Exploration of the characteristics and trends of electric vehicle crashes: a case study in Norway

With the rapid growth of electric vehicles (EVs) in the past decade, many new traffic safety challenges are also emerging. With the crash data of Norway from 2011 to 2018, this study gives an overview of the status quo of EV crashes. In the survey period, the proportion of EV crashes in total traffic crashes had risen from zero to 3.11% in Norway. However, in terms of severity, EV crashes do not show statistically significant differences from the Internal Combustion Engine Vehicle (ICEV) crashes. Compared to ICEV crashes, the occurrence of EV crashes features on weekday peak hours, urban areas, roadway junctions, low-speed roadways, and good visibility scenarios, which can be attributed to the fact that EVs are mainly used for urban local commuting travels in Norway. Besides, EVs are confirmed to be much more likely to collide with cyclists and pedestrians, probably due to their low-noise engines. Then, the separate logistic regression models are built to identify important factors influencing the severity of ICEV and EV crashes, respectively. Many factors show very different effects on ICEV and EV crashes, which implies the necessity of reevaluating many current traffic safety strategies in the face of the EV era. Although the Norway data is analyzed here, the findings are expected to provide new insights to other countries also in the process of the complete automotive electrification

PF-DMD: Physics-fusion dynamic mode decomposition for accurate and robust forecasting of dynamical systems with imperfect data and physics

The DMD (Dynamic Mode Decomposition) method has attracted widespread attention as a representative modal-decomposition method and can build a predictive model. However, the DMD may give predicted results that deviate from physical reality in some scenarios, such as dealing with translation problems or noisy data. Therefore, this paper proposes a physics-fusion dynamic mode decomposition (PFDMD) method to address this issue. The proposed PFDMD method first obtains a data-driven model using DMD, then calculates the residual of the physical equations, and finally corrects the predicted results using Kalman filtering and gain coefficients. In this way, the PFDMD method can integrate the physics-informed equations with the data-driven model generated by DMD. Numerical experiments are conducted using the PFDMD, including the Allen-Cahn, advection-diffusion, and Burgers' equations. The results demonstrate that the proposed PFDMD method can significantly reduce the reconstruction and prediction errors by incorporating physics-informed equations, making it usable for translation and shock problems where the standard DMD method has failed

Incorporating the standstill distance and time headway distributions into freeway car-following models and an application to estimating freeway travel time reliability

Standstill distances and following time headways are two important microsimulation model parameters associated with driver aggression. This paper investigates the distributions of standstill distances and time headways and incorporates these distributions into car-following models to estimate travel time reliability. By incorporating standstill distance and following headway into car-following models as stochastic parameters, a speed-density region can be generated, based on which various travel-time-reliability measures can be calculated. Key findings of this study are as follows: (1) Both standstill distances and time headways follow fairly dispersed distributions. Therefore, it is suggested that microsimulation models should include the option of allowing standstill distances and time headways to follow distributions as well as to be specified separately for different vehicle classes. (2) By incorporating stochastic standstill distance and time headway parameters in car-following models, travel-time-reliability measures can be estimated more precisely and faster compared with using VISSIM

Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset

Decentralized multiagent planning has been an important field of research in robotics. An interesting and impactful application in the field is decentralized vehicle coordination in understructured road environments. For example, in an intersection, it is useful yet difficult to deconflict multiple vehicles of intersecting paths in absence of a central coordinator. We learn from common sense that, for a vehicle to navigate through such understructured environments, the driver must understand and conform to the implicit "social etiquette" observed by nearby drivers. To study this implicit driving protocol, we collect the Berkeley DeepDrive Drone dataset. The dataset contains 1) a set of aerial videos recording understructured driving, 2) a collection of images and annotations to train vehicle detection models, and 3) a kit of development scripts for illustrating typical usages. We believe that the dataset is of primary interest for studying decentralized multiagent planning employed by human drivers and, of secondary interest, for computer vision in remote sensing settings.Comment: 6 pages, 10 figures, 1 tabl

A longitudinal analysis of the effectiveness of California’s ban on cellphone use while driving

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

Association of urban forest landscape characteristics with biomass and soil carbon stocks in Harbin City, Northeastern China

Background Urban forests help in mitigating carbon emissions; however, their associations with landscape patterns are unclear. Understanding the associations would help us to evaluate urban forest ecological services and favor urban forest management via landscape regulations. We used Harbin, capital city of the northernmost province in China, as an example and hypothesized that the urban forests had different landscape metrics among different forest types, administrative districts, and urban–rural gradients, and these differences were closely associated with forest carbon sequestration in the biomass and soils. Methods We extracted the urban forest tree coverage area on the basis of 2 GF-1 remote sensing images and object-oriented based classification method. The analysis of forest landscape patterns and estimation of carbon storage were based on tree coverage data and 199 plots. We also examined the relationships between forest landscape metrics and carbon storage on the basis of forest types, administrative districts, ring roads, and history of urban settlements by using statistical methods. Results The small patches covering an area of less than 0.5 ha accounted for 72.6% of all patches (average patch size, 0.31 ha). The mean patch size (AREA_MN) and largest patch index (LPI) were the highest in the landscape and relaxation forest and Songbei District. The landscape shape index (LSI) and number of patches linearly decreased along rural-urban gradients (p < 0.05). The tree biomass carbon storage varied from less than 10 thousand tons in the urban center (first ring road region and 100-year regions) to more than 100 thousand tons in the rural regions (fourth ring road and newly urbanized regions). In the same urban–rural gradients, soil carbon storage varied from less than five thousand tons in the urban centers to 73–103 thousand tons in the rural regions. The association analysis indicated that the total forest area was the key factor that regulates total carbon storage in trees and soils. However, in the case of carbon density (ton ha−1), AREA_MN was strongly associated with tree biomass carbon, and soil carbon density was negatively related to LSI (p < 0.01) and AREA_MN (p < 0.05), but positively related to LPI (p < 0.05). Discussion The urban forests were more fragmented in Harbin than in other provincial cities in Northeastern China, as shown by the smaller patch size, more complex patch shape, and larger patch density. The decrease in LSI along the rural-urban gradients may contribute to the forest carbon sequestrations in downtown regions, particularly underground soil carbon accumulation, and the increasing patch size may benefit tree carbon sequestration. Our findings help us to understand how forest landscape metrics are associated with carbon storage function. These findings related to urban forest design may maximize forest carbon sequestration services and facilitate in precisely estimating the forest carbon sink

Frailty in hypertensive population and its association with all-cause mortality: data from the National Health and Nutrition Examination Survey

ObjectivesThis study aimed to investigate the relationship between frailty and all-cause mortality in hypertensive population.MethodsWe used data from the National Health and Nutrition Examination Survey (NHANES) 1999–2002 and mortality data from the National Death Index. Frailty was assessed using the revised version of the Fried frailty criteria (weakness, exhaustion, low physical activity, shrinking, and slowness). This study aimed to evaluate the association between frailty and all-cause mortality. Cox proportional hazard models were used to evaluate the association between frailty category and all-cause mortality, adjusted for age, sex, race, education, poverty–income ratio, smoking, alcohol, diabetes, arthritis, congestive heart failure, coronary heart disease, stroke, overweight, cancer or malignancy, chronic obstructive pulmonary disease, chronic kidney disease, and taking medicine for hypertension.ResultsWe gathered data of 2,117 participants with hypertension; 17.81%, 28.77%, and 53.42% were classified as frail, pre-frail, and robust, respectively. We found that frail [hazard ratio (HR) = 2.76, 95% confidence interval (CI) = 2.33–3.27] and pre-frail (HR = 1.38, 95% CI = 1.19–1.59] were significantly associated with all-cause mortality after controlling for variables. We found that frail (HR = 3.02, 95% CI = 2.50–3.65) and pre-frail (HR = 1.35, 95% CI = 1.15–1.58) were associated with all-cause mortality in the age group ≥65 years. For the frailty components, weakness (HR = 1.77, 95% CI = 1.55–2.03), exhaustion (HR = 2.25, 95% CI = 1.92–2.65), low physical activity (HR = 2.25, 95% CI = 1.95–2.61), shrinking (HR = 1.48, 95% CI = 1.13–1.92), and slowness (HR = 1.44, 95% CI = 1.22–1.69) were associated with all-cause mortality.ConclusionThis study demonstrated that frailty and pre-frailty were associated with an increased risk of all-cause mortality in patients with hypertension. More attention should be paid to frailty in hypertensive patients, and interventions to reduce the burden of frailty may improve outcomes in these patients

The Misconception of Antibiotic Equal to an Anti-Inflammatory Drug Promoting Antibiotic Misuse among Chinese University Students.

Massive misuse of antibiotics is one of the most important reasons for antimicrobial resistance (AMR). Misconceptions of antibiotics contribute to antibiotic misuse behaviors. This study aims to examine whether university students hold the misconception that Antibiotic is a Xiaoyanyao (literally means anti-inflammatory drug in Chinese), and association between this misconception and antibiotic misuse behaviors. A cross-sectional study was conducted among university students using the cluster random sampling method in six universities of six regions in China (one university per region). The Chi-square test was used to assess the relationship between the misconception and antibiotic misuse behaviors. Logistic regression was conducted to identify the risk factors for antibiotic misuse behaviors. 11,192 of university students completed the entire questionnaire. There were 3882 (34.7%) students who were considered to have the misconception. Female students were more likely to have the misconception compared with males (36.7% vs. 32.6%, P < 0.001). Those students with a background of social science/humanities were more likely to have the misconception compared with those from science and medicine (44.1% vs. 30.3% vs. 20.1%, P < 0.001). Students came from rural areas compared with those from urban areas (37.5% vs. 32.5%, P < 0.001) were more likely to have the misconception. Students who had the misconception were 1.51 (95% CI 1.21⁻1.89, P < 0.001) times, 1.34 (95% CI 1.21⁻1.48, P < 0.001) times, and 1.36 (95% CI 1.24⁻1.50, P < 0.001) times more likely to report self-medication, request to obtain antibiotics, and take antibiotics prophylactically than those who did not have this misconception, respectively. The high proportion of university students' misconception on Antibiotic is a Xiaoyanyao is worth more attention. Effective health education and interventions need to be promoted among university students and the whole population