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

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

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

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

Deep Learning Based Anomaly Detection for Fog-Assisted IoVs Network

Internet of vehicles (IoVs) allows millions of vehicles to be connected and share information for various purposes. The main applications of IoVs are traffic management, emergency messages delivery, E-health, traffic, and temperature monitoring. On the other hand, IoVs lack in location awareness and geographic distribution, which is critical for some IoVs applications such as smart traffic lights and information sharing in vehicles. To support these topographies, fog computing was proposed as an appealing and novel term, which was integrated with IoVs to extend storage, computation, and networking. Unfortunately, it is also challenged with various security and privacy hazards, which is a serious concern of smart cities. Therefore, we can formulate that Fog-assisted IoVs (Fa-IoVs), are challenged by security threats during information dissemination among mobile nodes. These security threats of Fa-IoVs are considered as anomalies which is a serious concern that needs to be addressed for smooth Fa-IoVs network communication. Here, smooth communication refers to less risk of important data loss, delay, communication overhead, etc. This research work aims to identify research gaps in the Fa-IoVs network and present a deep learning-based dynamic scheme named CAaDet (Convolutional autoencoder Aided anomaly detection) to detect anomalies. CAaDet exploits convolutional layers with a customized autoencoder for useful feature extraction and anomaly detection. Performance evaluation of the proposed scheme is done by using the F1-score metric where experiments are carried out by exploiting a benchmark dataset named NSL-KDD. CAaDet also observes the behavior of fog nodes and hidden neurons and selects the best match to reduce false alarms and improve F1-score. The proposed scheme achieved significant improvement over existing schemes for anomaly detection. Identified research gaps in Fa-IoVs can give future directions to researchers and attract more attention to this new era

In Silico Modeling of the Immune System: Cellular and Molecular Scale Approaches

The revolutions in biotechnology and information technology have produced clinical data, which complement biological data. These data enable detailed descriptions of various healthy and diseased states and responses to therapies. For the investigation of the physiology and pathology of the immune responses, computer and mathematical models have been used in the last decades, enabling the representation of biological processes. In this modeling effort, a major issue is represented by the communication between models that work at cellular and molecular level, that is, multiscale representation. Here we sketch some attempts to model immune system dynamics at both levels

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

Safety evaluation of turbo roundabout considering autonomous vehicles operation

A microsimulation approach was carried out in this paper to evaluate the safety performance of turbo roundabouts in which the "CAVs" of connected autonomous vehicles are mixed with the "CVs" of conventional vehicles the research aims to evaluate the advantages in terms of safety and performance of turbo roundabouts. The paper shall also lead to describe the methodological path followed to build VISSIM models of turbo roundabout changing O_D matrix as real case applications, to calibrate the simulation models, and to estimate the potential conflicts when the percentages of CAVs are introduced into the traffic mix. The results, in accordance with the existing bibliography, have shown that the safety levels and the parameters that determine an improvement in the service level in a turbo roundabouts are significantly influenced not only by the geometric characteristics, but also by the distribution of vehicular flows. Therefore, it follows that in absence of crash data including CAVs, the surrogate measures of safety must be considered a strong approach to evaluate the safety performance of a roundabout so far, any road entity