Risk Of Vehicle Collision Research Articles

Incorporating Road Crossing Data into Vehicle Collision Risk Models for Moose (Alces americanus) in Massachusetts, USA.

Wildlife-vehicle collisions are a human safety issue and may negatively impact wildlife populations. Most wildlife-vehicle collision studies predict high-risk road segments using only collision data. However, these data lack biologically relevant information such as wildlife population densities and successful road-crossing locations. We overcome this shortcoming with a new method that combines successful road crossings with vehicle collision data, to identify road segments that have both high biological relevance and high risk. We used moose (Alces americanus) road-crossing locations from 20 moose collared with Global Positioning Systems as well as moose-vehicle collision (MVC) data in the state of Massachusetts, USA, to create multi-scale resource selection functions. We predicted the probability of moose road crossings and MVCs across the road network and combined these surfaces to identify road segments that met the dual criteria of having high biological relevance and high risk for MVCs. These road segments occurred mostly on larger roadways in natural areas and were surrounded by forests, wetlands, and a heterogenous mix of land cover types. We found MVCs resulted in the mortality of 3% of the moose population in Massachusetts annually. Although there have been only three human fatalities related to MVCs in Massachusetts since 2003, the human fatality rate was one of the highest reported in the literature. The rate of MVCs relative to the size of the moose population and the risk to human safety suggest a need for road mitigation measures, such as fencing, animal detection systems, and large mammal-crossing structures on roadways in Massachusetts.

Identification of local factors causing clustering of animal‐vehicle collisions

ABSTRACTEffective measures reducing risk of animal‐vehicle collisions (AVC) require defining high‐risk locations on roads where AVCs occur. Previous studies examined factors explaining locations of individual AVCs; however, some AVCs can form hotspots (i.e., clusters of AVCs) that can be explained by local factors. We therefore applied a novel kernel density estimation (KDE) method to AVCs for the Czech Republic from October 2006 to December 2011 to identify AVCs hotspots along roads. Our main goal was to identify local factors and their effect on the non‐random (clustered) occurrence of AVCs. The remaining solitary AVCs occurred randomly and are likely induced by other human factors on the global scale. The hotspot identification method followed by the selected data mining methods (KDE+ methods) identified factors causing local clustering of AVCs. Distance from forest (<350 m) or linear vegetation were important factors for estimating presence of clusters of AVCs; in open areas, AVC clusters were absent. Further research on effectiveness of measures reducing risk of AVC should focus on clusters of AVCs, not on the individual AVC. We recommend that state transportation agencies focus mitigation actions in forested areas. © 2018 The Authors. Journal of Wildlife Management Published by Wiley Periodicals, Inc.

The short-term impact of economic uncertainty on motor vehicle collisions

Stress and anxiety lead to attention loss and sleep deprivation and may reduce driving performance, increasing the risk of motor vehicle collision. We used evidence from a natural experiment to examine whether daily changes in economic uncertainty, potentially leading to attention or sleep loss, are associated with collisions in Great Britain. Daily data from the economic policy uncertainty index, derived from analysis of daily UK newspapers, were linked to the daily number of motor vehicle collisions in Great Britain over the period 2005–2015, obtained from the Department for Transport. Exploiting daily variations in economic uncertainty, we used a GARCH approach to model daily rates of motor vehicle collisions as a function of economic uncertainty, controlling for month and day of the week, monthly unemployment rates and weekly unleaded petrol prices. A spike in the daily economic uncertainty index was associated with an immediate increase in the number of motor vehicle collisions. Results were robust to various sensitivity analyses. Overall, daily increases in economic uncertainty are associated with short-term spikes in motor vehicle collisions. Preventive and traffic control measures may need to increase during periods of economic uncertainty.

Early ramp warning using vehicle behavior analysis

The ramp entrance connects the main road and the ramp and is the merging zone for vehicles from main road and ramp. As ramp entrance is considered to be the most accident-prone area, this paper focuses on the early warning to avoid collision of vehicles for the ramp entrance on freeway. Firstly, we studied the relation between the vehicle speed and collision risk by analyzing the vehicle trajectories. Secondly, the risk grades were defined based on the lag of the time when vehicles enter into the merging zone from the main road and the ramp. Finally, the risk grade was released by the information board and vehicle terminal to make driver aware of it, so that the vehicles can pass the merging zone in sequence rather than collide with each other. Theoretical analysis and experiment results show that the proposed method could realize early warning for the ramp entrance, reducing the possible traffic accidents.

Fueled by an Epidemic: A Spatial Analysis of Opioid-Positive Drivers Fatally Injured in Motor Vehicle Collisions in West Virginia, 2011-2015

Narcotics usage is associated with an increased risk of motor vehicle collision and opioid overdose deaths are elevated in West Virginia compared to other states in the United States. This analysis sought to determine the prevalence of narcotics among drivers fatally injured in motor vehicle collisions in West Virginia and to determine if these collisions were clustered in areas of the state where opioid use/abuse is high. Fatal crash data from 2011-2015 were obtained from the Fatality Analysis Reporting System and the locations of the collisions were plotted with spatial software. Spatial analyses, including nearest neighbor indexes, heat maps and hot-spots, were conducted to determine if and where clusters of opioid-positive crashes existed. The results of the spatial analyses were visually compared to the rates of opioid overdose deaths by county, which served as a proxy of opioid use/abuse. Of the 486 drivers, 19% (n=94) tested positive for opioids. A clustering of opioid positive crashes was detected in the state overall (nearest neighbor index=0.89, p-value=0.055). Hot-spots were detected in the lower regions of the state, which overlapped counties with the highest rates of opioid overdose deaths, and cold-spots were detected in areas with lower opioid overdose death rates. Individuals using narcotics may still operate motor vehicles, which may pose a threat to all road users in West Virginia. Public health interventions, education, or enforcement may be needed in areas of high opioid use/abuse to raise awareness of driving under the influence of drugs.

Consistent patterns of vehicle collision risk for six mammal species

The occurrence and rate of wildlife-vehicle collisions are related to both anthropocentric and environmental variables, however, few studies compare collision risks for multiple species within a model framework that is adaptable and transferable. Our research compares collision risk for multiple species across a large geographic area using a conceptually simple risk framework.We used six species of native terrestrial mammal often involved with wildlife-vehicle collisions in south-east Australia. We related collisions reported to a wildlife organisation to the co-occurrence of each species and a threatening process (presence and movement of road vehicles). For each species, we constructed statistical models from wildlife atlas data to predict occurrence across geographic space. Traffic volume and speed on road segments (also modelled) characterised the magnitude of threatening processes.The species occurrence models made plausible spatial predictions. Each model reduced the unexplained variation in patterns and distributions of species between 29.5% (black wallaby) and 34.3% (koala). The collision models reduced the unexplained variation in collision event data between 7.4% (koala) and 19.4% (common ringtail possum) with predictor variables correlating similarly with collision risk across species.Road authorities and environmental managers need simple and flexible tools to inform projects. Our model framework is useful for directing mitigation efforts (e.g. on road effects or species presence), predicting risk across differing spatial and temporal scales and target species, inferring patterns of threat, and identifying areas warranting additional data collection, analysis, and study.

The impact of medical and non-medical prescription opioid use on motor vehicle collision risk

BackgroundProduction and distribution of prescription opioid analgesic drugs (POs) has increased immensely across the globe. Both medical (MPO) and non-medical (NMPO) use of opioid medication are associated with increased rates of morbidity and mortality, including possibly motor vehicle collisions. The current study examined data from a population-level survey to determine the impact of any PO, MPO, and NMPO use on collision risk. MethodsData were based on the 2010–2011 Centre for Addiction and Mental Health Monitor, a regionally stratified general-population telephone survey of adults (N=3428). Three binary logistic regressions of self-reported collision involvement in the previous 12months were conducted, each consisting of: (1) measures of demographic characteristics, driving exposure, and binge drinking; and (2) one of three measures of PO use (any PO use, MPO use, or NMPO use). ResultsAny PO use and MPO use were associated with increased collision risk (OR=1.60, CI=1.06, 2.40 and OR=1.62, CI=1.07, 2.45, respectively). The odds ratio for NMPO use did not reach statistical significance (OR=1.86, CI=0.91, 3.77, p=0.09). ConclusionsThese results suggested that doctor-prescribed PO use is associated with a 62% increase in collision risk. Although not a statistically significant finding, NMPO use may also be associated with increased collision risk. The magnitude of increased collision risk associated with PO use was comparable to that of driving after alcohol or cannabis use.

The Relationship Between Objectively Measured Walking and Risk of Pedestrian-Motor Vehicle Collision.

Safe urban walking environments may improve health by encouraging physical activity, but the relationship between an individual's location and walking pattern and the risk of pedestrian-motor vehicle collision is unknown. We examined associations between individuals' walking bouts and walking risk, measured as mean exposure to the risk of pedestrian-vehicle collision. Walking bouts were ascertained through integrated accelerometry and global positioning system data and from individual travel-diary data obtained from adults in the Travel Assessment and Community Study (King County, Washington) in 2008-2009. Walking patterns were superimposed onto maps of the historical probabilities of pedestrian-vehicle collisions for intersections and midblock segments within Seattle, Washington. Mean risk of pedestrian-vehicle collision in specific walking locations was assessed according to walking exposure (duration, distance, and intensity) and participant demographic characteristics in linear mixed models. Participants typically walked in areas with low pedestrian collision risk when walking for recreation, walking at a faster pace, or taking longer-duration walks. Mean daily walking duration and distance were not associated with collision risk. Males walked in areas with higher collision risk compared with females, while vehicle owners, residents of single-family homes, and parents of young children walked in areas with lower collision risk. These findings may suggest that pedestrians moderate collision risk by using lower-risk routes.

Frequency of extreme weather events and increased risk of motor vehicle collision in Maryland.

Previous studies have shown increased precipitation to be associated with higher frequency of traffic collisions. However, data regarding how extreme weather events, projected to grow in frequency, intensity, and duration in response to a changing climate, might affect the risk of motor vehicle collisions is particularly limited. We investigated the association between frequency of extreme heat and precipitation events and risk of motor vehicle collision in Maryland between 2000 and 2012. Motor vehicle collision data was obtained from the Maryland Automated Accident Reporting System. Each observation in the data set corresponded to a unique collision event. This data was linked to extreme heat and precipitation events that were calculated using location and calendar day specific thresholds. A time-stratified case-crossover analysis was utilized to assess the association between exposure to extreme heat and precipitation events and risk of motor vehicle collision. Additional stratified analyses examined risk by road condition, season, and collisions involving only one vehicle. Overall, there were over 1.28 million motor vehicle collisions recorded in Maryland between 2000 and 2012, of which 461,009 involved injuries or death. There was a 23% increase in risk of collision for every 1-day increase in extreme precipitation event (Odds Ratios (OR) 1.23, 95% Confidence Interval (CI): 1.22, 1.27). This risk was considerably higher for collisions on roads with a defect or obstruction (OR: 1.46, 95% CI: 1.40, 1.52) and those involving a single vehicle (OR: 1.41, 95% CI: 1.39, 1.43). Change in risk associated with extreme heat events was marginal at best. Extreme precipitation events are associated with an increased risk of motor vehicle collisions in Maryland.

Mechanisms behind distracted driving behavior: The role of age and executive function in the engagement of distracted driving

Performing secondary tasks, such as texting while driving, is associated with an increased risk of motor vehicle collisions (MVCs). While cognitive processes, such as executive function, are involved in driving, little is known about the relationship between executive control and willingness to engage in distracted driving. This study investigated the relationship between age, behavioral manifestations of executive function, and self-reported distracted driving behaviors. Executive difficulty (assessed with the BRIEF-A) as well as demographics (age and gender) was considered as possible predictors of engagement in distracted driving behaviors. Fifty-nine young, middle, and older adults self-reported executive difficulty and weekly engagement in distracted driving behaviors. Results revealed that while partially accounted for by age, global executive difficulty was uniquely related to engagement in distracted driving behaviors. Older age was associated with fewer weekly self-reported distracted driving behaviors while higher self-reported executive difficulty was associated with more frequent weekly engagement in distracted behavior. No significant differences were found between young and middle-aged adults on distracted driving behaviors. Findings conclude that distracted driving is a ubiquitous phenomenon evident in drivers of all ages. Possible mechanisms underlying distracted driving behavior could potentially be related to deficits in executive function.

Risk of motor vehicle collisions associated with medical conditions and medications: rationale and study protocol.

Medical conditions and medications may be associated with motor vehicle collisions (MVCs), which pose a major public health problem worldwide. Further epidemiological assessment is necessary for certain diseases and medications. Moreover, since disease aetiology and patterns of medication use may differ among ethnicities and healthcare systems, a population-specific approach is necessary. The present epidemiological study is designed to assess the medical conditions and medications associated with the risk of fatal MVCs among at-fault drivers in the Korean population. A retrospective cohort will be constructed for individuals who died in MVCs between 2005 and 2014 in the Korean Traffic Accident Analysis System database, which is linked to the Korean National Health Insurance database between 2002 and 2014. In order to compare medical conditions and medication use among drivers who died in a fatal MVC with the general population, standardised prevalence ratios will be calculated. In the culpability study, we will identify conditions and drugs associated with MVCs, comparing drivers with higher levels of responsibility to those with lower levels of responsibility. In the case-crossover study, the transient effects of medical conditions and medications will be examined using a conditional logistic regression model that adjusts for confounders. The results of this study will help to characterise the associations of diseases and medications with fatal MVCs in an Asian population, with the goal of informing regulatory and clinical decision-making regarding patients with the relevant conditions and the establishment of strategies for improving traffic safety.

Medication use and the risk of motor vehicle collisions among licensed drivers: A systematic review

ObjectivesDriving under the influence of prescription and over-the-counter medication is a growing public health concern. A systematic review of the literature was performed to investigate which specific medications were associated with increased risk of motor vehicle collision (MVC). MethodsThe a priori inclusion criteria were: (1) studies published from English-language sources on or after January 1, 1960, (2) licensed drivers 15 years of age and older, (3) peer-reviewed publications, master’s theses, doctoral dissertations, and conference papers, (4) studies limited to randomized control trials, cohort studies, case-control studies, or case-control type studies (5) outcome measure reported for at least one specific medication, (6) outcome measure reported as the odds or risk of a motor vehicle collision. Fourteen databases were examined along with hand-searching. Independent, dual selection of studies and data abstraction was performed. ResultsFifty-three medications were investigated by 27 studies included in the review. Fifteen (28.3%) were associated with an increased risk of MVC. These included Buprenorphine, Codeine, Dihydrocodeine, Methadone, Tramadol, Levocitirizine, Diazepam, Flunitrazepam, Flurazepam, Lorazepam, Temazepam, Triazolam, Carisoprodol, Zolpidem, and Zopiclone. ConclusionsSeveral medications were associated with an increased risk of MVC and decreased driving ability. The associations between specific medication use and the increased risk of MVC and/or affected driving ability are complex. Future research opportunities are plentiful and worthy of such investigation.

Medication use and the risk of motor vehicle collision in West Virginia drivers 65 years of age and older: a case-crossover study.

BackgroundThe current generation of older adults reports a higher lifetime prevalence of prescription, over-the-counter, and recreational drug use. The purpose of this analysis is to characterize the drug usage and determine the risk of motor vehicle collision associated with individual medications in a population of drivers ≥65 years.MethodsA case-crossover study was conducted at West Virginia University Healthcare’s facilities using data obtained from the electronic health records (n = 611) of drivers ≥65 years admitted for medical treatment following a motor vehicle collision which occurred between Jan. 1, 2009 and June 30, 2014. Patients’ medication usage 14 days before collision were matched and compared to their medication usage during four control periods prior to collision. Odds ratios were then calculated for the most prevalent individual medications and pharmaceutical sub-classes using conditional logistic regression.ResultsAnalgesic, cardiovascular and gastrointestinal medicines were common. Few drivers tested positive for either licit or illicit drugs. Of those testing positive for drugs, benzodiazepines and opiates were prevalent. Drivers consuming Tramadol (adjusted OR 11.41; 95 % CI 1.27, 102.15) were at a significantly increased risk of motor vehicle collision.ConclusionsOlder adult drivers who have a prescription for this medication may need to be aware of the potential risk. Further research is necessary in a larger, more nationally representative population.

Bicycle trauma and alcohol intoxication

IntroductionAs bicycling has become more popular, admissions after bicycle trauma are on the rise. The impact of alcohol use on bicycle trauma has not been well studied. The aim of this study was to examine the effect of alcohol intoxication on injury burden following bicycle-related crashes. MethodsA retrospective review of trauma patients presenting to a Level I trauma center after bicycle-related crashes from January 2002 to December 2011 was conducted. Demographics, injury data, alcohol intoxication, helmet use, and clinical outcomes were reviewed. Blood alcohol level (BAL) was considered positive if >0.01 g/dL. Variables were compared between patients based on BAL: negative, 0.01–0.16 g/dL, and >0.16 g/dL. ResultsDuring the 10 year study period, 563 patients met study criteria; mean age was 33.5 ± 16.5 years, 87% were male, and mortality was 1%. On average, bicycle crashes increased over the study period by 4.4 collisions per year. BAL was tested in 211 (38%) patients. Mean BAL was 0.24 g/dL, with 37% of these patients being intoxicated (BAL ≥0.010 g/dL). Intoxicated patients were significantly less likely to wear a helmet (4.7% vs. 22.2%, p = 0.002) and to be involved in motor vehicle crash (59.0% vs. 81.2%, p < 0.001). There was no difference noted in the injury burden including ISS ≥16 (14.3% vs. 19.5%, p = 0.335) and AIS Head ≥3 (17.9% vs. 21.8%, p = 0.502). When comparing patients according to their BAL, there was a decreasing risk of motor vehicle collision with increasing BAL (81.2% for undetected, 76.5% for BAL ≤0.16 g/dL and 54.1% for BAL >0.16 g/dL, p < 0.001). The risk for a severe head injury (AIS Head ≥3) was significantly lower in helmeted patients (8.4% vs. 15.8%, p = 0.035). ConclusionsThe incidence of bicycle-related crashes is increasing and more than a third of patients tested for alcohol after bicycle-related crashes are found to be intoxicated. The injury burden in intoxicated patients, including head trauma, was not different compared to non-intoxicated patients. In addition, the risk for a collision with a motor vehicle was significantly lower. Nonetheless, these patients rarely utilize a helmet. The findings from this study can be used for the development and implementation of preventive strategies to minimize the injury burden associated with bicycle crashes and intoxicated cyclists.

When top predators become prey: Black bears alter movement behaviour in response to hunting pressure

The trade-off between predator avoidance and foraging is a key decision making factor that shapes an organism's adaptive behaviour and movement patterns. Human hunters act as top predators to influence the behaviour of free-ranging mammals, including large carnivorous species such as black bears (Ursus americanus). Analysing the effects of hunting on animal behavioural patterns is essential for understanding the extent to which animals detect and respond to human-induced disturbances. To this end, we assessed whether black bear movement behaviour changed with varying risk from spatially and temporally heterogeneous human predation. Levels of risk were categorized as either low (disturbance from dog training; n=19 bears) or high (disturbance from hunting activities; n=11 bears). Road types were either paved (risk due to vehicles) or non-paved (risk due to hunters) and were used as proxies for hunting effort and amount of disturbance. We began by testing the null hypothesis that bears’ distribution before the onset of human disturbance is spatially random. Next, to test temporal movement adjustment between the low and high risk levels, we measured the distance to the nearest road and the road crossing frequency using mixed effects models with risk level, time of day and sex as predictor variables.As disturbance near non-paved roads increased due to the start of the hunting activity, the mean distances of bears to non-paved roads increased while the mean distances of bears to paved roads decreased, despite the continual risk of vehicle collision. These behavioural responses were observed during day and night, with the frequency of crossing paved roads at night five times greater than in daytime during the hunting season.Our findings demonstrate that black bears are able to detect risky places and adjust their spatial movements accordingly. More specifically, bears can perceive changes in the level of risk from human hunting activities on a fine temporal scale.

Inequity in health: older rural driving and dementia

The number of drivers with dementia is expected to increase exponentially over the coming decades. Most individuals with moderate-to-severe dementia (table 1) are unfit to drive.1 Drivers with moderate-to-severe dementia...

Wolf diet in an agricultural landscape of north-eastern Turkey

AbstractIn this study, we investigated wolf feeding ecology in Kars province, north-eastern Turkey, by analysing 72 scat samples collected in spring 2013. Ongoing camera trap surveys suggest that large wild ungulates are exceptionally rare in the region. On the contrary, livestock is abundant. Accordingly, scats analysis revealed that livestock constituted most of the biomass intake for wolves, although small mammals were the most frequent prey items. Wild ungulates were occasional prey, and although wolves make use of the main village garbage dump as a food source, garbage remains were scarce in scat samples. Wolf dependence on anthropogenic resources, primarily livestock, generates human-wildlife conflicts in the study area. Uncontrolled carcass disposal seems to boost this wolf behaviour. Synanthropy enhances the probability of wolf-human encounters and thus increases the risk of direct persecution, vehicle collisions, and hybridisation with dogs. When livestock is not available, small mammals are an important alternative prey for wolves. This may increase interspecific competition, particularly with lynx, which is also lacking natural prey in the area. Our preliminary results contribute to wolf ecology and conservation in the Anatolian-Caucasian range, where further studies are urgently needed to generate baseline data.

The Prediction of Vehicle Collision Risk in Traffic Conflict Zone Based on Bayesian Network

The external traffic environment has a big influence to the traffic safety during the area of traffic conflict place，and to analysis the relationship between the external traffic environment factors and driving safety is helpful to improve the traffic safety. The method of comprehensive analysis the historical data and expert survey data is used to explore this question. And at the same time, the collision risk prediction model during the traffic conflict place is built by the Bayesian network. According to the data analyzing, the node variable, the state of variable and the conditional probability table of this model is also built. Finally, the software of Hugin is used to deal with the posteriori probability of collision risk, and the result proved that this model can predict the collision risk accurately during the traffic conflict area, and the data analyzing showed that the factor of the driver's intention, the vehicle speed and the headway have a significance influence to the traffic safety.

Binocular Visual Field Impairment in Glaucoma and At-Fault Motor Vehicle Collisions

To evaluate the association between the binocular visual field defects in drivers with glaucoma and the risk of motor vehicle collision (MVC) involvement. A retrospective cohort study was conducted on 438 drivers with glaucoma aged 55 years or older using data from 1994 through 2000. Demographic, clinical, and driving characteristics were obtained from chart abstractions and patient survey. Binocular field measures were generated by combining data from the monocular (central 24-degree radius) fields whereby the binocular field measure was defined as the more sensitive point at each monocular field location. Measures included threshold (TH), total deviation (TD), and pattern deviation (PD); severe impairment in these measures was defined as falling into the worst quartile. MVC data were obtained from police records. Rate ratios (RR) and 95% confidence intervals (CI) were calculated. Drivers with severely impaired PD measures were twice as likely to have an at-fault MVC compared with those not severely impaired (RR, 2.13; 95% CI, 1.21-3.75); those with severely impaired TH (RR, 1.49; 95% CI, 0.81-2.74) and TD (RR, 1.50; 95% CI, 0.82-2.74) also had an increased rate of at-fault MVCs, although these were not significant. When the binocular central visual field was stratified into 9 regions, drivers with impaired TH, TD, or PD had similarly elevated MVC rates in all regions compared with those not severely impaired, though not all reached statistical significance. On the basis of clinical measures of visual field routinely used in the management of glaucoma, drivers with glaucoma with severe PD field defects in the binocular field have a higher rate of at-fault MVC compared with those with less impaired or unimpaired binocular visual fields.

Individual flexibility in nocturnal activity reduces risk of road mortality for an urban carnivore

Many species living in developed areas adjust the timing of their activity and habitat selection to avoid humans, which may reduce their risk of conflict, including vehicle collisions. Understanding the behavioral adaptations to vehicles exhibited by species that thrive in urban areas could improve the conservation of many species that are threatened by road-caused mortality. We explored these behaviors using the seasonal distribution of 80 road-killed coyotes ( Canis latrans ) collected by civic employees and by comparing the activity patterns (step lengths) and road crossings made by 19 coyotes fitted with GPS collars with 3-h fix rates, 7 of which were killed in vehicle collisions. Coyotes were collected on roads most often in spring and fall, which corresponded to the most rapid changes in day length in our northern study area and when collared road-killed coyotes were more active during rush hour. Among collared coyotes, those that were killed on roads were most active and crossed roads most frequently at dusk. By contrast, surviving animals were most active and crossed roads most often near midnight year round and surprisingly, exhibited less avoidance of roads than did road-killed coyotes. Our results suggest that risk of vehicle collision is lower for coyotes that restrict the times at which they cross roads but some coyotes do not or cannot. Such behavioral flexibility to adapt to the timing of human activity relative to exogenous cues such as dawn and dusk may contribute to differences both among and within wildlife species in rates of coexistence with humans.