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University of Nebraska - Lincoln
Mid-America Transportation Center: Final Reports and Technical Briefs
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Full-Text Articles in Engineering
Analyzing Major Elements Of Crash Injury Severity Involving Priority-I Detriments Of Vision-Zero Plan, Muhammad Umer Farooq, Cole Madson, Aemal Khattak
Analyzing Major Elements Of Crash Injury Severity Involving Priority-I Detriments Of Vision-Zero Plan, Muhammad Umer Farooq, Cole Madson, Aemal Khattak
Mid-America Transportation Center: Final Reports and Technical Briefs
Road traffic crashes result in significant economic losses for individuals, their families, and entire nations. These losses stem from the expenses associated with injury treatment, as well as the productivity lost due to fatalities or disabilities caused by these injuries. The 2030 Agenda for Sustainable Development set an ambitious target of halving the global number of fatalities and injuries from road traffic crashes by 2020 and achieving zero deaths by 2030, commonly referred to as the 'Target Zero Plan.' The Target Zero Plan prioritizes traffic safety issues into three distinct levels. The three priority levels are determined based on the …
Road Work Zone Safety: Investigating Injury Severity In Motor Vehicle Crashes Using Random Effects Multinomial Logit Model, Aemal Khattak, Muhammad Umer Farooq
Road Work Zone Safety: Investigating Injury Severity In Motor Vehicle Crashes Using Random Effects Multinomial Logit Model, Aemal Khattak, Muhammad Umer Farooq
Mid-America Transportation Center: Final Reports and Technical Briefs
Work zones serve the purpose of facilitating maintenance and rehabilitation activities on roadways. However, these areas can also present unforeseen conditions to drivers, including narrowed right-of-way, lane shifts, and traffic disruptions. These conditions frequently contribute to vehicular crashes within work zones, resulting in property damage, injuries, and even loss of life. This paper aims to highlight work zone related crash data insights and presents statistical estimates of significant determinants of injury severity by analyzing ten-year crash data (2008-2018) from Nebraska, USA. The examination of crash data helped in highlighting work zone attributes that are empirically associated with serious injury crashes …
Exploring Statistical And Machine Learning-Based Missing Data Imputation Methods To Improve Crash Frequency Prediction Models For Highway-Rail Grade Crossings, Muhammad Umer Farooq, Aemal Khattak
Exploring Statistical And Machine Learning-Based Missing Data Imputation Methods To Improve Crash Frequency Prediction Models For Highway-Rail Grade Crossings, Muhammad Umer Farooq, Aemal Khattak
Mid-America Transportation Center: Final Reports and Technical Briefs
Highway-rail grade crossings (HRGCs) are critical spatial locations of transportation safety because crashes at HRGCs are often catastrophic, potentially causing several injuries and fatalities. Every year in the United States, a significant number of crashes occur at these crossings, prompting local and state organizations to engage in safety analysis and estimate crash frequency prediction models for resource allocation. These models provide valuable insights into safety and risk mitigation strategies for HRGCs. Furthermore, the estimation of these models is based on inventory details of HRGCs, and their quality is crucial for reliable crash predictions. However, many of these models exclude crossings …
A Heterogeneity-Based Temporal Stability Assessment Of Pedestrian Crash Injury Severity Using An Aggregated Crash And Hospital Data Set, M. Umer Farooq, Aemal Khattak
A Heterogeneity-Based Temporal Stability Assessment Of Pedestrian Crash Injury Severity Using An Aggregated Crash And Hospital Data Set, M. Umer Farooq, Aemal Khattak
Mid-America Transportation Center: Final Reports and Technical Briefs
This study utilized a unique approach to crash data analysis by examining the temporal stability of pedestrian crash injury severity and its contributory factors. Police-reported crash data and EMS-related injury data from Nebraska were obtained from 2014 to 2018, and random parameter ordered probit models for injury severity were estimated for each year to account for unobserved heterogeneity. Four discrete levels of injury severity were considered for model estimation: fatality, disabling injury/suspected serious injury, visible injury/possible injury, and no injury. Data were filtered based on several important variables of interest, such as pedestrian characteristics, crash characteristics, environmental and weather characteristics, …
Motor Vehicle Drivers' Knowledge Of Safely Traversing Highway-Rail Grade Crossings, Aemal Khattak, M. Umer Farooq, Abdul Farhan
Motor Vehicle Drivers' Knowledge Of Safely Traversing Highway-Rail Grade Crossings, Aemal Khattak, M. Umer Farooq, Abdul Farhan
Mid-America Transportation Center: Final Reports and Technical Briefs
This study investigates motor vehicle drivers’ socioeconomic, personality, and attitudinal factors associated with their knowledge of safely traversing highway-rail grade crossings (HRGCs). A survey of randomly selected Nebraska households solicited responses from licensed drivers (N= 980, response rate = 39 percent). Of the total thirty-one 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 …
The Effects Of Inaccurate And Missing Highway-Rail Grade Crossing Inventory Data On Crash Model Estimation And Crash Prediction, Aemal Khattak, M. Umer Farooq
The Effects Of Inaccurate And Missing Highway-Rail Grade Crossing Inventory Data On Crash Model Estimation And Crash Prediction, Aemal Khattak, M. Umer Farooq
Mid-America Transportation Center: Final Reports and Technical Briefs
ABSTRACT: Most highway-rail grade crossing (HRGC) crash models in the US rely on the Federal Railroad Administration’s (FRA) highway/rail crossing inventory database. Any errors and/or incomplete information in this database affects the estimated crash model parameters and subsequent crash predictions. Using 560 HRGCs in Nebraska, this study illustrates differences in crash predictions obtained from the FRA’s new (2020) Accident Prediction and Severity (APS) model when: 1) using the unaltered, original FRA HRGC inventory dataset as input, and 2) using a field-validated inventory dataset for those 560 HRGCs as input to the new APS model. Results showed that the predictions using …