Driving under the influence: a multi-center evaluation of vehicular crashes in the era of cannabis legalization

BackgroundEleven states have instituted laws allowing recreational cannabis use leading to growing public health concerns surrounding the effects of cannabis intoxication on driving safety. We hypothesized that after the 2016 legalization of cannabis in California, the use among vehicular injury patients would increase and be associated with increased injury severity.MethodsSan Diego County’s five adult trauma center registries in were queried from January 2010 to June 2018 for motor vehicle or motorcycle crash patients with completed toxicology screens. Patients were stratified as toxicology negative (TOX−), positive for only THC (THC+), only blood alcohol >0.08% (ETOH+), THC+ETOH, or THC+ with any combination with methamphetamine or cocaine (M/C). County medical examiner data were reviewed to characterize THC use in those with deaths at the scene of injury.ResultsOf the 11,491 patients identified, there were 61.6% TOX−, 11.7% THC+, 13.7% ETOH+, 5.0% THC+ETOH, and 7.9% M/C. THC+ increased from 7.3% to 14.8% over the study period and peaked at 14.9% post-legalization in 2017. Compared with TOX− patients, THC+ patients were more likely to be male and younger. THC+ patients were also less likely to wear seatbelts (8.5% vs 14.3%, p<0.001) and had increased mean Injury Severity Score (8.4±9.4 vs 9.0±9.9, p<0.001) when compared with TOX− patients. There was no difference in in-hospital mortality between groups. From the medical examiner data of the 777 deaths on scene, 27% were THC+.DiscussionTHC+ toxicology screens in vehicular injury patients peaked after the 2016 legalization of cannabis. Public education on the risks of driving under the influence of cannabis should be a component of injury prevention initiatives.Level of evidenceIII, Prognostic

Accident Hotspots in Southern Expressway of Sri Lanka: Interpolation Evaluation using GIS

In this study, the southern expressway, which is the first and lengthiest E class highway (126 km) in Sri Lanka, was analysed for roadside accident incidences. The primary objective of this paper is to identify the best-fit interpolation techniques for the hotspots' most distinctive causes of vehicular crashes. The accident details were collected from the Police Headquarters consisting of 966 accidents that took place during the period from 2015 to 2017. To identify accident hotspots, GIS-based interpolation techniques such as Ordinary Kriging, Kernel Density Estimation (KDE), Inverse Distance Weighting (IDW), and Nearest Neighbour Interpolation methods were used. The spatial interpolation outcome of the four methods was compared based on the standard Prediction Accuracy Index (PAI). The analysis was executed using QGIS and GeoDa. Results of PAI revealed that an IDW and KDE outperformed the other two interpolation methods. The left and right lanes of the expressway, spotted with 11 and 20 hotspots, respectively, indicate the right lane was 50% more prone to accidents than the left lane. Notably, nearly 5% of the entire road stretch is estimated as accident-prone spots in both lanes. Peak accidents were recorded during afternoon and evening hours, and buses were the most active vehicle type. Uncontrolled speeding was the primary reason for more than 50% of the accidents, while unsuccessful overtake accounted for more than 20% of the accidents on the highway. The road design modifications and warning sign placements at appropriate places may be recommended as countermeasures.

Will Weather Dampen Self-Driving Vehicles?

Innovative technologies that support implementation of automated vehicles continue to develop at a rapid pace. These advances strive to increase efficiency and safety throughout the global transportation network. One important challenge to these emergent technologies that remains underappreciated is how the vehicles will perform in adverse weather. Each year, weather-related vehicular crashes account for approximately 21% of all highway crashes in the United States. These crashes result in over 5,300 fatalities, injure over 418,000 people, and cost billions of dollars in insurance claims, liability, emergency services, congestion delays, rehabilitation, and environmental damage annually. Automated vehicles have the potential to significantly mitigate these statistics; however, public, private, and academic partnerships between the meteorological and transportation communities must be established to develop solutions to weather impacts now. To date, such interactions have been sparse and largely contribute to a lack of awareness in how these two communities may collaborate together. The purpose of this manuscript is to call the meteorological community to action and proactive engagement with the transportation community. A secondary goal is to make the transportation community aware of the advantages of teaming with the weather enterprise. Automated vehicles will not only increase travel safety, but also have benefits to the meteorological community through increasing availability of high-resolution surface data observations. The future challenges of these emergent technologies in the context of road weather implications focus on vehicle situational awareness and technological sensing capability in all weather conditions, and transforming how drivers and vehicles are informed of weather threats beyond sensing capabilities.

Residential accessibility’s relationships with crash rates per capita

This paper examines the relationship between residential accessibility, i.e., accessibility from a person’s home address, and their likelihood of being in a crash over a three-year period. We explore two potential relationships with accessibility. The first is that persons who live in areas with high destination accessibility may drive less and therefore are less likely to be in vehicular crashes. The second is that persons who live in high vehicle miles traveled (VMT) accessibility areas may be exposed to higher levels of traffic in their regular activity space and therefore may be more likely to be in crashes of all modal types. Examining traffic analysis zones in Knoxville, Tennessee, this research finds some evidence for each of these hypothesized effects. These oppositely directed effects have dominant influence within different travel-time thresholds. The first relationship between destination accessibility and fewer crashes is found to be strongest for 10-minute auto accessibility, whereas the second relationship between VMT accessibility and more crashes is found to occur at 10-minute, 20-minute, and 30-minute thresholds.

Effects Influencing Pedestrian–Vehicle Crash Frequency by Severity Level: A Case Study of Seoul Metropolitan City, South Korea

This study aimed to determine how built environments affect pedestrian–vehicle collisions. The study examined pedestrian–vehicular crashes that occurred between 2013 and 2015 in Seoul, Korea, by comparing and analyzing different effects of the built environment on pedestrian–vehicle crashes. Specifically, the study analyzed built environment attributes, land use environment, housing types, road environment, and traffic characteristics to determine how these factors affect the severity of pedestrian injury. The results of the statistical analysis appear to infer that the built environment attributes had dissimilar impacts on pedestrian collisions, depending on the injury severity. In general, both incapacitating and non-incapacitating injuries appear to be more likely to be caused by the built environment than fatal and possible injuries. These results highlight the need to consider injury severity when implementing more effective interventions and strategies for ensuring pedestrian safety. However, because of the small sample size, an expanded research project regarding this issue should be considered, as it would contribute to the development and implementation of effective policies and interventions for pedestrian safety in Korea. This study therefore offers practical information regarding the development of such an expanded study to inform future traffic safety policies in Seoul to establish a “safe walking city.”

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.

Developing a Department of Transportation Winter Severity Index

Adverse weather conditions are responsible for millions of vehicular crashes, thousands of deaths, and billions of dollars per year in economic and congestion costs. Many transportation agencies utilize a performance or mobility metric to assess how well they maintain road access; however, there is only limited consideration of meteorological impacts to the success of their operations. This research develops the Nebraska winter severity index (NEWINS), which is a daily event-driven index derived for the Nebraska Department of Transportation (NDOT). The NEWINS includes a categorical storm classification framework to capture atmospheric conditions and possible road impacts across diverse spatial regions of Nebraska. A 10-yr (2006–16) winter season database of meteorological variables for Nebraska was obtained from the National Centers for Environmental Information. The NEWINS is based on a weighted linear combination applied to the collected storm classification database to measure severity. The NEWINS results were compared to other meteorological variables, many used in other agencies’ winter severity indices. This comparison verified the NEWINS robustness for the observed events for the 10-yr period. An assessment of the difference between days with observed snow versus days with accumulated snow revealed 39% fewer snow-accumulated days than snow-observed days. Furthermore, the NEWINS results highlighted the greater number of events during the 2009/10 winter season and the lack of events during the 2011/12 winter season. It is expected that the NEWINS could help transportation personnel allocate efficiently resources during adverse weather events. Moreover, the NEWINS framework can be used by other agencies to assess their weather sensitivity.