1,720,970 research outputs found
Safety assessment in future scenarios with Automated Vehicles
Full text linkNowadays, several Advanced Driving Assistance Systems (ADAS) are installed in vehicles, helping drivers with sevral tasks. Human drivers are evermore less involved in driving thanks to the technological help. According to the automation rate of the vehicles and the human involvment, vehicles can be considered partially or fully automated. The partially automated vehicles (AVs) belong to the SAE level 2-3 and follow a cautious behavior
because they are still controlled for some tasks by human drivers. The fully automated ones belong to the SAE level 4-5, and their behavior is thought to be more aggressive since there
is no need for human drivers to take over maneuver or manage some driving tasks. The reliability of technologies is considered greater than the ones of men for managing and reacting to any changes in traffic conditions, so the behavior is more assertive, headway between vehicles reduced, and greater acceleration and deceleration.
Starting from these assumptions, in this thesis, three different vehicle typologies are studied, regular vehicles (RVs), Partially AVs (SAE level 2-3), and Fully AVs (SAE level 4-5) for crash assessments in future scenarios (short-term, mid-term, and long-term). This work aims at providing a methodological framework that can be used in every context and for every road type considering the introduction of technologies in traffic for safety assessments. This aspect is crucial since, practically speaking, plans for mobility and road design procedures require safety assessments projected in long temporal horizons. During
this considered period there is the great chance that the vehicle types circulating on roads drastically change. Not considering new vehicles and their interactions with RVs in future scenarios can lead to misestimations of safety.
The methodological framework was applied to a real-world case, in the context of the SUMP for the Province of Bari. The main results of this study highlight the importance of automation in traffic. Traffic made just of Fully AVs drastically decrease the crash frequency. Contrary, promiscuity of vehicles in traffic enhances the crash occurrence if compared to the
current scenario. In order to foresee the impact of such changes in traffic, an ad hoc Safety Performance Function for AVs was developed, with the intent of predicting crashes in the future with AVs
Road design influence on driving behaviors: The influence of curve design, a case study
Full text linkRoad geometry has always been a key feature for road safety concerns. It will become more crucial in the context of future transportation, especially with the advent of Connected and Autonomous Vehicles (CAVs). In fact, recognizable and intuitive road alignments would simplify the driving tasks for both humans and CAVs (independently from the rate of automation). Thus, not only building consistent and self-explaining roads is fundamental for new and old vehicles, but also adjusting the existent ones, operation that seems even harder. Since most of the existing roads would need massive interventions, policy makers and road designers might choose between making adjustments being compliant with the current regulations in toto or adopting countermeasures supported by specific safety assessments to make existing roads safer, also in the perspective of future changes. In this optic, the present study tries to investigate a typical geometric design issue of existing roads, i.e., the presence of a long segment followed by a sharp curve without transition curves on undivided two-way two-lane rural roads. This alignment does not reflect the current recommendations for road alignment, so it was investigated the effect of such a design on users and safety for a specific testbed. The users’ behavior was investigated recording the kinematic parameters of the traveling vehicles. This data collection was run using radar traffic counters, placed on the roadside throughout the entire layout of the investigated segment-curve, to get speed and acceleration. The data were elaborated to investigate driving behavior in free-flow conditions. A K-means cluster analysis was run to characterize the users’ behaviors in terms of speed and acceleration. Hard braking was found to be strongly related to high speed, as well as ongoing deceleration on curve was detected for all the vehicles with high speeds on the segment. Results about users’ behaviors were compared to the available crash dataset to understand the possible implications of human factors on occurred crashes and to simulate the decision process of safety-related adjustments of existing roads
Optimal planning of safety improvements on road sites belonging to different categories within large networks: An integrated multi-layer framework
Full text linkPlanning road safety interventions on large road networks implies several layers of complexity in the decisionmaking process. In fact, the following simultaneous problems should be addressed: estimating safety performances on the different road elements of the network, identifying sites showing high potential for improvement with respect to reference values, defining the possible types of safety measures to be implemented and their anticipated effect on traffic safety, limiting the number of interventions given fixed budget constraints. This study proposes an integrated multi-layer framework which takes into account the above-defined problems into a single optimization procedure which provides the number and type of safety interventions to be implemented over a wide road network composed of different categories of road elements. The proposed framework is based on the following peculiar aspects: the potential for safety improvement is quantitatively assessed based on the estimation of safety performances for each road category, a bi-level thresholding process integrated in the optimization process is used to highlight sites for interventions, the anticipated outcome of safety measures is quantitatively assessed as well through available crash reduction factors. The proposed methodology is applied to a case study which analyzes a sample of real roads belonging to a province-wide road network composed of various road elements (i.e., different categories of segments and intersections), under different budget constraints. Results demonstrate the applicability and flexibility of the proposed approach, which could be used for planning purposes, independently of the particular geographic location. Clearly, the approach is valid at the planning stage, given that several details of the different layers of analysis are necessarily simplified, while they should be studied in detail at the single intervention project stage
Road design influence on driving behaviors: The influence of curve design, a case study
Full text linkRoad geometry has always been a key feature for road safety concerns. It will become more crucial in the context of future transportation, especially with the advent of Connected and Autonomous Vehicles (CAVs). In fact, recognizable and intuitive road alignments would simplify the driving tasks for both humans and CAVs (independently from the rate of automation). Thus, not only building consistent and self-explaining roads is fundamental for new and old vehicles, but also adjusting the existent ones, operation that seems even harder. Since most of the existing roads would need massive interventions, policy makers and road designers might choose between making adjustments being compliant with the current regulations in toto or adopting countermeasures supported by specific safety assessments to make existing roads safer, also in the perspective of future changes. In this optic, the present study tries to investigate a typical geometric design issue of existing roads, i.e., the presence of a long segment followed by a sharp curve without transition curves on undivided two-way two-lane rural roads. This alignment does not reflect the current recommendations for road alignment, so it was investigated the effect of such a design on users and safety for a specific testbed. The users’ behavior was investigated recording the kinematic parameters of the traveling vehicles. This data collection was run using radar traffic counters, placed on the roadside throughout the entire layout of the investigated segment-curve, to get speed and acceleration. The data were elaborated to investigate driving behavior in free-flow conditions. A K-means cluster analysis was run to characterize the users’ behaviors in terms of speed and acceleration. Hard braking was found to be strongly related to high speed, as well as ongoing deceleration on curve was detected for all the vehicles with high speeds on the segment. Results about users’ behaviors were compared to the available crash dataset to understand the possible implications of human factors on occurred crashes and to simulate the decision process of safety-related adjustments of existing roads
Traffic microsimulation for road safety assessments of vehicle automation scenarios: Model comparison and sensitivity analysis
No full textThe possible implementation of Automated Vehicles (AVs) in the traffic flow implies that future safety conditions should be assessed by considering promiscuous traffic scenarios (composed of traditional vehicles and AVs). However, real safety performance data related to AVs are still scarce and unreliable. Thus, road safety assessments of future scenarios should necessarily be based on traffic simulations and surrogate safety measures (i.e., related to traffic conflicts, which are different from observed crashes). In the context of traffic simulation, the first step consists in assessing the currently available traffic models to match their characteristics with the possibility of reliably simulating AVs and their behavior in traffic, according to the specific variables of the investigated site (i.e., road type, road geometric design, number of intersections). After the model selection stage, a sensitivity analysis is necessary to identify the most crucial model parameters and their influence on the simulation output, with particular regard to those affecting safety performances. The sensitivity analysis paves the way for making traffic modelers aware of the effects of possible modifications to the simulation parameters in case of AV scenarios. In this research, this procedure was tested on a two-way two-lane rural road network. Among the analyzed traffic models, the Gipps model was highlighted as the most suitable to account for AVs in this specific context. The sensitivity analysis revealed that the most influencing parameters of the Gipps model in the context of safety assessments are: clearance, safety margin factor and sensitivity factor, which were revealed to be greatly influential on all types of traffic conflicts. Two other control tests with different simulated vehicle types were run to highlight the stability of the results coming from the sensitivity analysis in other scenarios
A safety assessment planning strategy proposal within the context of sustainable Urban mobility Plans: How to account for Connected and Autonomous vehicles in safety analysis in the SUMP?
No full textIn the context of Sustainable Urban Mobility Plans (SUMP), when road safety assessments are dealt with, different future scenarios are considered weighing the positive impacts of the proposed strategies for improving the transport system and road safety, globally. However, while considering those future scenarios, until now, the chance that Connected and Autonomous Vehicles (CAVs) will be introduced in the market has never been accounted. Neglecting CAVs can provide misleading results in terms of safety assessment. In this study, a general framework about how to include CAVs in SUMP safety assessments is provided. The general framework, which relies on traffic simulations and algorithms to count conflicts, was tested on two-way two-lane rural roads within the Province of Bari (Italy), where a SUMP was recently developed but the possible introduction of AVs has not been accounted, as it is a common practice in SUMP drafting. Results provided by simulations show a dramatic crash reduction when the traffic is made only of CAVs, while more dangerous situations are highlighted in the case of mixed traffic. Therefore, some countermeasures to handle mixed traffic, such as e.g., reserved lanes for CAVs in case of new roads, must be found and provided for stakeholders and practitioners while dealing with planning strategies
Credible Variable Speed Limits for Improving Road Safety: A Case Study Based on Italian Two-Lane Rural Roads
Full text linkIn an ever-changing driving environment where vehicles are becoming smarter, more autonomous, and more connected, a paradigmatic change in signals for drivers might be required. This need is correlated with road safety (social sustainability). There are several factors affecting road safety, and one of these, especially important on rural roads, is speed. One way to actively influence drivers’ speed is to intervene with regard to speed limit signs by providing credible and effective limits. This goal can be pursued by working on variable speed limits that align with the boundary conditions of the installation site. In this research, an analysis was conducted on the rural road network within the Metropolitan City of Bari (Italy) that involved collecting the speeds on each of the investigated two-way, two-lane rural roads of the network. In addition to the speeds, all the most relevant geometric details of the roads were considered, together with environmental factors like rainfall. A generalized linear model was developed to correlate the operating speed limits and other variables together with information about rainfall, which degrades tire–pavement friction and thus, road safety. After the development of this model, safety performance functions, depending on the amount of rain or number of days of rain, were calculated with the intent of predicting crash frequency, starting with the operative speed and rain conditions. Operative speed, speed limit, percentage of non-compliant drivers, traffic level, and site length were found to be associated with all typologies and locations of crashes investigated
Analysis of the Factors Influencing Speed Cushion Effectiveness in the Urban Context: A Case Study Experiment in the City of Bari, Italy
Full text linkThe installation of Traffic-Calming Devices (TCDs) is an extremely valuable countermeasure to prevent vulnerable road users from fatalities in urban contexts. Among all the TCDs, Berlin Speed Cushions (BSCs) seem to be one of the most promising because they reduce speeds but do not affect emergency vehicles. However, previous research on BSCs is limited and lacks some important aspects, such as the analysis of speeds at different distances from the cushion or the investigation of the influence of other context variables. In this study, BSCs of different lengths (2.20 m, 2.70 m, and 3.20 m) were deployed in the City of Bari on three roads belonging to the same area. To overcome the limitations of previous research, speeds were recorded using a laser-speed gun before and after the implementation of BSCs, in different conditions, in order to take into account the effect of the following factors: the time of day, day of the week, and average hourly traffic. An ANOVA analysis was performed, with speed as the dependent variable and the above-reported factors and the test road site (proxy variable for the cushion length) as factors, independently repeated for six distance ranges with respect to the cushion. The results reveal that speed evidently decreases immediately before (down to about 13 km/h) and after the cushion (down to about 12 km/h), time of the day is an important factor (speed decrease is much more evident during the morning than the evening), and the length of the cushion has some influence on speed decrease (the speed decrease is lower for the longest cushion)
Analysis of E-Scooter Crashes in the City of Bari
Full text linkThe remarkable impact that e-scooters have had on the transportation system drives research on this phenomenon. The widespread use of e-scooters also poses several new safety issues, which should be necessarily studied. The aim of this paper points in this direction, investigating the main contributing factors, causes, and patterns of recorded e-scooter crashes, considering also different crash types and severity, using the City of Bari (Italy) as a case study. The crash dataset based on police reports and referring to the period July 2020–November 2022 (i.e., the first period of e-scooter implementation in the City of Bari) was investigated. Crashes were clustered according to several variables. No fatal crashes occurred, even though crashes mostly resulted in injuries (70%). Considering road type, divided roads were found to be less safe than undivided ones, due to higher mean speeds than on other roads and to a less constrained e-scooter driving behavior. Calm (off-peak) daytime hours seem to lead to more frequent e-scooter crashes with respect to both peak and nighttime hours, even if the latter hours are associated with an increased severity. Once controlled for exposure, season, lighting conditions, and the private/sharing ratio do not seem influential. E-scooters are more prone to be involved in single-vehicle and pedestrian crashes at segments than other vehicles, but they show similar crash trends than other vehicles (i.e., angle crashes) at intersections. As emerged from traffic surveys, not all e-scooter users were found to use cycle paths. Combining this information with crash data, it seems that not using cycle paths is considerably less safe than using them. Besides engineering measures and policies, awareness campaigns should be promoted to elicit safe users’ behavior and to tackle the several violations and misbehaviors emerging from the crash data
Aberrant behaviors of drivers involved in crashes and related injury severity: Are there variations between the major cities in the same country?
Full text linkCrash data analyses based on accident datasets often do not include human-related variables because they can be hard to reconstruct from crash data. However, records of crash circumstances can help for this purpose since crashes can be classified considering aberrant behavior and misconduct of the drivers involved. In this case, urban crash data from the 10 largest Italian cities were used to develop four logistic regression models having the driver-related crash circumstance (aberrant behaviors: inattentive driving, illegal maneuvering, wrong interaction with pedestrian and speeding) as dependent variables and the other crash-related factors as predictors (information about the users and the vehicles involved and about road geometry and conditions). Two other models were built to study the influence of the same factors on the injury severity of the occupants of vehicles for which crash circumstances related to driver aberrant behaviors were observed and of the involved pedestrians. The variability between the 10 different cities was considered through a multilevel approach, which revealed a significant variability only for the inattention-related crash circumstance. In the other models, the variability between cities was not significant, indicating quite homogeneous results within the same country. The results show several relationships between crash factors (driver, vehicle or road-related) and human-related crash circumstances and severity. Unsignalized intersections were particularly related to the illegal maneuvering crash circumstance, while the night period was clearly related to the speeding-related crash circumstance and to injuries/casualties of vehicle occupants. Cyclists and motorcyclists were shown to suffer more injuries/casualties than car occupants, while the latter were generally those exhibiting more aberrant behaviors. Pedestrian casualties were associated with arterial roads, heavy vehicles, and older pedestrians
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