Time Of Crash Research Articles

Effect Of 5-hour Energy Shot® On Physiological And Performance Responses To Simulated Car Racing

The 5-Hour Energy Shot® continues to be one of the more popular energy drinks on the market. The maker claims that it improves work and/or exercise performance. PURPOSE: To determine if ingesting one 5-Hour Energy Shot® compared to a placebo causes measurable improvement in performance related to physiological variables during a simulated driving task (SDT). METHODS: Nineteen (11 males, 8 females), college-aged (21.8 + 1.55 yrs; 1.7 + 0.11 m in ht; 72.9 + 13.83 kg in wt), volunteers participated in a double-blind, cross-over, placebo-based study. The participants were tested prior to and then at 5 consecutive 1-hour intervals after ingesting either a randomly assigned non-caffeinated placebo (PL) (59 ml; 5 kcals) or the 5-Hour Energy Shot® (5HES) (59 ml; 4 kcals). The SDT was a solo-timed road race (Forza Horizon game) on an Xbox 360 gaming system. During each of the 6 data collection trials, heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), ear temperature (ETemp), skin temperature (STemp), drive time (DT), and number of crashes (#C) were recorded. Subjects were also evaluated for alertness and drink effectiveness at each of same time points. A 2-way repeated measures ANOVA was utilized to determine differences between the PL and the 5-HES treatments across the six test periods of the driving task. Significance was established at p < 0.05. Summary Of RESULTS: There was no statistically significant difference between PL and 5-HES across the six time periods relative to HR, SBP, DBP, ETemp, or #C driving tasks. However, the 5-HES group had a significantly lower STemp than the PL group at each time interval; with a 5-hour post-ingestion STemp of 89.55 + 2.30oF in the 5-HES group compared to 90.27 + 1.96oF in the PL group. The 5-HES group had a faster DT compared to the PL group at each of the time intervals; with a 5-hour post-ingestion DT of 197.47 + 50.39 sec in the 5-HES group compared to 201.09 + 52.38 sec in the PL group. CONCLUSIONS: When compared to a placebo, 5- Hour Energy Shot® did significantly improve driving time, along with a concomitant reduced skin temperature in college-aged participants during a simulated driving task.

Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach.

Road traffic injury accounts for a substantial human and economic burden globally. Understanding risk factors contributing to fatal injuries is of paramount importance. In this study, we proposed a model that adopts a hybrid ensemble machine learning classifier structured from sequential minimal optimization and decision trees to identify risk factors contributing to fatal road injuries. The model was constructed, trained, tested, and validated using the Lebanese Road Accidents Platform (LRAP) database of 8482 road crash incidents, with fatality occurrence as the outcome variable. A sensitivity analysis was conducted to examine the influence of multiple factors on fatality occurrence. Seven out of the nine selected independent variables were significantly associated with fatality occurrence, namely, crash type, injury severity, spatial cluster-ID, and crash time (hour). Evidence gained from the model data analysis will be adopted by policymakers and key stakeholders to gain insights into major contributing factors associated with fatal road crashes and to translate knowledge into safety programs and enhanced road policies.

Crash Classification by Congestion Type for Highways

Effective management of highway networks requires a thorough understanding of the conditions under which vehicular crashes occur. Such an understanding can and should inform related operational and resource allocation decisions. This paper presents an easily implementable methodology that can classify all reported crashes in terms of the operational conditions under which each crash occurred. The classification methodology uses link-based speed data. Unlike previous secondary collision identification schemes, it neither requires an a priori identification of the precipitating incident nor definition of the precipitating incident’s impact area. To accomplish this objective, the methodology makes use of a novel scheme for distinguishing between recurrent and non-recurrent congestion. A 500-crash case study was performed using a 274 km section of the I-40 in North Carolina. Twelve percent of the case study crashes were classified as occurring in non-recurrent congestion. Thirty-seven percent of the crashes in non-recurrent congestion classified were identified within unreported primary incidents or crashes influence area. The remainder was classified as primary crashes occurring in either uncongested conditions (84%) or recurrent congestion (4%). The methodology can be implemented in any advanced traffic management system for which crash time and link location are available along with corresponding archived link speed data are available.

Investigating the Impacts of Real-Time Weather Conditions on Freeway Crash Severity: A Bayesian Spatial Analysis.

This study presents an empirical investigation of the impacts of real-time weather conditions on the freeway crash severity. A Bayesian spatial generalized ordered logit model was developed for modeling the crash severity using the hourly wind speed, air temperature, precipitation, visibility, and humidity, as well as other observed factors. A total of 1424 crash records from Kaiyang Freeway, China in 2014 and 2015 were collected for the investigation. The proposed model can simultaneously accommodate the ordered nature in severity levels and spatial correlation across adjacent crashes. Its strength is demonstrated by the existence of significant spatial correlation and its better model fit and more reasonable estimation results than the counterparts of a generalized ordered logit model. The estimation results show that an increase in the precipitation is associated with decreases in the probabilities of light and severe crashes, and an increase in the probability of medium crashes. Additionally, driver type, vehicle type, vehicle registered province, crash time, crash type, response time of emergency medical service, and horizontal curvature and vertical grade of the crash location, were also found to have significant effects on the crash severity. To alleviate the severity levels of crashes on rainy days, some engineering countermeasures are suggested, in addition to the implemented strategies.

Real-time prediction of crash risk on freeways under fog conditions

The study aimed to develop a real-time crash risk prediction model on freeways under fog conditions. The data used include traffic surveillance data, fog-related crashes data, road geometry data, and visibility data of Interstate-5 (I-5), Interstate-10 (I-10), Interstate-15 (I-15) and Interstate-405 (I-405) in California, United States. The random forests method was applied for variable selection to identify and rank the most important variables. And then, the Bayesian logistic regression model was employed to develop the real-time crash risk prediction model. The model estimation results show that the explanatory variables contributing to crash risk are different in different time slices before crashes. There are common features: (a) visibility is negatively-correlated with the real-time crash risk under fog condition, and (b) average and standard deviation of vehicle count at upstream station are positively-correlated with crash risk. The model of time slice 3 (interval between 10 to 15 min prior to a crash time) performs best with the lowest false alarm rate and the highest overall accuracy and the largest area under the receiver operating characteristic (ROC) curve. And it can identify over 72% of fog-related crashes with the pre-specified threshold of 0.2.

Influence of Traffic Parameters on the Temporal Distribution of Crashes

The influence of several traffic parameters including the traffic hourly volume, average speed, and speed variance on the frequency and rate of the crash has been investigated by many researchers. In addition, the impact of human parameters on the spatial and temporal distribution of crashes has also been taken into account. However, the importance of traffic parameters on the temporal distribution of crashes has not been fully analyzed. Therefore, the main aim of this study was to investigate the effects of traffic parameters on the temporal distribution of crashes. Here, the spider plot was utilized for temporal analysis along with linear regression to estimate the impact of traffic hourly volume on the crash ratio. Also, the relationship between the crash's time and traffic parameters was determined using the Chi-square test. Accordingly, the results indicated a significant impact of traffic hourly volume on the crash ratio. Furthermore, it was observed that some traffic parameters have a significant impact on the temporal distribution of crash frequency and ratio and there are different temporal distribution patterns for different values of traffic parameters.

Investigation of injury severity in urban expressway crashes: A case study from Beijing.

Urban expressway is the main artery of traffic network, and an in-depth analysis of the crashes is crucial for improving the traffic safety level of expressways. This study intended to address the injury severity of expressways in Beijing by proposing Bayesian ordered logistic regression model. Crash data were collected from urban express rings and expressways in 2015 and 2016. The results showed that crash location, time and crash season are significant variables influencing injury severity. The findings revealed that the proposed model can address the ordinal feature of injury severity, while accommodating the data with small sample sizes that may not adequately represent population characteristics. The conclusions can provide the management departments with valuable suggestions for the injury prevention and safety improvement on the urban expressways.

Integrating complex systems science into road safety research and practice, Part 2: applying systems tools to the problem of increasing pedestrian death rates

ObjectivesTo provide a specific example of how systems dynamics tools can increase understanding of stakeholder ‘mental models’ and generate robust systems-based hypotheses about the escalating problem of rising pedestrian death...

Implications of monocular vision for racing drivers.

We performed two experiments to investigate how monocular vision and a monocular generalized reduction in vision (MRV) impact driving performance during racing. A total of 75 visually normal students or professional racing drivers, were recruited for the two experiments. Driving performance was evaluated under three visual conditions: normal vision, simulated monocularity and simulated monocular reduction in vision. During the driving scenario, the drivers had to detect and react to the sudden intrusion of an opponent's racing car into their trajectory when entering a turn. Generalized Linear Mixed Models (GLMMs) and ANOVA were then used to explore how monocular vision and monocular reduction in vision affect drivers' performance (crash and reaction time) while confronting them with critical situations. The results show that drivers under monocular condition are from 2.1 (95% CI 1.11-4.11, p = .024) to 6.5 (95% CI 3.91-11.13; p = .0001) times more likely to collide with target vehicles compared with their baseline (binocular) condition, depending on the driving situation. Furthermore, there was an average increase in reaction time from 64 ms (p = .029) to 126 ms (p = .015) under monocular condition, depending on the critical driving situation configuration. This study objectively demonstrates that monocularity has a significant impact on driving performance and safety during car racing, whereas performance under monocular reduction in vision conditions is less affected.

Helicopter Crash on the Spitsbergen Archipelago: Infrasound and Seismic Signals Decryption

A Convers Avia Airline Mi-8 helicopter crashed near the Russian settlement of Barentsburg on October 26, 2017. The moment of impact was recorded by the Barentsburg seismo-infrasound array 2.5 km from the crash site. The seismo-infrasound array consists of a micro-aperture infrasound group, including three low-frequency microphones and a broadband seismic station located at the same site. A detailed analysis of the obtained records of the seismometer and the low-frequency microphones is presented here. Two types of signals determined by the helicopter fall are detected in seismic channels: a wave caused by the water impact of the helicopter and propagating through the earth, and an air wave that was also generated by the water impact but propagating through the atmosphere. The second type of wave was also recorded by low-frequency microphones from the infrasonic microgroup. The procedure for determining the source coordinate by the complex analysis of acoustic and seismic signals is described. The difference in arrival times of seismic and acoustic waves is used to determine the distance to the source, and the back azimuth is calculated using the differences in the arrival times of the acoustic wave to the spaced microphones of the infrasound microarray. The records of the acoustic signals associated with the operation of the helicopter rotor before the crash and at the moment of water impact are examined. A detailed analysis of the frequency and amplitude composition of the received seismic and infrasound signals has allowed us to not only determine the exact place and time of the helicopter crash, but also to estimate the expected trajectory of its movement before the fall, as well as to recreate some details of the accident.

A novel feature extraction model for traffic injury severity and its application to Fatality Analysis Reporting System data analysis.

The prevention of severe injuries during crashes has become one of the leading issues in traffic management and transportation safety. Identifying the impact factors that affect traffic injury severity is critical for reducing the occurrence of severe injuries. In this study, the Fatality Analysis Reporting System data are selected as the dataset for the analysis. An algorithm named improved Markov Blanket was proposed to extract the significant and common factors that affect crash injury severity from 29 variables related to driver characteristics, vehicle characteristics, accidents types, road condition, and environment characteristics. The Pearson correlation coefficient test is applied to verify the significant correlation between the selected factors and traffic injury severity. Two widely used classification algorithms (Bayesian networks and C4.5 decision tree) were employed to evaluate the performance of the proposed feature selection algorithm. The calculation result of the correlation coefficient, accuracy of classification, and classification error rate indicated that the improved Markov Blanket not only could extract the significant impact factors but could also improve the accuracy of classification. Meanwhile, the relationship between five selected factors (atmospheric condition, time of crash, alcohol test result, crash type, and driver's distraction) and traffic injury severity was also analyzed in this study. The results indicated that crashes occurred in bad weather condition (e.g. fog or worse), in night time, in drunk driving, in crash type of single driver, and in distracted driving, which are associated with more severe injuries.

Adult moped-related injuries treated in U.S. emergency departments

Objective: Few studies have investigated U.S. emergency department (ED) moped-related injuries. Our objective was to determine the characteristics of moped crashes among adult riders and to identify associations between key variables.Methods: Data were obtained from the National Electronic Injury Surveillance System (NEISS) for 2002–2014. Descriptive and comparative analyses of the identified cases were performed.Results: Based on a probability sample of 2,453 cases, an estimated 108,229 U.S. adult moped-related ED visits occurred during the study period. Injuries increased 2.7-fold over time. Summer months and weekdays were the most common crash times. Three-fourths of injured riders were male, two-thirds were ages 23–59 years, and 77% were Caucasian. Among cases documented, one-half were helmeted, and almost two-thirds of crashes occurred at ≤20 mph. The predominant mechanism was a non-collision crash (60%). Skin-related and musculoskeletal diagnoses were most common. Almost one-fifth of patients were admitted or transferred. Regression analyses showed that males were less likely than females to be in crashes with multiple riders, and more likely to have used alcohol/drugs and to be in a crash with another motor vehicle (MVC). Older riders (≥40 years) were more likely than those younger to have used alcohol/drugs and to suffer torso injuries, and less likely to be in an MVC. As compared to other crash mechanisms, MVCs were more likely to involve injuries to the head/neck/face and torso. The likelihood of being admitted/transferred was higher for males, with increasing age, and for collision-related crashes. The higher likelihood of being admitted/transferred was also characterized by alcohol/drug use, by lower limb and torso injuries, and almost 8 times more likely for riders with serious head injuries.Conclusions: Moped injuries increased in all adult age groups during the study period. However, there were significant differences in contributing factors, crash mechanism, and outcomes by sex, race, and age. These findings suggest the need for targeted injury prevention interventions.

Avoiding momentum crashes: Dynamic momentum and contrarian trading

High momentum returns cannot be explained by risk factors, but they are negatively skewed and subject to occasional severe crashes. I explore the timing of momentum crashes and show that momentum strategies tend to crash in 1–3 months after the local stock market plunge. Next, I propose a simple dynamic trading strategy which coincides with the standard momentum strategy in calm times, but switches to the opposite contrarian strategy after a market crash and keeps the contrarian position for three months, after which it reverts back to the momentum position. The dynamic momentum strategy turns all major momentum crashes into gains and yields an average return, which is about 1.5 times as high as the standard momentum return. The dynamic momentum returns are positively skewed and not exposed to risk factors, have high Sharpe ratio and alpha, persist in different time periods and geographical markets around the globe.

Speed distribution and safety effects of license plate recognition: Analysis combining crash and toll record data in Hunan Province, China

With the Chinese motor expressway network rapidly expanding, the number of speed-related crashes has become an alarming phenomenon. Private vehicles that cannot be identified by enforcement cameras pose a threat to highway safety, specifically on expressways where it is problematic to initiate human enforcement. The purposes of this study are to investigate the speed characteristics of vehicles with no official identification (non–ID) on expressways and to compare key variables corresponding to the effects on casualties and injury severity. Data for expressway toll records and crash statistics from the expressway network of Hunan Province, China, have been collected and analyzed. The results highlighted: (1) The means and variance of non-ID-vehicles average travel speed are both higher than their normal counterpart; (2) There are relatively higher speeding rates among non-ID vehicles than their counterparts; (3) The injury severity of non-ID vehicles involved in crashes is significantly higher than that of normal vehicles. Moreover, according to the analysis of crash severity, drivers’ improper driving conduct, namely, speeding and fatigued driving, are the fundamental causes of disastrous crashes. Summer, senior drivers, invalid license, and alcohol intake also have an adverse effect on expressway safety. Similarly, crash time (evening) negatively affects injury severity.

Modeling bicyclist injury severity in bicycle–motor vehicle crashes that occurred in urban and rural areas: a mixed logit analysis

The purpose of this paper is to identify and compare the contributing factors to bicyclist injury severity in urban and rural areas. Two mixed logit models are developed for both the urban and rural areas separately to identify factors that significantly contribute to the injury severity outcome of bicyclists resulting from bicycle–motor vehicle crashes. Data collected from 2007 to 2014 in North Carolina are utilized for the model development. The model estimation results show that factors including bicyclist age from 25 to 54, driver age under 25, vehicle speed, and divided road are found to significantly affect the injury severity outcome of bicyclists in bicycle–motor vehicle crashes in rural areas only. In contrast, factors including drivers age over 60, van, single unit truck, head-on crash, motorist overtaking bicyclist, two-way roadway, road condition, and crash time are found to have a significant impact on the injury severity of bicyclists in urban areas only.

Pedestrian injury severity in motor vehicle crashes: An integrated spatio-temporal modeling approach

Traffic crashes are outcomes of human activities interacting with the diverse cultural, socio-economic and geographic contexts, presenting a spatial and temporal nature. This study employs an integrated spatio-temporal modeling approach to untangle the crashed injury correlates that may vary across the space and time domain. Specifically, this study employs Geographically and Temporally Weighted Ordinal Logistic Regression (GTWOLR) to examine the correlates of pedestrian injury severity in motor vehicle crashes. The method leverages the space- and time-referenced crash data and powerful computational tools. This study performed non-stationarity tests to verify whether the local correlates of pedestrian injury severity have a significant spatio-temporal variation. Results showed that some variables passed the tests, indicating they have a significantly varying spatio-temporal relationship with the pedestrian injury severity. These factors include the pedestrian age, pedestrian position, crash location, motorist age and gender, driving under the influence (DUI), motor vehicle type and crash time in a day. The spatio-temporally varying correlates of pedestrian injury severity are valuable for researchers and practitioners to localize pedestrian safety improvement solutions in North Carolina. For example, in near future, special attention may be paid to DUI crashes in the city of Charlotte and Asheville, because in such areas DUI-involved crashes are even more likely to cause severe pedestrian injuries that in other areas. More implications are discussed in the paper.

A clustering approach to developing car-to-two-wheeler test scenarios for the assessment of Automated Emergency Braking in China using in-depth Chinese crash data

Two-wheeled vehicles (motorized and non-motorized, referred to as TWs) are an important part of the transport system in China. They also represent an important challenge for road safety, with many TW user fatalities and injuries every year. Recently, active safety systems for cars, such as Automated Emergency Braking (AEB), promise to reduce road traffic fatalities and injuries. For these systems to work effectively, it is necessary to understand and define the complex traffic scenarios to be addressed. The aim of this study is to contribute to the development of test procedures for AEB specifically, drawing on the China In-Depth Accident Study (CIDAS) data from July 2011 to February 2016 to describe typical scenarios for crashes between cars and TWs by means of cluster analysis. In total, 672 car-to-TW crashes were extracted. The data was clustered according to five main crash characteristics: time of crash, view obstruction, pre-crash driving behavior of the car driver and the TW driver, and relative moving direction. The analysis resulted in six car-to-TW crash scenarios typical of China. In three scenarios the car and the TW travel perpendicularly to each other before the crash, in two they travel in the same direction, and in one they travel in opposite directions. Further, each scenario can be described with three characteristics (the road speed limit, the TW’s first contact point on the car, and the car’s first contact point on the TW) that can be included in an AEB test suite.Some scenarios were similar to those in the Euro New Car Assessment Program (Euro NCAP). For example, in one, a TW moving straight ahead was hit by a car moving perpendicularly, and in the other the car hit a TW traveling in the same direction. Both occurred in daytime, without a visual obstruction. However, in contrast to the Euro NCAP, typical scenarios in China included night-time scenarios, scenarios where the car or the TW was turning, and those in which the TW was hidden from the car by an obstruction. The results contribute to a proposed novel AEB test suite with realistic scenarios specific to China.

Prevalence of therapeutic drugs in blood of drivers involved in traffic crashes in the area of Bologna, Italy

IntroductionPsychoactive medicines, such as benzodiazepines and Z-drugs (BdZ), antidepressants and antipsychotics (AA) and medical opioids (MO), have an impairing effect on driving ability. In previous epidemiological studies performed on impaired and/or injured drivers, not all relevant psychoactive substances were included in the toxicological assessment, and their prevalence may be underestimated. This study aims to assess the prevalence of a large set of psychoactive substances (n=53) in Italian drivers involved in a road traffic crash and in predefined population subgroups. Materials and methodsThe toxicological analyses were performed on the whole blood of 1026 drivers involved in a road traffic crash in the area of Bologna, Italy, from January 2017 to March 2018. Analyses were performed using GC-FID (alcohol), GCMS (illicit drugs) and LC/HRMS (psychoactive drugs). The population was divided into subgroups according to gender, age and crash time. Descriptive statistics were used in order to assess differences among sub-groups. ResultsThe highest prevalence was found for alcohol (17.3%), followed by medicinal drugs (13.6%) and illicit drugs (5.5%). The prevalence of BdZ, AA and MO were 7.3%, 7.2% and 3.1%, respectively. The frequency of BDZ and AA was significantly higher in female drivers and showed higher prevalence at increasing age. The presence of medicinal drugs was significantly higher during the week and in crashes occurring during the day. ConclusionResults for alcohol and illicit drugs partially overlap with those reported in previous European and Italian studies, but the prevalence of BdZ was much higher. We also found a high prevalence of AA, which are rarely investigated in epidemiological studies performed on drivers, but may cause impairment of the ability to drive, especially when taken in combination with alcohol or other drugs. The pattern of medication use differs from that involving drugs of abuse, since it is mainly observed in female subjects and older drivers and does not follow the same weekly trend observed for alcohol and other illicit drugs.

Predictors of hospitalization and surgical intervention among patients with motorcycle injuries

BackgroundTo describe the distribution of injuries, hospitalization rates by body areas injured, and surgery-requiring admissions, and to identify independent predictors of admission to a regional hospital in Jamaica.MethodsA cross-sectional study...

Geovisual analysis of VGI for understanding people's behaviour in relation to multifaceted context

Geovisual analysis of VGI for understanding people's behaviour in relation to multifaceted context