•A random-parameters ordered probit model was developed to analyze injury severity of wrong-way driving crashes.•This approach takes into account the unobserved effects of roadway, vehicle, driver, ...etc.•Driver age, driver condition, lighting conditions, etc. significantly contribute to the injury severity of crashes.•Winter, urban setting, and number of vehicles involving in crash were found to be random parameters.
In the context of traffic safety, whenever a motorized road user moves against the proper flow of vehicle movement on physically divided highways or access ramps, this is referred to as wrong-way driving (WWD). WWD is notorious for its severity rather than frequency. Based on data from the U.S. National Highway Traffic Safety Administration, an average of 355 deaths occur in the U.S. each year due to WWD. This total translates to 1.34 fatalities per fatal WWD crashes, whereas the same rate for other crash types is 1.10. Given these sobering statistics, WWD crashes, and specifically their severity, must be meticulously analyzed using the appropriate tools to develop sound and effective countermeasures. The objectives of this study were to use a random-parameters ordered probit model to determine the features that best describe WWD crashes and to evaluate the severity of injuries in WWD crashes. This approach takes into account unobserved effects that may be associated with roadway, environmental, vehicle, crash, and driver characteristics. To that end and given the rareness of WWD events, 15 years of crash data from the states of Alabama and Illinois were obtained and compiled. Based on this data, a series of contributing factors including responsible driver characteristics, temporal variables, vehicle characteristics, and crash variables are determined, and their impacts on the severity of injuries are explored. An elasticity analysis was also performed to accurately quantify the effect of significant variables on injury severity outcomes. According to the obtained results, factors such as driver age, driver condition, roadway surface conditions, and lighting conditions significantly contribute to the injury severity of WWD crashes.
•This study used Louisiana crash data to determine key associated groups in wrong way driving crashes.•This study used multiple correspondence analysis that has advantage of removing noise without ...reducing the dataset.•High posted speed, inadequate lighting at night, and no physical separation show greater risks.•A group of countermeasures (for example, low mounted wrong way sign, refelctive arrows) are suggested.
Wrong way driving (WWD) has been a constant traffic safety problem in certain types of roads. Although these crashes are not large in numbers, the outcomes are usually fatalities or severe injuries. Past studies on WWD crashes used either descriptive statistics or logistic regression to determine the impact of key contributing factors. In conventional statistics, failure to control the impact of all contributing variables on the probability of WWD crashes generates bias due to the rareness of these types of crashes. Distribution free methods, such as multiple correspondence analysis (MCA), overcome this issue, as there is no need of prior assumptions. This study used five years (2010–2014) of WWD crashes in Louisiana to determine the key associations between the contribution factors by using MCA. The findings showed that MCA helps in presenting a proximity map of the variable categories in a low dimensional plane. The outcomes of this study are sixteen significant clusters that include variable categories like determined several key factors like different locality types, roadways at dark with no lighting at night, roadways with no physical separations, roadways with higher posted speed, roadways with inadequate signage and markings, and older drivers. This study contains safety recommendations on targeted countermeasures to avoid different associated scenarios in WWD crashes. The findings will be helpful to the authorities to implement appropriate countermeasures.
•Wrong-way Driving (WWD) is the movement of a vehicle in a direction opposite to the one designated for travel.•WWD crashes are common on arterials because of multiple access points.•The Bayesian ...partial proportional odds (PPO) model was used to model the severity of WWD crashes on arterials.•Lighting condition, presence of work zone, crash location, age and gender, airbag deployment are some of the factors that affect WWD crash severity.•Traditional countermeasures of the 4E’s can be tailored to mitigate WWD crashes. TSM&O strategies can also be used to mitigate WWD crashes in arterials.
Wrong-way Driving (WWD) is the movement of a vehicle in a direction opposite to the one designated for travel. WWD studies and mitigation strategies have exclusively been focused on limited-access facilities. However, it has been established that WWD crashes on arterial corridors are also severe and relatively more common. As such, this study focused on determining factors influencing the severity of WWD crashes on arterials. The analysis was based on five years of WWD crashes (2012–2016) that occurred on state-maintained arterial corridors in Florida. Police reports of 2,879 crashes flagged as “wrong-way” were downloaded and individually reviewed. The manual review of the police reports revealed that of the 2,879 flagged WWD crashes, only 1,890 (i.e., 65.6 %) occurred as a result of a vehicle traveling the wrong way. The Bayesian partial proportional odds (PPO) model was used to establish the relationship between the severity of these WWD crashes and different driver attributes, temporal factors, and roadway characteristics. The following variables were significant at the 90 % Bayesian Credible Interval (BCI): day of the week, lighting condition, presence of work zone, crash location, age and gender of the wrong-way driver, airbag deployment, alcohol use, posted speed limit, speed ratio (i.e., driver’s speed over the posted speed limit), and the manner of collision. Based on the model results, specific countermeasures on Education, Engineering, Enforcement, and Emergency response are discussed. Potential Transportation Systems Management and Operations (TSM&O) strategies for WWD detection systems on arterials to minimize WWD frequency and severity are also proposed.
Introduction: This study presents a comprehensive analysis of wrong-way driving (WWD) fatal crashes on divided highways in the United States over a 17-year period, from 2004 to 2020. The study aims ...to uncover trends, distribution patterns, and factors contributing to these fatal crashes. Data was extracted from the National Highway Traffic Safety Administration (NHTSA) Fatality Analysis Reporting System (FARS) database. Methods: Descriptive statistical analysis was used to reveal general crash characteristics, while trends were updated through an examination of the annual occurrence of WWD fatal crashes. The study further employed binomial logistic regression to compute odds ratios, identifying significant contributing factors. These factors encompassed temporal variables, crash characteristics, and driver characteristics. The odds ratios shed light on the relationship between WWD fatal crashes and other fatal crashes, allowing for the identification of key elements that drive WWD incidents. Results: On average, 302 WWD fatal crashes occurred annually, resulting in 6,953 fatalities during the study period. The frequency of WWD fatal crashes remained relatively stable, with a slight increase over time According to the model, variables include day of week, time of day, month, lighting conditions, weather conditions, roadway profile, collision type, passenger presence, driver age, gender, license status, and driver injury severity were found to significantly impact the occurrence of WWD fatal crashes. One significant finding is that road profiles like sag curves or hillcrests can increase the likelihood of WWD fatal crashes. Practical Application: The findings of this study contribute to an improved understanding of WWD fatal crashes on divided highways, thereby aiding in the development of strategies for prevention and mitigation.
This study delves into the wrong-way driving (WWD) research performed in the United States over the past five decades. The study employs a text network to summarize and synthesize major themes, data ...sources, and collaboration efforts from 123 previously published WWD articles collected from various online databases. The analysis shows that the majority of WWD studies were conducted between 2014 and 2020. Throughout the entire period, the primary focuses have been on WWD countermeasures, freeways, exit ramps, contributing factors, driver behavior, and detection systems. Over time, there has been a noticeable shift in the key themes, whereby earlier studies focused on freeways and driver behaviors, while the latter delved into exit ramps, limited access, and contributing factors among others. Most studies were from Florida, Texas, Illinois, and Alabama, with little collaboration of researchers across the States, except for Alabama and Illinois. Further, across the four States, there observed variations in the interests. Studies from Texas and Florida were centered on WWD countermeasures, detection systems, and the use of connected vehicles, while Illinois and Alabama investigated WWD crashes on exit ramps and interchanges. Despite the development and deployment of various countermeasures, there is still a need to develop crash modification factors to examine the effectiveness of the countermeasures. Furthermore, as technology continues to advance, connected and autonomous technology is expected to play a significant role in mitigating the WWD problem. The findings are essential for transportation agencies to evaluate the mitigation efforts and direct resources toward the right course of action.
•Seven wrong-way driving (WWD) countermeasures were evaluated regarding their safety effectivenss.•Existing data and study analysis, field testing, public opinion survey, and driving simulation were ...used in the evaluation.•Newly-developed S&PM standards will reduce confusion regarding freeway entry points.•Red RRFBs & wigwag beacons are two most effective WWD countermeasures, respectively.•Detection-triggered blank-out & flashing LED signs tied for third most effective.
Wrong-way crashes are a major cause for safety concerns along freeways and limited-access facilities. Although wrong-way crashes account for a relatively small portion of total crashes, the impact between two cars crashing into each other at high speeds in opposite directions often results in severe injuries or fatalities compared to any other type of crash. To seek solutions for mitigating wrong-way driving (WWD), multiple field tests involving a number of countermeasures using Intelligent Transportation Systems (ITS) technologies have been conducted in Florida. This study was aimed to evaluate these WWD countermeasures in Florida and develop recommendations regarding the most effective and informing WWD countermeasures through (1) analysis of existing data and studies, (2) field WWD testing using focus groups, (3) a public opinion survey, and (4) capturing human factors elements using simulation via a driving simulator. The results proved that red Rectangular Rapid Flashing Beacons (RRFBs) are the top countermeasure for mitigating WWD at freeway off-ramps, with wigwag flashing beacons as the second best, and detection-triggered blank-out signs and detection-triggered LED lights around “WRONG WAY” signs (tie) as third best. Red flush-mount Internally Illuminated Raised Pavement Markers (IIRPMs) were found to be statistically significantly effective for possible consideration as a supplemental countermeasure for mitigating WWD at freeway off-ramps. The countermeasure of delineators along off-ramps was found to be the least effective and was not considered for recommendation for deterring WWD at freeway off-ramps. This study further confirms that the newly-developed signing and pavement marking standards in Florida are a positive countermeasure on arterials to mitigate wrong-way entries onto freeway off-ramps.
Objective: Wrong-way driving (WWD) crashes result in 1.34 fatalities per fatal crash, whereas for other non-WWD fatal crashes this number drops to 1.10. As such, further in-depth investigation of WWD ...crashes is necessary. The objective of this study is 2-fold: to identify the characteristics that best describe WWD crashes and to verify the factors associated with WWD occurrence.
Methods: We collected and analyzed 15 years of crash data from the states of Illinois and Alabama. The final data set includes 398 WWD crashes. The rarity of WWD events and the consequently small sample size of the crash database significantly influence the application of conventional log-linear models in analyzing the data, because they use maximum-likelihood estimation. To overcome this issue, in this study, we employ multiple correspondence analysis (MCA) to define the structure of the crash data set and identify the significant contributing factors to WWD crashes on freeways.
Results: The results of the present study specify various factors that characterize and influence the probability of WWD crashes and can thus lead to the development of several safety countermeasures and recommendations. According to the obtained results, factors such as driver age, driver condition, roadway surface conditions, and lighting conditions were among the most significant contributors to WWD crashes.
Conclusions: Despite many other methods that identify only the contributing factors, this method can identify possible associations between various contributing factors. This is an inherent advantage of the MCA method, which can provide a major opportunity for state departments of transportation (DOTs) to select safety countermeasures that are associated with multiple safety benefits.
•Factors associated with the injury severity of at-fault drivers involved in wrong-way driving (WWD) crashes were analyzed.•A partial proportional odds (PPO) model was developed to quantify the ...effect of these parameter on injury severity.•Based on the calculated average direct pseudo-elasticities, these factors were prioritized and several countermeasures were recommended.•The performance of three ordered-response models was also explored.
For more than five decades, wrong-way driving (WWD) has been notorious as a traffic safety issue for controlled-access highways. Numerous studies and efforts have tried to identify factors that contribute to WWD occurrences at these sites in order to delineate between WWD and non-WWD crashes. However, none of the studies investigate the effect of various confounding variables on the injury severity being sustained by the at-fault drivers in a WWD crash. This study tries to fill this gap in the existing literature by considering possible variables and taking into account the ordinal nature of injury severity using three different ordered-response models: ordered logit or proportional odds (PO), generalized ordered logit (GOL), and partial proportional odds (PPO) model. The findings of this study reveal that a set of variables, including driver’s age, condition (i.e., intoxication), seatbelt use, time of day, airbag deployment, type of setting, surface condition, lighting condition, and type of crash, has a significant effect on the severity of a WWD crash. Additionally, a comparison was made between the three proposed methods. The results corroborate that the PPO model outperforms the other two models in terms of modeling injury severity using our database. Based on the findings, several countermeasures at the engineering, education, and enforcement levels are recommended.
Wrong-way driving (WWD) has been a constant traffic safety problem in certain types of roads. These crashes are mostly associated with fatal or severe injuries. This study aims to determine ...associations between various factors in the WWD crashes. Past studies on WWD crashes used either descriptive statistics or logistic regression to identify the impact of key contributing factors on frequency and/or severity of crashes. Machine learning and data mining approaches are resourceful in determining interesting and non-trivial patterns from complex datasets. This study employed association rules ‘Eclat’ algorithm to determine the interactions between different factors that result in WWD crashes. This study analyzed five years (2010–2014) of Louisiana WWD crash data to perform the analysis. A broad definition of WWD crashes (both freeway exit ramp WWD crashes and median crossover WWD crashes on low speed roadways) was used in this study. The results of this study confirmed that WWD fatalities are more likely to be associated with head-on collisions. Additionally, fatal WWD crashes tend to be involved with male drivers and off-peak hours. Driver impairment was found as a critical factor among the top twenty rules. Despite several other studies identifying only the WWD contributing factors, this study determined several influencing patterns in WWD crashes. The findings can provide an excellent opportunity for state departments of transportation (DOTs) and local agencies to develop safety strategies and engineering solutions to tackle the issues associated with WWD crashes.
Despite extensive research on traffic injury severities, relatively little is known about the factors contributing to truck-involved crashes in developing countries, especially in the context of ...Bangladesh. Because of the unavailability of authentic crash data sources, this study collected data from alternative sources such as online English news media reports. The current study prepared a database of 144 truck-involved fatal crash reports during the period of 12 months (January 2021 to December 2021). The crash reports contain a bag of 15,300 words. Several state-of-the-art text mining tools were utilized to identify crash patterns, including word cloud analysis, word frequency analysis, word co-occurrence network analysis, rapid automatic keyword extraction, and topic modeling. The analysis revealed several important crash contributing factors, such as the type of vehicle involved (auto-rickshaw, bus, van, motorcycle), the manner of collision (head-on), the time of the day (morning, night), driver behavior (speeding, overtaking, wrong-way driving), and environmental factors (dense fog). In addition, “coming from opposite direction” and “head-on collision” are two important sequences of events in truck-involved crashes. Truck drivers are also involved in crashes with trains at rail crossings. The findings of this research can assist policymakers in identifying crash avoidance strategies to lower truck-related crashes in Bangladesh.
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