evaluating the safety benefit of retrofitting motorways section with barriers meeting a new eu standard comparison of observational before after methodologies

Abstract The road safety barriers are today designed and installed in compliance with the European standards for Road Restraint Systems (EN 1317), which lays down common requirements for the testing and certification in all EU countries. The introduction of the European Union (EU) regulation for safety barriers, which is based on performance, has encouraged European road agencies to perform an upgrade of the old barriers installed before 2000, with the expectation that there will be safety benefits at the retrofitted sites. Due to the high cost of such treatments, a benefit-cost analysis (BCA) is often used for site selection and ranking and to justify the investment. To this aim a crash modification factor (CMF) has to be applied and errors in the estimation of benefits are directly reflected in the reliability of BCA. Despite the benefits of empirical Bayes before–after (EB–BA) analysis or similar rigorous methods are well-known in the scientific world, these approaches are not always the standard for estimating the effectiveness of safety treatments. To this aim, the differences between the EB–BA and a naive comparison of observed crashes before and after the treatment are presented in the paper. Crash modification factors for total and target crashes are estimated by performing an EB–BA based on data from a motorway in Italy. As expected the results suggest a strong safety benefit for the ran-off-road crashes by reducing the number of severe crashes (fatal and injury). The statistical significance of results obtained by the EB–BA approach show that the retrofits are still cost-effective. The comparison pointed out as selection bias effects can overestimate the safety benefit of the retrofits when a naive approach is used to estimate the CMF and how those can significantly affect a benefit-cost analysis

Investigating the influence of segmentation in estimating safety performance functions for roadway sections

Safety performance functions (SPFs) are crucial to science-based road safety management. Success in developing and applying SPFs, apart data quality and availability, depends fundamentally on two key factors: the validity of the statistical inferences for the available data and on how well the data can be organized into distinct homogeneous entities. The latter aspect plays a key role in the identification and treatment of road sections or corridors with problems related to safety. Indeed, the segmentation of a road network could be especially critical in the development of SPFs that could be used in safety management for roadway types, such as motorways (freeways in North America), which have a large number of variables that could result in very short segments if these are desired to be homogeneous. This consequence, from an analytical point of view, can be a problem when the location of crashes is not precise and when there is an overabundance of segments with zero crashes. Lengthening the segments for developing and applying SPFs can mitigate this problem, but at a sacrifice of homogeneity. This paper seeks to address this dilemma by investigating four approaches for segmentation for motorways, using sample data from Italy. The best results were obtained for the segmentation based on two curves and two tangents within a segment and with fixed length segments. The segmentation characterized by a constant value of all original variables inside each segment was the poorest approach by all measures. Keywords: Road safety management, Rural motorways, Safety performance functions, Segmentation, Crash prediction, General estimating equatio

Observed Risk and User Perception of Road Infrastructure Safety Assessment for Cycling Mobility

The opportunities for data collection in smart cities and communities provide new approaches for assessing risk of roadway components. This paper presents and compares two different methodological approaches for cycling safety assessment of objective and perceived risk. Objective risk was derived from speed and direction profiles collected with Global Navigation Satellite System (GNSS) and camera installed on an instrumented bicycle. Safety critical events between cyclists and other road users were identified and linked to five different roadway components. A panel of experts was asked to score the severity of the safety critical events using a Delphi process to reach consensus. To estimate the perceived risk, a web-based survey was provided to the city bicyclist community asking them to score the same five roadway components with a 4-point Likert scale. A comparison between perceived and objective risk classification of the roadway components showed a good agreement when only higher severity conflicts were considered. The research findings support the notion that it is possible to collect information from bicycle probe data that match and user perceptions and thus, utilizing them to take advantage of such data in advancing the goals of in smart cities and communities

Structural health monitoring of asphalt pavements using smart sensor networks: A comprehensive review

Abstract Early, effective and continuous monitoring allows to reduce costs and to extend life of road infrastructure. For this reason, over the years, more and more efforts have been made to implement more advanced and effective monitoring systems at ever more contained costs, going from impractical manual and destructive methods through automated in vehicle equipment to the most recent wireless sensor network (WSN) embedded into the pavement. The purpose of this paper is to provide a comprehensive, up-to-date critical literature review of wireless sensor networks for pavement health monitoring, considering, also, the experience gained for wired sensor as fundamental point of reference. This work presents both the methodology used to collect and analyse the current bibliography and provides a description and comments fundamental characteristics of wireless sensor networks for pavement monitoring for damage detection purposes, among which energy supply, the detection method, the hardware and network architecture and the performance validation procedures. A brief analysis of other possible complementary applications of smart sensor networks, such as traffic and surface condition monitoring, is provided. Finally, a comment is provided on the gaps and possible directions that future research could follow to allow the extensive use of wireless sensor networks for pavement health condition monitoring

A Collaborative System to Manage Information Sources Improving Transport Infrastructure Data Knowledge

The present paper describes the WIKI RoadSMap project implemented within a start-up research program. The main objective of the project is to create a system that applies innovative technologies to information gathered to enable the acquisition of greater local knowledge and analysis of issues related to road infrastructure and directly and indirectly connected elements. By applying semantic analysis technology for the extraction, collection, integration and publication of data, WIKI RoadSMap allows users to acquire greater knowledge in order to optimize choices related to road infrastructure. The system allows more detailed and targeted dissemination of data related to the design, management and maintenance of an infrastructure. The source and type of data needed are different and heterogeneous, including information 'posted' by people with private and/or commercial purposes, or available at road agencies and/or public administrations or related to specific surveys carried out. The system platform should be available on the Web and on smartphones, both providing different levels of access and subscriptions. The spread and use of WIKI RoadSMap could have a positive impact on the market with regard to the supply of materials and specialized technical skills and companies operating in the areas of interest

In-vehicle stereo vision system for identification of traffic conflicts between bus and pedestrian

Abstract The traffic conflict technique (TCT) was developed as "surrogate measure of road safety" to identify near-crash events by using measures of the spatial and temporal proximity of road users. Traditionally applications of TCT focus on a specific site by the way of manually or automated supervision. Nowadays the development of in-vehicle (IV) technologies provides new opportunities for monitoring driver behavior and interaction with other road users directly into the traffic stream. In the paper a stereo vision and GPS system for traffic conflict investigation is presented for detecting conflicts between vehicle and pedestrian. The system is able to acquire geo-referenced sequences of stereo frames that are used to provide real time information related to conflict occurrence and severity. As case study, an urban bus was equipped with a prototype of the system and a trial in the city of Catania (Italy) was carried out analyzing conflicts with pedestrian crossing in front of the bus. Experimental results pointed out the potentialities of the system for collection of data that can be used to get suitable traffic conflict measures. Specifically, a risk index of the conflict between pedestrians and vehicles is proposed to classify collision probability and severity using data collected by the system. This information may be used to develop in-vehicle warning systems and urban network risk assessment

Traffic conflicts analyses for 2+1 road sections

The additional passing lanes and 2+1 roads improve significant road safety. Studies indicate sections with additional passing lanes (relief or alternately), which may cause reduction in the number of accidents by 50%. However, how geometric design affects the safety performance of such sections is not in depth investigated. Previous studies are carried out with two approaches, i.e. the most often, based on analysis of observed crashes and more rarely by using microsimulation study. In the case of microsimulation research, traffic conflict theory can be applied as a surrogate measure of safety. One of the main problem in simulated conflicts study is the validation of simulation results against real world conditions. The aim of the paper is to assess the reliability of traffic conflict measures obtained by microsimulation against real world observation. Conflicts were detected and classified from video recording and analysis of vehicle trajectories in the merging area on 2+1 roads in Poland. Conducted studies focus only on lane changing conflicts, locations and TTCs values of observed conflicts between vehicles were primarily identified. Observed conflicts are than compared with microsimulated one, to assess if there is a correlation in the two

Rendimiento de los sistemas para mantenerse en el carril (LSS) en alineaciones curvilíneas

Lane support systems (LSS) are based on computer vision and they are expected to give safety benefits. However, despite the assumed technology readiness, there is still a lot of uncertainty regarding the needs of vision systems for “reading” the road and limited results are still available from in-field testing. In this framework an experimental test of LSS performance was carried out in two-lane rural roads with different geometric alignments. LSS faults in daylight and dry pavement conditions were detected on average in 2 % of the road sections, but with significant differences basing on horizontal curvature radius. Additionally, the increase of fault probability of failure to 8% was observed in road sections with a radius of less than 200 m. A curvature radius of 200 m is a relevant geometric constrain in mountain roads in which curves with a smaller radius are common.Los sistemas para mantenerse en el carril (LSS) se basan en la visión artificial y se espera que ellos brinden beneficios de seguridad. No obstante, a pesar de la supuesta preparación tecnológica, todavía hay mucha incertidumbre con respecto a las necesidades de los sistemas de visión para "leer" la carretera, ya que son  limitados los resultados que están disponibles en las pruebas de campo. En tal marco, se desarrolló una prueba experimental de desempeño LSS que fue realizada en carreteras rurales de dos carriles con diferentes alineaciones geométricas. Las fallas de LSS, en condiciones de luz diurna y pavimento seco, fueron detectadas en promedio en un 2% de los tramos de la vía, pero con diferencias significativas en función del radio de curvatura horizontal. Adicional, se observó un aumento de la probabilidad de falla al 8% en los tramos de carretera con un radio de menos de 200 m. Un radio de curvatura de 200 m es una restricción geométrica relevante en carreteras de montaña donde las curvas con un radio menor son usuales

Monitoring Bicycle Safety through GPS data and Deep Learning Anomaly Detection

Cycling has always been considered a sustainable and healthy mode of transport. Moreover, during Covid-19 period, cycling was further appreciated. by citizens as an individual opportunity of mobility. As a counterpart of the growth in the num.ber ofbicyclists and of riding k:ilometres, bicyclist safety has become a challenge as the unique road transport mode with an increasing trend of crash fatalities in EU (Figure 1). When compared to the traditional road safety network screening. availability of suitable data for crashes involving bicyclists is more difficult because of underreporting and traffic flow issues. In such framework, new technologies and digital transformation in smart cities and communities is offering new opportunities of data availability which requires also different approaches for collection and analysis. An experimental test was carried out to collect data ftom different users with an instrumented bicycle equipped with Global Navigation Satellite Systems (GNSS) and cameras. A panel of experts was asked to review the collected data to identify and score the severity of the safety critical events (CSE) reaching a good consensus. Anyway, manual observation and classi.fication of CSE is a time consu.ming and unpractical approach when large amount of data must be analysed. Moreover, due to the complex correlation between precrash driving behaviour and due to high dimensionality of the data, traditional statistical methods might not be appropriate in t.bis context. Deep learning-based model have recently gained significant attention in the lit.erature for time series data analysis and for anomaly detection, but generally applied to vehicles' mobility and not to micro-mobility. We present and discuss data requirements and treatment to get suitable infonnation from the GNSS devices, the development of an experimental :framework: where convolutional neural networks (CNN) is applied to integrate multiple GPS data streams of bicycle kinematics to detect the occurrence of a CSE