Gregorio Gecchele

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.

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.

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.

Traffic-Calming Measures Affecting Perceived Speed in Approaching Bends: On-Field Validated Virtual Environment

With the aim of reducing the number of road traffic deaths around the world, the United Nations General Assembly proclaimed 2011–2020 the Decade of Action for Road Safety. Excessive speed is one of the main problems to overcome. The aim of this study was to test the effectiveness of traffic-calming measures in reducing drivers’ speed along a road with a dangerous bend in an inland area near Venice, Italy. The driving simulator of the Transportation Laboratory of the University of Padua and the simulated scenario were validated by reproducing the study site environment. A driving simulator experiment was conducted to analyze changes in speed profiles associated with various countermeasures: evenly spaced guideposts, tall guideposts, narrowing guideposts, and dragons teeth markings. Tall and narrowing guideposts served to reduce drivers’ speed by up to 2.7 km/h. Unlike tall guideposts, which produced no detrimental effect on drivers’ behavior, narrowing guideposts led drivers to occupy more variable positions within the lane. In view of this at-risk behavior, the convenience of the option to produce an apparent narrowing of the lane on real roads was discussed. This study found that the use of driving simulators was a reliable research tool to reproduce drivers’ real behavior. The study also provided effective, low-cost measures to counteract excessive speed on dangerous road sections.

Collision prediction in roundabouts: a comparative study of extreme value theory approaches

This work contributes to study the application of extreme value theory (EVT) in road safety analysis, estimating the risk of being involved in an entering–circulating collision in single-lane roundabouts. Detailed trajectory data of the vehicles were derived from a driving simulator experiment, and the time-to-collision (TTC) was used as a surrogate measure of safety. Three EVT approaches were applied, tested and compared: (1) the Generalized Extreme Value distribution used in the block maxima (BM) approach, (2) the Generalized Pareto Distribution used in the peak-over-threshold approach (POT), with negated-TTC (nTTC), and (3) shifted-reciprocal-TTC (srTTC). Case-study results analysis showed that BM and POT with shifted-reciprocal-TTC confidence intervals included the number of observed crashes, while POT with negated-TTC did not include it. According to these findings, both BM and POT-with-shifted-reciprocal-TTC appear promising and deserve further attention in order to develop effective ready-to-practice crash prediction models, useful in intersection design and operational analysis.

An advanced driver assistance system for improving driver ability. Design and test in virtual environment

This work presents initial findings of a research project aimed at designing an assistance system able to improve driver ability and reduce accident risk. The proposed system is an innovative Advanced Driver Assistance System based on the integration of two main components: a training procedure based on precision teaching, and a control equipment monitoring driver behavior and providing feedbacks during regular driving and in critical traffic conditions. With reference to the first component, two driving simulator experiments were designed and implemented. Experiments aimed at demonstrating the effectiveness of precision teaching for training drivers and observing the effectiveness of the feedbacks system in enhancing vehicle control. Vehicle lateral position was considered as main dependent measure. Feedbacks were successful in improving vehicle position within the lane with lateral variability being reduced after three trials.

Automatic Incident Detection on Freeway Ramp Junctions. A Fuzzy Logic-Based System Using Loop Detector Data

Vehicle loop detectors or other equipment installed on highway sections are commonly used for monitoring traffic flow conditions on road networks. For operational analysis, it is essential to be able to distinguish low levels of service due to over-saturated conditions from those caused by extraordinary events such as incidents. In the case of incidents, prompt responses are crucial for activating any required countermeasures, such as rescue activation or traffic detours. Automatic Incident Detection methods for basic freeway segments are widely reported in the literature, but their application to freeway ramp merging zones is limited. This work introduces a control system which can identify incidents from vehicle loop detector data on freeway ramp merging zones. The system was developed with fuzzy logic concepts and calibrated with data from micro-simulation experiments. The main finding of this study is that the detection system, despite its simplicity, shows excellent False Alarm Rate (FAR) and satisfactory Detection Rate (DR) and Mean Time To Detection (MTTD), generally better than those obtained with the traditional California#7 comparative algorithm.