Background: This paper compares a hybrid traffic flow model with benchmark macroscopic and microscopic models. The proposed hybrid traffic flow model may be applied considering a mixed traffic flow and is based on the combination of the macroscopic cell transmission model and the microscopic cellular automata.Modelled variables: The hybrid model is compared against three microscopic models, namely the Krauß model, the intelligent driver model and the cellular automata, and against two macroscopic models, the Cell Transmission Model and the Cell Transmission Model with dispersion, respectively. To this end, three main applications were considered: (i) a link with a signalised junction at the end, (ii) a signalised artery, and (iii) a grid network with signalised junctions.Results: The numerical simulations show that the model provides acceptable results. Especially in terms of travel times, it has similar behaviour to the microscopic model. By contrast, it produces lower values of queue propagation than microscopic models (intrinsically dominated by stochastic phenomena), which are closer to the values shown by the enhanced macroscopic cell transmission model and the cell transmission model with dispersion. The validation of the model regards the analysis of the wave propagation at the boundary region.

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 “operational benefits” 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 “do nothing” scenario (current; DN) and the “modeled compliance” (MC) scenario, in which travelers’ 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.

In this work some experiments are made in order to assess the feasibility of a human-like ACC (adaptive cruise control) system. The proposed system is able to understand driverpsilas attitudes and driving-styles by means of a self-calibration process that can be (re)initialized on request. Three different speed-control logics have been tested: one tries to learn from actual driverspsila behaviors by using an artificial neural network (ANN) approach, the second is based on the calibration of a linear function aimed to be mimic of the driver response to stimuli, the third is based on the calibration of a polynomial function instead of a linear one. A microscopic traffic model, accurately calibrated and validated for different aims and in a previous work, has been adapted and used in order to generate a long car-following trajectory on which the speed control logics have been calibrated and compared. This has allowed for a sufficient amount of accurate laboratory data at a relatively low cost. Comparison of the tested speed-control logics show that a fully adaptive human-like ACC system is feasible and worth further more costly developments.

This paper analyses travellers’ behaviour with respect to route choice in a context where an Advanced Traveller Information System (ATIS) is in place. ATIS are important applications in the field of intelligent transportation systems (ITS). However, the practical impact of ATIS is still a matter for debate, and identification of expected route choice behaviour under ATIS is one of the main ways to assess their practical importance. Travellers’ choices are frequently explored by means of stated preference (SP) approaches. In this paper we discuss some issues to be addressed when an SP survey is carried out, with particular reference to cases where a repeated choice approach is employed in the survey. Our analysis concerns an application of the SP approach in a pilot study aimed at identifying the effects of ATIS accuracy on travellers’ compliance with information. This paper aims to make two major contributions. First of all, empirical analyses based on proper indicators and statistical tests are suggested in order to evaluate how the collected data have to be handled in order to eliminate transient route-choice observations. These are due to the warm-up phase inherently associated with the survey method adopted, dealing with repeated choices. Secondly, we analyse (stationary) route choice in order to assess the effects of information reliability (and the kind of information) on both route choice and compliance.