Subasish Das

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Verified

Subasish verified their affiliation via an institutional email.

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• PhD, Transportation Engineering
• Assistant Professor at Texas State University

In the U.S., around 14% of total crash fatalities are pedestrian related. In 2011, 4,432 pedestrians were killed and 69,000 pedestrians were injured in vehicle-pedestrian crashes in the U.S. Vehicle-pedestrian crashes have become a key concern in Louisiana due to the high percentage of fatalities in recent years. In 2012, pedestrians accounted for...

Raised pavement markers (RPM) are intended as safety devices on roadways. Intuitively convinced by their safety benefits, the Louisiana Department of Transportation and Development (LADOTD) has been using RPMs for many years on all freeways in the state. Because of the not-sopositive RPM Crash Modification Factor published in the first Highway Safe...

Narrow, rural two-lane highways are mostly characterized by low design features, light traffic volumes with high crash rates and particularly high fatal crash rates. There are about 5,000 miles of narrow, rural two-lane highways administrated by the Louisiana Department of Transportation and Development (LADOTD). Running-off-roadway (ROR) crashes a...

Undivided roadways have consistently exhibited low safety performance, particularly in urban or suburban areas where roadside development is relatively intense. Changing a four-lane undivided road to a divided roadway by either building a boulevard cross-section or installing a physical barrier is a desirable option to improve safety performance of...

Crashes involving farm equipment vehicles are a significant safety concern on public roads, particularly in rural and agricultural regions. These vehicles display unique challenges due to their slow-moving operational speed and interactions with faster vehicles, often leading to severe crashes. This study analyzed crashes involving farm equipment v...

Motorcyclists face an increased risk of injury severity at curved roadway segments due to a wide range of factors. Given the complex and evolving nature of factors influencing motorcyclist injury severity on curved roadway segments, there is a need for research that not only captures these contributing factors dynamics but also considers temporal v...

Recent advancements in sequence modeling have led to the emergence of Structured State Space Models (SSMs) as an efficient alternative to Recurrent Neural Networks (RNNs) and Transformers, addressing challenges in long-range dependency modeling and computational efficiency. While RNNs suffer from vanishing gradients and sequential inefficiencies, a...

Young motorcyclists, particularly those aged 15 to 24 years old, face a heightened risk of severe crashes due to factors such as speeding, traffic violations, and helmet usage. This study aims to identify key factors influencing crash severity by analyzing 10,726 young motorcyclist crashes in Texas from 2017 to 2022. Two advanced tabular deep learn...

Visual object detection utilizing deep learning plays a vital role in computer vision and has extensive applications in transportation engineering. This paper focuses on detecting pavement marking quality during daytime using the You Only Look Once (YOLO) model, leveraging its advanced architectural features to enhance road safety through precise a...

Child bicyclists (14 years and younger) are among the most vulnerable road users, often experiencing severe injuries or fatalities in crashes. This study analyzed 2,394 child bicyclist crashes in Texas from 2017 to 2022 using two deep tabular learning models (ARM-Net and MambaNet). To address the issue of data imbalance, the SMOTEENN technique was...

Pedestrian crashes represent a critical traffic safety issue, often resulting in fatal outcomes and raising significant equity concerns. This study analyzed detailed records of pedestrian-involved crashes in California from 2018 to 2021, employing a novel clustering framework enhanced by the SHapley Additive exPlanations approach. The proposed meth...

The ongoing increase in the generation of baby boomers is expected to significantly impact demographic changes, leading to a higher proportion of elderly individuals in the population. According to the 2020 census data, 1 in every 6 people in the United States were aged 65 years and over. Due to the rise of this specific age group population and th...

This study used a detailed explainable AI for automatic machine learning, AutoGluon, to predict pedestrian injury severity using data collected over five years (2016-2021) in Louisiana. The final dataset includes forty variables related to pedestrian characteristics, environmental circumstances, and vehicle specifications. Pedestrian injury severit...

Roadside and median barriers have proven effective in preventing crashes; however, a significant number of crashes still occur that involve road barriers across United States. This study focuses on analyzing the factors related to barrier crashes across Texas. The dataset includes 63,475 crashes involving road barriers and covering six years of cra...

Speeding has been distinguished as one of the most frequent and persistent contributing factors and is a critical contributing factor to the degree of injury severity. In the United States, at least a quarter of nationwide annual fatal crashes during the last decade involved speeding. There is still a need for an overarching look at crashes involvi...

Previous research has demonstrated that crashes involving motorcycles and fixed objects often result in severe injuries. However, there is a scarcity of stand-alone studies examining the contributing factors related to this specific type of crash. This study sought to address this gap by analyzing a comprehensive dataset comprising 1,198 crash obse...

This systematic review explores the theoretical foundations, evolution, applications, and future potential of Kolmogorov-Arnold Networks (KAN), a neural network model inspired by the Kolmogorov-Arnold representation theorem. KANs distinguish themselves from traditional neural networks by using learnable, spline-parameterized functions instead of fi...

The present study investigates the efficacy of speed cameras in managing vehicle speeds and their influence on various driver demographics within the operational context of the cameras. The research, conducted as a case study in Iran, comprises both macroscopic and detailed analyses, quantifying vehicle speeds at different spatial points relative t...

The transition rate to electric vehicles (EVs) has accelerated globally as indicated by a notable rise in the number of used EVs in the market. However, most existing studies focused on the attributes related to the new EVs. This study explores the factors influencing consumer purchase intentions of used EVs using structural equation modeling (SEM)...

Tabular data, widely used in industries like healthcare, finance, and transportation, presents unique challenges for deep learning due to its heterogeneous nature and lack of spatial structure. This survey reviews the evolution of deep learning models for tabular data, from early fully connected networks (FCNs) to advanced architectures like TabNet...

Frontage roads play a vital role in the U.S.’s highway system because they serve as critical access routes between principal arterials, freeways, and surrounding businesses. Despite their importance, there have been only limited studies conducting an in-depth analysis of frontage road safety. The limited availability of suitable data for frontage r...

Ensuring the safety of work zones is a top priority for transportation agencies because of the dangers posed by vehicles changing lanes and paths within these areas. Recent statistics highlight the seriousness of this issue, showing a shocking 46% increase in fatal collisions within work zones in 2019 compared with 2011. Therefore, this study exami...

Ambulance crashes constitute a matter of utmost concern within public health, posing potential risks to both patients and emergency responders. Despite this critical importance, investigating the underlying causes of these collisions is difficult because of the scarcity of comprehensive and relevant datasets. To bridge this research gap and gain va...

Crash counts are non-negative integer events often analyzed using crash frequency models such as the negative binomial (NB) distribution. However, due to their random and infrequent nature, crash data usually exhibit unique characteristics, such as excess zero observations that the NB distribution cannot adequately model. The negative binomial-Lind...

Identifying factors that significantly affect drivers that are repeatedly involved in traffic violations or non-fatal crashes (defined here as recidivist drivers) is very important in highway safety studies. This study sought to understand the relationship between a set of variables related to previous driving violations and the duration between a...

Background To estimate global and regional trends from 2000 to 2020 of the number of persons visually impaired by cataract and their proportion of the total number of vision-impaired individuals. Methods A systematic review and meta-analysis of published population studies and gray literature from 2000 to 2020 was carried out to estimate global a...

Speed is widely recognized as having significant safety impacts, especially on the severity of crashes. However, these effects are complex and generally have not been captured in the Highway Safety Manual (HSM) (American Association of State Highway and Transportation Officials (AASHTO) 2010). The current Federal Highway Administration (FHWA) proje...

Speed is widely recognized as having significant safety impacts, especially on the severity of crashes. However, these effects are complex and generally have not been captured in the Highway Safety Manual (HSM) (American Association of State Highway and Transportation Officials 2010). The current Federal Highway Administration (FHWA) project develo...

Social media networks, like X (formerly Twitter), emerged as a revolutionary means of communication, drastically changing how information is disseminated. This study attempted to synthesize the current state of social media usage by transit agencies, accomplishing three primary tasks: (1) conducting a comprehensive review of agencies' social media...

Amid the COVID-19 pandemic, automated delivery vehicles (ADVs) have gained even more significance due to their touchless capabilities, aligning perfectly with the need for contactless delivery services to safeguard public health. This study collected and conducted quantitative analysis on the most relevant 86 news articles focused on ADVs by employ...

Pedestrians are at a significantly higher risk of suffering serious injuries or death in road traffic incidents. In 2021 alone there were 7,388 pedestrian fatalities and 60,577 injuries. Addressing this critical issue, our study introduced a novel methodology for predicting the severity of pedestrian crashes. This method leveraged advanced deep lea...

Summary Background Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injur...

Posted speed limits (PSLs) are a highly complex issue involving engineering, human factors, and political and societal concerns. On a national level, recent research along with calls to change how speed limit are set, especially for city streets, have generated extensive discussion on future speed limit setting procedures. Within this TxDOT project...

Background Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensiv...

Summary Background Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injur...

To accurately analyze and understand the causes of traffic crashes involving pedestrians and bicyclists, the Pedestrian and Bicycle Crash Analysis Tool (PBCAT) was developed. However, manual data entry in the tool is labor intensive. Thus, a more automated method is needed for large datasets. This study developed deep-learning models to automate th...

Pedestrian-involved hit-and-run (PIHR) crashes represent a significant public health concern, and identifying patterns in these crashes can aid in developing effective countermeasures. Cluster correspondence analysis (CCA) is a multidimensional statistical technique that combines dimension reduction and clustering to identify patterns in categorica...

In the U.S., the interstate highway system is categorized as a controlled-access or limited-access route, and it is unlawful for pedestrians to enter or cross this type of highway. However, pedestrian-vehicle crashes on the interstate highway system pose a distinctive safety concern. Most of these crashes involve 'unintended pedes-trians', drivers...

The objective of this current study was to evaluate the impact of digital billboards (DBs) on selfreported and observed driving behavior, given their established association with distracted driving. This investigation focused on driver behavior in Iran using a dual-pronged approach. Initially, self-reported driving behavior was analyzed using a Dri...

The current study examined 1,465 crash observations (2017-2021) from Louisiana, identifying significant variables grouped into three major categories: drivers', crash, and road characteristics. Considering crash injury severity as a dependent variable, we employed classic Multinomial Logit (MNL) model, and several other models to address unobserved...

Background Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important follow...

Background Disorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditio...

Background: To estimate global and regional trends from 2000 to 2020 of the number of persons visually impaired by cataract and their proportion of the total number of vision-impaired individuals. Methods: A systematic review and meta-analysis of published population studies and gray literature from 2000 to 2020 was carried out to estimate global...

The Safe System Approach (SSA) has emerged as a comprehensive framework for enhancing traffic safety through system-wide interventions. This systematic review, conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzes 82 relevant studies categorized based on the SSA pillars: safe road u...

This research leverages a novel deep learning model, Inception-v3, to predict pedestrian crash severity using data collected over five years (2016–2021) from Louisiana. The final dataset incorporates forty different variables related to pedestrian attributes, environmental conditions, and vehicular specifics. Crash severity was classified into thre...

Advancements in computing power have recently made it possible to utilize machine learning and deep learning to push scientific computing forward in a range of disciplines, such as fluid mechanics, solid mechanics, materials science, etc. The incorporation of neural networks is particularly crucial in this hybridization process. Due to their intrin...

The concept of distracted pedestrians and its impact on highway safety has gained increasing attention in recent years. However, studies focusing exclusively on distracted pedestrian crashes are less pervasive than distracted driving. In addition, most prior studies investigate the harmful effect of cellphone usage while walking, without considerin...

Identifying factors that significantly affect drivers that are repeatedly involved in traffic violations or non-fatal crashes (defined here as recidivist drivers) is very important in highway safety studies. This study sought to understand the relationship between a set of variables related to previous driving violations and the duration between a...

Despite extensive research on traffic injury severities, relatively little is known about the factors contributing to truck-involved crashes in developing countries, especially in the context of Bangladesh. Because of the unavailability of authentic crash data sources, this study collected data from alternative sources such as online English news m...

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The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) is a multinational collaborative research study with >10,000 collaborators around the world. GBD generates a time series of summary measures of health, including prevalence, cause-specific mortality (CSMR), years of life lost (YLLs), years lived with disability (YLDs), and disabi...

Pedestrian red-light violation is one of the crucial causes of pedestrian crashes at urban intersections, which cause considerable injuries and casualties to this vulnerable road group of road users. The objective of this study is to evaluate the risk of pedestrian-vehicle collisions by clustering the pedestrians' red-light violations using surroga...

A notable shortcoming in contemporary digital navigation systems is their failure to incorporate live weather data. This study investigates the possibility of improving urban navigation experiences by integrating real-time weather data. We developed a navigation tool that utilizes weather data from the OpenWeather API, providing users with real-tim...