Examination of factors associated with the temporal stability assessment of crash severity by using generalised linear model-A case study.

Road crashes are a major public safety concern in Pakistan. Prior studies in Pakistan investigated the impact of different factors on road crashes but did not consider the temporal stability of crash data. This means that the recommendations based on these studies are not fully effective, as the impact of certain factors may change over time. To address this gap in the literature, this study aims to identify the factors contributing to crash severity in road crashes and examine how their impact varies over time. In this comprehensive study, we utilized Generalised Linear Model (GLM) on the crash data between the years 2013 to 2017, encompassing a total sample of 802 road crashes occurred on the N-5 road section in Pakistan, a 429-kilometer stretch connecting two big cities of Pakistan, i.e., Peshawar and Lahore. The purpose of the GLM was to quantify the temporal stability of the factors contributing crash severity in each year from 2013 to 2017. Within this dataset, 60% (n = 471) were fatal crashes, while the remaining 40% (n = 321) were non-fatal. The results revealed that the factors including the day of the week, the location of the crashes, weather conditions, causes of the crashes, and the types of vehicles involved, exhibited the temporal instability over time. In summary, our study provides in-depth insights aimed at reducing crash severity and potentially aiding in the development of effective crash mitigation policies in Pakistan and other nations having similar road safety problems. This research holds great promise in exploring the dynamic safety implications of emerging transportation technologies, particularly in the context of the widespread adoption of connected and autonomous vehicles.

The impact of COVID-19 restrictions on motorcycle crashes in Taiwan.

Taiwan is one of the countries with the highest motorcycle per capita globally, and motorcycle crashes are predominant among traffic crashes. This study examines the impact of coronavirus disease 2019 restrictions on motorcycle crashes. We analyzed the trend of motorcycle crashes in Taipei City from 2019 to 2020 using the dataset provided by the Department of Transportation, Taipei City Government, Taiwan. We found 47,108 and 51,441 motorcycle crashes in 2019 and 2020, involving 61,141 and 67,093 motorcycles, respectively. Mopeds had the highest risk in 2020, followed by heavy motorcycles [≥550 cubic capacity (cc)] and scooters compared to 2019. Food delivery motorcycle crashes increased for scooters (0.93% in 2019 to 3.45% in 2020, P < .0001) and heavy motorcycles (250 < cc < 550) (0.90% in 2019 to 3.38% in 2020, P < .0001). While fatalities remained under 1%, 30% to 51% of motorcyclists sustained injuries. Food delivery with scooters or heavy motorcycles (250 < cc < 550) was significantly associated with motorcyclist injuries and deaths. Compared with 2019, the adjusted odds ratios of motorcyclist injuries and deaths in 2020 were 1.43 (95% confidence interval = 1.05-1.94) for heavy motorcycles (≥550 cc) and 1.07 (95% confidence interval = 1.04-1.09) for scooters. This study shows that coronavirus disease 2019 restrictions was associated with elevated risks of crashes, injuries, and deaths among motorcyclists, reflecting the general preference for private transport over public transport. The popularity of food delivery services also contributed to increased motorcycle crashes.

Sex-related disparities in vehicle crash injury and hemodynamics.

Objective: Multiple studies evaluate relative risk of female vs. male crash injury; clinical data may offer a more direct injury-specific evaluation of sex disparity in vehicle safety. This study sought to evaluate trauma injury patterns in a large trauma database to identify sex-related differences in crash injury victims. Methods: Data on lap and shoulder belt wearing patients age 16 and up with abdominal and pelvic injuries from 2018 to 2021 were extracted from the National Trauma Data Bank for descriptive analysis using injuries, vital signs, International Classification of Disease (ICD) coding, age, and injury severity using AIS (Abbreviated Injury Scale) and ISS (Injury Severity Score). Multiple linear regression was used to assess the relationship of shock index (SI) and ISS, sex, age, and sex*age interaction. Regression analysis was performed on multiple injury regions to assess patient characteristics related to increased shock index. Results: Sex, age, and ISS are strongly related to shock index for most injury regions. Women had greater overall SI than men, even in less severe injuries; women had greater numbers of pelvis and liver injuries across severity categories; men had greater numbers of injury in other abdominal/pelvis injury regions. Conclusions: Female crash injury victims' tendency for higher (AIS) severity of pelvis and liver injuries may relate to how their bodies interact with safety equipment. Females are entering shock states (SI > 1.0) with lesser injury severity (ISS) than male crash injury victims, which may suggest that female crash patients are somehow more susceptible to compromised hemodynamics than males. These findings indicate an urgent need to conduct vehicle crash injury research within a sex-equity framework; evaluating sex-related clinical data may hold the key to reducing disparities in vehicle crash injury.

A method to enhance drivers' hazard perception at night based on "knowledge-attitude-practice" theory.

During nighttime driving, the inherent challenges of low-illuminance conditions often lead to an increased crash rate and higher fatalities by impairing drivers' ability to recognize imminent hazards. While the severity of this issue is widely recognized, a significant research void exists with regard to strategies to enhance hazard perception under such circumstances. To address this lacuna, our study examined the potential of an intervention grounded in the knowledge-attitude-practice (KAP) framework to bolster nighttime hazard detection among drivers. We engaged a cohort of sixty drivers split randomly into an intervention group (undergoing specialized training) and a control group and employed a holistic assessment that combined eye movement analytics, physiological response monitoring, and driving performance evaluations during simulated scenarios pre- and post-intervention. The data showed that the KAP-centric intervention honed drivers' visual search techniques during nighttime driving, allowing them to confront potential threats with reduced physiological tension and ensuring more adept vehicle handling. These compelling findings support the integration of this methodology in driver training curricula and present an innovative strategy to enhance road safety during nighttime journeys.

Statistical estimation of fatal and serious injuries saved by iRAP protocols in 74 countries.

OBJECTIVE: Road traffic crashes cause 1.19 million deaths and millions more injuries annually. The persistently high burden has drawn attention from national and international stakeholders worldwide. Unsafe road infrastructure is one of the major risk factors for traffic safety, particularly in low- and middle-income countries. METHODS: Aiming to eliminate high-risk roads in all countries, the International Road Assessment Programme (iRAP) developed a robust and evidence-based approach to support country transportation agencies. RESULTS: Thus far, the iRAP protocols have been used to collect 1.8 million kilometers of Crash Risk Mapping and 1.5 million kilometers of Star Rating and FSI estimations in 128 countries. Deploying an observational before-and-after (or pre-post) study design, this report estimated the fatal and series injuries (FSI) saved through use of the iRAP protocols. The study is based on 441,753 kilometers of assessed roads from 1,039 projects in 74 countries. Our results show that the implementation of iRAP's proposed countermeasures saves about 159,936 FSI annually. Throughout the lifetime of the implemented countermeasures, a total of 3.2 million FSI could be saved. CONCLUSION: While quantifying the success of the iRAP protocols, our results suggest an opportunity to save many millions more lives on the roads through expanding iRAP implementation to more regions and countries.

Does the priority of ambulance guarantee no delay? a MIPSSTW model of emergency vehicle routing optimization considering complex traffic conditions for highway incidents.

Globally, traffic accidents on the highway network contribute significantly to a high fatality rate, drawing considerable attention from health institutions. The efficiency of transportation plays a vital role in mitigating the severe consequences of these incidents. This study delves into the issues of emergency vehicles experiencing delays despite having priority. Therefore, we construct mixed-integer linear programming with semi-soft time windows (MIPSSTW) model for optimizing emergency vehicle routing in highway incidents. We analyze the time-varying and complex traffic situations and respectively propose corresponding estimation approaches for the travel time of road segments, intersections on the urban road network, and ramp-weave sections on the highway network. Furthermore, we developed a modified cuckoo search(MCS) algorithm to solve this combinatorial problem. Optimization strategies of Lévy flight and dynamic inertial weight strategy are introduced to strengthen the exploration capability and the diversity of solution space of the CS algorithm. Computational experiments based on the Chinese emergency medical system data are designed to validate the efficacy and effectiveness of the MIPSSTW model and MCS algorithm. The results show that our works succeed in searching for high-quality solutions for emergency vehicle routing problems and enhance the efficacy of strategic decision-making processes in the realm of incident management and emergency response systems.

Injury Patterns and Gender in Italy.

Introduction: Globally, injuries pose significant public health challenges, with road traffic accidents in particular being responsible for considerable morbidity, mortality, and economic distress. Italy has been significantly impacted due to its high population density and frequency of road traffic and domestic incidents. Method: This study set out to investigate the incidence of self-reported road traffic and home and leisure accidents in the Italian general population. A particular emphasis was placed on exploring possible gender differences across varying age groups. The data was obtained from the European Health Interview Survey and a representative sample of the Italian population was analyzed. Results: The analysis revealed that regardless of age, women experienced a reduced risk of road traffic accidents compared to men. However, gender disparities in home-leisure accidents were observed to be age-dependent. Women under the age of 25 exhibited a lower likelihood of home-leisure accidents and serious accidents necessitating hospital admission in comparison to their male counterparts. In contrast, women aged 65 and above had an increased likelihood of home-leisure accidents as opposed to men in the same age category. Conclusions: The findings of this study highlight the importance of considering age and gender as significant factors in the occurrence of different types of accidents, offering insight into how injury rates vary between these demographic groups within Italy.

Epidemiology and Characteristics of Ankle and Foot Injuries in Electric Scooter-Related Accidents.

Background: The use of electric scooters (e-scooters) continues to increase as a simple, inexpensive means of transport, resulting in a sharp increase in the incidence of scooter-related accidents. No study to date has closely examined the injury extent to the lower leg, joints, and extremities from e-scooter-related accidents. Here, we investigated the epidemiology and injury patterns of such accidents, focusing on injuries to the ankle and foot. Methods: Based on data from a single tertiary hospital's database, the demographics of 563 patients with scooter-associated injuries were analyzed retrospectively. Among the patients, 229 patients who were injured by e-scooter riding were further investigated. Based on the data, the general demographics of whole scooter-associated injuries and the injury characteristics and fracture cases of the lower leg, ankle, and foot were analyzed. Results: During the 4-year study period, the number of patients injured by e-scooters increased every year. Lower extremities were the most common injury site (67.2%) among riders, whereas injuries to the head and neck (64.3%) were more common in riders of non-electric scooters. Among the lower leg, ankle, and foot injuries of riders (52 cases), the ankle joint (53.8%) was the most commonly injured site, followed by the foot (40.4%) and lower leg (21.2%). The fracture group scored significantly higher on the Abbreviated Injury Scale than the non-fracture group (p < 0.001). Among the fracture group (20 cases), ankle fractures (9 cases) were most common, including pronation external rotation type 4 injuries (4 cases) and pilon fractures (2 cases). Five patients (25%) had open fractures, and 12 patients (60%) underwent surgical treatment. Conclusions: The ankle and foot are the most common injury sites in e-scooter-related accidents. Given the high frequency and severity of e-scooter-related ankle and foot injuries, we suggest that more attention be paid to preventing these types of injuries with greater public awareness of the dangers of using e-scooters.

Assessing the predictability of surrogate safety measures as crash precursors based on vehicle trajectory data prior to crashes.

This study aims to investigate the predictability of surrogate safety measures (SSMs) for real-time crash risk prediction. We conducted a year-long drone video collection on a busy freeway in Nanjing, China, and collected 20 rear-end crashes. The predictability of SSMs was defined as the probability of crash occurrence when using SSMs as precursors to crashes. Ridge regression models were established to explore contributing factors to the predictability of SSMs. Four commonly used SSMs were tested in this study. It was found that modified time-to-collision (MTTC) outperformed other SSMs when the early warning capability was set at a minimum of 1 s. We further investigated the cost and benefit of SSMs in safety interventions by evaluating the number of necessary predictions for successful crash prediction and the proportion of crashes that can be predicted accurately. The result demonstrated these SSMs were most efficient in proactive safety management systems with an early warning capability of 1 s. In this case, 308, 131, 281, and 327,661 predictions needed to be made before a crash could be successfully predicted by TTC, MTTC, DRAC, and PICUD, respectively, achieving 75 %, 85 %, 35 %, and 100 % successful crash identifications. The ridge regression results indicated that the predefined threshold had the greatest impact on the predictability of all tested SSMs.

A variable speed limit control approach for freeway tunnels based on the model-based reinforcement learning framework with safety perception.

To improve the traffic safety and efficiency of freeway tunnels, this study proposes a novel variable speed limit (VSL) control strategy based on the model-based reinforcement learning framework (MBRL) with safety perception. The MBRL framework is designed by developing a multi-lane cell transmission model for freeway tunnels as an environment model, which is built so that agents can interact with the environment model while interacting with the real environment to improve the sampling efficiency of reinforcement learning. Based on a real-time crash risk prediction model for freeway tunnels that uses random deep and cross networks, the safety perception function inside the MBRL framework is developed. The reinforcement learning components fully account for most current tunnels' application conditions, and the VSL control agent is trained using a deep dyna-Q method. The control process uses a safety trigger mechanism to reduce the likelihood of crashes caused by frequent changes in speed. The efficacy of the proposed VSL strategies is validated through simulation experiments. The results show that the proposed VSL strategies significantly increase traffic safety performance by between 16.00% and 20.00% and traffic efficiency by between 3.00% and 6.50% compared to a fixed speed limit approach. Notably, the proposed strategies outperform traditional VSL strategy based on the traffic flow prediction model in terms of traffic safety and efficiency improvement, and they also outperform the VSL strategy based on model-free reinforcement learning framework when sampling efficiency is considered together. In addition, the proposed strategies with safety triggers are safer than those without safety triggers. These findings demonstrate the potential for MBRL-based VSL strategies to improve traffic safety and efficiency within freeway tunnels.

Outcome evaluation of the p drivers Program: Randomised controlled trial of a program to improve safe driving among novice drivers.

BACKGROUND: Globally, road traffic crashes are the leading cause of death for young adults. The P Drivers Project was a trial of a behavioural change program developed for, and targeted at, young Australian drivers in their initial months of solo driving when crash risk is at its highest. METHODS: In a parallel group randomised controlled trial, drivers (N = 35,109) were recruited within 100 days of obtaining their probationary licence (allowing them to drive unaccompanied) and randomised to an intervention or control group. The intervention was a 3 to 6-week multi-stage driving behaviour change program (P Drivers Program). Surveys were administered at three time points (pre-Program, approximately one month post-Program and at 12 months after). The outcome evaluation employed an on-treatment analysis comprising the 2,419 intervention and 2,810 control participants who completed all required activities, comparing self-reported crashes and police-reported casualty crashes (primary outcome), infringements, self-reported attitudes and behaviours (secondary outcomes) between groups. RESULTS: The P Drivers Program improved awareness of crash risk factors and intentions to drive more safely, relative to the controls; effects were maintained after 12-months. However, the Program did not reduce self-reported crashes or police-reported casualty crashes. In addition, self-reported violations, errors and risky driving behaviours increased in the intervention group compared to the control group as did recorded traffic infringements. This suggests that despite the Program increasing awareness of risky behaviour in novice drivers, behaviour did not improve. This reinforces the need to collect objective measures to accompany self-reported behaviour and intentions. CONCLUSIONS: The P Drivers Program was successful in improving attitudes toward driving safety but the negative impact on behaviour, lack of effect on crashes, and the large loss to follow-up fail to support the use of a post-licensing behaviour change program to improve novice driver behaviour and reduce crashes. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry: 363,293 (ANZCTR, 2012).

Employee Grievance Redressal and Corporate Ethics: Lessons from the Boeing 737-MAX Crashes.

Two Boeing 737-MAX passenger planes crashed in October 2018 and March 2019, suspending all 737-MAX aircraft. The crashes put Boeing's corporate practices and culture under the spotlight. The main objective of this paper is to use the case of Boeing to highlight the importance of efficient employee grievance redressal mechanisms and an independent external regulator. The methodology adopted is a qualitative analysis of statements of various whistleblowers and Boeing and the Federal Aviation Administration (FAA) stakeholders. It suggests that employee feedback flowing up the chain of command should be more flexible and dealt with more seriousness. It recommends that companies adopt a cooling-off period or a lifetime restriction for employees who have gone through the revolving door between regulators and the industry. The Boeing 737-MAX case, which emphasizes the ethical obligations of the job, can offer value to engineers, engineering educators, managers, ombudsmen, and human resource professionals.

Integrated driving risk surrogate model and car-following behavior for freeway risk assessment.

Drivers' risk perception plays a crucial role in understanding vehicle interactions and car-following behavior under complex conditions and physical appearances. Therefore, it is imperative to evaluate the variability of risks involved. With advancements in communication technology and computing power, real-time risk assessment has become feasible for enhancing traffic safety. In this study, a novel approach for evaluating driving interaction risk on freeways is presented. The approach involves the integration of an interaction risk perception model with car-following behavior. The proposed model, named the driving risk surrogate (DRS), is based on the potential field theory and incorporates a virtual energy attribute that considers vehicle size and velocity. Risk factors are quantified through sub-models, including an interactive vehicle risk surrogate, a restrictions risk surrogate, and a speed risk surrogate. The DRS model is applied to assess driving risk in a typical scenario on freeways, and car-following behavior. A sensitivity analysis is conducted on the effect of different parameters in the DRS on the stability of traffic dynamics in car-following behavior. This behavior is then calibrated using a naturalistic driving dataset, and then car-following predictions are made. It was found that the DRS-simulated car-following behavior has a more accurate trajectory prediction and velocity estimation than other car-following methods. The accuracy of the DRS risk assessments was verified by comparing its performance to that of traditional risk models, including TTC, DRAC, MTTC, and DRPFM, and the results show that the DRS model can more accurately estimate risk levels in free-flow and congested traffic states. Thus the proposed risk assessment model provides a better approach for describing vehicle interactions and behavior in the digital world for both researchers and practitioners.

Effects of executive load on crashes and near-crashes for young versus older drivers.

With the increasing use of infotainment systems in vehicles, secondary tasks requiring executive demand may increase crash risk, especially for young drivers. Naturalistic driving data were examined to determine if secondary tasks with increasing executive demand would result in increasing crash risk. Data were extracted from the Second Strategic Highway Research Program Naturalistic Driving Study, where vehicles were instrumented to record driving behavior and crash/near-crash data. executive and visual-manual tasks paired with a second executive task (also referred to as dual executive tasks) were compared to the executive and visual-manual tasks performed alone. Crash/near-crash odds ratios were computed by comparing each task condition to driving without the presence of any secondary task. Dual executive tasks resulted in greater odds ratios than those for single executive tasks. The dual visual-manual task odds ratios did not increase from single task odds ratios. These effects were only found in young drivers. The study shows that dual executive secondary task load increases crash/near-crash risk in dual task situations for young drivers. Future research should be conducted to minimize task load associated with vehicle infotainment systems that use such technologies as voice commands.

Real-time crash prediction on express managed lanes of Interstate highway with anomaly detection learning.

To facilitate efficient transportation, I-4 Express is constructed separately from general use lanes in metropolitan area to improve mobility and reduce congestion. As this new infrastructure would undoubtedly change the traffic network, there is a need for more understanding of its potential safety impact. Unfortunately, many advanced real-time crash prediction models encounter an important challenge in their applicability due to their demand for a substantial volume of data for direct modeling. To tackle this challenge, we proposed a simple yet effective approach - anomaly detection learning, which formulates model as an anomaly detection problem, solves it through normality feature recognition, and predicts crashes by identifying deviations from the normal state. The proposed approach demonstrates significant improvement in the Area Under the Curve (AUC), sensitivity, and False Alarm Rate (FAR). When juxtaposed with the prevalent direct classification paradigm, our proposed anomaly detection learning (ADL) consistently outperforms in AUC (with an increase of up to 45%), sensitivity (experiencing up to a 45% increase), and FAR (reducing by up to 0.53). The most performance gain is attained through the combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in an ensemble, resulting in a 0.78 AUC, 0.79 sensitivity, and a 0.22 false alarm rate. Furthermore, we analyzed model features with a game-theoretic approach illustrating the most correlated features for accurate prediction, revealing the attention of advanced convolution neural networks to occupancy features. This provided crucial insights into improving crash precaution, the findings from which not only benefit private stakeholders but also extend a promising opportunity for governmental intervention on the express lane. This work could promote express lane with more efficient resource allocation, real-time traffic management optimization, and high-risk area prioritization.

Investigating built environment and traffic flow impact on crash frequency in urban road networks.

While numerous studies have examined the factors that influence crash occurrence, there remains a gap in understanding the intricate relationship between built environment, traffic flow, and crash occurrences across different spatial units. This study explores how built environment attributes, and dynamic traffic flow characteristics affect crash frequency by focusing on proposed traffic density-based zones (TDZs). Utilizing a comprehensive dataset from Greater Melbourne, Australia, this research emphasizes on the dynamic traffic flow variables and insights from the Macroscopic Fundamental Diagram model, considering parameters such as shockwave velocity and congestion index. The association between the potential influencing factors and crash frequency is examined using a random parameter negative binomial regression model. Results indicate that the data segmentation based on TDZs is instrumental in establishing a more refined crash model compared to traditional planning-based zones, as demonstrated by improved goodness-of-fit measures. Factors including density (e.g., employment density), network design (e.g., road density and highway density), land use diversity (e.g., job-housing balance and land use mixture), and public transit accessibility (e.g., bus route density) are significantly associated with crash occurrence. Furthermore, the unobserved heterogeneity effects of the shockwave velocity and congestion index on crashes are revealed. The study highlights the significance of incorporating dynamic traffic flow variables in understanding crash frequency variations across different spatial units. These findings can inform optimal real-time traffic monitoring, environmental design, and road safety management strategies to mitigate crash risks.

Risk field modeling of urban tunnel based on APF.

OBJECTIVE: The purpose of this paper is to explore the changing laws of driving safety in the complex and changing driving environment in urban tunnels, to analyze the evolution of driving risk fields caused by changes in adjacent vehicles, driving behavior characteristics and road environment, and to reveal the formation mechanism of tunnel driving danger zones. METHODS: The kinetic field, behavioral field and potential field models are constructed according to the APF theory. The driving safety risks arising from the surrounding vehicles, driving behavior characteristics and changes in the tunnel environment are analyzed in the process of driving from the open section to the exit of the tunnel. RESULTS: The magnitude of the risk field force is inversely proportional to the spacing of the vehicles and the distance between the tunnel sidewalls, and is proportional to the relative speed between the vehicles and the slope of the longitudinal slope. Under the same conditions, the vehicle at the entrance and exit of the tunnel is subjected to a greater force of travel risk than inside the tunnel, and the effect of speed on the force of the risk field is greater than the distance. CONCLUSIONS: The established model better describes the trend of driving risk during the driving of vehicles in urban tunnels, and the research findings can provide theoretical support for the design and traffic management of urban tunnels.

Risk factors associated with driving after marijuana use among West Virginia college students during the COVID-19 pandemic.

OBJECTIVES: The purpose of this study was to assess sociodemographic and behavioral risk factors associated with driving after marijuana use (DAMU) among West Virginia college students. METHODS: Participants were recruited from West Virginia University between September and November 2022. The study sample was restricted to students who were ≥18 years of age; reported recently driving; possessed a current, valid driver's license from any US state; and were enrolled for at least one credit hour in the Fall 2022 semester. RESULTS: Among respondents (N = 772), 28.9% reported DAMU. Students who had a GPA of B (adjusted odds ratio [AOR]: 2.17, 95% confidence interval [CI]: 1.06-4.42), smoked or ingested marijuana in the past year (AOR: 26.51, 95% CI: 10.27-68.39), drove after drinking (AOR: 2.38, 95% CI: 1.18-4.79), and used both marijuana and alcohol concurrently and then drove (AOR: 10.39, 95% CI: 2.32-46.54) associated with DAMU. Individuals who felt the behavior was somewhat dangerous or not dangerous or thought their peers approved of DAMU showed significant associations with DAMU. CONCLUSIONS: As DAMU was prevalent, future interventions that raise awareness of the danger and potential consequences of DAMU may be needed to reduce this risky behavior on college campuses.

Examining the impact of legalization on the prevalence of driving after using cannabis: A comparison of rural and non-rural parts of Canada.

OBJECTIVE: The purpose of this study was to examine the likelihood of driving after using cannabis, and of being a passenger with someone who is driving after using cannabis, in rural areas and non-rural areas before and after legalization. METHODS: A multi-wave analysis of Canada's National Cannabis Survey was conducted using logistic regression with interactions to predict the prevalence of driving after using cannabis, and of being a passenger with someone who is driving after using cannabis, in relation to place of residence (rural or non-rural) and in the weeks and months before and after legalization. Three time points were compared: pre-legalization, two months following legalization and 1 year after legalization. RESULTS: At the national level, there are no significant differences between the predicted estimates of driving after using cannabis for those who live in rural and non-rural areas. However, when examining the impact of legalization, we found a significant increase in driving after using cannabis among rural residents directly following legalization. Furthermore, it was observed that this increase in driving after using cannabis returns to pre-legalization rates one year after legalization. By contrast, in the weeks and months following legalization, driving after using cannabis decreased among those living in non-rural areas, and slowly increased soon thereafter. No significant differences were observed, in either time period or group, in the prevalence of being a passenger with someone who is driving after using cannabis. CONCLUSIONS: The finding of significantly higher risk of driving after use of cannabis soon after legalization in rural areas suggests a need for more attention to address immediate concerns for public safety. The increased potential for traffic injuries and deaths in other jurisdictions contemplating legalization supports the call for more and better targeted prevention efforts in rural communities that have far too often been overlooked and under-served.