Mohammadreza Koloushani

The concentration of surface air methane (CH₄) measured in parts per million by volume (ppmv) near the soil/atmosphere interface should, in theory, have a positive correlation with surface methane emissions fluxes, measured in grams per square meter per day (gm^-2d^-1). Some researchers suggest that CH₄ flux can be reasonably inferred from simple measurements of CH₄ concentrations near the landfill surface. Ground-based and drone-based surface emissions monitoring (SEMs) were performed at several municipal solid waste landfills as tracer correlation method (TCM) testing was being used to measure total methane emissions from the same landfills. The TCM data and SEM data were used to establish a new simple correlation to convert surface methane concentrations in ppmv to localized surface methane emission flux in gm^-2d^-1.The SEM data obtained from ten ground and drone monitoring campaigns were log-transformed and geospatially treated using inverse distance weighting to the power of 2 to predict methane surface concentrations in the entire footprint of the SEM measurements area. The developed new correlation equation was then used to convert every predicted surface methane concentration to an emissions flux. The total estimate of surface emissions from the entire landfill was obtained by integrating the predicted fluxes over the area of the footprint of the SEM measurement area. The use of the new developed correlation resulted in higher total emissions estimates than other correlations reported in the literature and should be considered more conservative. Not including other factors, the proposed approach provides estimate of total methane emissions with a coefficient of variation of 20%. This study introduces a novel approach that utilizes a developed correlation between surface methane concentrations and surface emissions fluxes to estimate total methane emissions from municipal solid waste landfills or from a specified area. This study provides an additional use of the quarterly SEM data.

Transportation authorities aim to boost road safety by identifying risky locations and applying suitable safety measures. The Highway Safety Manual (HSM) is a vital resource for US transportation professionals, aiding in the creation of Safety Performance Functions (SPFs), which are predictive models for crashes. These models rely on negative binomial distribution-based regression and misinterpreting them due to unmet statistical assumptions can lead to erroneous conclusions, including inaccurately assessing crash rates or missing high-risk sites. The Florida Department of Transportation (FDOT) has introduced context classifications to HSM SPFs, complicating the assumption of violation identification. This study, part of an FDOT-sponsored project, investigates the established statistical diagnostic tests to identify model violations and proposes a novel approach to determine the optimal spatial regions for empirical Bayes adjustment. This adjustment aligns HSM SPFs with regression assumptions. This study employs a case study involving Florida roads. Results indicate that a 20-mile radius offers an optimal spatial sample size for modeling crashes of all injury levels, ensuring accurate assumptions. For severe-injury crashes, which are less frequent and harder to predict, a 60-mile radius is suggested to fulfill statistical modeling assumptions. This methodology guides FDOT practitioners in assessing the conformity of HSM SPFs with intended assumptions and determining appropriate region sizes.

Transportation authorities aim to boost road safety by identifying risky locations and applying suitable safety measures. The Highway Safety Manual (HSM) is a vital resource for US transportation professionals, aiding in the creation of Safety Performance Functions (SPFs), which are predictive models for crashes. These models rely on Negative Binomial distribution-based regression and misinterpreting them due to unmet statistical assumptions can lead to erroneous conclusions, including inaccurately assessing crash rates or missing high-risk sites. The Florida Department of Transportation (FDOT) has introduced context classifications to HSM SPFs, complicating assumption violation identification. This study, part of an FDOT-sponsored project, investigates established statistical diagnostic tests to identify model violations and proposes a novel approach to determine optimal spatial regions for Empirical Bayes adjustment. This adjustment aligns HSM-SPFs with regression assumptions. The study employs a case study involving Florida roads. Results indicate that a 20-mile radius offers an optimal spatial sample size for modeling crashes of all injury levels, ensuring accurate assumptions. For severe injury crashes, which are less frequent and harder to predict, a 60-mile radius is suggested to fulfill statistical modeling assumptions. This methodology guides FDOT practitioners in assessing the conformity of HSM-SPFs with intended assumptions and determining appropriate region sizes.

Negative binomial-based safety performance functions (SPFs) have been extensively used by United States Department of Transportation professionals for predictive crash analysis. Recently, the Florida Department of Transportation (FDOT) has developed a context classification approach and incorporated it into crash prediction models, which has the potential to significantly enhance their accuracy and reliability. The additional modeling contexts and parameters make it more challenging to diagnose and remedy modeling problems, however. Particularly for roadway segments with low annual average daily traffic (AADT), short lengths, or low counts of severe crashes, the SPF models significantly underestimate the actual number of crashes. This uncertainty in SPF predictions can lead FDOT practitioners to reach misleading conclusions, such as failing to detect sites with genuinely high crash rates. This project intends to establish thresholds for certain SPF parameters to ensure reliable crash predictions are obtained across various context classes. For this purpose, we (a) developed a functional statistical model that quantifies economic loss relative prediction errors as a function of AADT volume and (b) calculated the minimum context-specific AADT threshold for each segment length group, roadway category, context classification, and crash severity combination. Employing the developed AADT thresholds confirmed up to 89% reduction in SPF prediction errors for the most represented context class. In light of the results obtained, we are able to conclude that context-specific AADT thresholds perform well in significantly reducing prediction errors for the thresholded segments and contexts on Florida roadways.

Using survey-based inferential statistics, Vandana VALLURI, Mary Moses PIDAKALA, and Nihal Kumar SINGH examine undergraduate medical students' perceptions of roadway safety issues. Based on my perspective, the authors conduct excellent research and t...

There has been extensive visual-disability-focused safety research resulting from various factors/conditions that have a significant impact on a driver’s ability to maneuver safely. One such critical condition among the major environmental issues that contribute to traffic crashes is daytime sun glare-induced temporary visual impairment. Nevertheless, the contribution of relevant factors to the choice of potential actions at the time of such crashes has not been widely studied. Approximately 3,000 crashes per year in the United States are attributed to sun glare, which may indicate that the driver had no control over the situation. Thus, this study investigated several factors to identify crashes that occurred, with acceptable certainty, under the direct influence of sun glare over 4 years (2014 to 2017) in Florida—“the Sunshine State.” A multinomial logistic regression model was developed to formulate relationships between crash-related contributing factors and to evaluate the likelihood of alternative actions being adopted by drivers experiencing sun glare. The results indicated that running red lights/stop signs was the most likely action one might take while experiencing this adverse condition, particularly on local roadways. A further consequence of sun glare is that drivers tend to follow the car in front too closely along segments with higher annual average daily traffic. The findings could help safety officials support the use of emerging intelligent transportation system technologies (i.e., automated traffic signal performance measures and cooperative intersection collision avoidance systems) to alert drivers of the possibility of a temporary vision impairment exactly when high-glare conditions exist.

Roadway crashes are an increasing concern worldwide, and pedestrians are among the most vulnerable road users because of several special characteristics, including their fragility. While focusing on pedestrian-involved crashes that occurred far away from intersections, a more systematic investigation is needed to determine whether there is a relationship between the severity of these crashes and the land use types surrounding the crash locations. To investigate this issue, a network-based logistic regression model was employed to assess the impact of the distance between pedestrian-involved crash locations and the centroid of an associated land use of various types. In addition, a set of logistic models was developed to assess the effect of traffic- and environment-related factors, such as daylight and the average annual daily traffic (AADT), on the severity of crashes occurring around each land use. Several types of land use appeared to be influential in predicting pedestrian-involved crash severity. The probability of a severe pedestrian-involved crash increased around commercial land use types including retail stores or night clubs whereas it decreased around university campuses. Moreover, among the crash attributes, daylight contributed to the severity of pedestrian-involved crashes regardless of the surrounding land use type while average traffic was found to be a statistically significant factor for the crashes that occurred particularly around parking lots and office buildings. Pedestrian-involved crashes mostly occurred in the vicinity of office buildings during daylight hours, and the pedestrians were less likely to be severely injured. The findings provided valuable insight into the measurements concerning pedestrian safety with respect to various types of land use.

Over the last three decades, traffic crashes have been one of the leading causes of fatalities and economic losses in the U.S.; compared with other age groups, this is especially concerning for the youth population (those aged between 16 and 24), mostly due to their inexperience, greater inattentiveness, and riskier behavior while driving. This research intends to investigate this issue around selected Florida university campuses. We employed three methods: (1) a comparative assessment for three selected counties using both planar Euclidean Distance and Roadway Network Distance-based Kernel Density Estimation methods to determine high-risk crash locations, (2) a crash density ratio difference approach to compare the maxima-normalized crash densities for the youth population and those victims that are 25 and up, and (3) a logistic regression approach to identify the statistically significant factors contributing to young-driver-involved crashes. The developed GIS maps illustrate the difference in spatial patterns of young-driver crash densities compared to those for other age groups. The statistical findings also reveal that intersections around university areas appear to be significantly problematic for youth populations, regardless of the differences in the general perspective of the characteristics of the selected counties. Moreover, the speed limit countermeasures around universities could not effectively prevent young-driver crash occurrences. Hence, the results of this study can provide valuable insights to transportation agencies in terms of pinpointing the high-risk locations around universities, assessing the effectiveness of existing safety countermeasures, and developing more reliable plans with a focus on the youth population.

In this study, we selected four real-world rear-end crash scenarios with different crash characteristics. The vehicles involved in those crashes were not equipped with any crash avoidance systems. We then used the accident reconstruction method to build those crash scenarios in PC-Crash software. Then, different FCW/AEB safety algorithms have been defined for a subject vehicle model in each crash scenario and each scenario was simulated for a set of input parameters such as vehicle speed, brake intensity, and driver reaction time. The range and distribution of input parameters were extracted from the related field crash data and available literature. A total number of 16000 simulations have been conducted which produced input-output datasets for further investigations. Finally, the effects of input parameters on simulation outcomes including crash occurrence, AEB activation, injury risk, and vehicle damage have been quantified using the Boruta algorithm. The results indicated that the overall effectiveness of the AEB system was a 57% reduction of rear-end crashes, a 52% reduction of injury severity (striking vehicle’s passengers), and a 47% reduction of damages for striking vehicles. The results also showed that the available AEB algorithms were more effective for the average speed equal to or less than 80 kmph. The speed of the subject vehicle, type of AEB algorithm, sensor detection range, and driver reaction time were the most important parameters on crash outcomes. In addition, the results indicated that the performance of FCW had a direct impact on the effectiveness of the AEB system for the integrated FCW + AEB system.