Major Traffic Safety Reform and Road Traffic Injuries Among Low-Income New York Residents, 2009-2021.

Objectives. To evaluate the effects of a comprehensive traffic safety policy-New York City's (NYC's) 2014 Vision Zero-on the health of Medicaid enrollees. Methods. We conducted difference-in-differences analyses using individual-level New York Medicaid data to measure traffic injuries and expenditures from 2009 to 2021, comparing NYC to surrounding counties without traffic reforms (n = 65 585 568 person-years). Results. After Vision Zero, injury rates among NYC Medicaid enrollees diverged from those of surrounding counties, with a net impact of 77.5 fewer injuries per 100 000 person-years annually (95% confidence interval = -97.4, -57.6). We observed marked reductions in severe injuries (brain injury, hospitalizations) and savings of $90.8 million in Medicaid expenditures over the first 5 years. Effects were largest among Black residents. Impacts were reversed during the COVID-19 period. Conclusions. Vision Zero resulted in substantial protection for socioeconomically disadvantaged populations known to face heightened risk of injury, but the policy's effectiveness decreased during the pandemic period. Public Health Implications. Many cities have recently launched Vision Zero policies and others plan to do so. This research adds to the evidence on how and in what circumstances comprehensive traffic policies protect public health. (Am J Public Health. 2024;114(6):633-641. https://doi.org/10.2105/AJPH.2024.307617).

Translating global evidence into local implementation through technical assistance: a realist evaluation of the Bloomberg philanthropies initiative for global Road safety.

BACKGROUND: Traffic-related crashes are a leading cause of premature death and disability. The safe systems approach is an evidence-informed set of innovations to reduce traffic-related injuries and deaths. First developed in Sweden, global health actors are adapting the model to improve road safety in low- and middle-income countries via technical assistance (TA) programs; however, there is little evidence on road safety TA across contexts. This study investigated how, why, and under what conditions technical assistance influenced evidence-informed road safety in Accra (Ghana), Bogotá (Colombia), and Mumbai (India), using a case study of the Bloomberg Philanthropies Initiative for Global Road Safety (BIGRS). METHODS: We conducted a realist evaluation with a multiple case study design to construct a program theory. Key informant interviews were conducted with 68 government officials, program staff, and other stakeholders. Documents were utilized to trace the evolution of the program. We used a retroductive analysis approach, drawing on the diffusion of innovation theory and guided by the context-mechanism-outcome approach to realist evaluation. RESULTS: TA can improve road safety capabilities and increase the uptake of evidence-informed interventions. Hands-on capacity building tailored to specific implementation needs improved implementers' understanding of new approaches. BIGRS generated novel, city-specific analytics that shifted the focus toward vulnerable road users. BIGRS and city officials launched pilots that brought evidence-informed approaches. This built confidence by demonstrating successful implementation and allowing government officials to gauge public perception. But pilots had to scale within existing city and national contexts. City champions, governance structures, existing political prioritization, and socio-cultural norms influenced scale-up. CONCLUSION: The program theory emphasizes the interaction of trust, credibility, champions and their authority, governance structures, political prioritization, and the implement-ability of international evidence in creating the conditions for road safety change. BIGRS continues to be a vehicle for improving road safety at scale and developing coalitions that assist governments in fulfilling their role as stewards of population well-being. Our findings improve understanding of the complex role of TA in translating evidence-informed interventions to country-level implementation and emphasize the importance of context-sensitive TA to increase impact.

Exploring the challenges faced by Dutch truck drivers in the era of technological advancement.

Introduction: Despite their important role in the economy, truck drivers face several challenges, including adapting to advancing technology. The current study investigated the occupational experiences of Dutch truck drivers to detect common patterns. Methods: A questionnaire was distributed to professional drivers in order to collect data on public image, traffic safety, work pressure, transport crime, driver shortage, and sector improvements. Results: The findings based on 3,708 respondents revealed a general dissatisfaction with the image of the industry and reluctance to recommend the profession. A factor analysis of the questionnaire items identified two primary factors: 'Work Pressure', more common among national drivers, and 'Safety & Security Concerns', more common among international drivers. A ChatGPT-assisted analysis of textbox comments indicated that vehicle technology received mixed feedback, with praise for safety and fuel-efficiency improvements, but concerns about reliability and intrusiveness. Discussion: In conclusion, Dutch professional truck drivers indicate a need for industry improvements. While the work pressure for truck drivers in general may not be high relative to certain other occupational groups, truck drivers appear to face a deficit of support and respect.

The efficacy of hazard perception training and education: A systematic review and meta-analysis.

BACKGROUND: Hazard perception (HP) has been argued to improve with experience, with numerous training programs having been developed in an attempt to fast track the development of this critical safety skill. To date, there has been little synthesis of these methods. OBJECTIVE: The present study sought to synthesise the literature for all road users to capture the breadth of methodologies and intervention types, and quantify their efficacy. DATA SOURCES: A systematic review of both peer reviewed and non-peer-reviewed literature was completed. A total of 57 papers were found to have met inclusion criteria. RESULTS: Research into hazard perception has focused primarily on drivers (with 42 studies), with a limited number of studies focusing on vulnerable road users, including motorcyclists (3 studies), cyclists (7 studies) and pedestrians (5 studies). Training was found to have a large significant effect on improving hazard perception skills for drivers (g = 0.78) and cyclists (g = 0.97), a moderate effect for pedestrians (g = 0.64) and small effect for motorcyclists (g = 0.42). There was considerable heterogeneity in the findings, with the efficacy of training varying as a function of the hazard perception skill being measured, the type of training enacted (active, passive or combined) and the number of sessions of training (single or multiple). Active training and single sessions were found to yield more consistent significant improvements in hazard perception. CONCLUSIONS: This study found that HP training improved HP skill across all road user groups with generally moderate to large effects identified. HP training should employ a training method that actively engages the participants in the training task. Preliminary results suggest that a single session of training may be sufficient to improve HP skill however more research is needed into the delivery of these single sessions and long-term retention. Further research is also required to determine whether improvements in early-stage skills translate to improvements in responses on the road, and the long-term retention of the skills developed through training.

The development of a road safety policy index and its application in evaluating the effects of road safety policy.

The paper presents an exploratory study of a road safety policy index developed for Norway. The index consists of ten road safety measures for which data on their use from 1980 to 2021 are available. The ten measures were combined into an index which had an initial value of 50 in 1980 and increased to a value of 185 in 2021. To assess the application of the index in evaluating the effects of road safety policy, negative binomial regression models and multivariate time series models were developed for traffic fatalities, fatalities and serious injuries, and all injuries. The coefficient for the policy index was negative, indicating the road safety policy has contributed to reducing the number of fatalities and injuries. The size of this contribution can be estimated by means of at least three estimators that do not always produce identical values. There is little doubt about the sign of the relationship: a stronger road safety policy (as indicated by index values) is associated with a larger decline in fatalities and injuries. A precise quantification is, however, not possible. Different estimators of effect, all of which can be regarded as plausible, yield different results.

Overtaking maneuvers on two-lane highways under the microscope: Exploration of a multidimensional framework for the analysis of safety, comfort and efficiency using simulator data.

An unreasonable overtaking attempt on two-lane highways could cause drivers to suffer in terms of driving safety, comfort, and efficiency. Several external factors related to the traffic environment (e.g., speed and car type of surrounding vehicles), were found to be the significant factors in drivers' overtaking performance in the previous studies. However, the microscopic decision-making (e.g., the moments of the occupation of the opposite lane) mechanisms during overtaking, by means of which drivers react to changes in the external traffic environment and adjust their overtaking trajectories, are still need to be explored. Hence, this study had three goals: (i) To explore the spatial characteristics of micro-decisions (MDs) (such as the start and end point) in overtaking trajectories; (ii) To measure three types of performance indicators (i.e., safety, comfort, and efficiency) for the execution of overtaking maneuvers; (iii) To quantitatively explain the microscopic decision-making mechanism in overtaking. Data for overtaking trajectories were collected from driving a simulation experiment where 52 Chinese student drivers completed a series of overtaking maneuvers on a typical two-lane highway under different traffic conditions. Two analyses were conducted: firstly, the distributions of the relative distance between the ego and surrounding vehicles at four key points (i.e., the start, entry, back, and end) in the overtaking trajectory were investigated and clustered to uncover the spatial characteristics of the MDs. Secondly, the safety, comfort, and efficiency of the overtaking were measured by the aggregations of multi-targets collision risks, triaxial acceleration variances, and spatial consumptions respectively based on the Data Envelopment Analysis (DEA), which were further applied in a two-stage SEM model to reveal the quantitative interrelationships among the external factors, microscope decisions and performances in overtaking. We confirmed that the MDs could be considered as the mediating variables between the external factors and overtaking performances. In the presence of the more hazardous traffic environment (e.g., faster traffic flow and impeded by a truck), the safety, comfort and efficiency of overtaking would be deteriorated inevitably. But drivers would execute the overtaking under the longer passing sight distance, migrate their trajectories forward, and shorten the spatial duration to significantly improve the overtaking performances. Based on this mechanism, a overtaking trajectory optimization strategy for the advanced or automatic driving system, was confirmed and concluded that 1) the passing gap should be firstly planned according to the sight distance acceptance of different drivers, which directly determine the upper limit of the safety performance in the overtaking; 2) the trajectory forward migration and shortening the whole duration in overtaking could be effective to enhance the overtaking performances of the overtaking on the two-lane highway; 3) the guidance of the stable control of the steering wheel and gas/brake pedals is essential in the overtaking.

Comprehensive Assessment of Artificial Intelligence Tools for Driver Monitoring and Analyzing Safety Critical Events in Vehicles.

Human factors are a primary cause of vehicle accidents. Driver monitoring systems, utilizing a range of sensors and techniques, offer an effective method to monitor and alert drivers to minimize driver error and reduce risky driving behaviors, thus helping to avoid Safety Critical Events (SCEs) and enhance overall driving safety. Artificial Intelligence (AI) tools, in particular, have been widely investigated to improve the efficiency and accuracy of driver monitoring or analysis of SCEs. To better understand the state-of-the-art practices and potential directions for AI tools in this domain, this work is an inaugural attempt to consolidate AI-related tools from academic and industry perspectives. We include an extensive review of AI models and sensors used in driver gaze analysis, driver state monitoring, and analyzing SCEs. Furthermore, researchers identified essential AI tools, both in academia and industry, utilized for camera-based driver monitoring and SCE analysis, in the market. Recommendations for future research directions are presented based on the identified tools and the discrepancies between academia and industry in previous studies. This effort provides a valuable resource for researchers and practitioners seeking a deeper understanding of leveraging AI tools to minimize driver errors, avoid SCEs, and increase driving safety.

How would autonomous vehicles behave in real-world crash scenarios?

Autonomous Vehicles (AVs) have the potential to revolutionize transportation systems by enhancing traffic safety. Safety testing is undoubtedly a critical step for enabling large-scale deployment of AVs. High-risk scenarios are particularly important as they pose significant challenges and provide valuable insights into the driving capabilities of AVs. This study presents a novel approach to assess the safety of AVs using in-depth crash data, with a particular focus on real-world crash scenarios. First, based on the high-definition video recording of the whole process prior to the crash occurrences, 453 real-world crashes involving 596 passenger cars from China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database were reconstructed. Pertinent static and dynamic elements needed for the construction of the testing scenarios were extracted. Subsequently, 596 testing scenarios were created via each passenger car's perspective within the simulation platform. Following this, each of the crash-involved passenger cars was replaced with Baidu Apollo, a famous automated driving system (ADS), for counterfactual simulation. Lastly, the safety performance of the AV was assessed using the simulation results. A logit model was utilized to identify the fifteen crucial scenario elements that have significant impacts on the test results. The findings demonstrated that the AV could avoid 363 real-world crashes, accounting for approximately 60.91% of the total, and effectively mitigated injuries in the remaining 233 unavoidable scenarios compared to a human driver. Moreover, the AV maintain a smoother speed in most of the scenarios. The common feature of these unavoidable scenarios is that the AV is in a passive state, and the crashes are not caused by the AV violating traffic rules, but rather caused by abnormal behavior exhibited by the human drivers. Additionally, seven specific scenarios have been identified wherein AVs are unable to avoid a crash. These findings demonstrate that, compared to human drivers, AVs can avoid crashes that are difficult for humans to avoid, thereby enhancing traffic safety.

Analysis and regulation of driving behavior in the entrance zone of freeway tunnels: Implementation of visual guidance systems in China.

In China, visual guidance systems are commonly used in tunnels to optimize the visual reference system. However, studies focusing specifically on visual guidance systems in the tunnel entrance zone are limited. Hence, a driving simulation test is performed in this study to quantitatively evaluate the effectiveness of (i) visual guidance devices at different vertical positions (pavement and roadside) and (ii) a multilayer visual guidance system for regulating driving behavior in the tunnel entrance zone. Furthermore, the characteristics of driving behavior and their effects on traffic safety in the tunnel entrance zone are examined. Data such as the vehicle position, area of interest (AOI), throttle position, steering wheel angle, and lane center offset are obtained using a driving simulation platform and an eye-tracking device. As indicators, the first fixation position (FP), starting deceleration position (DP), average throttle position (TPav), number of deceleration stages (N|DS), gradual change degree of the vehicle trajectory (G|VT), and average steering wheel angle (SWAav) are derived. The regulatory effect of visual guidance devices on driving performance is investigated. First, high-position roadside visual guidance devices effectively reduce decision urgency and significantly enhance deceleration and lane-keeping performance. Specifically, the advanced deceleration performance (AD), smooth deceleration performance (SD), trajectory gradualness (TG), and trajectory stability (TS) in the tunnel entrance zone improve by 63%, 225%, 269%, and 244%, respectively. Additionally, the roadside low-position visual guidance devices primarily target the trajectory gradualness (TG), thus resulting in improvements by 80% and 448% in the TG and TS, respectively. Meanwhile, the pavement visual guidance devices focus solely on enhancing the TS and demonstrates a relatively lower improvement rate of 99%. Finally, the synergistic effect of these visual guidance devices facilitates the multilayer visual guidance system in enhancing the deceleration and lane-keeping performance. This aids drivers in early detection and deceleration at the tunnel entrance zone, reduces the urgency of deceleration decisions, promotes smoother deceleration, and improves the gradualness and stability of trajectories.

Safety performance of dedicated and preferential bus lanes using multivariate negative binomial models for Bogotá, Colombia.

Public transport priority systems such as Bus Rapid Transit (BRT) and Buses with High Level of Service (BHLS) are top-rated solutions to mobility in low-income and middle-income cities. There is scientific agreement that the safety performance level of these systems depends on their functional, operational, and infrastructure characteristics. However, there needs to be more evidence on how the different characteristics of bus corridors might influence safety. This paper aims to shed some light on this area by structuring a multivariate negative binomial model comparing crash risk on arterial roads, BRT, and BHLS corridors in Bogotá, Colombia. The analyzed infrastructure includes 712.1 km of arterial roads with standard bus service, 194.1 km of BRT network, and 135.6 km of BHLS network. The study considered crashes from 2015 to 2018 -fatalities, injuries, and property damage only- and 30 operational and infrastructure variables grouped into six classes -exposure, road design, infrastructure, public means of transport, and land use. A multicriteria process was applied for model selection, including the structure and predictive power based on [i] Akaike information criteria, [ii] K-fold cross-validation, and [iii] model parsimony. Relevant findings suggest that in terms of observed and expected accident rates and their relationship with the magnitude of exposure -logarithm of average annual traffic volumes at the peak hour (LOG_AAPHT) and the percentage of motorcycles, cars, buses, and trucks- the greatest risk of fatalities, injuries, and property damage occurs in the BHLS network. BRT network provides lower crash rates in less severe collisions while increasing injuries and fatalities. When comparing the BHLS network and the standard design of arterial roads, BHLS infrastructure, despite increasing mobility benefits, provides the lowest safety performance among the three analyzed networks. Individual factors of the study could also contribute to designing safer roads related to signalized intersection density and curvature. These findings support the unique characteristics and traffic dynamics present in the context of Bogotá that could inform and guide decisions of corresponding authorities in other highly dense urban areas from developing countries.

Injury risk curves to guide safe speed limits on Swedish roads using German crash data supplemented with estimated non-injury crashes.

Vision Zero postulates that no one should be killed or seriously injured in road traffic; therefore, it is necessary to define evidence-based speed limits to mitigate impact severity. The overall aims to guide the definition of safe speeds limits by establishing relations between impact speed and the risk of at-least-moderate (MAIS2+) and at-least-severe (MAIS3+) injuries for car occupants in frontal and side crashes in Sweden. As Swedish in-depth data are unavailable, the first objective was to assess the applicability of German In-depth Accident Study (GIDAS) data to Sweden. The second was to create unconditional injury risk curves (risk of injury given involvement in any crash), rather than risk curves conditional on the GIDAS sampling criterion of suspected-injury crashes. Thirdly, we compared the unconditional and conditional risk curves to quantify the practical implications of this methodological choice. Finally, we provide an example to demonstrate how injury risk curves facilitate the definition of safe, evidence-based speed limits in Sweden. Characteristics important for the injury outcome were similar between GIDAS and Swedish data; therefore, the injury risk curves using German GIDAS data are applicable to Sweden. The regression models yielded the following results for unconditional injury risk curves: 10 % MAIS2+ at 25 km/h impact speed for frontal head-on crashes, 20 km/h for frontal car-to-object crashes, 55 km/h in far-side crashes, and 45 km/h in near-side crashes. A 10 % MAIS3+ risk was reached between 70 and 75 km/h for all crash types. Conditional injury risk curves gave substantially different results; the 10 % MAIS3+ risk in near-side crashes was 140 km/h, twice the unconditional value. For example, if a 10 % MAIS3+ risk was acceptable, treating remaining uncertainty conservatively, assuming compliance with speed limits and that Automated Emergency Braking takes 20 km/h of the travel speed before impact in longitudinal traffic, the safe speed limit for car occupants on most Swedish roads would be 80 km/h and 60 km/h in intersections.

Modeling temporal correlation and heterogeneity in real-time conflict rates using Bayesian Tobit models for signalized intersections.

The use of real-time traffic conflicts for safety studies provide more insight into how important dynamic signal cycle-related characteristics can affect intersection safety. However, such short-time window for data collection raises a critical issue that the observed conflicts are temporally correlated. As well, there is likely unobserved heterogeneity across different sites that exist in conflict data. The objective of this study is to develop real-time traffic conflict rates models simultaneously accommodating temporal correlation and unobserved heterogeneity across observations. Signal cycle level traffic data, including traffic conflicts, traffic and shock wave characteristics, collected from six signalized intersections were used. Three types of Tobit models: conventional Tobit model, temporal Tobit (T-Tobit) model, and temporal grouped random parameters (TGRP-Tobit) model were developed under full Bayesian framework. The results show that significant temporal correlations are found in T-Tobit models and TGRP-Tobit models, and the inclusion of temporal correlation considerably improves the goodness-of-fit of these Tobit models. The TGRP-Tobit models perform best with the lowest Deviance Information Criteria (DIC), indicating that accounting for the unobserved heterogeneity can further improve the model fit. The parameter estimates show that real-time traffic conflict rates are significantly associated with traffic volume, shock wave area, shock wave speed, queue length, and platoon ratio.

The association between health literacy and pedestrian safety behavior among adults: a cross-sectional study.

INTRODUCTION: Pedestrians are considered the most vulnerable and complex road users as human behavior constitutes one of the fundamental reasons for traffic-related incidents involving pedestrians. However, the role of health literacy as a predictor of Pedestrian safety behavior remains underexplored. Therefore, the current study was designed to examine the level of health literacy and its association with the safety behavior of adult pedestrians in the city of Tabriz. METHODS: This cross-sectional analytical study was conducted among individuals aged 18 to 65 years in the metropolitan area of Tabriz from January to April 2023. Data were collected using the HELIA standard questionnaire (Health Literacy Instrument for adults), comprising 33 items across 5 domains (access, reading, understanding, appraisal, decision-making and behavior), as well as the Pedestrian Behavior Questionnaire (PBQ) consisting of 29 items. Data were analyzed using descriptive and analytical statistics (independent t-tests, ANOVA, and Pearson correlation coefficient) via SPSS-22 software. RESULTS: Based on the results, 94% (376 individuals) had excellent health literacy levels, and their safety behavior scores were at a good level. Health literacy and safety behavior were higher among the age group of 31 to 45 years, women, married individuals, those who read books, and individuals with higher education. However, safety behavior showed no significant association with education level (P > 0.05). There was a significant and positive relationship between health literacy and all its domains and pedestrian safety behavior (r = 0.369, P < 0.001). CONCLUSION: This study underscores the significant impact of health literacy on pedestrians' safety behavior. The findings reveal that higher levels of health literacy are associated with better safety behavior among individuals aged 18 to 63. Demographic factors such as age, gender, marital status, and education level also play a role in shaping both health literacy and safety behavior. By recognizing these relationships, interventions can be tailored to improve health literacy levels and promote safer pedestrian practices, ultimately contributing to a healthier and safer community in Tabriz city.

Assessing the predictability of surrogate safety measures as crash precursors based on vehicle trajectory data prior to crashes.

This study aims to investigate the predictability of surrogate safety measures (SSMs) for real-time crash risk prediction. We conducted a year-long drone video collection on a busy freeway in Nanjing, China, and collected 20 rear-end crashes. The predictability of SSMs was defined as the probability of crash occurrence when using SSMs as precursors to crashes. Ridge regression models were established to explore contributing factors to the predictability of SSMs. Four commonly used SSMs were tested in this study. It was found that modified time-to-collision (MTTC) outperformed other SSMs when the early warning capability was set at a minimum of 1 s. We further investigated the cost and benefit of SSMs in safety interventions by evaluating the number of necessary predictions for successful crash prediction and the proportion of crashes that can be predicted accurately. The result demonstrated these SSMs were most efficient in proactive safety management systems with an early warning capability of 1 s. In this case, 308, 131, 281, and 327,661 predictions needed to be made before a crash could be successfully predicted by TTC, MTTC, DRAC, and PICUD, respectively, achieving 75 %, 85 %, 35 %, and 100 % successful crash identifications. The ridge regression results indicated that the predefined threshold had the greatest impact on the predictability of all tested SSMs.

A variable speed limit control approach for freeway tunnels based on the model-based reinforcement learning framework with safety perception.

To improve the traffic safety and efficiency of freeway tunnels, this study proposes a novel variable speed limit (VSL) control strategy based on the model-based reinforcement learning framework (MBRL) with safety perception. The MBRL framework is designed by developing a multi-lane cell transmission model for freeway tunnels as an environment model, which is built so that agents can interact with the environment model while interacting with the real environment to improve the sampling efficiency of reinforcement learning. Based on a real-time crash risk prediction model for freeway tunnels that uses random deep and cross networks, the safety perception function inside the MBRL framework is developed. The reinforcement learning components fully account for most current tunnels' application conditions, and the VSL control agent is trained using a deep dyna-Q method. The control process uses a safety trigger mechanism to reduce the likelihood of crashes caused by frequent changes in speed. The efficacy of the proposed VSL strategies is validated through simulation experiments. The results show that the proposed VSL strategies significantly increase traffic safety performance by between 16.00% and 20.00% and traffic efficiency by between 3.00% and 6.50% compared to a fixed speed limit approach. Notably, the proposed strategies outperform traditional VSL strategy based on the traffic flow prediction model in terms of traffic safety and efficiency improvement, and they also outperform the VSL strategy based on model-free reinforcement learning framework when sampling efficiency is considered together. In addition, the proposed strategies with safety triggers are safer than those without safety triggers. These findings demonstrate the potential for MBRL-based VSL strategies to improve traffic safety and efficiency within freeway tunnels.

GIS-based intelligent planning approach of child-friendly pedestrian pathway to promote a child-friendly city.

Pedestrian safety, particularly for children, relies on well-designed pathways. Child-friendly pathways play a crucial role in safeguarding young pedestrians. Shared spaces accommodating both vehicles and walkers can bring benefits to pedestrians. However, active children playing near these pathways are prone to accidents. This research aims to develop an efficient method for planning child-friendly pedestrian pathways, taking into account community development and the specific needs of children. A mixed-methods approach was employed, utilizing the Datang community in Guangzhou, China, as a case study. This approach combined drawing techniques with GIS data analysis. Drawing methods were utilized to identify points of interest for children aged 2-6. The qualitative and quantitative fuzzy analytic hierarchy process assessed factors influencing pathway planning, assigning appropriate weights. The weighted superposition analysis method constructed a comprehensive cost grid, considering various community elements. To streamline the planning process, a GIS tool was developed based on the identified factors, resulting in a practical, child-friendly pedestrian pathway network. Results indicate that this method efficiently creates child-friendly pathways, ensuring optimal connectivity within the planned road network.

Evaluation of safety margins for cone beam CT-based adaptive prostate radiotherapy.

Adaptive radiotherapy is characterized by the use of a daily imaging system, such as CBCT (Cone-Beam Computed Tomography) images to re-optimize the treatment based on the daily anatomy and position of the patient. By systematically re-delineating the Clinical Target Volume (CTV) at each fraction, target delineation uncertainty features a random component instead of a pure systematic. The goal of this work is to identify the random and systematic contributions of the delineation error and compute a new relevant Planning Target Volume (PTV) safety margin. 169 radiotherapy sessions from 10 prostate cancer patients treated on the Varian ETHOS treatment system have been analyzed. Intra-patient and inter-patient delineation variabilities were computed in six directions, by considering the prostate as a rigid, non-rotating volume. By doing so, we were able to directly compare the delineations done by the physicians on daily CBCT images with the initial delineation done on the CT-sim and MRI, and sort them by direction using the polar coordinates of the points. The computed variabilities were then used to compute a PTV margin based on Van Herk margin recipe. The total margin computed with random and systematic delineation uncertainties was of 2.7, 2.4, 5.6, 4.8, 4.9 and 3.6 mm in the left, right, anterior, posterior, cranial and caudal directions, respectively. According to our results, the gain offered by the separation of the delineation uncertainty into systematic and random contributions due to the adaptive delineation process justifies a reduction of the PTV margin down to 3 to 5 mm in every direction.

Creating safety amidst chronic contamination: A mixed-method analysis of residents' experiences in a Southern Italian steel town.

This study analyses how residents create safety in Taranto, Italy, a city located next to one of the largest steel plants in Europe. Combining long-term ethnographic research with an online-based survey, our study shows that most respondents recognise and criticise the presence of environmental risks in their daily lives but encounter such risks in complex ways. Contrary to previous scholarship suggesting that pollution can result in alienating residents from their lived environment, this research shows that acute awareness of environmental risks does not necessarily undermine attachment to place but rather can co-exist with or even strengthen it. Our findings propose first that residents experience and understand environmental risk mostly through air pollution, but often situate risks outside of their own neighbourhood and inscribe different meanings to such risk. Second, residents mitigate environmental risk through practices aimed at creating safety, such as moving away from the industrial area or using everyday practices and reflecting on their responsibility for actions. Third, we argue that residents create safety through an attachment and entitlement to place and emotional detachment from pollution and institutional failures. Finally, in line with residents' concerns about safety and how to secure it, this study embraces a shift in its analytical focus from risk to the quest for safety. By doing so, it provides novel insights into environmental risk perception in industrially polluted areas and reveals the often-contradictory sentiments and practices that such areas invoke in residents.

Flight safety assessment based on a modified human error risk quantification approach.

In risk and safety assessments of aviation systems, engineers generally pay more attention to the risks of hardware or software failure and focus less on the risks caused by human errors. In this paper, a (FRAHE) method is proposed for identifying this critical error type and determining the risk severity of human errors. This method accounts for the human error probability as well as the impacts of human errors on the system. The fuzzy inference approach is employed in this paper to address the uncertainty and issues of imprecision that arise from insufficient information and scarce error data and a risk assessment model of human error is developed. The model can be used to precisely describe the relationship between the output risk severity and the input risk indicators, including the human error probability, the error impact probability, and the human error consequence. A case study of the approach task is presented to demonstrate the availability and reasonability of the model. The risk-based modeling method can not only provide valuable information for reducing the occurrence of critical errors but also be used to conduct prospective analyses to prevent unsafe incidents or aviation accidents.

Outcome evaluation of the p drivers Program: Randomised controlled trial of a program to improve safe driving among novice drivers.

BACKGROUND: Globally, road traffic crashes are the leading cause of death for young adults. The P Drivers Project was a trial of a behavioural change program developed for, and targeted at, young Australian drivers in their initial months of solo driving when crash risk is at its highest. METHODS: In a parallel group randomised controlled trial, drivers (N = 35,109) were recruited within 100 days of obtaining their probationary licence (allowing them to drive unaccompanied) and randomised to an intervention or control group. The intervention was a 3 to 6-week multi-stage driving behaviour change program (P Drivers Program). Surveys were administered at three time points (pre-Program, approximately one month post-Program and at 12 months after). The outcome evaluation employed an on-treatment analysis comprising the 2,419 intervention and 2,810 control participants who completed all required activities, comparing self-reported crashes and police-reported casualty crashes (primary outcome), infringements, self-reported attitudes and behaviours (secondary outcomes) between groups. RESULTS: The P Drivers Program improved awareness of crash risk factors and intentions to drive more safely, relative to the controls; effects were maintained after 12-months. However, the Program did not reduce self-reported crashes or police-reported casualty crashes. In addition, self-reported violations, errors and risky driving behaviours increased in the intervention group compared to the control group as did recorded traffic infringements. This suggests that despite the Program increasing awareness of risky behaviour in novice drivers, behaviour did not improve. This reinforces the need to collect objective measures to accompany self-reported behaviour and intentions. CONCLUSIONS: The P Drivers Program was successful in improving attitudes toward driving safety but the negative impact on behaviour, lack of effect on crashes, and the large loss to follow-up fail to support the use of a post-licensing behaviour change program to improve novice driver behaviour and reduce crashes. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry: 363,293 (ANZCTR, 2012).