A multiple correspondence analysis of at-fault motorcycle-involved crashes in Alabama

According to the U.S. National Highway Traffic Safety Administration, in 2012, more than 4950 motorcyclists were killed in traffic accidents. Compared to passenger car occupants, mile for mile, motorcyclists are more than 26 times more at risk to dying in crashes. Due to the high fatality rate associated with motorcycle crashes, factors contributing to this type of crash must be identified in order to implement effective safety countermeasures. Given that the available datasets are large and complex, identifying the key factors contributing to crashes is a challenging task. Using multiple correspondence analysis, as an exploratory data analysis technique to determine the dataset structure, we identified the roadway/environmental, motorcycle, and motorcyclist‐related variables influencing at‐fault motorcycle‐involved crashes. This study used the latest available dataset (2009 to 2013) from the Critical Analysis Reporting Environment database to study motorcycle crashes in the state of Alabama. The most significant contributors to the frequency and severity of at‐fault motorcycle‐involved crashes were found to be light conditions, time of day, driver condition, and weather conditions

Development of Innovative, Adaptable Video Learning Modules for the Civil Engineering Classroom

As engineering and technology continue to evolve, so should the use of such innovations in engineering pedagogy. Standard course learning modules have not often utilized technology to assist in learning of engineering principles and concepts; that is, until the COVID-19 pandemic required teachers and students to use technology more frequently in a virtual teaching/learning environment. Therefore, it is even more critical now that engineering pedagogy be adapted to incorporate technology in the classroom to enhance student learning of complex engineering concepts. In this study, a team of Civil Engineering professors has set out to incorporate technology into their classrooms to help students gain a stronger understanding of the fundamental building blocks of Civil Engineering. A series of comprehensive educational video and simulation-based learning modules were created for the Civil Engineering subdisciplines of environmental, geotechnical, transportation, and structural engineering. The development and implementation of such technology-based learning modules offer new opportunities to teach students the complex concepts of Civil Engineering through visual means. The efficacy of the learning modules were evaluated through student assessment surveys for: (1) the appropriateness of the module in aiding the introduction of course content, (2) the effectiveness of the module in enhancing student understanding of course content, and (3) the overall perception of students of the module. Implementation of the modules into the classroom has shown that students responded positively to the modules, referencing the modules as both engaging and comprehensive in aiding their understanding of course content

A Comprehensive Assessment of Highway Inventory Data Collection Methods

The implementation of the Highway Safety Manual (HSM) at the state level has the potential to allow transportation agencies to proactively address safety concerns. However, the widespread utilization of HSM faces significant barriers as many state departments of transportations (DOTs) do not have sufficient HSM-required highway inventory data. Many techniques have been utilized by state DOTs and local agencies to collect highway inventory data for other purposes. Nevertheless, it is unknown which of these methods or any combination of them is capable of efficiently collecting the required dataset while minimizing cost and safety concerns. The focus of this study is to characterize the capability of existing methods for collecting highway inventory data vital to the implementation of the recently published HSM. More specifically, this study evaluated existing highway inventory methods through a nationwide survey and a field trial of identified promising highway inventory data collection (HIDC) methods on various types of highway segments. A comparative analysis was conducted to present an example on how to incorporate weights provided by state DOT stakeholders to select the most suitable HIDC method for the specific purpose

Factors influencing the patterns of wrong-way driving crashes on freeway exit ramps and median crossovers: Exploration using ‘Eclat’ association rules to promote safety

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

Fatal crashes at highway rail grade crossings: A U.S. based study

Crashes at highway rail grade crossings (HRGCs) are often involved with fatalities due to the momentum of a train. This study collected nine years (2010–2018) of fatal HRGC crashes from the Fatality Analysis Reporting System (FARS) to perform the analysis. The Taxicab Correspondence Analysis (TCA) was applied to this dataset. This method identified several patterns that trigger HRGC-related fatal crashes. The findings indicate that fatal crashes involving multiple fatalities are often highly associated with alcohol-influenced drivers, poor lighting conditions, and inclement weather. The fatal crash that occurs during the daylight with the uninfluenced driver is less likely to involve more than one fatality. The results also recognized the combinations of vehicle type and speed are associated with fatal crashes at rail grade crossings. The relatively low-speed limit crossings and larger utility vehicles are more likely to be associated with fatal crashes because large vehicles require a longer time to cross railroads at a low speed. The relatively high-speed limit crossing and smaller or lighter vehicles, especially the motorcycle, are highly associated with fatal crashes

Evaluating the Safety and Mobility Impacts of American Dream Complex: Phase I (Feasibility Study, and Data Acquisition)

69A3551847102Traffic congestion and motor vehicle crashes are perceived as pivotal concerns that are particularly difficult to manage in high-density urban areas. Thus, mitigating traffic congestion and improving users' safety on roadways are top priorities of the United States Department of Transportation (USDOT). American Dream Complex, located outside New York City, is an entertainment and retail center that was officially opened in October 2019. The complex is expected to attract over 40 million annual visitors once fully operational, which may potentially result in substantial mobility and safety issues for road users in the area. The present research work evaluates the mobility and safety concerns of the transportation network in the vicinity of the American Dream Complex due to its partial official opening. In terms of mobility, the performance of four surrounding corridors was explored by incorporating travel time inflation (TI) as a performance measure. Based on the results obtained from the mobility analysis, no considerable congestion was observed on the opening day of the American Dream Complex on surrounding corridors. Additionally, StreetLight data was also explored for Interstate 95, NJ Route 3, and NJ Route 120 for a period of 120 days before and 120 days after the opening of the complex. Findings showed an increase in the trips made after the opening; however, the travel duration was not significantly impacted due to the opening. To achieve the second goal of this study, the research team developed an innovative artificial intelligence (AI)-based video analytic tool to assess intersection safety using surrogate safety measures. To extract the trajectory data, the proposed work integrates a real-time AI detection algorithm, YOLO-V5, with tracking using the Deep SORT algorithm. The proposed approach achieved a relative accuracy between 95 and 98 percent in detecting and tracking vehicle trajectories

A Real-time Proactive Intersection Safety Monitoring System Based on Video Data

69A3551847102In recent years, identifying road users' behavior and conflicts at intersections have become an essential data source for evaluating traffic safety. According to the Federal Highway Administration (FHWA), in 2020, more than 50% of fatal and injury crashes occurred at or near the intersections, necessitating further investigation. This study developed an innovative artificial intelligence (AI)-based video analytic tool to assess intersection safety using surrogate safety measures. Surrogate safety measures (e.g., Post-encroachment Time (PET) and Time-to-Collision (TTC)) are extensively used to identify future threats, such as rear-end and left-turning collisions due to vehicle and road users' interactions. To extract the trajectory data, this project integrates a real-time AI detection model - YOLO-v5 with a tracking framework based on the DeepSORT algorithm. 54 hours of high-resolution video data were collected at six signalized intersections (including three3-leg intersections and three 4-leg intersections) in Glassboro, New Jersey. Non-compliance behaviors, such as red-light running and pedestrian jaywalking, are captured to better understand the risky behaviors at these locations. The proposed approach achieved an accuracy of 92% to 98% for detecting and tracking the road users' trajectories. Additionally, a user-friendly web-based application was developed that provides directional traffic volumes, pedestrian volumes, vehicles running a red light, pedestrian jaywalking events, and PET and TTC for crossing conflicts between two road users. In addition, an extreme value theory (EVT) was used to estimate the number of crashes at each intersection utilizing the frequency of PETs and TTCs. Finally, the intersections were ranked based on the calculated score considering the severity of crashes. Overall, the developed tool as well as the crash estimation model and ranking method, can provide valuable information for engineers and policymakers to assess the safety of intersections and implement effective countermeasures to mitigate the intersection-involved crashes

Local Access Management Regulations

The primary objective of this research is to identify and recommend strategies, tools, and guidelines to facilitate access management on local (i.e., county and municipal) roads intersecting and/or impacting state highways in New Jersey. State of New Jersey has an access management code that applies to state highways, but it does not apply to local roads. Local authorities in New Jersey do not have uniform access management codes, regulations, standards for local roads, as a result of which issues arise when developments take place on local roads near intersections with state routes. This research evaluated if/when similar issues are encountered in other states, how other state departments of transportation (DOTs) address access management on local roads, and how New Jersey local government and officials perceive access management on local roads near state highways. The study included a review of the literature and best practices, several stakeholder meetings, structured interviews with officials from other states DOTs, a survey of in-state officials, case studies involving intersections between local roads and state highways, and synthesis of results

Assessment of the First-and-Last-Mile Problem in Underserved Communities: Case Study in Camden City, NJ

The first-/last-mile (FLM) trip is the distance a person needs to complete before boarding a transit stop or after alighting. Challenges to completing the FLM trip significantly affect disadvantaged groups who are highly reliant on transit, including people with disability, people of color, older populations, and low-income travelers. To evaluate the FLM trip, this study referred to the current literature and identified the factors associated with challenges in completing the FLM trip. The identified elements were categorized into safety, accessibility, demographic, and health-related factors. The analytic hierarchy process (AHP) method was utilized to weigh these factors and evaluate the FLM problem in the study area, Camden City, New Jersey. To implement this, a questionnaire was designed and distributed to transportation professionals in New Jersey. The experts were required to rank the importance of each factor based on their experience and in consideration of the study area. Finally, the study utilized the multi-criteria analysis AHP approach supported by ArcGIS to model the problem spatially. A data inventory of Camden City was completed to run the spatial analysis. The outcome presented in this paper is a spatial evaluation of the FLM barriers in the study area. In addition, it provides a prioritization of the factors associated with FLM issues concerning underserved communities. This research offers a comprehensive approach to understanding the barriers to fair and accessible transportation systems and helps professionals overcome these barriers and maintain equitable transportation networks

Evaluation of navigation performances of GPS devices near interchange area pertaining to wrong-way driving

Based on past studies, exit ramp terminals are the common locations for drivers to enter a physically separated highway in the wrong direction. Currently, many drivers, especially nonlocal drivers, often rely on voice-guided navigation apps and Global Positioning System (GPS) devices to navigate their routes on and off freeways. A few studies have reported that GPS devices sometimes give drivers wrong information and cause wrong-way entry into a freeway, especially at some confusing interchanges, such as partial cloverleaf and compressed diamond interchanges. The access points located close to exit ramps may also cause a problem for GPS devices in sending accurate voice-guidance. It is unknown if current GPS devices are capable of properly informing drivers regarding turning movements in advance of exit ramp terminals at some common interchanges. The objective of this study is to evaluate the most commonly used GPS devices/navigation apps to identify existing problems and their potential for reducing wrong-way driving (WWD) incidents at interchange terminals. Field experiments were conducted at 10 common freeway interchanges or interchanges with nearby access driveways in the state of Alabama. Results show that most GPS devices have difficulty in providing correct guidance when the spacing between an access point and an exit ramp is less than 300 feet. Our comparison of five different GPS devices used on the same routes reveals that navigation apps have more limitations in guiding drivers than stand-alone GPS devices. Recommendations are offered to help GPS mapping companies improve their devices or add new features to reduce the occurrence of WWD