Mohammad Jalayer

Evaluating the safety risk of roadside features for rural two-lane roads using reliability analysis

The severity of roadway departure crashes mainly depends on the roadside features, including the sideslope, fixed-object density, offset from fixed objects, and shoulder width. Common engineering countermeasures to improve roadside safety include: cross section improvements, hazard removal or modification, and delineation. It is not always feasible to maintain an object-free and smooth roadside clear zone as recommended in design guidelines. Currently, clear zone width and sideslope are used to determine roadside hazard ratings (RHRs) to quantify the roadside safety of rural two-lane roadways on a seven-point pictorial scale. Since these two variables are continuous and can be treated as random, probabilistic analysis can be applied as an alternative method to address existing uncertainties. Specifically, using reliability analysis, it is possible to quantify roadside safety levels by treating the clear zone width and sideslope as two continuous, rather than discrete, variables. The objective of this manuscript is to present a new approach for defining the reliability index for measuring roadside safety on rural two-lane roads. To evaluate the proposed approach, we gathered five years (2009-2013) of Illinois run-off-road (ROR) crash data and identified the roadside features (i.e., clear zone widths and sideslopes) of 4500 300ft roadway segments. Based on the obtained results, we confirm that reliability indices can serve as indicators to gauge safety levels, such that the greater the reliability index value, the lower the ROR crash rate.

Wrong-way driving crashes: A multiple correspondence approach to identify contributing factors

ABSTRACT 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.

Supervised association rules mining on pedestrian crashes in urban areas: identifying patterns for appropriate countermeasures

ABSTRACT In 2011, 4,432 pedestrians were killed (14% of total traffic crash fatalities), and 69,000 pedestrians were injured in vehicle-pedestrian crashes in the United States. Particularly in Louisiana, vehicle-pedestrian crashes have become a key concern because of the high percentage of fatalities in recent years. In 2012, pedestrians were accounted for 17% of all fatalities due to traffic crashes in Louisiana. Alcohol was involved in nearly 44% of these fatalities. This research utilized ‘a priori’ algorithm of supervised association mining technique to discover patterns from the vehicle-pedestrian crash database. By using association rules mining, this study aims to discover vehicle-pedestrian crash patterns using eight years of Louisiana crash data (2004–2011). The results indicated that roadway lighting at night helped in alleviating pedestrian crash severity. In addition, a few groups of interest were identified from this study: male pedestrians’ greater propensity towards severe and fatal crashes, younger female drivers (15–24) being more crash-prone than other age groups, vulnerable impaired pedestrians even on roadways with lighting at night, middle-aged male pedestrians (35–54) being inclined towards crash occurrence, and dominance of single vehicle crashes. Based on the recognized patterns, this study recommends several countermeasures to alleviate the safety concerns. The findings of this study will help traffic safety professionals in understanding significant patterns and relevant countermeasures to raise awareness and improvements for the potential decrease of pedestrian crashes.

Multiple Correspondence Analysis of Pedestrian Crashes in Rural Illinois

During the five-year period between 2010 and 2014, there were 24,178 pedestrian crashes in Illinois. Approximately 4.39% of these pedestrian crashes occurred in rural areas; and approximately 40% of the crashes resulted in a severe injury or a fatality. Thus, pedestrian safety problems exist in rural locales, and the factors contributing to these problems need to be investigated. The goal of this study is to answer the question: “Which variable categories, when acting together, contribute more to the occurrence of pedestrian crashes in rural areas?” Crashes are random events stemming from the convergence of various factors. However, traditional statistical tools can only make pairwise comparisons of dependent and independent variables. Therefore, it is necessary to apply an analytical tool that can identify complex underlying structures in crash data and spot associations among variable categories that contribute to crash occurrence. The multiple correspondence analysis (MCA) method, which is used in this study, can do just that. According to the obtained results, categories of variables such as roadway functional class, the number of lanes, lighting conditions, weather conditions, traffic control devices, driver condition, and pedestrian condition were proved to contribute to pedestrian crashes in rural Illinois.

Wrong-way driving crashes: A random-parameters ordered probit analysis of injury severity

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.

Modelling single-vehicle, single-rider motorcycle crash injury severity: an ordinal logistic regression approach

ABSTRACT Motorcycles represent an increasing proportion of traffic fatalities in the United States, accounting for more than 12.7% of the total traffic casualties within 2005–2014. Specifically, in North Carolina, motorcycles comprise less than 1% of vehicles involved in crashes but account for more than 7% of total fatalities, representing a top state in the United States. This study tries to investigate the motorcycle crashes in North Carolina more in depth. In doing so, five years’ (2009–2013) worth of crash data was obtained from the Federal Highway Administration’s Highway Safety Information System database. A partial proportional odds (PPO) logistic regression model was developed to examine the influence of the explanatory variable on the ordered dependent variable, that is, injury severity. Moreover, two other popular ordered-response models, that is, proportional odds and non-proportional odds models, as well as one similar unordered-response model, that is, multinomial logit model, were also developed to evaluate their performances compared to the PPO model. Older riders, DUI riding, not wearing helmets, crashes during summer and weekends, darkness, crashes with fixed objects, reckless riding, and speeding were found to increase the severity of injuries. In contrast, younger riders, winter season, adverse weather condition, and wet surface were associated with lower injury severities. Furthermore, crashes in rural areas, overturn/rollover, and crashes happened while negotiating a curve showed fluctuating effects of injury severity. According to two information criteria calculated for all three developed models fitted to the same data, the PPO model was found to outperform the other models and provide more reliable results. Based on the obtained average direct pseudo-elasticities, this study determines the effect of the various identified variables and develops several safety countermeasures as a resource for policy-makers to prevent or mitigate the severity of motorcycle crashes in North Carolina.