Massimiliano Gastaldi

Advanced driver assistance systems: Using multimodal redundant warnings to enhance road safety.

This study investigated whether multimodal redundant warnings presented by advanced assistance systems reduce brake response times. Warnings presented by assistance systems are designed to assist drivers by informing them that evasive driving maneuvers are needed in order to avoid a potential accident. If these warnings are poorly designed, they may distract drivers, slow their responses, and reduce road safety. In two experiments, participants drove a simulated vehicle equipped with a forward collision avoidance system. Auditory, vibrotactile, and multimodal warnings were presented when the time to collision was shorter than five seconds. The effects of these warnings were investigated with participants performing a concurrent cell phone conversation (Exp. 1) or driving in high-density traffic (Exp. 2). Braking times and subjective workload were measured. Multimodal redundant warnings elicited faster braking reaction times. These warnings were found to be effective even when talking on a cell phone (Exp. 1) or driving in dense traffic (Exp. 2). Multimodal warnings produced higher ratings of urgency, but ratings of frustration did not increase compared to other warnings. Findings obtained in these two experiments are important given that faster braking responses may reduce the potential for a collision.

Post-quake urban road network functionality assessment for seismic emergency management in historical centres

Abstract In this paper, a procedure for the evaluation of the interaction between existing buildings and urban roadway networks after a seismic event is proposed. This question is relevant in historical urban centres, where urbanisation typology has evidenced, in the past, lack of road networks’ residual connectivity in post-earthquake conditions. The present study proposes a method for the evaluation of the residual functionality in urban context, moving from the execution of building surveys, the application of probabilistic concepts and fuzzy logic with the aim to define probable urban road network damage scenarios. The methodology is applied to the Municipality of Conegliano (northeastern Italy) evaluating the potential seismic damage scenario to the historical urban centre and highlighting criticisms in the post-earthquake rescue operations.

Advances in Uncertainty Treatment in FHWA Procedure for Estimating Annual Average Daily Traffic Volume

This paper proposes an approach that estimates annual average daily traffic (AADT) of a road section from incomplete data. This approach improves the accuracy and interpretability of the results while preserving the concept of the current FHWA procedure. When the short-period traffic count (SPTC) of a road section is given, a road group with a known AADT and a similar traffic pattern is identified. The AADT of the road section in question is estimated by adjusting the AADT associated with the road group by the degree of similarity. The uncertainty associated with the similarity is measured by nonspecificity and discord. The model was tested with data obtained in the Province of Venice, Italy. The analysis considered the characteristics of SPTC, including duration and day of the week. Estimates obtained with the proposed method and two existing methods were compared. The proposed method was found to produce more accurate results than the previous methods. Weekday 48-h short counts were found to be the best sample SPTC for AADT estimation. Furthermore, the measures of uncertainty helped to interpret the quality of the estimates. SPTCs with a lower value of discord were found to yield better AADT estimates, and a high value of nonspecificity indicated uncertainty with respect to matching with the groups. These measures can also indicate the need for additional data collection.

Evaluating the impact of processing spoken words on driving

The potential impact on driving of the processing of a single, auditorily presented word is analyzed in this work. Because driving is a complex cognitive activity that involves the integration and coordination of multiple subprocesses, the authors narrowed the scope of the research to concentrate on one critical task involved in driving: driver braking response. If two tasks have to be performed concurrently and both of them require access to a capacity-limited system, then performance in one or both of the tasks will dramatically worsen because the two processes will compete for access to cognitive resources. It has been shown that both word recognition and driving require central resources; therefore, these tasks are likely to interfere with each other. In the experiments, participants were required to perform two tasks during simulated driving. In the word recognition task, participants had to categorize auditorily presented words. In the braking task, participants depressed a brake pedal in response to the lead cars brake lights. The interval of time between the onset of the tasks’ stimuli was varied. Braking responses were substantially slower as the overlap between tasks increased. This finding demonstrates that the processing of a single word hinders driving performance. The experiments carried out have significant implications in the field of road safety. Many situations, such as cell phone ringing, cell phone conversations, auditory tips from navigation systems, and auditory alerts from driver warning systems, are similar to those studied. The experiments suggest that all these situations can negatively affect a drivers response time, increasing the likelihood of near misses and accidents.

Empirical Analysis of Vehicle Time Headways and Speeds on Rural Two-Lane, Two-Way Roads

Accurate knowledge of vehicle time headway (TH) and speed distributions is essential in many traffic engineering applications, such as capacity and level-of-service analysis, and in several contexts (operations at road segments, priority junctions, roundabouts, merging maneuvers). Other fields of interest are represented by vehicle generation in traffic microsimulation models and driving simulation applications. This paper presents results from an experimental analysis of vehicle TH and speed distributions on two-lane, two-way roads. The focus is on rural roads in Northern Italy, which are characterized by various levels of traffic and where data were collected by radar sensors. A trendless analysis of observed THs was carried out. Analysis then focused on estimating a well-known TH model, gamma-generalized queuing. Statistical analysis of the available data allowed testing of a set of speed distribution models, highlighting their goodness of fit with reference to empirical distributions. Completing the analysis, bivariate distributions were estimated to represent headway and speed distributions. The traffic parameters considered to affect TH distributions were flow rate and flow composition (percentage of heavy vehicles). Exogenous conditions, such as weather and road geometric futures, were common to all analyzed time periods and cross sections. The final goal of the analysis was to create a picture of the relationship between traffic conditions and speed and headway distributions for a typical road.

Comparison of Clustering Methods for Road Group Identification in FHWA Traffic Monitoring Approach: Effects on AADT Estimates

Defining road groups is the first step in the Federal Highway Administration (FHWA) factor approach procedure for annual average daily traffic (AADT) estimation and is one of the main sources of errors in AADT estimates. This paper focuses on a comparative analysis of cluster analysis methods to identify road groups with similar traffic patterns according to different combinations of seasonal adjustment factors calculated for passenger vehicles and trucks. The aim is to highlight the differences among methods and input variables in the AADT estimation process, optimizing information commonly available to analysts. The analysis made use of traffic data from 50 automatic traffic recorder (ATR) sites in the Province of Venice, Italy. The estimation accuracy of the clustering methods was assessed and compared by considering the values of mean absolute percent error in AADT estimates. The performance of clustering methods was found to differ, depending on data sets and traffic patterns. Particularly significant for the accuracy of AADT estimates was the choice to use seasonal adjustment factors disaggregated by vehicle type as input variables.

Fitting Time Headway–Vehicle Speed Bivariate Distributions: Operational Procedure for Two-Way Two-Lane Roads

Knowledge of traffic flow characteristics at cross sections helps analysts, software developers, and practitioners to identify, model, and simulate traffic flows as inputs in operational analysis. This work presents a complete procedure for constructing bivariate time headway (TH)–vehicle speed (VS) distributions starting from raw traffic data on two-way two-lane roads. The general framework of the procedure, together with functional components and their mutual interactions, was designed and implemented. The effect of flow rate in both directions (analyzed and opposite) on TH-VS distributions was examined. The possibility of associating the TH-VS probability density function with several kinds of traffic conditions in both directions and lanes is a significant and interesting advance with respect to previous works. The procedure was applied to a set of four cross sections belonging to the road network of the province of Venice (northeast Italy). This application enabled testing of the various steps of the procedure and acquisition of better knowledge concerning the way the findings can be used for traffic flow description and simulation.

Sustainability Evaluation of Transportation Policies: A Fuzzy-Based Method in a “What to” Analysis

The widely debated concepts of sustainability and sustainable development represent nowadays an essential aspect in transportation studies, in particular for the analyses of interactions between transportation and land-use systems. In this paper the three-dimensional concept of sustainability (social, economic and environmental sustainability) is formalized by a Fuzzy-Based Evaluation Method, which has already been applied for evaluating the sustainability of alternative transportation policies. The method is tested as a tool to interpret the preferences expressed by the decision makers, to identify the most important characteristics of alternative transportation policies and to support the design of hypothetical transportation services, following a “What to” analysis.

Fuzzy Logic Models of Gap-Acceptance Behavior at Roundabouts

Gap-acceptance behavior at intersections has been extensively studied in the field of traffic theory and engineering using various methods. An interesting application of gap-acceptance theory regards roundabouts, which differ from ordinary unsignalized intersections in terms of geometry and driving behavior. Several studies on gap-acceptance at roundabouts can be found in the literature, but, to our knowledge, the fuzzy logic approach has never been used to analyze this type of problem. This chapter describes the development of a gap-acceptance model based on fuzzy system theory and specifically applicable to traffic entering a roundabout. As an alternative to probabilistic discrete choice models, fuzzy system based models can be considered to be appropriate for describing gap-acceptance behavior at roundabouts, because they allow to represent the uncertainty and vagueness that characterizes various aspects of the choice situation under study. Possible applications of fuzzy logic models of gap-acceptance behavior include roundabout entry capacity estimation and use in the context of traffic micro-simulation software. The study is based on data derived from on site observations carried out at a roundabout near Venice, Italy. The performance of the model, evaluated using the Receiver Operating Characteristic (ROC) curve analysis, indicates that fuzzy models can be considered an alternative to the use of random utility models.

Caffeinated Chewing Gum as Countermeasure to Drivers’ Passive Task-Related Fatigue Caused by Monotonous Roadway

This study analyzed driver passive task-related fatigue caused by a monotonous environment and the effectiveness of caffeinated chewing gum as a countermeasure. Data collected by a driving simulator in the laboratory were used to measure changes in driving performance. A self-perceived measure of fatigue was also analyzed. Seventy-two subjects were asked to drive for 70 min along a straight road after receiving one of the following substances (treatments): caffeinated chewing gum, a cup of coffee, or placebo chewing gum. The 72 subjects were subdivided into three groups of 24 each, and all participants were asked to take part in two driving sessions: one control drive without administration (no treatment) and one with administration (one of the treatments). The negative effects on driving performance of prolonged driving and the effectiveness of the standard deviation of the lateral position in representing worsening driving performance were demonstrated. This analysis indicated that intake of caffeine in the form of caffeinated chewing gum (100 mg caffeine) improved driving performance in less than 10 min. Drinking an ordinary cup of coffee (with the same caffeine content) did not improve driving performance in the same short time interval.