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Our goal was to examine the role of adolescent perceived deviant peer affiliation in mediating or moderating the association between adolescent attention-deficit/hyperactivity disorder (ADHD) symptoms and young adult driving risk in females with and without ADHD. The overall sample included 228 ethnically and socioeconomically diverse girls with or without a diagnosis of ADHD in childhood (Wave 1; 6-12 years) followed through adolescence (Wave 2; 11-18 years) and into young adulthood (Wave 3; 17-24 years). A subsample of 103 girls with a driving license by Wave 3 and with full data for all study variables was utilized in this investigation. In adolescence, mothers and teachers reported on ADHD symptoms (inattention and hyperactivity/impulsivity), and participants reported on perceived deviant peer affiliation. In young adulthood, participants reported on driving behavior and outcomes, including number of accidents, number of moving vehicle citations, and ever having driven illegally. Covariates included age and adolescent oppositional defiant disorder/conduct disorder. Inattention directly predicted citations. Perceived deviant peer affiliation mediated the association between inattention and (a) accidents and (b) citations. In addition, perceived deviant peer affiliation moderated the association between hyperactivity/impulsivity and accidents, with hyperactivity/impulsivity predicting accidents only for those with low perceived deviant peer affiliation. Perceived deviant peer affiliation appears to play an important role in the association between ADHD symptoms and driving outcomes. Our findings provide preliminary evidence that both ADHD symptoms and peer processes should be targeted in interventions that aim to prevent negative driving outcomes in young women with and without ADHD. (Contains 1 figure, 1 footnote, and 2 tables.)

A pervasive problem in estimating the costs of pollution is that optimizing individuals may compensate for increases in pollution by reducing their exposure, resulting in estimates that understate the full welfare costs. To account for this issue, measurement error, and environmental confounding, we estimate the health effects of ozone using daily boat traffic at the port of Los Angeles as an instrumental variable for ozone. We estimate that ozone causes at least $44 million in annual costs in Los Angeles from respiratory related hospitalizations alone and that the cost of avoidance behavior is at least $11 million per year. (Contains 5 tables, 3 figures and 38 footnotes.)

Three respondents provide cogent commentary on the author's first article, "Does the Adolescent Brain Make Risk Taking Inevitable? A Skeptical Appraisal." Two respondent papers argue that the author mischaracterized valid and useful developmental and biological arguments affirming adolescents' singular risk propensities; the third response raises innovative biological perspectives suggesting unique adolescent brain abilities that strongly challenge current developmental discourse. In his rejoinder to the three respondents, the author presents new statistical and biological data to argue that current adolescent-risk theories will not survive rigorous, age-comparative socioeconomic analyses, objective inventories of cognitive capacities across the life span, and long overdue concession that grown-up outcomes show "risk taking" is not just (or even mainly) an adolescent event. (Contains 3 tables, 1 figure and 1 note.)

A Spanish-language program was developed to educate California migrant farmworker families about motor vehicle safety using a bingo-like game similar to one popular in Mexico. The game disseminated safe-driving information in weekly bilingual newspapers and on Spanish radio and television. Assessments suggest that the media campaigns favorably affected farmworker knowledge, awareness, and driving behavior. (Author/SV)

According to 1987 National Institute of Drug Abuse statistics, more than 67 percent of teens drink, 25 percent drink weekly, and more teens die and are injured in drinking and driving accidents than any other manner. This article describes a Friday Night Live program featuring powerful testimonials and an intense multimedia program to promote safe partying. Includes three references. (MLH)

Road Traffic Injuries are one of the world's leading cause of death, with greatest burden falling on nations with lower and moderate incomes. They are consistently ranked in top 10 leading causes of mortality worldwide for persons of all ages. The biggest advantage of classifying victim degree of injuries in road accidents can pave a way for safer roads and reduced accident rates. This article employs California based SWITRS dataset to propose a novel approach namely Stacked DCL-X model for classifying ' ' v i c t i m _ d e g r e e _ o f _ i n j u r y ′ ′ . It classifies injuries that might take place due to collisions occurring between vehicles and near by pedestrians, obstacles etc. on roads. To verify the superiority of our proposed model, several Machine Learning algorithm-based classification models are stacked together to classify ' ' v i c t i m _ d e g r e e _ o f _ i n j u r y ′ ′ . A total of 1 27 000 accidents are considered from SWITRS dataset when determining the ' ' v i c t i m _ d e g r e e _ o f _ i n j u r y ′ ′ . Machine Learning classifiers implemented in this article includes XGBoost, CatBoost, LightGBM, Decision Tree, Random Forest, Gradient Boosting and Stacked DCL-X. In addition, the algorithm used at feature selection step is Harris Hawk Optimization algorithm, a Nature Inspired Algorithm to select the best features. Prediction results shows that the proposed Stacked DCL-X model provides good stability, fewer hyper-parameters, and highest accuracy under different levels of training data volume. The values of Accuracy, Mean Square Error, and ROC-Auc in Stacked DCL-X model are 87.52, 0.5677 and 97.43, respectively. Moreover, confusion matrix and evaluation metrics of the proposed model provides better results than state-of-the-art classifiers. Statistical analysis has also been performed using Friedman's rank test on different datasets to ensure the superiority of our proposed Stacked DCL-X model. The findings of this study would be helpful in classifying the ' ' v i c t i m _ d e g r e e _ o f _ i n j u r y ′ ′ . These findings are highly significant in smart city projects to effectively establish timely proactive strategies and improve road traffic safety. [ABSTRACT FROM AUTHOR]

Precrash scenario analysis for autonomous vehicles (AVs) is critical for improving the safety of autonomous driving, yet the scenario differences between different driving modes are unexplored. Using the precrash scenario typology of the USDOT, this study classified 484 AV crash reports from the California DMV from 2018 to 2022, revealing the differences in the scenario proportions of the three modes of autonomous driving, driving takeover, and conventional driving in 34 types of scenarios. The results showed that there were significant differences in the proportion of six scenarios such as "Lead AV stopped" and "Lead AV decelerating" among different driving modes p < 0.05 . To analyze the relative risk of different driving modes in specific scenarios, an evaluation model of the risk level of AV precrash scenarios was established using the analytic hierarchy process (AHP). The findings indicated that ​ autonomous driving has the highest risk rating and poses the greatest danger in Scenario 1, while conventional driving is associated with Scenario 2b, and driving takeover corresponds to Scenario 3, respectively. In-depth analysis of the crash characteristics and causes of these three typical scenarios was conducted, and suggestions were made from the perspectives of autonomous driving system (ADS) and drivers to reduce the severity of crashes. This study compared precrash scenarios of AV by different driving modes, providing references for the optimization of ADS and the safety of human-machine codriving. [ABSTRACT FROM AUTHOR]

Traffic accidents are a nationwide public health concern, but autonomous vehicles (AVs) have the potential to significantly reduce accident severity and frequency by eliminating their most common cause, human error. By analyzing the data published by California's Department of Motor Vehicles, researchers have identified the factors that influence AV crash severity, however, none do so through a literature review. This paper's aims are multi-faceted: to understand AVs' operation on public roadways by identifying and classifying the factors contributing to accident severity, to develop a list of strategies that address the public's safety concerns, and to acknowledge the ethics of unavoidable collisions. To fulfill these objectives, a comprehensive literature review was conducted based on a keyword search. Following a multi-step screening and exclusion process, detailed review was performed of 107 relevant publications, and the factors contributing to increased crash severity were classified into 14 categories. The literature revealed that AVs are not at fault in most accidents, although they have a higher propensity than conventional cars to be involved in rear-end collisions, and they are specifically designed to minimize the number of accidents, but may face unavoidable ones. For the benefit of policymakers and manufacturers, 11 strategies that address the moral dilemma of these accidents and 7 strategies that address concerns about AV safety to improve public perception were identified. [ABSTRACT FROM AUTHOR]

An accurate wind resource dataset is required for assessing the potential energy yield of floating offshore wind farms that are expected along the California outer continental shelf. The National Renewable Energy Laboratory has developed and disseminated an updated wind resource dataset offshore of California, using the Weather Research and Forecasting Model, referred to as the CA20 dataset. As compared to buoy lidar measurements that have become available recently, the CA20 dataset showed significant positive biases for 100-m wind speeds along Northern California wind energy lease areas. To investigate the meteorological drivers for the model errors, we first consider two 1-yr simulations run with two different planetary boundary layer (PBL) parameterizations: the Mellor–Yamada–Nakanishi–Niino (MYNN) PBL scheme (the chosen configuration in the CA20 dataset) and the Yonsei University PBL scheme (which significantly reduces the bias in modeled winds). By comparing the 1-yr simulations to the concurrent lidar buoy observations, we find that errors are larger with the MYNN PBL scheme in warm seasons. We then dive deeper into the analysis by running simulations for short-term (3-day) case studies to evaluate the sensitivity of initial/boundary condition forcings on model results. By analyzing the short-term simulations, we find that during synoptic-scale northerly flows driven by the North Pacific high and inland thermal low, a coastal warm bias in the MYNN simulation is mainly responsible for the modeled wind speed bias by altering the boundary layer thermodynamics. The results of our analysis will help guide the creation of an updated version of the CA20 dataset. [ABSTRACT FROM AUTHOR]

• The negative traffic safety impact of California's prior traffic violator school (TVS) citation dismissal policy is well documented in past California TVS evaluations. • Using advanced inferential statistical techniques, the current study evaluated the substantive changes to California's Traffic Violator School program as required by California Assembly Bill 2499 (Chapter 599, Statutes of 2010). • The program changes implemented by AB 2499 appear to be associated with a specific deterrent effect as evidenced by a reliable and statistically significant reduction in subsequent traffic crashes and convictions of those receiving a masked TVS conviction as opposed to a countable conviction. • Although the results suggest that this relationship exists primarily among TVS drivers with less elevated prior records, the change in status from a TVS citation dismissal to a TVS masked conviction has reduced the negative traffic safety impact of the TVS citation dismissal policy in effect prior to the implementation of AB 2499. • Several recommendations are offered to enhance the positive traffic safety impact of the TVS program by further combining its educational elements with the Department's post-license control program by way of the Negligent Operator Treatment System. Introduction: The negative traffic safety impact of California's prior traffic violator school (TVS) citation dismissal policy is well documented in past California TVS evaluations. Method : Using advanced inferential statistical techniques, the current study evaluated the substantive changes to California's traffic violator school program as required by California Assembly Bill (AB) 2499. The program changes implemented by AB 2499 appear to be associated with a specific deterrent effect as evidenced by a reliable and statistically significant reduction in subsequent traffic crashes of those receiving a masked TVS conviction as opposed to a countable conviction. Results: The results suggest that this relationship exists primarily among TVS drivers with less elevated prior records. The change in status from a TVS citation dismissal to a TVS masked conviction has reduced the negative traffic safety impact of the TVS citation dismissal policy in effect prior to the implementation of AB 2499. Several recommendations are offered to enhance the positive traffic safety impact of the TVS program by further combining its educational elements with the state's post license control program by way of the Negligent Operator Treatment System. Practical Applications: The findings and recommendations have implications to all states and jurisdictions utilizing pre-conviction diversion programs and/or demerit point systems associated with traffic violations. [ABSTRACT FROM AUTHOR]

Secondary crashes (SCs) are typically defined as the crash that occurs within the spatiotemporal boundaries of the impact area of the primary crashes (PCs), which will intensify traffic congestion and induce a series of road safety issues. Predicting and analyzing the time and distance gaps between the SCs and PCs will help to prevent the occurrence of SCs. In this paper, a combined data-driven method of static and dynamic approaches is applied to identify SCs. Then, the random forests (RF) method is implemented to predict the two gaps using temporal, primary crash, roadway, and real-time traffic characteristics data collected from 2016 to 2019 at California interstate freeways. Subsequently, the SHapley Additive explanation (SHAP) approach is employed to interpret the RF outputs. The results show that the traffic volume, speed, lighting, and population are considered the most significant factors in both gaps. Furthermore, the main and interaction effects of factors are also quantified. High volume possibly promotes the time and distance gaps, while low volume inhibits them. And volume affects the distance gap inconsiderably when it falls between 300 and 400 veh/5 min. Traffic conditions with high speed and low volume are strongly associated with short-time and short-distance gaps. Darker surroundings probably accelerate the occurrence of SCs. Moreover, crashes involving the violation categories of improper turns or unsafe lane changes likely result in long time and distance gaps. These results have important implications for proposing traffic management and improving road safety. [ABSTRACT FROM AUTHOR]

Intelligent traffic control and guidance system is an effective way to solve urban traffic congestion, improve road capacity and guarantee drivers' travel safety, while short-term traffic flow prediction is the core of intelligent traffic control and guidance system. To investigate the long-term memory and the dynamic feature of short-time traffic flow time series, a hybrid model was proposed by integrating autoregressive fractionally integrated moving average (ARFIMA) model and nonlinear autoregressive (NAR) neural network model to predict short-time traffic flow, in which ARFIMA model can address the long-term memory of linear component and NAR neural network can accommodate the dynamic feature of nonlinear residual component. First, the ARFIMA model was employed to predict the linear component of traffic flow, and the results were compared with those of autoregressive integrated moving average (ARIMA) model. Next, the NAR neural network model was adopted to forecast the nonlinear residual components, and the weighted results were considered as the predicted flow of the hybrid model. The proposed hybrid model was validated by using the cross-sectional traffic flow data in California freeways obtained from the open-access PeMS database. The results showed that the ARFIMA model considering the long-term memory can effectively predict the short-term traffic flow, and the prediction accuracy of the hybrid model is better than that of the singular models. The findings provide an alternative for the short-term traffic flow prediction with lower error and higher accuracy. [ABSTRACT FROM AUTHOR]

Traffic crashes are one of the most severe problems that affect communities worldwide. According to the World Health Organization, the annual number of global fatalities due to traffic crashes is about 1.35 million. Many factors affect the likelihood and the severity of a crash. In this paper, different factors are studied to evaluate their impact on the frequency, the severity, and the type of crashes at intersections, in order to boost road safety. California crash data have been obtained from the Highway Safety and Information System (HSIS) database and have been analyzed statistically. The analysis has included several factors such as severity, type of intersection, and the characteristics of the driver, roadway, environment, and vehicle. A negative binomial regression has been conducted by using the Statistical Analysis System (SAS®) Software for building three models. As a result, different parameters have been considered significant factors in each model. From all the models, the type of intersection and collision, the area of the crash, the type of surface type and its condition, the number of lanes, the age, and the gender have been considered significant factors for crashes at intersections. The result of this study could help reduce the likelihood and severity of crashes. [ABSTRACT FROM AUTHOR]

Regulatory and sociological resistance to new market-driven technologies, particularly to those that rely on collection and analysis of personal data, is prevalent even in cases where the technology creates large social value and saves lives. This article is a case study of such tragic technology resistance, focusing on tracking devices in cars which allow auto insurers to monitor how policyholders drive and adjust the premiums accordingly. Growing empirical work reveals that such "usage-based insurance" induces safer driving, reducing fatal accidents by almost one third, and resulting in more affordable and fair premiums. Yet, California prohibits this technology and other states limit its effectiveness, largely in the interest of privacy protection. The article evaluates the justifications fueling the restrictive regulation vis-à-vis the loss of lives resulting from this regulation. It concludes that the social benefits of the tracking technology dramatically outweigh the privacy and related costs. [ABSTRACT FROM AUTHOR]

• Factors affecting injury severity in rollover crashes were explored. • Random parameters logit model with unobserved heterogeneity in means and variances. • Findings reveal temporal instability of model specifications across individual years. • Aggregate-to-component shift quantified using out-of-sample prediction. • Implications for mitigating the associated driver injury severity. Single-vehicle rollover crashes have been acknowledged as a predominant highway crash type resulting in serious casualties. To investigate the heterogeneous impact of factors determining different injury severity levels in single-vehicle rollover crashes, the random parameters logit model with unobserved heterogeneity in means and variances was employed in this paper. A five-year dataset on single-vehicle rollover crashes, gathered in California from January 1, 2013, to December 31, 2017, was utilized. Driver injury severities that were determined to be outcome variables include no injury, minor injury, and severe injury. Characteristics pertaining to the crash, driver, temporal, vehicle, roadway, and environment were acknowledged as potential determinants. The results showed that the gender indicator specified to minor injury was consistently identified as a significant random parameter in four years' models and the joint five-year model, excluding the 2016 crash model where the night indicator associated with no injury was observed to produce the random effect. Additionally, two series of likelihood ratio tests were conducted to assess the year-to-year and aggregate-to-component temporal stability of model estimation results. Marginal effects of explanatory variables were also calculated and compared to analyze the temporal stability and interpret the results. The findings revealed an overall temporal instability of model specifications across individual years, while there is no significant aggregate-to-component variation. Injury severities were observed to be stably affected by several variables, including improper turn indicator, under the influence of alcohol indicator, old driver indicator, seatbelt indicator, insurance indicator, and airbag indicator. Furthermore, the year-to-year and aggregate-to-component shift was quantified and characterized by calculating the differences in probabilities between within-sample observations and out-of-sample predictions. The overall results imply that continuing to expand and refine the model to incorporate more comprehensive datasets can result in more robust and stable injury severity prediction, thus benefiting in mitigating the associated driver injury severity. [ABSTRACT FROM AUTHOR]

Introduction: Previous studies show evidence for associations of prenatal exposure to organophosphate (OP) pesticides with poorer childhood neurodevelopment. As children grow older, poorer cognition, executive function, and school performance can give rise to risk-taking behaviors, including substance abuse, delinquency, and violent acts. We investigated whether prenatal OP exposure was associated with these risk-taking behaviors in adolescence and young adulthood in a Mexican American cohort.Methods: We measured urinary dialkyl phosphates (DAPs), non-specific metabolites of OPs, twice (13 and 26 weeks gestation) in pregnant women recruited in 1999-2000 in the Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) study, a birth cohort set in a primarily Latino agricultural community in the Salinas Valley, California. We followed up children throughout their childhood and adolescence; at the 18-year visit, adolescent youth (n = 315) completed a computer-based questionnaire which included questions about substance use, risky sexual activity, risky driving, and delinquency and police encounters. We used multivariable models to estimate associations of prenatal total DAPs with these risk-taking behaviors.Results: The prevalence of risk-taking behaviors in CHAMACOS youth ranged from 8.9% for smoking or vaping nicotine to 70.2% for committing a delinquent act. Associations of total prenatal DAPs (geometric mean = 132.4 nmol/L) with risk-taking behavior were generally null and imprecise. Isolated findings included a higher risk for smoking or vaping nicotine within the past 30 days (relative risk [RR] per 10-fold increase in prenatal DAPs = 1.89, 95% CI: 1.00, 3.56) and driving without a license (RR = 1.74, 95% CI: 1.25, 2.42). There were no consistent differences by sex or childhood adversity.Discussion: We did not find clear or consistent evidence for associations of prenatal OP exposure with risk-taking behaviors in adolescence/early adulthood in the CHAMACOS population. Our small sample size may have prevented us from detecting potentially subtle associations of early life OP exposure with these risk-taking behaviors. [ABSTRACT FROM AUTHOR]

The onset of autonomous driving has provided fertile ground for discussions about ethics in recent years. These discussions are heavily documented in the scientific literature and have mainly revolved around extreme traffic situations depicted as moral dilemmas, i.e. situations in which the autonomous vehicle (AV) is required to make a difficult moral choice. Quite surprisingly, little is known about the ethical issues in focus by the AV industry. General claims have been made about the struggles of companies regarding the ethical issues of AVs but these lack proper substantiation. As private companies are highly influential on the development and acceptance of AV technologies, a meaningful debate about the ethics of AVs should take into account the ethical issues prioritised by industry. In order to assess the awareness and engagement of industry on the ethics of AVs, we inspected the narratives in the official business and technical reports of companies with an AV testing permit in California. The findings of our literature and industry review suggest that: (i) given the plethora of ethical issues addressed in the reports, autonomous driving companies seem to be aware of and engaged in the ethics of autonomous driving technology; (ii) scientific literature and industry reports prioritise safety and cybersecurity; (iii) scientific and industry communities agree that AVs will not eliminate the risk of accidents; (iv) scientific literature on AV technology ethics is dominated by discussions about the trolley problem; (v) moral dilemmas resembling trolley cases are not addressed in industry reports but there are nuanced allusions that unravel underlying concerns about these extreme traffic situations; (vi) autonomous driving companies have different approaches with respect to the authority of remote operators; and (vii) companies seem invested in a lowest liability risk design strategy relying on rules and regulations, expedite investigations, and crash/collision avoidance algorithms. [ABSTRACT FROM AUTHOR]

Objective: Automated Truck-Mounted Attenuators (ATMAs) have the potential to improve work zone safety by removing the human driver out of a vehicle that is positioned in work zones to absorb impact from errant vehicles. However, this automated technology is expensive and can be detrimental to safety and project success if operated incorrectly (e.g., operating limitations and procedures not followed). Therefore, it is important to understand users' perceptions of ATMAs and how training can improve appropriate adoption of this technology. The objective of this study was to evaluate how work zone workers perceive the usefulness of and the capabilities of automation in Truck-Mounted Attenuators.Methods: A survey study was conducted with 13 Department of Transportation (DOT) workers in Colorado and California. Each of the DOT workers in this study had some previous experience with the ATMA, either in real-world applications and/or formal training. The survey collected information on participant job specifications, experience with the ATMA, training received, trust in the ATMA, usability of the HMIs, and operating capabilities of the automation.Results: Workers reported an overall positive acceptance of this technology. This was supported by their expectation that it would reduce crash severity; that there was a reasonable workload associated with operating procedures for the automation; and by their overall trust in the automation's reliability. However, workers noted concerns regarding their trust in the automation under various contexts, such as poor visibility and denser traffic volumes. Further, trust in the technology was greatest among workers with higher levels of ATMA training and longer experience working with the ATMA.Conclusions: This research presents a novel perspective on user acceptance of ATMA technology. These findings can help jurisdictions achieve the safety improvements that investment and deployment of automation in work zones offers, by identifying the disconnect between operators and technology. [ABSTRACT FROM AUTHOR]

The aim of the study is to fill the research gap by developing the crash severity models for distinct crash types. The data are extracted from the Highway Safety Information System (HSIS) for the analysis of crashes of two severity levels (injury and non-injury) in the intersections of the state highway system in California. To identify significant variables of different crash types on the basis of crash severities, a mixed binary logit model was employed. The results demonstrate that the covariates have different effects on crash severities under various crash types. Among a large number of covariates, the number of vehicles involved, and rural roadway class demonstrate a positive significant impact on crash severities across five and four crash types, respectively. Following them, the urbanized intersections in the non-urban areas, the mainline number of lanes at the intersection and mainline signal mast arm exhibit modestly positive influence on crash severities under two crash types. On the contrary, some other variables showing a negative influence on crash severities were right or left independent alignment and cross-street number of lanes for three crash types, and lighted intersections for two crash types. [ABSTRACT FROM AUTHOR]

Connected and Automated Vehicles (CAVs) can potentially improve the performance of the transportation system by reducing human errors. This paper investigates the safety impact of CAVs in a mixed traffic with conventional vehicles at intersections. Analyzing real-world AV crashes in California revealed that rear-end crashes at intersections are the dominant crash type. Therefore, to enhance our understanding of the future interactions between human-driven vehicles with CAVs at intersections, a simulation framework was developed to model the mixed traffic environment of Automated Vehicles (AV), cooperative AVs, and conventional human-driven vehicles. In order to model AVs driving behavior, Adaptive Cruise Control (ACC) and cooperative ACC (CACC) models are utilized. Particularly, this study explores system improvements due to automation and connectivity across varying CAV market penetration scenarios. ACC and CACC car following models are used to mimic the behavior of AVs and cooperative AVs. Real-world connected vehicle data are utilized to modify and tune the acceleration/deceleration regimes of the Wiedemann model. Next, the driving volatility concept capturing variability in vehicle speeds was utilized to calibrate the simulation to represent the safety performance of a real-world environment. Two surrogate safety measures are used to evaluate the safety performance of a representative intersection under different market penetration rate of CAVs: the number of longitudinal conflicts and driving volatility. At low levels of ACC market penetration, the safety improvements were found to be marginal, but safety improved substantially with more than 40% ACC penetration. Additional safety improvements can be achieved more quickly through the addition of cooperation and connectivity through CACC. Furthermore, ACC/CACC vehicles were found to improve mobility performance in terms of average speed and travel time at intersections. [ABSTRACT FROM AUTHOR]

Context: Increases in tree cover at dry forest margins are a global phenomenon. Yet, how pre-existing tree cover interacts with terrain and water balance to influence tree cover change is not well-understood, nor whether subsequent disturbances restore prior tree cover patterns or create novel patterns. Objectives: To assess how terrain, water balance and pre-existing patterns of tree cover influenced late twentieth century tree cover change, and how subsequent wildfires altered tree cover patterns. Methods: We analyzed tree canopy cover at four sites at the forest-steppe ecotone on the eastern side of the Sierra Nevada, California, U.S.A., using aerial photographs from 1953/1955, 1999/2002 and 2016. Influences on tree cover change were assessed using statistical modeling, and tree cover in 1953/1955 was compared with post-wildfire tree cover in 2016. Results: From 1953 to 2002, area with > 25% canopy cover increased by 1.5 to 5-fold and treeless area decreased by 17–111%. Mesic areas and areas of sparse tree cover close to existing forest tended to gain more canopy cover. Subsequent wildfires caused a mix of net tree cover loss, little change and gain relative to 1953/1955, but at all sites areas with > 25% cover and < 10% cover in 1953/55 experienced net losses and gains respectively by 2016. Conclusions: Accounting for initial tree cover and its configuration are crucial to assessing water balance and terrain effects on tree cover change. Our work highlights how wildfires can generate novel patterns of tree cover relative to historical baselines, especially following decades of fire exclusion. [ABSTRACT FROM AUTHOR]

Introduction: This trial assesses the effects of a community-level alcohol prevention intervention in California on alcohol-related motor vehicle crashes.Study Design: The study is a group RCT with cities as the unit of assignment to condition and as the unit of analysis.Setting/participants: A total of 24 California cities with populations between 50,000 and 450,000 were chosen at random and roughly matched into pairs before randomly assigning 12 each to the intervention and control conditions.Intervention: The intervention, aimed at reducing excessive drinking among adolescents and young adults, included driving under the influence sobriety checkpoints, saturation patrols, and undercover operations to reduce service of alcohol to intoxicated patrons in bars, all including high visibility so the public would be aware of them. A measure of overall intervention intensity or dosage was created.Main Outcome Measures: The outcome was a monthly percentage of all motor vehicle crashes that were single vehicle nighttime crashes for drivers aged 15-30 years.Results: Multilevel analyses were conducted to examine intervention effects on alcohol-related crashes among drivers aged 15-30 years. Crash data were obtained in 2018 with data preparation and analysis conducted in 2019. Intent-to-treat analyses indicated a 17% reduction in the percentage of alcohol-involved crashes among drivers aged 15-30 years relative to controls, which translates to about 310 fewer crashes. Dosage was found to have a statistically significant effect on crashes among this age group, although not in the expected direction.Conclusions: Enhanced alcohol enforcement operations involving both community health and law enforcement agencies can help to reduce alcohol-impaired driving and related consequences among young people. Including measures of intervention dosage raises interesting questions about the understanding of the impact of the community intervention. Future studies should continue to further develop implementation strategies that may more effectively and efficiently reduce community alcohol-related harm. [ABSTRACT FROM AUTHOR]

This study aimed to investigate how land-use pattern affects crash frequency at traffic analysis zone (TAZ) level. Traffic, road network, land use, population and crash data were collected from Los Angeles County, California in 2014. K-means clustering analysis was first conducted to divide land use at each TAZ into five different patterns. Geographically weighted Poisson regression (GWPR) models were then developed to investigate the associations between crash counts and land-use patterns. The elasticity was calculated to compare the safety effect of each explanatory factor across different patterns. The results of this study indicated that land use combinations at TAZs can be divided into different patterns using land-use mix and proportions of different land use types, and that each land use combination can be assigned with a certain safety level. The effects of contributing factors on crash frequency are different across different land-use patterns. The results suggest that proper combinations of different land uses can improve safety performance at the urban and road network planning stage. [ABSTRACT FROM AUTHOR]

As robots and artificial intelligence (AI) increase their influence over society, policymakers are increasingly regulating them. But to regulate these technologies, we first need to know what they are. And here we come to a problem. No one has been able to offer a decent definition of robots and AI--not even experts. What's more, technological advances make it harder and harder each day to tell people from robots and robots from "dumb" machines. We have already seen disastrous legal definitions written with one target in mind inadvertently affecting others. In fact, if you are reading this you are (probably) not a robot, but certain laws might already treat you as one. Definitional challenges like these aren't exclusive to robots and AI. But today, all signs indicate we are approaching an inflection point. Whether it is citywide bans of "robot sex brothels" or nationwide efforts to crack down on "ticket scalping bots," we are witnessing an explosion of interest in regulating robots, human enhancement technologies, and all things in between. And that, in turn, means that typological quandaries once confined to philosophy seminars can no longer be dismissed as academic. Want, for example, to crack down on foreign "influence campaigns" by regulating social media bots? Be careful not to define "bot" too broadly (like the California legislature recently did), or the supercomputer nestled in your pocket might just make you one. Want, instead, to promote traffic safety by regulating drivers? Be careful not to presume that only humans can drive (as our Federal Motor Vehicle Safety Standards do), or you may soon exclude the best drivers on the road. [ABSTRACT FROM AUTHOR]

This study characterized nitrogen oxides (NOx) and ammonia (NH 3) emissions from five in-use goods movement vehicles, including one diesel vehicle without selective catalytic reduction (SCR), two diesel vehicles with SCR, and two ultra-low NOx natural gas vehicles equipped with three-way catalysts (TWCs). Emissions testing was performed under real-world driving conditions in typical goods movement routes in the South Coast Air Basin (SCAB) of California. NOx emissions varied depending on the vehicle and the route. The no-SCR diesel vehicle showed the highest NOx emissions over all the routes. One of the SCR-equipped vehicles showed NOx emission rates that were two times higher than the other SCR-equipped vehicle with similar engine model year and mileage, which may be attributed to catalyst deterioration. The SCR-equipped diesel vehicles were within two to three times 0.2 g/bhp-hr certification standard over the different routes. The natural gas vehicles showed average NOx emissions around or below the optional Low NOx emissions standard of 0.02 g/bhp-hr. The three-bin moving average window (MAW) method was utilized to show the NOx emissions across various modes of operation, including idle, low load, and medium/high load. The highest fraction of vehicle operation was found in either the low load or medium/high load bins, depending on the route. NOx emissions for each of the bins varied between different vehicles, and are further analyzed in this paper. NH 3 emissions formation was favored for the TWC-equipped natural gas vehicles, which produced about 20 times more NH 3 emissions than the SCR-equipped diesel vehicles. [Display omitted] • Significant NOx reductions for the natural gas vehicles compared to the SCR-equipped diesel vehicles. • NH 3 formation favored in the TWC for the natural gas vehicles and produced during acceleration events. • Low load and medium/high load conditions dominated vehicle operation and NOx emissions. [ABSTRACT FROM AUTHOR]

In order to ensure safe lane change and avoid traffic accidents, an effective lane change assist system is required. In a lane change assist system, it is very important to obtain the following elements in time, such as actual distance between vehicles, minimum safety distance, and warning signal. To this end, this paper analyzed four kinds of lane change angle collision scenes. Initial position, initial velocity, acceleration, heading angle, and kinematics of vehicles were used to calculate the position of potential angle collision points between lane change vehicle and obstacle vehicles. Then, actual distance model was constructed based on potential angular collision points. The minimum safety distance model was also established under the two most unfavorable conditions. In order to achieve the lane change warning, three early warning rules were formulated. We verified the validity of models and early warning rules using vehicle driving video data of Interstate 80 in California. Models and early warning rules constructed in our research can be applied to the advanced active safety systems of vehicle, such as vehicle lane change assist system and active collision early warning system, which can improve the active safety and reduce traffic accidents. [ABSTRACT FROM AUTHOR]

Cellphone use while driving is an increasingly serious threat for traffic safety and is prohibited in many jurisdictions. In California, the use of handheld cellphones while driving has been prohibited since July 1, 2008. Using interrupted time series analysis, this study explores the effectiveness of the ban by specifically analyzing the crashes caused by cellphone usage in California from 2002 to 2014. These crashes were thought to be able to reflect the role of the ban more accurately than total crashes. The ban was found effective in reducing the cellphone usage-caused crashes in terms of both crash frequency and crash proportion. The study also confirms that crashes caused by cellphone use produce more severe outcomes than other crashes. These findings show that the ban on handheld cellphone use while driving plays an important role for improving traffic safety in California. In addition, it is found that the ban motivates drivers to switch from handheld cellphones to hands-free cellphones, but in terms of crash severity, hands-free cellphone usage and handheld cellphone usage do not show significant differences. These findings support a complete ban on cellphone use while driving--not just a prohibition of handheld cellphone use. The study results are expected to provide new insights for future policy-making related to cellphone use while driving. [ABSTRACT FROM AUTHOR]

Introduction: Roadside sobriety checkpoints are an intervention in which law enforcement officers stop passing vehicles to check whether drivers are impaired. There is clear evidence that a program of roadside sobriety checkpoints is an effective approach to reducing alcohol-involved motor vehicle crashes, likely because of general deterrent effects across the entire population of drivers. The aim of this study is to assess the duration of time over which individual roadside sobriety checkpoints are associated with alcohol-involved motor vehicle crashes, within the context of a broader checkpoint program.Methods: In August 2018, the authors accessed incident-level data for alcohol-involved motor vehicle crashes and roadside sobriety checkpoints for the City of Los Angeles, California, 2013-2017. Counts of crashes and checkpoints were computed within three different temporal units: days (n=1,826), weeks (n=260), and months (n=60). The number of checkpoints were then calculated at different lagged periods (up to 7 days, up to 4 weeks, and up to 3 months). Autoregressive integrated moving average analyses related counts of checkpoints over these lagged periods to subsequent crashes.Results: Fewer alcohol-involved motor vehicle crashes occurred when there were more roadside sobriety checkpoints over the previous 4 days, 5 days, 6 days, 7 days, and 1 week.Conclusions: Individual roadside sobriety checkpoints affected alcohol-involved motor vehicle crashes in Los Angeles, California for approximately 1 week. The temporal configuration of individual checkpoints is an important consideration when designing an overall roadside sobriety checkpoint program. [ABSTRACT FROM AUTHOR]

Autonomous Vehicles (AV) technology is emerging. Field tests on public roads have been on going in several states in the US as well as in Europe and Asia. During the US public road tests, crashes with AV involved happened, which becomes a concern to the public. Most previous studies on AV safety relied heavily on assessing drivers’ performance and behaviors in a simulation environment and developing automated driving system performance in a closed field environment. However, contributing factors and the mechanism of AV-related crashes have not been comprehensively and quantitatively investigated due to the lack of field AV crash data. By harnessing California’s Report of Traffic Collision Involving an Autonomous Vehicle Database, which includes the AV crash data from 2014 to 2018, this paper investigates by far the most current and complete AV crash database in the US using statistical modeling approaches that involve both ordinal logistic regression and CART classification tree. The quantitative analysis based on ordinal logistic regression and CART models has successfully explored the mechanism of AV-related crash, via both perspectives of crash severity and collision types. Particularly, the CART model reveals and visualize the hierarchical structure of the AV crash mechanism with knowledge of how these traffic, roadway, and environmental contributing factors can lead to crashes of various serveries and collision types. Statistical analysis results indicate that crash severity significantly increases if the AV is responsible for the crash. The highway is identified as the location where severe injuries are likely to happen. AV collision types are affected by whether the vehicle is on automated driving mode, whether the crashes involve pedestrians/cyclists, as well as the roadway environment. The method used in this research provides a proven approach to statistically analyze and understand AV safety issues. And this benefit is potential be even enhanced with an increasing sample size of AV-related crashes records in the future. The comprehensive knowledge obtained ultimately facilitates assessing and improving safety performance of automated vehicles. [ABSTRACT FROM AUTHOR]