Aemal J. Khattak’s research while affiliated with University of Nebraska–Lincoln and other places

Published literature is sparse on non-motorist (pedestrians and bicyclists) crashes at Highway-rail Grade Crossings (HRGCs), despite their involvement in rail-related crashes. A critical aspect of crash prediction models for HRGs is crash exposure, which measures activities of interest at specific locations. While data on motor vehicle and train traffic at HRGCs are available from different sources, non-motorist traffic counts are not readily available. Current Federal Railroad Administration (FRA) models focus on vehicular crash exposure, overlooking non-motorized traffic; gathering non-motorized traffic data is crucial for improving crash prediction models. In this study, non-motorist traffic videos were recorded for 1,848 hours from various urban and suburban HRGCs in Nebraska followed by application of the AI based You Only Look Once Version 8 (YOLOv8) algorithm for automated non-motorist volume detection. Additionally, data on grade crossing characteristics, including population density and land use, were collected to create a comprehensive non-motorist database. Statistical and AI models were developed to analyze non-motorist exposure in terms of daily traffic volumes, utilizing physical, dynamic, and temporal characteristics of HRGCs. The models indicated that sidewalks, improved visibility, and cloudy weather conditions were associated with increased non-motorist traffic volume. Conversely, higher motor vehicle traffic levels, adverse weather conditions (rain and snow), industrial zones, and greater number of traffic lanes were linked with lower non-motorist traffic. This foundational study aims to enhance crash prediction models at HRGCs by incorporating non-motorist traffic factors, potentially improving crash prediction models and non-motorist safety at HRGCs.

Highway-rail grade crossing (HRGC) crash prediction models’ effectiveness hinges on the input data accuracy and precision. This paper investigates the impact of inaccurate HRGC inventory data on the modeling of HRGC crashes. Specifically, the research explores data gaps by obtaining samples of Federal Railroad Administration rail crossing inventory data. These inventory data were checked for accuracy by visiting the rail crossings and comparing the inventory elements to their field conditions. Any inaccurate records were corrected; the process created an accurate inventory of the rail crossings under consideration. The corrected inventory data was subsequently used for crash predictions using the U.S. Department of Transportation accident prediction formula (U.S. DOT APF), released in 2020. To fit for the U.S. DOT APF, the corrected inventory data from Nebraska was used for the case 1 study, which applied a multiple imputation algorithm to augment the empirical data to verify improvements in the model’s goodness of fit. The results showed that the adjusted Akaike information criterion (AIC) improved from 1,074 to 1,068 when only 7% of the total inventory dataset was corrected, and to 813 assuming all verified corrected data obtained through data imputation. In case 2, the filtered inventory data from four Midwest states (i.e., Kansas, Iowa, Missouri, and Nebraska) were utilized to address data stratification issues in the U.S. DOT APF. Results showed that the adjusted AIC improved from 1,442 to 1,431 when the latest annual average daily traffic data and properly stratified variables (i.e., road surface, traffic control) were included in the U.S. DOT APF. The findings emphasize the need for regular HRGC inventory data verification and improved data-updating processes for more accurate HRGC crash predictions.

This research project embarked on a crucial endeavor to enhance safety and efficiency at highway-rail grade crossings (HRGCs) through the innovative development and application of real-time optimized traffic signal preemption strategies. Recognizing the significant risks associated with HRGCs, especially in urban areas where such crossings are in close proximity to signalized intersections, this study aimed to address the complexities of traffic flow and preemptive signal operations to improve both safety and mobility. The project progressed through the completion of four major tasks: 1) Review and Identification of Limitations: Conducting a holistic review of existing preemption operations, national guidelines, and current engineering practices, the study began with studying in current HRGC preemption strategies. 2) Effectiveness Verification: Through the development of microsimulation models and sensitivity analysis, the project rigorously tested the efficacy of various preemption plans across different HRGC scenarios. 3) Standard Optimization Process: Aiming to maximize safety and operational efficiency, a standard optimization process for designing preemption strategies was developed. 4) Guideline Development: A significant outcome of the project was the development of a guideline that provides a standardized process for evaluating the effectiveness of signal control at HRGCs and adjacent arterials. This research represents a significant step forward in traffic safety and efficiency management at HRGCs, providing a model for similar traffic situations in other regions and laying the groundwork for future technological advancements in the field. The developed guideline serves to offer technical support in terms of application conditions, plan formation, and system operations, aiming to facilitate implementation while enhancing coordination between railway and highway agencies.

This paper focuses on traffic safety and operational performance of rural, minor approach stop-controlled intersections with free right-turn (FRT) ramps. Studies on the guidelines, safety, and operational analysis of FRT ramps are limited. Therefore, this paper formulates a comprehensive framework of FRT ramp studies and applied it to Nebraska. The research compared 68 rural FRT ramp intersections with 24 similar non-FRT rural intersections to identify differences in crash frequencies, crash rates, and crash severity using 2010 to 2019 crash data from Nebraska Department of Transportation (DOT). The analysis did not show any statistically significant differences between the two intersection groups. Using a calibrated and validated microsimulation model, traffic operations at FRT ramp and non-FRT intersections were modeled and analyzed to study 324 scenarios, based on varying traffic and roadway geometry. Assuming a 20-year lifespan, using the operation data, cost–benefit analysis was conducted for combinations of discount rates (4%, 6%, 8%), major road annual average daily traffic (AADT) (5,000, 10,000, 15,000), minor road AADT (2,500, 5,000, 7,500), percentage of right-turning traffic (10%, 25%, 50%), FRT ramp radius (650, 1,200, 1,800 ft), and speed limit (45, 55, 65 mph). Using the results, this paper provides a guideline for Nebraska DOT for FRT ramp construction, reconstruction, or removal. Traffic agencies in Nebraska and the Midwest may make more informed decisions on FRT ramp based on guidance in this paper. However, the widely applicable methodology presented here to determine FRT ramp’s feasibility can be used in other locations in the United States without appreciable loss of generality.

Highway-rail grade crossing (HRGC) crash prediction models’ effectiveness hinges on the input data accuracy and precision. This paper investigates the impact of inaccurate HRGC inventory data on the modeling of HRGC crashes. In specific, the research explores existing data gaps by obtaining samples of Federal Railroad Administration (FRA) rail crossing inventory data, which contains some inaccurate records. Those inaccurate records were corrected by visiting the HRGCs, and the crossing corrected inventory data used for crash predictions using the U.S. Department of Transportation recommended accident prediction formula (U.S. 2020 DOT APF) for HRGCs. To fit for the U.S. DOT APF, the corrected inventory data from Nebraska was used for case 1 study, which applied a multiple imputation algorithm to augment the empirical data to verify improvements in the model’s goodness-of-fit. The results show that the adjusted AIC improves from 1074 to 1068 when only 7% of the total inventory dataset is corrected, and to 813 assuming all verified corrected data obtained through data imputation. In case 2, the filtered inventory data from four Midwest states (i.e., Kansas, Iowa, Missouri, and Nebraska) were utilized to address data stratification issues in the U.S. DOT APF. The results show that the adjusted AIC improves from 1442 to 1431 when adopting latest AADT data and properly stratified variables (i.e., road surface, traffic control) are included in the U.S. DOT APF. The findings emphasize the need for regular HRGC inventory data verification and improved data-updating processes for robust traffic crash prediction modeling for HRGCs.

Aggressive driving significantly impacts traffic safety, and heavy‐duty vehicle drivers are more liable for causing serious crashes. This paper analyzes drivers' aggressive driving behavior from the vehicle type perspective and identifies the influencing factors of aggressive driving behavior through artificial neural network (ANN) and Shapley additive explanations (SHAPs). Using Kaggle's open‐source aggressive driving data, we establish an ANN model to identify driving styles, where road conditions, environmental conditions, and vehicle parameters are independent variables and driving style is a dependent variable. The following measurements, including accuracy, recall, precision, and F 1 score, are used to evaluate the model's performance, and the neural network got 85.33%, 82.32%, 84.16%, and 0.8308, respectively. To illustrate the influence of independent variables, the SHAP algorithm is used to analyze the model's feature importance. It was found that illumination and weather conditions influenced the model's performance along with the vehicle length. The number of lanes relates to driving style, and there were more aggressive driving behaviors on two‐lane roads than on single‐lane roads. Besides, heavy‐duty vehicle drivers were more likely to drive aggressively in wet road conditions and indulge in aggressive driving behaviors at night. Particularly, drivers of heavy‐duty vehicles were more likely to drive aggressively, provided that the vehicle in front was also a heavy‐duty vehicle. These findings inform heavy‐duty vehicle drivers to reduce aggressive driving behavior. The information is suitable for inclusion in driver education programs, thus improving traffic safety.

This study investigates motor vehicle drivers’ socioeconomic, personality, and attitudinal factors associated with their knowledge of safely traversing highway-rail grade crossings (HRGCs). A three-step mail-based survey of randomly selected Nebraska households solicited responses from licensed drivers (N = 980, response rate = 39%). Of the total 31 questions on the questionnaire, nine pertained to respondents’ knowledge of safely navigating HRGCs (e.g., what does a crossbuck sign require a driver to do when approaching a rail crossing?). Correct answers to the questions provided a measure of respondents’ knowledge of safely traversing HRGCs and led to their classification in five ordered categories. A random parameter probit model then assessed associations among respondents’ socioeconomic, personality, and attitudinal characteristics and the five ordered categories of their HRGCs negotiation knowledge. The estimated model revealed that drivers with negative or indifferent attitudes toward HRGCs, who were unemployed, or engaged in risky driving behavior around HRGCs were likely to be less informed about safe HRGCs navigation. Similarly, drivers that disliked waiting at HRGCs and those who did not receive information on HRGCs safety had lower levels of knowledge of safely negotiating HRGCs. Attentive drivers at HRGCs and those who routinely stopped in response to active train warning devices were associated with higher levels of knowledge. Drivers with negative or indifferent attitudes toward HRGCs were less knowledgeable about safe HRGCs navigation. The research findings are useful for targeted driver education and traffic safety programs, safety professionals, and policymakers engaged in HRGCs safety.