied here to estimation of the speed of the whole platoon by including intervehicle spacings as additional measurements (assumed to be reference measurements). The closed solution of an optimization problem that ensures strict observation of the true intervehicle spacings concludes the estimation process. The stationary counterpart of the devised filter is suitable for application to position data, regardless of the data collection technique used, e.g., video cameras.