Mahmud Hossain

Pavement marking retroreflectivity (RL) is a critical characteristic for enhancing road safety and infrastructure compatibility with advanced driver assistance systems. This study investigates factors contributing to RL degradation of thermoplastic markings, focusing on marking line types and rural lane configurations. RL data from the Alabama Department of Transportation (DOT) were analyzed for the Montgomery area in 2020 and 2021. Data from six sources, including the Topologically Integrated Geographic Encoding and Referencing System, Highway Performance Monitoring System, and Critical Analysis Reporting Environment, were used to extract relevant factors featuring road, traffic flow, location, and marking characteristics. Apart from beta regression, paired t-tests were performed to examine RL degradation, particularly statistical variations between curve and adjacent straight segments. Moreover, over 200 locations were manually reviewed using Google Earth Pro Street View to identify additional contributing factors contributed to high RL degradation, not apparent through statistical analysis. The results revealed that centerlines in residential, business, or mixed-use areas experienced greater RL degradation. White lanelines showed statistically significant RL degradation on roadways with high posted speed limits. White right edgelines exhibited higher degradation on curved compared with straight segments with similar road and traffic characteristics and identical RL qualitative states. Additional factors influencing RL degradation included the presence of right-turn lanes near intersections or ramps, U-turn and left-turn facilities, bridges, shoulders, residential driveways, and surrounding landscape features. These findings can support pavement management system in identifying high-priority segments for restriping, optimizing maintenance efforts, and reducing costs.

Despite significant research efforts into wrong-way driving crashes, the fatality rate in the United States remains persistently high year after year. However, few studies have concentrated on how the driver’s familiarity with the road affects wrong-way driving. This study aims to examine if there is a difference in contributing factors to fatal wrong-way driving crashes by local and non-local drivers by utilizing Fatality Analysis Reporting System (FARS) data from 2016 to 2020. Descriptive statistics were first used to give insight into the data, and then the association rule mining method was applied to help uncover the hidden connections between contributing factors to wrong-way driving crashes for both local and non-local drivers. The findings indicated that several factors, including intoxicated drivers, an urban environment, and late-night hours from 12 A.M. to 6 A.M., play a significant role in causing local wrong-way driving crashes. On the other hand, non-lighted conditions in a rural setting significantly contributed to fatal wrong-way driving crashes by non-local drivers.

As technology advances, there is a growing demand for understanding the fundamental concepts of connected and autonomous vehicles (CAVs) and their relative impact on different aspects of transportation engineering. Various transportation courses are regularly offered in civil engineering programs, including but not limited to an introduction to transportation engineering, transportation planning/modeling, highway design, transportation safety, traffic engineering, traffic simulation, and intelligent transportation systems. Instructors often face challenges in identifying critical CAV topics for these courses, making it necessary to explore critical course content related to CAVs. The objectives of this study are (1) to identify critical CAV topics for different transportation engineering courses and (2) to emphasize upcoming CAV topics crucial for a successful transition into the CAV system. To achieve these objectives, a national survey was conducted among transportation educators and practitioners. The survey was distributed with the assistance of Transportation Research Board Standing Committees to ensure broad participation. Among the 48 respondents from 33 states, faculty members who had already integrated CAV topics into their courses and practitioners were prioritized while estimating the weighted average of each topic. The findings of this research can serve as valuable resources for redesigning transportation engineering courses by incorporating CAVs. The identified high-priority CAV topics can play a crucial role in raising awareness among undergraduate and graduate students about the diversity of transportation engineering and motivating them to explore emerging CAV technologies and opportunities within the field.

The development of connected and autonomous vehicles (CAVs) has progressed rapidly with advancements in intelligent transportation systems, automated driving technologies, and regulatory reforms worldwide. As the efficiency and safety of surface transportation heavily rely on highway geometric design, understanding the impact of CAVs on design control elements is essential to fully capitalize the benefits CAVs may offer. The objective of this study is to conduct a comprehensive review of existing literature to synthesize how CAVs can influence the controlling criteria for highway geometric design. Over 30 articles were thoroughly reviewed to gain insights into the impacts of CAVs on highway design and potential explanations for them. As CAVs could potentially reduce the minimum required stopping sight distance, they create opportunities to decrease other geometric dimensions, such as horizontal curve radius, length of vertical curves, horizontal sight offset, extra widening, and so on. However, this optimization is more feasible in an environment with fewer human-driven vehicles. Moreover, further related research needs to be done in mixed traffic streams and complex driving conditions. The critical discussions of this study can offer valuable insights, shedding light on the potential benefits and implications that an automated transportation sector may bring to future road geometry design.

Secondary crashes (SCs) are a major concern, posing additional safety threats to both non-involved vehicles and incident responders. The objective of this study was to identify the affiliated factors contributing to SCs on roadways with a speed limit of 55 mph or above. Traditional police-investigated crash dataset was analyzed, spanning more than four years (January 2016–February 2020) for the entire state of Alabama. As the crash database did not directly include information on SCs and did not allow for linking a primary crash with a subsequent SC, a data extraction process was developed to identify SCs and understand their characteristics. Association rule mining (ARM) was applied to identify crash patterns based on maximum injury severity levels. The generated rules were filtered based on support, confidence, and lift, and then validated by the lift increase criterion. The results revealed complex relationships between risk factors and severity of SCs. In relation to SCs with injuries, single-vehicle crashes were frequently observed during peak hours and when drivers swerved to avoid objects/persons/vehicles. In contrast, concerning SCs with possible/no injuries, single-vehicle collisions were more likely to occur when drivers failed to notice objects/persons/vehicles and were involved in speeding. On urban interstates, single-vehicle SCs were frequently associated with injuries, while rear-end SCs were often linked to possible/no injuries. The findings of this study can be helpful in enhancing existing traffic incident management programs to mitigate the occurrence of SCs.

Animal-vehicle crashes (AVCs) are a significant issue in Louisiana that requires attention. Data on AVCs that occurred from 2015 to 2020 were obtained from the Louisiana Department of Transportation and Development (DOTD), including 14,349 crashes with major injury (KA), minor injury (BC), and no injury (O) severity groups. Aiming to find the collective association of attributes from AVC data, which are categorical in nature, this study utilized two data mining methods: multiple correspondence analysis (MCA) and association rule mining (ARM). Five hierarchical clusters that were generated from the BC and O AVC datasets were particularly significant. Among several other findings, MCA revealed that BC and O AVCs are more concentrated on parish roads during the spring season, while O AVCs in the fall and winter tend to occur on highways with speed limits of 50 mph or higher. ARM revealed that moderate-speed parish roads are frequently associated with KA and BC AVCs, particularly in residential areas and during the spring season, and they often involve young drivers. The findings of this study can be particularly beneficial by considering the spatiotemporal factors associated with animal concentration and movement to develop targeted interventions and mitigation strategies.