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The BIM (Building Information Modeling) approach potential in the civil engineering field opened novel scenarios in the design idea concept, from planning to executive and constructive phases. The related advantages are numerous and not only limited to a real-time interaction among the involved subjects, that can actually operate in an optimized 3D shared environment. Owing to the sharing information philosophy and to the features of various "smart objects" combined in the project, this innovation reduces potential errors and increases the effectiveness of the design solution in terms of both functionality and cost. Despite these advantages, the highway alignment design problem remains very complicated and not easy to solve without appropriate supporting tools. In recent years, several efforts have been spent in defining highway optimization procedures for helping designers in the selection of an optimal solution in compliance with numerous different constraints. Introducing these procedures in a BIM environment may represent a crucial step in the improvement of the highway design procedures, exploiting the full representation and modelling potential of the approach. In this paper, the authors present the advantages of a 3D highway alignment optimization algorithm, based on the Particle Swarm Optimization method, and its possible implementation in a BIM platform. A proper I-BIM environment can exploit the potential of the alignment optimization algorithms, simplifying the analysis of the different solutions, the final representation and the eventual manual modifications.

Abstract. In this paper, we investigated drivers' visual behaviour while travelling a road regularly opened to traffic in order to evaluate the effectiveness of the traditional scientific models and propose, at the same time, further measures useful for understanding the complex phenomenon. As is known, drivers acquire the necessary information for knowing the road geometry by visually detecting certain areas of the surrounding context. Some models in the literature have shown in a simple and convincing way these mechanisms, but they are valid only with specific assumptions, often very restrictive, such as a two-lane road, horizontal sign clearly visible and no interaction with other vehicles. For this reason, in this study we wanted to investigate different conditions, by estimating the visual strategy of some regular drivers on a three-lane road in presence of other vehicles. The visual behaviour was surveyed with the Tobii Glasses Eye Tracker and the resulting raw data were further manipulated by us to extract more useful information for our purposes. In particular, we quantified the driver's dedicated attention to the various elements present inside the environmental context, both static (road edges, road signs, dashboard, etc.) and dynamic (other vehicles), meaning by this term those that could potentially collide with the trajectories of our vehicle. The achieved results, highlighting the limits of validity of some recent studies, contain some proposed indexes useful to give a better understanding of the visual behaviour in order to detect any eventual weakness of the road.

This study proposes a prediction model about the trajectories a vehicle, in isolated conditions, along a curve of a road. As we know, the road environment induces stress on users and, under certain conditions, influences driving behavior. It is of advantage then, to isolate and identify those conditions from among the numerous variables, which are actually the most significant so as to prevent or mitigate the occurrence of dangerous maneuvers. On the basis of an experiment performed using an instrumented vehicle, we collected a data base to which we subsequently applied Neuro-Fuzzy techniques for the selection of the most representative variables. We then used these data to prepare a nonlinear dynamic Hammerstein-Wiener's model able to predict the track paths along curves. The findings were encouraging since almost all the results obtained from the validation checks proved satisfactory. This research is the first step in the identification of complex systems and could be applied in road safety measures and design of new and existing roads.

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This paper analyses the driver’ visual behaviour in the different conditions of ‘isolated vehicle’ and ‘disturbed vehicle’. If the meaning of the former is clear, the latter condition considers the influence on the driving behaviour of various objects that could be encountered along the road. These can be classified in static (signage, stationary vehicles at the roadside, etc.) and dynamic objects (cars, motorcycles, bicycles). The aim of this paper is to propose a proper analysis regarding the driver’s visual behaviour. In particular, the authors examined the quality of the visually informa-tion acquired from the entire road environment, useful for detecting any critical safety condition. In order to guaran-tee a deep examination of the various possible behaviours, the authors combined the several test outcomes with other variables related to the road geometry and with the dynamic variables involved while driving. The results of this study are very interesting. As expected, they obviously confirmed better performances for the ‘isolated vehicle’ in a rural two-lane road with different traffic flows. Moreover, analysing the various scenarios in the disturbed condition, the proposed indices allow the authors to quantitatively describe the different influence on the visual field and effects on the visual behaviour, favouring critical analysis of the road characteristics. Potential applications of these results may contribute to improve the choice of the best maintenance strategies for a road, to select the optimal signage location, to define forecasting models for the driving behaviour and to develop useful instruments for intelligent transportation systems.

Vehicle control can be described with lateral and longitudinal control measures. The Standard Deviation of Lateral Position (SDLP) is probably the most common measure to reflect lateral control. Indices such as mean Lateral Position (MLP) and Time-to-Line Crossing (TLC) have also been used to describe driver behaviour. Even though all these measures have demonstrated their value, in some specific cases, these measures may indicate that driver behaviour is deteriorated while that may not necessarily be the case. When negotiating curves for example, most drivers prefer to not to follow the centre of the lane. We propose a new index, called the Cumulative Lateral Position (CLP), an index that does not suffer from drawbacks of the earlier mentioned measures in these conditions. We also applied the CLP in a practical case. In a simulator experiment drivers negotiated three types of curves: traditional circular (CIR), clothoid (CLO), and a new curve, a polynomial curve with continuous curvature (CON). Results show that the CLP index, unlike the older measures, is able to well summarise the trajectory on a road curve and is sensitive in distinguishing different driving behaviour with respect to variations in road geometry, even in cases where these differences are small. The proposed methodology can be used to evaluate both new and existing roads design solutions, and showed in this experiment that driving behaviour was safest in the continuous curve.