Francesco Galante

Perceptual Measures to Influence Operating Speeds and Reduce Crashes at Rural Intersections: Driving Simulator Experiment

The aim of this paper is to investigate, by means of a dynamic driving simulator experiment, the behavior of road users at rural intersections in relation to perceptual measures designed for increasing hazard detection. In the experiment 10 configurations of tangents were tested: Alt1, base tangent; Alt2, four-leg base intersection; Alt3, intersection with reduced sight distance; and Alt4 through Alt10, intersections with perceptual treatments. The Virtual Environment for Road Safety high-fidelity dynamic-driving simulator, operating at the Technology Environment Safety Transport Road Safety Laboratory located in Naples, Italy, was used. Analysis of the results used two approaches: (a) explorative description of data by cluster analysis and (b) inferential procedures about population using statistical tests. Results showed that the speed behavior in the tangents was significantly affected by the presence of the intersections and by the perceptual treatments. Intersections without perceptual treatments significantly affected driver speeds in the 250 m preceding the intersection. Perceptual treatments helped the driver to detect the intersection earlier and to slow down. Dragon teeth markings, colored intersection area, and raised median island performed better than the other perceptual treatments. They produced significant average speed reduction in the 150 m preceding the intersection ranging between 16 km/h and 23 km/h. Study results support real-world implementation of perceptual measures in rural intersections because they are low-cost, fast implementation measures with a high potential to be cost-effective.

Prediction of Drivers' Speed Behavior on Rural Motorways Based on an Instrumented Vehicle Study

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.

Coupling instrumented vehicles and driving simulators: Opportunities from the DRIVE IN2 project

DRIVE IN2 is an automotive research project within the field of Intelligent Transportation Systems, especially Advanced Driving Assistance Systems (ADAS). The project originates from the idea that the development of new ADAS and evaluation of their effect have to take drivers into account, as well as their behavior while driving: the benefits of adopting new in-vehicle technologies depend also on their adoption and usage by drivers. To this aim, the project develops a Driver-In-the-Loop framework to position observation of the drivers at the center of the research activities. Observations are carried out by coupling different research tools, namely instrumented vehicle and driving simulators. The premise and methodological framework of the research project are presented and discussed. Some preliminary activities with particular reference to validating the driving simulation environment are also described.

Identification of Driving Behaviors with Computer-Aided Tools

Identification of driving behavior is a crucial task in several Intelligent Transportation Systems applications, both to increase safety and assist drivers. Here we identify driving behaviors by means of an analytical model. In order to estimate the model parameters, data are collected with an instrumented vehicle. The paper presents the model, the procedure for the estimation of the parameters and the results of the proposed framework with respect to a pilot experiment to assess the feasibility and potential of the approach. Some practical implementations of the proposed model are presented. In particular, road safety assessment is introduced in greater depth to show the potential of the approach. For this purpose, a modified (and original) version of some surrogate measures of safety is introduced.

Effects of traffic control devices on rural curve driving behavior

This study investigated, by means of a dynamic driving simulator experiment, driver behavior at curves on rural two-lane highways in relation to different advance warning signs, perceptual measures, and delineation treatments. The tested treatments were intended to alert drivers to the presence of low-radius curves and to affect their behavior in the approach to the curve as well as along the curve itself. The study results showed that the advance warning signs, perceptual measures, and delineation treatments tested in the driving simulator experiment produced significant effects on driver behavior. The perceptual treatments (i.e., colored transverse strips, dragon teeth markings, colored median island) were the most effective treatments because they produced significant speed reductions in the approach tangent as well as inside the curve. Deceleration behavior in the approach to the curve was affected significantly by the presence of treatments that helped drivers to detect the curve earlier; early detection provided more time to perform deceleration maneuvers at lower rates. The study results strongly supported the real-world implementation of colored transverse strips, dragon teeth markings, and the colored median island. Implementation of the tested measures should be conducted on similar rural highways to validate general application of the results of this study to other regions.

Application of Data Fusion for Route Choice Modelling by Route Choice Driving Simulator

Modelling route choices is one of the most significant tasks in transportation models. Route choice models under Advanced Traveller Information Systems (ATIS) are often developed and calibrated by using, among other, Stated Preferences (SP) surveys. Different types of SP approaches can be adopted, alternatively based on Travel Simulators (TSs) or Driving Simulators (DSs). Here a pilot study is presented, aimed at setting up an SP-tool based on driving simulator developed at the Technical University of Bari. The obtained results are analysed in order to check the accordance with expectations in particular the results of application of data fusion technique are shown in order to explain how data collected by DSs, can be used to reduce the effect of choice of behaviour in unrealistic scenarios in TSs.

Validity of Mental Workload Measures in a Driving Simulation Environment

Automated in-vehicle systems and related human-machine interfaces can contribute to alleviating the workload of drivers. However, each new functionality can also introduce a new source of workload, due to the need to attend to new tasks and thus requires careful testing before being implemented in vehicles. Driving simulators have become a viable alternative to on-the-road tests, since they allow optimal experimental control and high safety. However, for each driving simulator to be a useful research tool, for each specific task an adequate correspondence must be established between the behavior in the simulator and the behavior on the road, namely, the simulator absolute and relative validity. In this study we investigated the validity of a driving-simulator-based experimental environment for research on mental workload measures by comparing behavioral and subjective measures of workload of the same large group of participants in a simulated and on-road driving task on the same route. Consistent with previous studies, mixed support was found for both types of validity, although results suggest that allowing more and/or longer familiarization sessions with the simulator may be needed to increase its validity. Simulator sickness also emerged as a critical issue for the generalizability of the results.

The impact of the leading vehicle type on car-following behaviours

Modelling car-following in an effective and accurate way is of great importance for several areas of application, such as microscopic traffic simulation and ADAS (Advanced Driving Assistance Systems). Heterogeneity can be observed in driving behaviors if car-following data are analyzed. Part of this dispersion depends on the inherent heterogeneity across drivers, that could react in different ways to very similar stimuli. However, another source of dispersion could be related to a misleading identification of the stimuli or to an improper identification of the context in which the stimuli are evaluated. This work is oriented to analyze if a non-negligible part of the observed heterogeneity can be explained by considering the type of the leading vehicle. Observation of car-following trajectories has been carried out in a large experiment involving one hundred drivers, with different leading vehicles. These observation have been interpreted by means of behavioral models and the parameters of these models have been separately identified for different types of leading vehicles. Comparison of modelling parameters, as well as directly observed variables as speed and spacing, allows for testing if the type of leading vehicle actually influences the car-following behavior.