Portrait of Dr. Subasish Das

Dr. Subasish Das

  • Assistant Professor at Ingram School of Engineering, College of Science & Engineering

Scholarly and Creative Works

2025

  • Liu, J., Antariksa, G., Somvanshi, S., & Das, S. (2025). Revealing equity gaps in pedestrian crash data through explainable artificial intelligence clustering. Transportation Research Part D: Transport and Environment.
  • Antariksa, G., Tamakloe, R., Liu, J., & Das, S. (2025). Automated and Explainable Artificial Intelligence to Enhance Prediction of Pedestrian Injury Severity. IEEE Transactions on Intelligent Transportation Systems.
  • Das, S., Dzinyela, R., Dadashova, B., Westfall, G., Silvestri-Dobrovolny, C., Adanu, E. K., & Lord, D. (2025). Analysis of Motorcyclists Crash Severity using Cluster Correspondence and Hierarchical Binary Logit Models. Multimodal Transportation.
  • Kutela, B., Kinero, A., Shita, H., Das, S., Ruseruka, C., Chengula, T. J., & Novat, N. (2025). Understanding Spatial-temporal Attributes Influencing Electric Vehicle’s Charging Stations Utilization: A Multi-City Study. Green Energy and Intelligent Transportation.
  • Mimi, M. S., Chakraborty, R., Barua, S., Das, S., Khan, M. N., & Dadashova, B. (2025). Demographic risk factors and injury severity scores in Substance-use behaviour related traffic crashes. Transportation Research Part F: Traffic Psychology and Behaviour.
  • Das, S., Sheykhfard, A., Azmoodeh, M., & Kutela, B. (2025). Analyzing purchase intentions of used electric vehicles through consumer experiences: A structural equation modeling approach. Transport Policy.
  • Mimi, M. S., Chakraborty, R., Liu, J., Barua, S., & Das, S. (2025). Exploring patterns in older pedestrian involved crashes during nighttime. Accident Analysis & Prevention.
  • Rahman, M. A., Chakraborty, R., Das, S., Mohammed, N.-H., Hossain, M. M., & Junaed, S. (2025). Identifying attribute associations in fatal speeding crashes using latent class clustering and association rule mining. Journal of Transportation Safety & Security.
  • Kinero, A., Kutela, B., Das, S., & Hossain, A. (2025). Who should be responsible for setting standards for how automated vehicles are used? Insights of the US perspective from a 2021 Nationwide Survey. Sustainable Futures, 100718.
  • Das, S., Dzinyela, R., Liu, J., Dadashova, B., & Silvestri-Dobrovolny, C. (2025). Understanding patterns of factor influences in motorcycle crashes with fixed objects. Taylor & Francis, 17(5), 483–509.
  • Somvanshi, S., Tusti, A. G., Polock, S. B. B., Mimi, M. S., Islam, M. M., Dutta, A., & Das, S. (2025). Applying MambaAttention, TabPFN, and TabTransformers to Classify SAE Automation Levels in Crashes. SSRN 5229751.
  • Jafari, M., Starewich, M., Hossain, A., Barua, S., Alnawmasi, N., Ye, X., & Das, S. (2025). Assessing motorcyclist injury severity on curved road segments with temporal dynamics and unobserved heterogeneity. Scientific Reports, 15(1), 13110.
  • Das, S., & Chakraborty, R. (2025). Uncovering the role of restraint usage in driver ejection: a data mining investigation of fatal and injury crashes. Traffic Injury Prevention. Retrieved from 601 University Dr, San Marcos, TX 78666
  • Somvanshi, S., Islam, M. M., Mimi, M. S., Polock, S. B. B., Chhetri, G., & Das, S. (2025). A Survey on Structured State Space Sequence (S4) Models. arXiv Preprint arXiv:2503.18970. Retrieved from 601 University Dr, San Marcos, TX 78666
  • Das, S., Somvanshi, S., Chakraborty, R., & Dutta, A. K. (2025). Crash severity analysis of child bicyclists using arm-net and mambanet. arXiv Preprint arXiv:2503.11003.
  • Antariksa, G., Chakraborty, R., Somvanshi, S., Das, S., Jalayer, M., Patel, D. R., & Mills, D. (2025). Comparative Analysis of Advanced AI-based Object Detection Models for Pavement Marking Quality Assessment during Daytime.
  • Somvanshi, S., Tusti, A. G., Chakraborty, R., & Das, S. (2025). Applying Tabular Deep Learning Models to Estimate Crash Injury Types of Young Motorcyclists. arXiv Preprint arXiv:2503.10474.
  • Tusti, A. G., Dutta, A. K., Javed, S. A., & Das, S. (2025). Driving Education Advancements of Novice Drivers: A Systematic Literature Review. arXiv Preprint arXiv:2503.05762.
  • Das, S., Javed, S. A., Tusti, A. G., & Pandey, B. (2025). From Maneuver to Mishap: A Systematic Literature Review on U-Turn Safety Risks. arXiv Preprint arXiv:2502.12556.
  • Somvanshi, S., Islam, M. M., Polock, S. B. B., Chhetri, G., Anderson, D., Dutta, A., & Das, S. (2025). Quantum Computing in Transportation Engineering: A Survey. Available at SSRN 5141686.
  • Das, S., Somvanshi, S., Barua, S., & Liu, J. (2025). Gen-AI for Transportation Planning. Gen-AI for Transportation Planning.
  • Ye, X., Newman, G., Zhai, W., Retchless, D., Das, S., Ham, Y., … Zhang, Z. (n.d.). Toward Coastal Infrastructure Resiliency: An AI-Enabled Decision Support Framework for Multiscale Comprehension and Stakeholder Empowerment. Transactions of the American Philosophical Socie, 114(1), 65–97.
  • Hossain, A., Das, S., Jafari, M., Junaed, S., & Codjoe, J. (n.d.). Behavioral Insights into Older Driver Involved Crashes at High-Speed Signalized Intersections (Hssis): A Random Parameter Ordered Probit Approach. Available at SSRN 5089636.
  • Kinero, A., Kutela, B., Das, S., & Hossain, A. (n.d.). Who Should Be Responsible for Setting Standards for How Automated Vehicles are Used? Insights from the Nationwide Survey. SSRN.
  • Somvanshi, S., Antariksa, G., & Das, S. (2025). Enhanced Balanced-Generative Adversarial Networks to Predict Pedestrian Injury Types. Available at SSRN 4847615.
  • Hossain, A., Sun, X., Das, S., Jafari, M., & Rahman, M. A. (2025). Investigating Pedestrian-Vehicle Crashes on Interstate Highways: Applying Random Parameter Logit Model. Available at SSRN 4649205.
  • Banihashemi, M., Das, S., Dadvar, S., & Liu, J. (2025). COVID-19 Era Crash Fatality/Severe Injury and Proven Speed-Crash Relations. TRR.
  • Kutela, B., Das, S., kabir, N., & Vierkant, V. (2025). The Autopilot Paradox: Public Perception of Sleeping while Driving Semi-Automated Cars Boniphace Kutela Applied Mobilities. Applied Mobilities.
  • Dzinyela, R., Das, S., Jafari, M., & Khan, N. (2025). Applying Hybrid Dimension Reduction and Econometric Model to Investigate Rider Behaviors in Roadway Departure Motorcycle Crashes. Transportation Letters: The International Journal of Transportation Research.
  • Sheykhfard, A., Jones, S., Sadeghvaziri, E., Koppel, S., Das, S., & Nankali, M. (2025). Integrating Empirical and Subjective Evidence on Young Drivers’ Risk Perceptions and Crash Factors. TRR.
  • Kinero, A., Kutela, B., Das, S., & Hossain, A. (2025). Who Should Be Responsible for Setting Standards for How Automated Vehicles are Used? Insights from the Nationwide Survey. SSRN.
  • Hossain, A., Das, S., Sun, X., Hasan, A. S., & Jalayer, M. (2025). A Hybrid Data Mining Framework to Investigate Roadway Departure Crashes on Rural Two-Lane Highways: Applying Fast and Frugal Tree with Association Rules Mining. Accident Analysis and Prevention.
  • Kutela, B., Chengula, T. J., Ngeni, F., Lipu, C., Kidando, E., Liu, J., & Das, S. (2025). Examining Patterns of GPS-Related Traffic Crashes: Insights from a Matched Case–Control Approach through Crash Narratives. Journal of Transportation Engineering, Part A: Systems, 151(9).
  • Hossain, A., Das, S., jafari, M., Starewich, M., Chakraborty, R., & Kutela, B. (2025). Behavioral and psychological determinants of pedestrian collisions on arterial roads with evidence from random parameter models. Scientific Reports, 15(1).
  • Somvanshi, S., Aibinu, M. O., Chakraborty, R., Islam, M. M., Mimi, M. S., Koirala, D., … Das, S. (2025). Not Just Another Survey on Physics-Informed Neural Networks (PINNs): Foundations, Advances, and Open Problems. Advances, and Open Problems.
  • Sheykhfard, A., Haghighi, F., Zadeh, A. A., Das, S., Oshanreh, M. M., Shaaban, K., & Soltani, A. (2025). Evaluating U-left turn and direct left turn movements at signalized intersections using traffic conflict indices. Journal of Traffic and Transportation Engineering (English Edition), 12(4).
  • Chakraborty, R., Liu, J., Tusti, A. G., Mimi, M. S., & Das, S. (2025). Impact of lighting conditions on nighttime crash severity among older and elderly drivers. Journal of Transportation Safety & Security.
  • Hossain, A., Dzinyela, R., Jafari, M., Barua, S., Chakraborty, R., & Das, S. (2025). Assessing risk factors in ambulance-involved collisions: understanding the impact of COVID-19 pandemic. Transportmetrica A: Transport Science.
  • Chakraborty, R., Javed, S. A., Hossain, A., Mills, D., & Das, S. (2025). Identifying patterns in backing maneuver crashes utilizing differential evolution optimization algorithm.
  • Das, S., Liu, J., Dzinyela, R., Dadashova, B., & Silvestri-Dobrovolny, C. (2025). Clustering patterns of roadway departure related motorcycle crashes using dimension reduction analysis. Journal of Transportation Safety & Security.
  • Sakib, N., Paul, T., Das, S., & Hossain, A. (2025). Exploring the factors affecting injury severity in highway and non-highway crashes in Bangladesh applying machine learning and SHAP. IATSS Research.
  • Huang, X., Ye, X., Stewart, K., & Das, S. (Eds.). (2025). Urban Human Mobility Practices, Analytics, and Strategies for Smart Cities. New York, NY, USA: Routledge.
  • Jafari, M., Das, S., Tamakloe, R., Hossain, A., & KHan, N. (2025). Uncovering Individual Heterogeneity in Pedestrian Crash Severity with Mixed Logit Models: A Louisiana Case Study. Transportation Research Record, 1–28.
  • Sheykhfard, A., Qiao, F., Das, S., & Lord, D. (2025). A predictive analysis of crash proneness among freight drivers: insight into latent risk dimensions. Transportation Research Part F: Traffic Psychology and Behaviour, 114, 30–48.
  • Liu, J., Chakraborty, R., Somvanshi, S., & Das, S. (2025). Impact of operating speed, roadway curvature, and precipitation on roadway departure risk in rural two-lane roads. Travel Behaviour and Society, 41, 101055. Retrieved from 601 University Dr, San Marcos, TX 78666
  • Chakraborty, R., Mills, D. A., & Das, S. (2025). Children on Wheels: Identifying Crash Determinants using Cluster Correspondence Analysis  . Accident Analysis & Prevention, 1–20.
  • Hossain, A., Barua, S., Das, S., & Starewich, M. J. (2025). Ambulance crash risk dynamics: a baseline (2017–2019) vs. pandemic-era (2020–2022) comparative study using a random parameter logit model. Transportmetrica A: Transport Science, 1–39. Retrieved from 601 University Drive, San Marcos, TX-78666
  • Kinero, K., Kasubi, F., Hossain, A., Das, S., & Kutela, B. (2025). Perception of cyber attacks on automated vehicles and its influence on road sharing and ridership: Insights of the US perspective from a 2021 nationwide survey. Transportation Research Part F: Traffic Psychology and Behaviour, (111). Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S1369847825001007
  • Jafari, M., Das, S., Starewich, M. J., & Das, S. (2025). SUV-pedestrian crash severity modelling considering unobserved heterogeneity in means and variances. Transportmetrica A: Transport Science, 1–45. Retrieved from https://www.tandfonline.com/doi/abs/10.1080/23249935.2025.2453509
  • Liu, J., Chakraborty, R., Somvanshi, S., & Das, S. (2025). Impact of operating speed, roadway curvature, and precipitation on roadway departure risk in rural two-lane roads. Travel Behaviour and Society, 41, 101055. Retrieved from 601 University Dr, San Marcos, TX 78666
  • Jafari, M., Das, S., Barua, S., Mimi, M. S., & Starewich, M. (2025). Crash outcomes of yellow school buses: Random parameter and correlated random parameter logit models with heterogeneity in means. Accident Analysis & Prevention, 219, 108109. Retrieved from 601 University Dr, San Marcos, TX 78666
  • Tamakloe, R., Khorasani, M., Das, S., & Kim, I. (2025). Pattern recognition in crash clusters involving vehicles with advanced driving technologies. Accident Analysis & Prevention, 218, 108072.
  • Hossain, A., Das, S., Sun, X., Hasan, A. S., Jalayer, M., & Rahman, M. A. (2025). A hybrid data mining framework to investigate roadway departure crashes on rural two-lane Highways: Applying Fast and Frugal Tree with Association Rules Mining. Accident Analysis & Prevention, 217, 108066.
  • Islam, M. M., Liu, J., Chakraborty, R., & Das, S. (2025). Evaluating crash risk factors of farm equipment vehicles on county and non-county roads using interpretable tabular deep learning (TabNet). Accident Analysis & Prevention, 217.
  • Kutela, B., Das, S., Javed, S. A., Sheykhfard, A., Ngeni, F., Lyimo, S. M., … Langa, N. (2025). Understanding the intersection of transportation safety and quality of life: Insights from community surveys in Austin, Texas. Cities, 162, 105964.
  • Antariksa, G., Koeshidayatullah, A., Das, S., & Lee, J. (2025). XAI-driven contamination for self-supervised denoising with pixel-level anomaly detection in seismic data. Journal of Applied Geophysics, 238, 105723.
  • Agheli, A., Aghabayk, K., Sadeghi, M., & Das, S. (2025). E-scooter crash severity in the United Kingdom: A comparative analysis using machine learning techniques and random parameters logit with heterogeneity in means and variances. IATSS Research, 49, 155–168.

2024

  • Somvanshi, S., Javed, S. A., Islam, M. M., Pandit, D., & Das, S. (2024). A survey on Kolmogorov-Arnold Network. ACM Computing Survey.
  • Khan, M. N., & Das, S. (2024). Advancing Traffic Safety through the Safe System Approach: A Systematic Review. Accident Analysis & Prevention.
  • Liu, J., Das, S., & Khan, M. (2024). Decoding the impacts of contributory factors and addressing social disparities in crash frequency analysis. Accident Analysis & Prevention. https://doi.org/10.1016/j.aap.2023.107375
  • Hossain, M., Zhou, H., Sun, X., Hossain, A., & Das, S. (2024). Crashes involving distracted pedestrians: Identifying risk factors and their relationships to pedestrian severity levels and distraction modes. Accident Analysis & Prevention. https://doi.org/10.1016/j.aap.2023.107359
  • Brauer, M., Roth, G. A., & Das, S. (2024). Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. The Lancet.
  • Das, S., Jafari, M., Hossain, A., Chakraborty, R., & Mimi, M. S. (2024). Toll road crash severity using mixed logit model incorporating heterogeneous mean structures. Transportmetrica A Transport Science.
  • Vollset, S. E., Ababneh, H. S., & Das, S. (2024). Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021. The Lancet.
  • Kutela, B., Novat, N., Kinero, A., Samulel, O., & Das, S. (2024). Understanding user behaviors and safety concerns on shared use paths in Edmonton, Canada. Cities.
  • Naghavi, M., Ong, K. L., & Das, S. (2024). Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021.
  • Sheykhfard, A., Azmoodeh, M., Kutela, B., Das, S., & Fountas, G. (2024). From self-reports to observations: Unraveling digital billboard influence on drivers. Transportation Research Part F: Psychology and Behaviour.
  • Wei, Z., Das, S., Wu., Y., Li, Z., & Zhang, Y. (2024). Modeling the lagged impacts of hourly weather and speed variation factors on the segment crash risk of rural interstate freeways: Applying a space–time-stratified case-crossover design. Accident Analysis & Prevention.
  • Kutela, B., Salum, J. H., Seif, S. R., Das, S., & Kidando, E. (2024). Navigating the blame game: Investigating automated vehicle fault in collisions under mixed traffic conditions. Robotics and Autonomous Systems.
  • Das, S., Barua, S., & Hossain, A. (2024). Unraveling the complex relationship between weather conditions and traffic safety. Journal of Transportation Safety & Security.
  • Das, S., Dzinyela, R., Liu, J., Dadashova, B., & Silvestri-Dobrovolny, C. (2024). Understanding patterns of factor influences in motorcycle crashes with fixed objects. Journal of Transportation Safety & Security.
  • Chakraborty, R., Javed, S. A., Das, S., Kutela, B., & Khan, M. N. (2024). Impact of level 2 automation on driver behavior: A study using association rules mining. Transportation Research Part F: Traffic Psychology and Behaviour.
  • Kutela, B., Shita, H., Das, S., Kapaya, L., & Tarimo, E. (2024). Exploring the Role of Sponsoring Agencies in Shaping the MUTCD Using Supervised and Unsupervised Text Mining. Journal of Transportation Engineering, Part A: Systems.
  • Dzinyela, R., Shirazi, M., Das, S., & Lord, D. (2024). The negative Binomial-Lindley model with Time-Dependent Parameters: Accounting for temporal variations and excess zero observations in crash data. Accident Analysis & Prevention.
  • Somvanshi, S., Das, S., Javed, S. A., Antariksa, G., & Hossain, A. (2024). A Survey on Deep Tabular Learning. arXiv Preprint.
  • Kavianpour, S., Haghighi, F., Sheykhfard, A., Das, S., Fountas, G., & Oshanreh, M. M. (2024). Assessing the risk of pedestrian crossing behavior on suburban roads using structural equation model. Journal of Traffic and Transportation Engineering.
  • Sheykhfard, A., Haghighi, F., Saeidi, S., SafariTaherkhani, M., & Das, S. (2024). Understanding the influence of environmental factors on driver speed: A structural equation modeling analysis.
  • Kutela, B., Novat, N., Novat, N., Kalambay, P., & Das, S. (2024). Exploring Diversity of Activities on Shared-Use Paths: Factors and Implications for Planning and Design. Journal of Transportation Engineering, Part A: Systems.
  • Dzinyela, R., Adanu, E. K., Gupta, H., Koirala, P., Alnawmasi, N., Das, S., & Lord, D. (2024). Analyzing fatal crash patterns of recidivist drivers across genders and age Groups: A hazard-based duration approach. Accident Analysis & Prevention.
  • Kutela, B., Menon, N., Herman, J., Ruseruka, C., & Das, S. (2024). A regression-content analysis approach to assess public satisfaction with shared mobility measures against COVID-19 pandemic.
  • Liu, J., Zhan, F. B., Khan, M. N., & Das, S. (2024). Spatial analysis of geographical disparities in pedestrian safety. Transport Policy.
  • Hossain, A., Sun, X., Islam, S., Rahman, A., & Das, S. (2024). Single-vehicle roadway departure crashes at rural two-lane highway curved segments: a diagnosis using pattern recognition. International Journal of Transportation Science and Technology.
  • Dzinyela, R., Jafari, M., Das, S., Shimu, T. H., Alnawmasi, N., & Lord, D. (2024). Unconstrained and partially constrained temporal modelling of pedestrian injury severities. Transportmetrica A: Transport Science.
  • Das, S. (2024). Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020. Eye.
  • Chakraborty, R., Das, S., & Khan, M. N. (2024). Uncovering pedestrian midblock crash severity patterns using association rules mining. Transportmetrica A: Transport Science.
  • Hossain, A., Sun, X., Das, S., Jafari, M., & Codjoe, J. (2024). Investigating older driver crashes on high-speed roadway segments: a hybrid approach with extreme gradient boosting and random parameter model. Transportmetrica A: Transport Science.
  • Kong, X., Zhang, Y., Chen, X., Das, S., & Sheykhfard, A. (2024). Case Study on the Relationship Between Socio-Demographic Characteristics and Work-from-Home Behavior Before, During, and After the COVID-19 Pandemic. Transportation Research Record. https://doi.org/10.1177/03611981231172946
  • Oliaee, A. H., Das, S., & Le, M. (2024). Automating Pedestrian Crash Typology using Transformer Models. Transportation Research Record.
  • Ye, X., Newman, G., Zhai, W., Retchless, D., Das, S., Ham, Y., … Huang, X. (n.d.). Towards coastal infrastructure resiliency: an AI-enabled decision-support framework for multi-scale comprehension and stakeholder empowerment. Transactions of the American Philosophical Society.
  • Steinmetz, J. D., Seeher, K. M., & Das, S. (2024). Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. The Lancet Neurology. https://doi.org/10.1016/S1474-4422(24)00038-3
  • Wei, Z., Das, S., Wu, Y., Li, Z., & Zhang, Y. (2024). Modelling the Lagged Impacts of Hourly Weather and Speed Variation Factors on the Segment Crash Risk of Rural Interstate Freeways: Applying a Space-Time-Stratified Case-Crossover Design. Accident Analysis and Prevention. https://doi.org/10.2139/ssrn.4379772
  • Faroughi, S. A., Pawar, N. M., Fernandes, C., Raissi, M., Das, S., Kalantari, N. K., & Mahjour, S. K. (2024). Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing for Fluid and Solid Mechanics. Journal of Computing and Information Science in Engineering.
  • Ahmed Hossain, Xiaoduan Sun, Das, S., Monire Jafari, & M Ashifur Rahman. (2024). Investigating pedestrian-vehicle crashes on interstate highways: applying random parameter binary logit model with heterogeneity in means. Accident Analysis & Prevention. https://doi.org/https://doi.org/10.1016/j.aap.2024.107503
  • Kutela, B., Das, S., Shita, H., & Novat, N. (2024). Understanding Socio-Demographic Factors associated with Shared-Use-Paths (SUPs) Utilization. Journal of Cycling and Micromobility Research.
  • Khan, M. N., Das, S., & Liu, J. (2024). Predicting Pedestrian-Involved Crash Severity Using Inception-v3 Deep Learning Model  . Accident Analysis & Prevention.

2023

  • Khodadadi, A., Tsapakis, I., Shirazi, M., Das, S., & Lord, D. (2023). Derivation of the Empirical Bayesian method for the Negative Binomial-Lindley generalized linear model with application in traffic safety. Accident Analysis & Prevention, 170. https://doi.org/https://doi.org/10.1016/j.aap.2022.106638
  • Wei, Z., Das, S., Wu, Y., Li, Z., & Zhang, Y. (2023). Investigating the exposure-lag-response association of hourly weather and speed variation factors on rural freeway crash risk: A space-time-stratified case-crossover study. Accident Analysis & Prevention.
  • Al-Gharabi, A., Zubaidi, H., & Das, S. (2023). A scientometric analysis and bibliometric review of driver injury severity crash studies. Al-Qadisiyah Journal for Engineering Sciences, 16(1).
  • Das, S., & Zubaidi, H. (2023). City transit rider tweets: Understanding sentiments and politeness. Journal of Urban Technology, 30(1), 111–126.
  • Dobrovolny, C., Dadashova, B., Tabesh, M., Das, S., Kwon, H., Bligh, R., … Hallmark, S. (2023). Addressing Encroachment-Related Safety Issues in Work Zones: A Guide. Washington DC: NCHRP.
  • Das, S., Warner, J., Lavrenz, S., & Khanal, B. (2023). Safety Enhancements at Short-Storage-Space Railroad Crossings.
  • Dobrovolny, C., Dadashova, B., Tabesh, M., Das, S., Kwon, H., Bligh, R., … Hallmark, S. (2023). Determination of Work Zone Encroachments. NCHRP Web-Only Report.
  • J., M., Storey, B., Das, S., Habermann, J., & Bullard, L. (2023). Long-Term Vegetation Management Strategies for Roadsides and Roadside Appurtenances (350th ed.).
  • Das, S., Tsapakis, I., Torbic, D., Li, S., & Wang, Y. (2023). Developing AI-driven Safe Navigation Tool. SafeD UTC Report.
  • Moberg, M., Hamilton, E., & Das, S. (2023). Global, regional, and national mortality due to unintentional carbon monoxide poisoning. The Lancet Public Health.
  • Tamakloe, R., Adanu, E., Atandzi, J., Das, S., Lord, D., & Park, D. (2023). Stability of factors influencing walking-along-the-road pedestrian injury severity outcomes under different lighting conditions: A random parameters logit approach with heterogeneity in means and out-of-sample predictions. Accident Analysis & Prevention.
  • Sheykhfard, A., Haghighi, F., Kavianpour, S., Das, S., Farahani, P., & Fountas, G. (2023). Risk assessment of pedestrian red-light violation behavior using surrogate safety measures: Influence of human, road, vehicle, and environmental factors. IATSS Research. https://doi.org/https://doi.org/10.1016/j.iatssr.2023.11.003
  • Islam, S., Uddin, R., & Das, S. (2023). The burden of diseases and risk factors in Bangladesh, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet Global Health. https://doi.org/https://doi.org/10.1016/S2214-109X(23)00432-1
  • Ye, X., Li, S., Das, S., & Du, J. (2023). Enhancing routes selection with real-time weather data integration in spatial decision support systems. Spatial Information Research. https://doi.org/https://doi.org/10.1007/s41324-023-00564-8
  • Hossain, A., Sun, X., Alam, S., Das, S., & Sheykhfard, A. (2023). Crash Contributing Factors and Patterns Associated with Fatal Truck-Involved Crashes in Bangladesh: Findings from the Text Mining Approach. Transportation Research Record. https://doi.org/https://doi.org/10.1177/03611981231209031
  • Cullen, P., Cullen, A. E., Francis, K. L., & Das, S. (2023). Interpersonal violence and gender inequality in adolescents: a systematic analysis of Global Burden of Disease data from 1990 to 2019. Journal of Adolescent Health. https://doi.org/https://doi.org/10.1016/j.jadohealth.2023.08.044
  • Kutela, B., Das, S., & Sener, I. (2023). Exploring the Shared Use Pathway: A Review of the Design and Demand Estimation Approaches. Urban, Planning and Transport Research, 11(1). https://doi.org/10.1080/21650020.2023.2233597
  • Sheykhfard, A., Haghighi, F., Das, S., & Fountas, G. (2023). Evasive actions to prevent pedestrian collisions in varying space/time contexts in diverse urban and non-urban areas. Accident Analysis & Prevention. https://doi.org/https://doi.org/10.1016/j.aap.2023.107270
  • Das, S., Kutela, B., & Menon, N. (2023). Unlocking the narrative: using text mining to reveal the hidden factors behind suicide related traffic crashes. Archives of Suicide Research. https://doi.org/10.1080/13811118.2023.2247026
  • Das, S., Dutta, A., Tamakloe, R., & Khan, M. (2023). Analyzing the time-varying patterns of contributing factors in work zone-related crashes. Journal of Transportation Safety & Security, 1–28. https://doi.org/https://doi.org/10.1080/19439962.2023.2246020
  • Sheykhfard, A., Haghighi, F., Papadimitriou, E., Das, S., & Gelder, P. (2023). Exploring the Influence of Signal Countdown Timers on Driver Behavior: An Analysis of Pedestrian–Vehicle Conflicts at Signalized Intersections. Transportation Research Record. https://doi.org/10.1177/03611981231186987
  • Das, S., Kong, X., Wei, Z., Xiao, X., Mills, D., & Hossain, A. (2023). Probing into Driver Speeding Patterns and Their Influence on Child Occupancy in Urban Areas. Transportation Research Record. https://doi.org/https://doi.org/10.1177/03611981231188
  • Das, S., Hossain, A., Rahman, M., Sheykhfard, A., & Kutela, B. (2023). Case Study on the Traffic Collision Patterns of E-Scooter Riders. Transportation Research Record. https://doi.org/https://doi.org/10.1177/03611981231185770
  • Tamakloe, R., Das, S., Adanu, E. K., & Park, D. (2023). Key factors affecting motorcycle-barrier crash severity: an innovative cluster-regression technique. Transportmetrica A: Transport Science, 1–25. https://doi.org/10.1080/23249935.2023.2230310
  • Hossain, M., Zhou, H., & Das, S. (2023). Data mining approach to explore emergency vehicle crash patterns: A comparative study of crash severity in emergency and non-emergency response modes. Accident Analysis & Prevention. https://doi.org/https://doi.org/10.1016/j.aap.2023.107217
  • Kutela, B., Msechu, K., Kidando, E., Das, S., & Kitali, A. (2023). Eliciting the influence of roadway and traffic conditions on hurricane evacuation decisions using regression-content analysis approach. Travel Behaviour and Society. https://doi.org/https://doi.org/10.1016/j.tbs.2023.100623
  • Das, S., Sheykhfard, A., Liu, J., & Khan, M. (2023). Understanding non-motorists’ views on automated vehicle safety through Bayesian network analysis and latent dirichlet allocation. International Journal of Transportation Science and Technology. https://doi.org/10.1016/j.ijtst.2023.06.002
  • Das, S., Khodadadi, A., & Liu, J. (2023). Short-Duration Crash Modeling to Understand the Impact of Operating Speed on Freeway Crashes During COVID-19. Transportation Research Record. https://doi.org/10.1177/03611981231169283
  • Sheykhfard, A., Haghighi, F., & Das, S. (2023). How does talking with passengers threatens pedestrian life? An analysis of drivers’ performance based on real-world driving data. Transportation Research Part F: Traffic Psychology and Behaviour, 95, 464–479. https://doi.org/10.1016/j.trf.2023.05.010
  • Das, S., Hossein, A., Vierkant, V., & Liu, J. (2023). Using Bidirectional Encoder Representations from Transformers (BERT) to Classify Traffic Crash Severity Types. Natural Language Processing, 3. https://doi.org/10.1016/j.nlp.2023.100007
  • Rahman, M., Das, S., Codjoe, J., Mitran, E., Sun, X., Abedi, K., & Hossain, M. (2023). Applying Data Mining Methods to Explore Animal-Vehicle Crashes. Transportation Research Record. https://doi.org/10.1177/03611981231166688
  • Das, S., Vierkant, V., Gonzalez, J., Kutela, B., & Sheykfard, A. (2023). Bayesian Network for Motorcycle Crash Severity Analysis. Transportation Research Record. https://doi.org/10.1177/03611981231164386
  • Hasan, A., Jalayer, M., Das, S., & Kabir, M. (2023). Application of Machine Learning Models and SHAP to Examine Crashes Involving Young Drivers in New Jersey. International Journal of Transportation Science and Technology. https://doi.org/10.1016/j.ijtst.2023.04.005
  • Das, S., Khodadadi, A., & Liu, J. (2023). Scientometric and Bibliographic Analysis of Pedestrian Safety Research. Transportation Research Record. https://doi.org/https://doi.org/10.1177/03611981231167
  • Hossain, A., Sun, X., Thapa, R., Hossain, M., & Das, S. (2023). Exploring association of contributing factors to pedestrian fatal and severe injury crashes under dark-no-streetlight condition. IATSS Research. https://doi.org/https://doi.org/10.1016/j.iatssr.2023.03.002
  • Mahdi, A., Zubaidi, H., & Das, S. (2023). A scientometric analysis and bibliometric review of driver injury severity crashes studies. Al-Qadisiyah Journal for Engineering Sciences. https://doi.org/10.30772/qjes.v16i1.870
  • Das, S., Weng, Y., & Paal, S. (2023). Applying Few-Shot Learning in Classifying Pedestrian Crash Typing. Transportation Research Record. https://doi.org/10.1177/03611981231157393
  • Das, S., Hossain, A., Le, M., Pratt, M., & Wu, J. (2023). Classifying Pedestrian Maneuver Types Using the Advanced Language Model. Transportation Research Record. https://doi.org/10.1177/03611981231155187
  • Rahman, M., Dey, K., Pyrialakou, V., & Das, S. (2023). Factors influencing safety perceptions of sharing roadways with autonomous vehicles among vulnerable roadway users. Journal of Safety Research. https://doi.org/10.1016/j.jsr.2023.02.010
  • Sheykhfard, A., Haghighi, F., Fountas, G., Das, S., & Khanpour, A. (2023). How do driving behavior and attitudes toward road safety vary between developed and developing countries? Evidence from Iran and the Netherlands. Journal of Safety Research. https://doi.org/10.1016/j.jsr.2023.02.005
  • Das, S., Tamakloe, R., Zubaidi, H., Obaid, I., & Rahman, M. (2023). Bicyclist injury severity classification using a random parameter logit model. International Journal of Transportation Science and Technology. https://doi.org/https://doi.org/10.1016/j.ijtst.2023.02.001
  • Dey, K., Rahman, M., Das, S., & Williams, A. (2023). Left turn phasing selection considering vehicle to vehicle and vehicle to pedestrian conflicts. Journal of Traffic and Transportation Engineering. https://doi.org/10.1016/j.jtte.2021.07.006
  • Hossain, A., Sun, X., Alam, S., & Das, S. (2023). Crash Contributing Factors and Patterns Associated with Fatal Truck-involved Crashes in Bangladesh: Findings from Text Mining Approach. Transportation Research Record. https://doi.org/10.6084/m9.figshare.21937229.v1
  • Das, S., Rahman, M., Kabir, N., Oviedo-Trespalacios, O., Dey, K., & Hossain, M. (2023). Do people act differently while using ridesharing services with children? Transportation Research Part A: Policy and Practice, 171. https://doi.org/https://doi.org/10.1016/j.trf.2023.05.010

2022

  • Faroughi, S. A., Pawar, N. M., Fernandes, C., Das, S., Kalantari, N., & Mahjour, K. (2022). Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing. Arxiv. https://doi.org/https://doi.org/10.48550/arXiv.2211.07377
  • Dixon, K., Park, E. S., Brewer, M., Wu, L., Geedipally, S., Srinivasan, R., … Rista, E. (2022). NCHRP Report 995: Guidelines for Treatments to Mitigate Opposite Direction Crashes. Transportation Research Board. https://doi.org/10.17226/26586
  • Das, S., Le, M., Fitzpatrick, K., & Wu, D. (2022). Did Operating Speeds During COVID-19 Result in More Fatal and Injury Crashes on Urban Freeways? Transportation Research Record: Journal of the Transportation Research Board. https://doi.org/10.1177/03611981221109597
  • Obaid, I., Alnedawi, A., Aboud, G. M., Tamakloe, R., Zubaidi, H., & Das, S. (2022). Factors associated with driver injury severity of motor vehicle crashes on sealed and unsealed pavements: Random parameter model with heterogeneity in means and variances. International Journal of Transportation Science and Technology. https://doi.org/10.1016/j.ijtst.2022.04.002
  • Das, S., & Sarkr, S. (2022). News media mining to explore speed-crash-traffic association during COVID-19. Transportation Research Record: Journal of the Transportation Research Board.
  • Hossain, M. M., Zhao, H., Rahman, M. A., Das, S., & Sun, X. (2022). Cellphone-Distracted Crashes of Novice Teen Drivers: Understanding Associations of Contributing Factors for Crash Severity Levels and Cellphone Usage Types. Traffic Injury Prevention. https://doi.org/https://doi.org/10.1080/15389588.2022.2097667
  • Das, S., Park, E. S., & Sarkar, S. (2022). Impact of operating speed measures on traffic crashes: Annual and daily level models for rural two-lane and rural multilane roadways. Journal of Transportation Safety & Security. https://doi.org/https://doi.org/10.1080/19439962.2022.2098441
  • Hasan, A. S., Kabir, M. A. B., Jalayer, M., & Das, S. (2022). Severity modeling of work zone crashes in New Jersey using machine learning models. Journal of Transportation Safety & Security. https://doi.org/https://doi.org/10.1080/19439962.2022.2098442
  • Das, S., Le, M., Hossain, A., Wu, D., & Pratt, M. (2022). Classifying Pedestrian Maneuver Types using Advanced Language Model. Transportation Research Record: Journal of the Transportation Research Board.
  • Kong, X., Das, S., Zhang, Y., Wu, L., & Wallis, J. (2022). In-Depth Understanding of Pedestrian–Vehicle Near-Crash Events at Signalized Intersections: An Interpretable Machine Learning Approach. Transportation Research Record: Journal of the Transportation Research Board. https://doi.org/https://doi.org/10.1177/03611981221136138
  • Das, S., Hossain, M., Rahman, A., Kong, X., Sun, X., & Mamun, G. (2022). Understanding Patterns of Moped And Seated Motor Scooter (50 Cc Or Less) Involved Fatal Crashes Using Cluster Correspondence Analysis. Transportmetrica A: Transport Science.
  • Hossain, M., Zhou, H., Das, S., Sun, X., & Hossain, A. (2022). Young Drivers and Cellphone Distraction: Pattern Recognition from Fatal Crashes. Journal of Transportation Safety & Security.
  • Das, S., & Trisha, N. (2022). Understanding social media usage patterns by transit agencies.
  • Das, S., Tamakloe, R., Zubaidi, H., & Vierkant, V. (2022). Virtual Public Engagement and Communications in a Transportation Conference during COVID-19.
  • Das, S., Dutta, A., Rahman, M., & Sun, X. (2022). Pattern recognition from light delivery vehicle crash characteristics. Journal of Transportation Safety & Security, 14(12), 2055–2073.
  • Wei, Z., Das, S., & Zhang, Y. (2022). Short duration crash prediction for rural two-lane roadways: applying explainable artificial intelligence. Transportation Research Record, 2676(12).
  • Hosseini, P., Khoshsirat, S., Jalayer, M., Das, S., & Zhou, H. (2022). Application of text mining techniques to identify actual wrong-way driving (WWD) crashes in police reports. International Journal of Transportation Science and Technology. https://doi.org/10.1016/j.ijtst.2022.12.002
  • Geedipally, S., Dixon, K., Wu, L., Pratt, M., Avelar, R., Das, S., … Saini, G. (2022). Calibrating the Highway Safety Manual Predictive Methods for Texas Highways.
  • Rahman, M., Das, S., & Sun, X. (2022). Understanding the drowsy driving crash patterns from correspondence regression analysis. Journal of Safety Research. https://doi.org/https://doi.org/10.1016/j.jsr.2022.10.017
  • Das, S., Wei, Z., & Dutta, A. (2022). Rules mining on hybrid electric vehicle consumer complaint database. Journal of Transportation Safety & Security, 1–21. https://doi.org/10.1080/19439962.2022.2147614
  • Kong, X., Das, S., Zhang, Y., Wei, Z., & Yuan, C. (2022). In-Depth Understanding of Pedestrian- Vehicle Near-Crash Events at Signalized Intersections: An Interpretable Machine Learning Approach. International Journal of Transportation Science and Technology. https://doi.org/10.1177/03611981221136138
  • Kong, X., Das, S., Zhang, Y., Wu, L., & Wallis, J. (2022). In-Depth Understanding of Near-Crash Events Through Pattern Recognition. Transportation Research Record: Journal of the Transportation Research Board. https://doi.org/10.1177/03611981221097395
  • Rahman, M., Sun, X., & Das, S. (2022). Understanding the Drowsy Driving Crash Patterns from Corresponding Regression Analysis. Journal of Safety Research.
  • Das, S., Fitzpatrick, K., Wu, L., Wei, Z., Tsapakis, I., Paal, S., … Park, E. (2022). Develop a Real-Time Decision Support Tool for Rural Roadway Safety Improvements. TXDOT.
  • Das, S. (2022). Applying Explainable Machine Learning Techniques in Daily Crash Occurrence and Severity Modeling for Rural Interstates. Transportation Research Record. https://doi.org/10.1177/03611981221134629
  • Rahman, M., Das, S., Sun, X., Sun, M., & Hossain, M. (2022). Using Unsupervised Learning to Investigate Injury-Associated Factors of Animal-Vehicle Crashes. International Journal of Injury Control and Safety Promotion. https://doi.org/10.1080/17457300.2022.2125532
  • Das, S., X, K., & Hossain, M. (2022). Exploration on Prior Driving Modes for Automated Vehicle Collisions. International Journal of Urban Sciences. https://doi.org/10.1080/12265934.2022.2142650
  • Sohrabi, S., Wang, Y., Das, S., & Paal, S. (2022). Safe Route-Finding: A Review of Literature and Future Directions. Accident Analysis and Prevention. https://doi.org/10.1016/j.aap.2022.106816
  • Das, S., Wei, Z., & Dutta, A. (2022). Rules Mining and Narrative Analysis of Consumer Complaints on Hybrid Electric Vehicles.
  • Das, S., Aman, J. J. C., & Rahman, M. A. (2022). Content Analysis on Homelessness Issues at Airports by News Media Mining. Transportation Research Record: Journal of the Transportation Research Board. https://doi.org/10.1177/03611981221108151
  • Haberman, J., Holik, W., Huang, W., Das, S., Rista, E., & Clotch, D. (2022). NCHRP 06-18 Report: Guide for Snow and Ice Control Operations. Washington, D.C.: Transportation Research Board. Retrieved from https://onlinepubs.trb.org/onlinepubs/nchrp/docs/NCHRP_06-18_Final_Report.pdf
  • Das, S., Tamakloe, R., Kutela, B., & Hossain, A. (2022). Pattern Recognition from Injury Severity Types of Frontage Roadway Crashes. Journal of Transportation Safety and Security. https://doi.org/10.1080/19439962.2022.2123581
  • Rahman, S., Das, S., & Sun, X. (2022). Single-Vehicle Run-Off Road Crashes Because of Cellphone Distraction: Finding Patterns with Rule Mining. Transportation Research Record: Journal of the Transportation Research Board. https://doi.org/10.1177/03611981221122781
  • Das, S., Wei, Z., & Ravuri, V. (2022). Safety and Operations of Automated Delivery Vehicles. SafeD.
  • Das, S. (2022). Artificial Intelligence in Highway Safety. Boca Raton, FL, USA: CRC Press. Retrieved from https://www.amazon.com/Artificial-Intelligence-Highway-Safety-Subasish/dp/0367436701/
  • Ashifur Rahman, M., Das, S., & Sun, X. (2022). Using Cluster Correspondence Analysis to Explore Rainy Weather Crashes in Louisiana. Transportation Research Record: Journal of the Transportation Research Board, 2676(8), pp 159--173. https://doi.org/10.1177/03611981221082582
  • Kong, X., Li, Z., Zhang, Y., & Das, S. (2022). Bridge Deck Deterioration: Reasons and Patterns. Transportation Research Record: Journal of the Transportation Research Board, 2676(7), pp 570--584. https://doi.org/10.1177/03611981221080140
  • Tamakloe, R., Sam, E. F., Bencekri, M., Das, S., & Park, D. (2022). Mining groups of factors influencing bus/minibus crash severities on poor pavement condition roads considering different lighting status. Traffic Injury Prevention, 23(5), pp 308--314. https://doi.org/10.1080/15389588.2022.2066658
  • Das, S., Mousavi, S. M., & Shirinzad, M. (2022). Pattern recognition in speeding related motorcycle crashes. Journal of Transportation Safety \& Security, 14(7), pp 1121--1138. https://doi.org/10.1080/19439962.2021.1877228
  • Khodadadi, A., Tsapakis, I., Shirazi, M., Das, S., & Lord, D. (2022). Derivation of the Empirical Bayesian method for the Negative Binomial-Lindley generalized linear model with application in traffic safety. Accident Analysis \& Prevention, 170, 106638. https://doi.org/10.1016/j.aap.2022.106638
  • Fitzpatrick, K., Do, A., Avelar, R., Pratt, M., & Das, S. (2022). Improving Pedestrian Safety at Signalized Intersections: Impacts of Corner Radius. ITE Journal, 92(6), pp 37--43. Retrieved from https://trid.trb.org/view/2006270
  • Kong, X., Zhang, A., Xiao, X., Das, S., & Zhang, Y. (2022). Work from home in the post-COVID world. Case Studies on Transport Policy, 10(2), pp 1118--1131. https://doi.org/10.1016/j.cstp.2022.04.002
  • Das, S., Kong, X. J., Lavrenz, S. M., Wu, L., & Jalayer, M. (2022). Fatal crashes at highway rail grade crossings: A U.S. based study. International Journal of Transportation Science and Technology, 11(1), pp 107--117. https://doi.org/10.1016/j.ijtst.2021.03.002
  • Das, S., Trisha, N., Sener, I., & Walk, M. (2022). TCRP Synthesis 156: Uses of Social Media in Public Transportation. TCRP.
  • Tamakloe, R., Das, S., Aidoo, E. N., & Park, D. (2022). Factors affecting motorcycle crash casualty severity at signalized and non-signalized intersections in Ghana: Insights from a data mining and binary logit regression approach. Accident Analysis \& Prevention, 165, 106517. https://doi.org/10.1016/j.aap.2021.106517
  • Das, S., Sun, X., Dadashova, B., Rahman, M. S., & Sun, M. (2022). Identifying Patterns of Key Factors in Sun Glare-Related Traffic Crashes. Transportation Research Record: Journal of the Transportation Research Board, 2676(2), pp 165--175. https://doi.org/10.1177/03611981211037891
  • Tsapakis, I., Das, S., Anderson, P., Jessberger, S., & Holik, W. (2022). Improving Stratification Procedures and Accuracy of Annual Average Daily Traffic (AADT) Estimates for Non-Federal Aid-System (NFAS) Roads. Transportation Research Record: Journal of the Transportation Research Board, 2676(2), pp 393--406. https://doi.org/10.1177/03611981211043544
  • Kutela, B., Das, S., & Dadashova, B. (2022). Mining patterns of autonomous vehicle crashes involving vulnerable road users to understand the associated factors. Accident Analysis \& Prevention, 165. https://doi.org/10.1016/j.aap.2021.106473
  • Das, S., Tabesh, M., Dadashova, B., & Dobrovolny, C. (2022). Diagnosis of Encroachment-Related Work-Zone Crashes by Applying Pattern Recognition. Transportation Research Record. https://doi.org/https://doi.org/10.1177/03611981231152254
  • Das, S., Sun, X., Goel, S., Sun, M., Rahman, A., & Dutta, A. (2022). Flooding Related Traffic Crashes: Findings from Association Rules. Journal of Transportation Safety \& Security, 14(1), pp 111--129. https://doi.org/10.1080/19439962.2020.1734130
  • Das, S., & Kong, X. (2022). Quantifying Bridge Element Vulnerability over Time. Transportation Research Record: Journal of the Transportation Research Board, 2676(1), pp 460--471. https://doi.org/10.1177/03611981211036380
  • Das, S., & Dutta, A. (2022). Twelve-Year Analysis of Transportation Research Board Annual Meeting’s Official Hashtag. Transportation Research Record: Journal of the Transportation Research Board, 2676(1), pp 763--772. https://doi.org/10.1177/03611981211037232
  • Fitzpatrick, K., Avelar, R., Pratt, M. P., Das, S., Lord, D., Texas A\&M Transportation Institute, & Administration, F. H. (2022). Crash Modification Factor for Corner Radius, Right-Turn Speed, and Prediction of Pedestrian Crashes at Signalized Intersections (p. 136p). Retrieved from https://www.fhwa.dot.gov/publications/research/safety/21105/21105.pdf

2021

  • Fitzpatrick, K., Das, S., Pratt, M. P., Dixon, K., & Gates, T. (2021). Development of a Posted Speed Limit Setting Procedure and Tool. Transportation Research Board. Retrieved from http://www.trb.org/Main/Blurbs/182154.aspx
  • Fitzpatrick, K., Das, S., Pratt, M. P., Dixon, K., & Gates, T. (2021). Posted Speed Limit Setting Procedure and Tool: User Guide. Transportation Research Board. Retrieved from http://www.trb.org/Main/Blurbs/182038.aspx
  • Das, S., Trisha, N. F., Sener, I. N., & Walk, M. (2021). Uses of Social Media in Public Transportation. Transportation Research Board. Retrieved from https://www.trb.org/Main/Blurbs/182636.aspx
  • Hosseini, P., Jalayer, M., Das, S., & Board, T. R. (2021). A Multiple Correspondence Approach to Identify Contributing Factors Related to Work Zone Crashes (p. 19p). Retrieved from https://annualmeeting.mytrb.org/OnlineProgram/Details/15753
  • Das, S., & Board, T. R. (2021). American Generations and Traffic Fatalities: Exploratory Evaluation from Person Level Data (p. 17p). Retrieved from https://annualmeeting.mytrb.org/OnlineProgram/Details/15775
  • Hosseini, P., Jalayer, M., Das, S., Zhou, H., & Board, T. R. (2021). Identifying Wrong-Way Driving (WWD) Crashes in Police Reports Using Text Mining Techniques (p. 16p). Retrieved from https://annualmeeting.mytrb.org/OnlineProgram/Details/15775
  • Das, S., Dutta, A., & Board, T. R. (2021). Light Delivery Vehicles Crashes: Identifying Insights using Joint Dimension Reduction and Clustering (p. 16p). Retrieved from https://annualmeeting.mytrb.org/OnlineProgram/Details/15793
  • Das, S., Theel, M., & Board, T. R. (2021). Pandemic and Transportation Research: Bibliometric Analysis and Topic Modeling (p. 16p). Retrieved from https://annualmeeting.mytrb.org/OnlineProgram/Details/15738
  • Das, S., Lavrenz, S. M., Wu, L., Jalayer, M., Kong, X., & Board, T. R. (2021). Pattern Recognition from Rail Grade Crossing Fatal Crashes (p. 17p). Retrieved from https://annualmeeting.mytrb.org/OnlineProgram/Details/15713
  • Das, S., Wang, R., & Board, T. R. (2021). Racism discussed in Transportation Research: Bibliometric Analysis and Topic Modeling (p. 16p). Retrieved from https://annualmeeting.mytrb.org/OnlineProgram/Details/15738
  • Shipp, E., Das, S., Smith, S., Wu, L., Sun, X., Texas A\&M Transportation Institute, … Administration, F. H. (2021). Louisiana’s Alcohol-Impaired Driving Problem: An Analysis of Crash and Cultural Factors (p. 185p). Retrieved from https://www.ltrc.lsu.edu/pdf/2021/FR_655.pdf
  • Geedipally, S. R., Brewer, M., Wunderlich, R., Pratt, M. P., Wu, L., Das, S., … Administration, F. H. (2021). Examine Trade-Offs Between Center Separation and Shoulder Width Allotment for a Given Roadway Width (p. 146p). Retrieved from https://static.tti.tamu.edu/tti.tamu.edu/documents/0-7035-R1.pdf
  • Fitzpatrick, K., Avelar, R., Pratt, M. P., Das, S., & Lord, D. (2021). Crash Modification Factor for Corner Radius, Right-Turn Speed, and Prediction of Pedestrian Crashes at Signalized Intersections. Federal Highway Administration. Retrieved from https://www.fhwa.dot.gov/publications/research/safety/21106/21106.pdf
  • Das, S., Datta, S., Zubaidi, H. A., & Obaid, I. A. (2021). Applying interpretable machine learning to classify tree and utility pole related crash injury types. IATSS Research, 45(3), pp 310--316. https://doi.org/10.1016/j.iatssr.2021.01.001
  • Sun, M., Sun, X., Rahman, M., Akter, M., & Das, S. (2021). Modeling two-way stop-controlled intersection crashes with zero-inflated models on Louisiana rural two-lane highways. IATSS Research, 45(3), pp 303--309. https://doi.org/10.1016/j.iatssr.2020.12.007
  • Kong, X., Das, S., Zhou, Hongmin “Tracy,” & Zhang, Y. (2021). Patterns of near-crash events in a naturalistic driving dataset: Applying rules mining. Accident Analysis \& Prevention, 161. https://doi.org/10.1016/j.aap.2021.106346
  • Das, S., Tsapakis, I., & Khodadadi, A. (2021). Safety performance functions for low-volume rural minor collector two-lane roadways. IATSS Research, 45(3), pp 347--356. https://doi.org/10.1016/j.iatssr.2021.02.004
  • Das, S. (2021). Understanding Fatal Crash Reporting Patterns in Bangladeshi Online Media using Text Mining. Transportation Research Record: Journal of the Transportation Research Board, 2675(10), pp 960--971. https://doi.org/10.1177/03611981211014200
  • Das, S., Tamakloe, R., Zubaidi, H., Obaid, I., & Alnedawi, A. (2021). Fatal pedestrian crashes at intersections: Trend mining using association rules. Accident Analysis \& Prevention, 160. https://doi.org/10.1016/j.aap.2021.106306
  • Das, S., Sun, X., & Sun, M. (2021). Rule-based safety prediction models for rural two-lane run-off-road crashes. International Journal of Transportation Science and Technology, 10(3), pp 235--244. https://doi.org/10.1016/j.ijtst.2020.08.001
  • Das, S., Dutta, A., & Tsapakis, I. (2021). Topic Models from Crash Narrative Reports of Motorcycle Crash Causation Study. Transportation Research Record: Journal of the Transportation Research Board, 2675(9), pp 449--462. https://doi.org/10.1177/03611981211002523
  • Das, S. (2021). Autonomous vehicle safety: Understanding perceptions of pedestrians and bicyclists. Transportation Research Part F: Traffic Psychology and Behaviour, 81, pp 41--54. https://doi.org/10.1016/j.trf.2021.04.018
  • Fitzpatrick, K., Das, S., Gates, T., Dixon, K. K., & Park, E. S. (2021). Considering Roadway Context in Setting Posted Speed Limits. Transportation Research Record: Journal of the Transportation Research Board, 2675(8), pp 590--602. https://doi.org/10.1177/0361198121999618
  • Kong, X., Das, S., & Zhang, Y. (2021). Mining patterns of near-crash events with and without secondary tasks. Accident Analysis \& Prevention, 157. https://doi.org/10.1016/j.aap.2021.106162
  • Khodadadi, A., Tsapakis, I., Das, S., Lord, D., & Li, Y. (2021). Application of different negative binomial parameterizations to develop safety performance functions for non-federal aid system roads. Accident Analysis \& Prevention, 156. https://doi.org/10.1016/j.aap.2021.106103
  • Das, S., Dutta, A., & Geedipally, S. R. (2021). Applying Bayesian data mining to measure the effect of vehicular defects on crash severity. Journal of Transportation Safety \& Security, 13(6), pp 605--621. https://doi.org/10.1080/19439962.2019.1658674
  • Ashifur Rahman, M., Sun, X., Das, S., & Khanal, S. (2021). Exploring the Influential Factors of Roadway Departure Crashes on Rural Two-Lane Highways with Logit Model and Association Rules Mining. International Journal of Transportation Science and Technology, 10(2), pp 167--183. https://doi.org/10.1016/j.ijtst.2020.12.003
  • Das, S., Wei, Z., Kong, X., & Xiao, X. (2021). Mining crowdsourced data on bicycle safety critical events. Transportation Research Interdisciplinary Perspectives, 10. https://doi.org/10.1016/j.trip.2021.100360
  • Zubaidi, H., Obaid, I., Alnedawi, A., Das, S., & Haque, M. M. (2021). Temporal instability assessment of injury severities of motor vehicle drivers at give-way controlled unsignalized intersections: A random parameters approach with heterogeneity in means and variances. Accident Analysis \& Prevention, 156, 106151. https://doi.org/10.1016/j.aap.2021.106151
  • Das, S. (2021). Exploratory Analysis of Unmanned Aircraft Sightings using Text Mining. Transportation Research Record: Journal of the Transportation Research Board, 2675(5), pp 291--300. https://doi.org/10.1177/0361198120987230
  • Kong, X. J., Das, S., Zhang, Y., & Xiao, X. (2021). Lessons learned from pedestrian-driver communication and yielding patterns. Transportation Research Part F: Traffic Psychology and Behaviour, 79, pp 35--48. https://doi.org/10.1016/j.trf.2021.03.011
  • Das, S., Geedipally, S. R., & Fitzpatrick, K. (2021). Inclusion of speed and weather measures in safety performance functions for rural roadways. IATSS Research, 45(1), pp 60--69. https://doi.org/10.1016/j.iatssr.2020.05.001
  • Rahman, M. T., Dey, K., Das, S., & Sherfinski, M. (2021). Sharing the road with autonomous vehicles: A qualitative analysis of the perceptions of pedestrians and bicyclists. Transportation Research Part F: Traffic Psychology and Behaviour, 78, pp 433--445. https://doi.org/10.1016/j.trf.2021.03.008
  • Kong, X., Das, S., Zhou, H., & Zhang, Y. (2021). Characterizing phone usage while driving: Safety impact from road and operational perspectives using factor analysis. Accident Analysis \& Prevention, 152. https://doi.org/10.1016/j.aap.2021.106012
  • Park, E. S., Fitzpatrick, K., Das, S., & Avelar, R. (2021). Exploration of the relationship among roadway characteristics, operating speed, and crashes for city streets using path analysis. Accident Analysis \& Prevention, 150. https://doi.org/10.1016/j.aap.2020.105896
  • Das, S., Kong, X., & Tsapakis, I. (2021). Hit and run crash analysis using association rules mining. Journal of Transportation Safety \& Security, 13(2), pp 123--142. https://doi.org/10.1080/19439962.2019.1611682
  • Zubaidi, H. A., Obaid, I. A., Alnedawi, A., & Das, S. (2021). Motor vehicle driver injury severity analysis utilizing a random parameter binary probit model considering different types of driving licenses in 4-legs roundabouts in South Australia. Safety Science, 134. https://doi.org/10.1016/j.ssci.2020.105083
  • Jalayer, M., Pour-Rouholamin, M., Patel, D., Das, S., & Parvardeh, H. (2021). A Penalized-Likelihood Approach to Characterizing Bridge-Related Crashes in New Jersey. Traffic Injury Prevention, 22(1), pp 63--67. https://doi.org/10.1080/15389588.2020.1842379
  • Das, S. (2021). Data Dive into Transportation Research Record Articles: Authors, Coauthorships, and Research Trends. TR News, (331), pp 25--31. Retrieved from http://www.trb.org/Publications/Blurbs/182002.aspx
  • Das, S. (2021). Traffic volume prediction on low-volume roadways: a Cubist approach. Transportation Planning and Technology, 44(1), pp 93--110. https://doi.org/10.1080/03081060.2020.1851452

2020

  • Das, S., Dutta, A., & Tsapakis, I. (2020). Automated vehicle collisions in California: Applying Bayesian latent class model. IATSS Research, 44(4), pp 300--308. https://doi.org/10.1016/j.iatssr.2020.03.001
  • Das, S., Ashraf, S., Dutta, A., & Tran, L.-N. (2020). Pedestrians Under Influence (PUI) Crashes: Patterns from Correspondence Regression Analysis. Journal of Safety Research, 75, pp 14--23. https://doi.org/10.1016/j.jsr.2020.07.001
  • Dadashova, B., Griffin, G. P., Das, S., Turner, S., & Sherman, B. (2020). Estimation of Average Annual Daily Bicycle Counts using Crowdsourced Strava Data. Transportation Research Record: Journal of the Transportation Research Board, 2674(11), pp 390--402. https://doi.org/10.1177/0361198120946016
  • Das, S., Dutta, A., & Brewer, M. A. (2020). Case Study of Trend Mining in Transportation Research Record Articles. Transportation Research Record: Journal of the Transportation Research Board, 2674(10), pp 1--14. https://doi.org/10.1177/0361198120936254
  • Das, S., & Dutta, A. (2020). Extremely serious crashes on urban roadway networks: Patterns and trends. IATSS Research, 44(3), pp 248--252. https://doi.org/10.1016/j.iatssr.2020.01.003
  • Das, S., Dutta, A., & Sun, X. (2020). Patterns of Rainy Weather Crashes: Applying Rules Mining. Journal of Transportation Safety \& Security, 12(9), pp 1083--1105. https://doi.org/10.1080/19439962.2019.1572681
  • Rahman, M., Sun, X., & Das, S. (2020). Reconfiguring Urban Undivided Four-Lane Highways to Five-Lane: A Nonideal but Very Effective Solution for Crash Reduction. Journal of Transportation Engineering, Part A: Systems, 146(10), 04020116. https://doi.org/10.1061/JTEPBS.0000422
  • Geedipally, S. R., Das, S., Pratt, M. P., & Lord, D. (2020). Determining Skid Resistance Needs on Horizontal Curves for Different Levels of Precipitation. Transportation Research Record: Journal of the Transportation Research Board, 2674(9), pp 358--370. https://doi.org/10.1177/0361198120929334
  • Das, S., & Geedipally, S. (2020). Rural Speed Safety Project for USDOT Safety Data Initiative: Findings and Outcome. ITE Journal, 90(9), pp 38--42. Retrieved from https://trid.trb.org/view/1742688
  • Das, S., Islam, M., Dutta, A., & Shimu, T. H. (2020). Uncovering Deep Structure of Determinants in Large Truck Fatal Crashes. Transportation Research Record: Journal of the Transportation Research Board, 2674(9), pp 742--754. https://doi.org/10.1177/0361198120931507
  • Kong, X., Das, S., Jha, K., & Zhang, Y. (2020). Understanding Speeding Behavior from Naturalistic Driving Data: Applying Classification Based Association Rule Mining. Accident Analysis \& Prevention, 144. https://doi.org/10.1016/j.aap.2020.105620
  • Das, S. (2020). Identifying key patterns in motorcycle crashes: findings from taxicab correspondence analysis. Transportmetrica A: Transport Science, 17(4), pp 593--614. https://doi.org/10.1080/23249935.2020.1802362
  • Das, S., Storey, B., Shimu, T. H., Mitra, S., Theel, M., & Maraghehpour, B. (2020). Severity analysis of tree and utility pole crashes: Applying fast and frugal heuristics. IATSS Research, 44(2), pp 85--93. https://doi.org/10.1016/j.iatssr.2019.08.001
  • Das, S., Dutta, A., Dey, K., Jalayer, M., & Mudgal, A. (2020). Vehicle involvements in hydroplaning crashes: Applying interpretable machine learning. Transportation Research Interdisciplinary Perspectives, 6, 100176. https://doi.org/10.1016/j.trip.2020.100176
  • Das, S., Le, M., & Dai, B. (2020). Application of machine learning tools in classifying pedestrian crash types: A case study. Transportation Safety and Environment, 2(2), pp 106--119. https://doi.org/10.1093/tse/tdaa010
  • Das, S., & Griffin, G. P. (2020). Investigating the Role of Big Data in Transportation Safety. Transportation Research Record: Journal of the Transportation Research Board, 2674(6), pp 244--252. https://doi.org/10.1177/0361198120918565
  • Avelar, R., Geedipally, S., Das, S., Wu, L., Kutela, B., Lord, D., … Administration, F. H. (2020). Evaluation of Roadside Treatments to Mitigate Roadway Departure Crashes: Technical Report (p. 120p). Retrieved from https://static.tti.tamu.edu/tti.tamu.edu/documents/0-6991-R1.pdf
  • Das, S., & Tsapakis, I. (2020). Interpretable Machine Learning Approach in Estimating Traffic Volume on Low-volume Roadways. International Journal of Transportation Science and Technology, 9(1), pp 76--88. https://doi.org/10.1016/j.ijtst.2019.09.004
  • Das, S., Geedipally, S., Avelar, R., Wu, L., Fitzpatrick, K., Banihashemi, M., … Administration, F. H. (2020). Rural Speed Safety Project for USDOT Safety Data Initiative: [supporting dataset]. Texas A\&M Transportation Institute. Retrieved from https://rosap.ntl.bts.gov/view/dot/48840
  • Turner, S., Martin, M., Griffin, G., Le, M., Das, S., Wang, R., … Technology. (2020). Exploring Crowdsourced Monitoring Data for Safety (p. 45p). Retrieved from https://www.vtti.vt.edu/utc/safe-d/wp-content/uploads/2020/04/TTI-Student-05_Final-Research-Report_Final.pdf
  • Das, S., Geedipally, S., Avelar, R., Wu, L., Fitzpatrick, K., Banihashemi, M., … Administration, F. H. (2020). Rural Speed Safety Project for USDOT Safety Data Initiative (p. 120p). Retrieved from https://rosap.ntl.bts.gov/view/dot/48840
  • Tsapakis, I., Das, S., Khodadadi, A., Lord, D., Morris, J., Li, E., … Technology. (2020). Use of Disruptive Technologies to Support Safety Analysis and Meet New Federal Requirements (p. 37p). Retrieved from https://safed.vtti.vt.edu/wp-content/uploads/2021/04/Final-Version-04-113-Use-of-Disruptive-Technologies-to-Support-Safety-Analysis-and-Meet-New-Federal-Requirements.pdf
  • Das, S., Le, M., Pratt, M. P., & Morgan, C. (2020). Safety effectiveness of truck lane restrictions: a case study on Texas urban corridors. International Journal of Urban Sciences, 24(1), pp 35--49. https://doi.org/10.1080/12265934.2019.1585929

2019

  • Jalayer, M., O’Connell, M., Zhou, H., Szary, P., Das, S., & Board, T. R. (2019). Application of Unmanned Aerial Vehicle to Inspect and Inventory Interchange Assets to Mitigate Wrong-Way Entries (p. 16p). Retrieved from https://trid.trb.org/view/1573022
  • Sun, M., Sun, X., Shan, D., Armstrong, D., Das, S., & Board, T. R. (2019). Louisiana Pedestrian Crash Analysis with Multinomial Logit Model and Bayesian Network (p. 9p). Retrieved from https://trid.trb.org/view/1573239
  • Das, S., Tsapakis, I., & Datta, S. (2019). Safety Performance Functions of Low-Volume Roadways. Transportation Research Record: Journal of the Transportation Research Board, 2673(12), pp 798--810. https://doi.org/10.1177/0361198119853559
  • Das, S., Wang, R., Safety Through Disruption University Transportation Center (Safe-D), Office of the Assistant Secretary for Research, & Technology. (2019). Exploring Crowdsourced Monitoring Data for Safety - Travel Patterns using GPS Waypoint Data (TTI-Student-05) [supporting dataset]. Virginia Tech Transportation Institute Dataverse. Retrieved from https://rosap.ntl.bts.gov/view/dot/50717
  • Das, S. (2019). \#TRBAM: Social Media Interactions from Transportation’s Largest Conference. TR News, (324), pp 18--23. Retrieved from http://www.trb.org/Main/Blurbs/180592.aspx
  • Tsapakis, I., Sharma, S., Dadashova, B., Geedipally, S., Sanchez, A., Le, M., … Administration, F. H. (2019). Evaluation of Highway Safety Improvement Projects and Countermeasures: Technical Report (p. 244p). Retrieved from http://tti.tamu.edu/documents/0-6961-R1.pdf
  • Sun, X., & Das, S. (2019). Estimating Annual Average Daily Traffic for Low-Volume Roadways: A Case Study in Louisiana (p. pp 190--201). Transportation Research Board. Retrieved from http://www.trb.org/Publications/Blurbs/179567.aspx
  • Das, S., Minjares-Kyle, L., Wu, L., & Henk, R. H. (2019). Understanding Crash Potential Associated with Teen Driving: Survey Analysis Using Multivariate Graphical Method. Journal of Safety Research, 70, pp 213--222. https://doi.org/10.1016/j.jsr.2019.07.009
  • Das, S., Bibeka, A., Sun, X., Zhou, Hongmin “Tracy,” & Jalayer, M. (2019). Elderly Pedestrian Fatal Crash-Related Contributing Factors: Applying Empirical Bayes Geometric Mean Method. Transportation Research Record: Journal of the Transportation Research Board, 2673(8), pp 254--263. https://doi.org/10.1177/0361198119841570
  • Das, S., Dutta, A., Lindheimer, T., Jalayer, M., & Elgart, Z. (2019). YouTube as a Source of Information in Understanding Autonomous Vehicle Consumers: Natural Language Processing Study. Transportation Research Record: Journal of the Transportation Research Board, 2673(8), pp 242--253. https://doi.org/10.1177/0361198119842110
  • Jalayer, M., O’Connell, M., Zhou, H., Szary, P., & Das, S. (2019). Application of Unmanned Aerial Vehicles to Inspect and Inventory Interchange Assets to Mitigate Wrong-Way Entries. ITE Journal, 89(7), pp 36--42. Retrieved from https://trid.trb.org/view/1659126
  • Das, S., Dutta, A., Kong, X., & Sun, X. (2019). Hit and Run Crashes: Knowledge Extraction from Bicycle Involved Crashes using First and Frugal Tree. International Journal of Transportation Science and Technology, 8(2), pp 146--160. https://doi.org/10.1016/j.ijtst.2018.11.001
  • Das, S., Jha, K., Fitzpatrick, K., Brewer, M., & Shimu, T. H. (2019). Pattern Identification from Older Bicyclist Fatal Crashes. Transportation Research Record: Journal of the Transportation Research Board, 2673(6), pp 638--649. https://doi.org/10.1177/0361198119841863
  • Trueblood, A. B., Pant, A., Kim, J., Kum, H.-C., Perez, M., Das, S., & Shipp, E. M. (2019). A Semi-Automated Tool for Identifying Agricultural Roadway Crashes in Crash Narratives. Traffic Injury Prevention, 20(4), pp 413--418. https://doi.org/10.1080/15389588.2019.1599873
  • Das, S., Geedipally, S. R., Dixon, K., Sun, X., & Ma, C. (2019). Measuring the Effectiveness of Vehicle Inspection Regulations in Different States of the U.S. Transportation Research Record: Journal of the Transportation Research Board, 2673(5), pp 208--219. https://doi.org/10.1177/0361198119841563
  • Fitzpatrick, K., McCourt, R., & Das, S. (2019). Current Attitudes among Transportation Professionals with Respect to the Setting of Posted Speed Limits. Transportation Research Record: Journal of the Transportation Research Board, 2673(4), pp 778--788. https://doi.org/10.1177/0361198119838504
  • Das, S., Dutta, A., Medina, G., Minjares-Kyle, L., & Elgart, Z. (2019). Extracting patterns from Twitter to promote biking. IATSS Research, 43(1), pp 51--59. https://doi.org/10.1016/j.iatssr.2018.09.002
  • McCourt, R., Fitzpatrick, K., Koonce, P., & Das, S. (2019). Speed Limits: Leading to Change. ITE Journal, 89(4), pp 38--43. Retrieved from https://trid.trb.org/view/1598110
  • Turner, S., Benz, R., Hudson, J., Griffin, G., Lasley, P., Dadashova, B., … Administration, F. H. (2019). Improving the Amount and Availability of Pedestrian and Bicyclist Count Data in Texas (p. 100p). Retrieved from http://tti.tamu.edu/documents/0-6927-R1.pdf
  • Das, S., Dutta, A., Avelar, R., Dixon, K. K., Sun, X., & Jalayer, M. (2019). Supervised association rules mining on pedestrian crashes in urban areas: identifying patterns for appropriate countermeasures. International Journal of Urban Sciences, 23(1), pp 30--48. https://doi.org/10.1080/12265934.2018.1431146
  • Fitzpatrick, K., Das, S., Texas A\&M Transportation Institute, Safety through Disruption University Transportation Center (Safe-D), Office of the Assistant Secretary for Research, & Technology. (2019). Vehicle Operating Speed on Urban Arterial Roadways (p. 31p). Retrieved from http://hdl.handle.net/10919/89097

2018

  • Das, S., Minjares-Kyle, L., Dixon, K., Palanisamy, A., & Dutta, A. (2018). \#TRBAM: Exploring Knowledge Management, Research Trends, and Networks by Social Media Mining (p. 20p). Retrieved from https://trid.trb.org/view/1495087
  • Minjares-Kyle, L., Das, S., Medina, G., & Henk, R. H. (2018). Knowledge about Crash Risk Factors and Self-Reported Driving Behavior: Exploratory Analysis on Multi-State Teen Driver Survey (p. 19p). Retrieved from https://trid.trb.org/view/1495088
  • Bibeka, A., Das, S., Martin, M. W., Jalayer, M., & Munira, S. (2018). Macro-Level Analysis of Association between Non-motorized Trips, Socio-Economic Characteristics, and Crime (p. 16p). Retrieved from https://trid.trb.org/view/1494623
  • Rahman, M., Sun, X., & Das, S. (2018). Safety Performance Evaluation of Urban Undivided Four-Lane to Five-Lane Conversion in Louisiana (p. 17p). Retrieved from https://trid.trb.org/view/1497216
  • Das, S., Le, M., Pratt, M. P., & Morgan, C. (2018). Safety Performance of Truck Lane Restrictions in Texas: Empirical Bayes Observational Before–After Analysis (p. 17p). Retrieved from https://trid.trb.org/view/1495903
  • Das, S., Medina, G., Minjares-Kyle, L., & Elgart, Z. (2018). Social Media Hashtags Associated with Bike Commuting: Applying Natural Language Processing Tools (p. 18p). Retrieved from https://trid.trb.org/view/1495848
  • Das, S., Mudgal, A., Dutta, A., & Geedipally, S. R. (2018). Vehicle Consumer Complaint Reports Involving Severe Incidents: Mining Large Contingency Tables. Transportation Research Record: Journal of the Transportation Research Board, 2672(32), pp 72--82. https://doi.org/10.1177/0361198118788464
  • Das, S., Dutta, A., Dixon, K., Minjares-Kyle, L., & Gillette, G. (2018). Using Deep Learning in Severity Analysis of At-Fault Motorcycle Rider Crashes. Transportation Research Record: Journal of the Transportation Research Board, 2672(34), pp 122--134. https://doi.org/10.1177/0361198118797212
  • Dadashova, B., Griffin, G., Das, S., Turner, S., Graham, M., Texas A\&M Transportation Institute, … Administration, F. H. (2018). Guide for Seasonal Adjustment and Crowdsourced Data Scaling (p. 78p). Retrieved from https://static.tti.tamu.edu/tti.tamu.edu/documents/0-6927-P6.pdf
  • Pratt, M. P., Geedipally, S. R., Wilson, B., Das, S., Brewer, M., Lord, D., … Administration, F. H. (2018). Pavement Safety-Based Guidelines for Horizontal Curve Safety (p. 176p). Retrieved from https://static.tti.tamu.edu/tti.tamu.edu/documents/0-6932-R1.pdf
  • Fitzpatrick, K., Das, S., Texas A\&M Transportation Institute, Safety through Disruption University Transportation Center (Safe-D), Office of the Assistant Secretary for Research, & Technology. (2018). Vehicle Operating Speed on Urban Arterial Roadways (TTI-01-04) [supporting dataset]. Virginia Tech Transportation Institute Dataverse. Retrieved from http://hdl.handle.net/10919/89097
  • Fitzpatrick, K., Das, S., Texas A\&M Transportation Institute, Safety through Disruption University Transportation Center (Safe-D), Office of the Assistant Secretary for Research, & Technology. (2018). Vehicle Operating Speed on Urban Arterial Roadways (TTI-01-04) [supporting dataset]. Virginia Tech Transportation Institute Dataverse. Retrieved from http://hdl.handle.net/10919/89097
  • Das, S., Brimley, B. K., Lindheimer, T. E., & Zupancich, M. (2018). Association of reduced visibility with crash outcomes. IATSS Research, 42(3), pp 143--151. https://doi.org/10.1016/j.iatssr.2017.10.003
  • Das, S., Sun, X., Dixon, K., & Rahman, M. (2018). Safety effectiveness of roadway conversion with a two way left turn lane. Journal of Traffic and Transportation Engineering (English Edition), 5(4), pp 309--317. https://doi.org/10.1016/j.jtte.2017.11.002
  • Turner, S., Sener, I., Martin, M., White, L. D., Das, S., Hampshire, R., … Associates, Inc. (2018). Guide for Scalable Risk Assessment Methods for Pedestrians and Bicyclists (p. 122p). Retrieved from https://safety.fhwa.dot.gov/ped_bike/tools_solve/fhwasa18032/fhwasa18032.pdf
  • Das, S., Dutta, A., Jalayer, M., Bibeka, A., & Wu, L. (2018). Factors Influencing the Patterns of Wrong-Way Driving Crashes on Freeway Exit Ramps and Median Crossovers: Exploration using ‘Eclat’ Association Rules to Promote Safety. International Journal of Transportation Science and Technology, 7(2), pp 114--123. https://doi.org/10.1016/j.ijtst.2018.02.001
  • Das, S., Avelar, R., Dixon, K. K., & Sun, X. (2018). Investigation on the wrong way driving crash patterns using multiple correspondence analysis. Accident Analysis \& Prevention, 111, pp 43--55. https://doi.org/10.1016/j.aap.2017.11.016

2017

  • Das, S., Minjares-Kyle, L., Avelar, R. E., Dixon, K. K., Bommanayakanahalli, B., & Board, T. R. (2017). Improper Passing Related Crashes on Rural Roadways: Using Association Rules Negative Binomial Miner (p. 17p). Retrieved from https://trid.trb.org/view/1439264
  • Das, S., Avelar, R. E., Dixon, K. K., Sun, X., & Board, T. R. (2017). Pedestrian Crash Analysis Using Association Rules Mining (p. 19p). Retrieved from https://trid.trb.org/view/1437503
  • Das, S., Brimley, B. K., Lindheimer, T., Zupancich, M., & Board, T. R. (2017). Safety Impacts of Reduced Visibility in Inclement Weather (p. 17p). Retrieved from https://trid.trb.org/view/1438547
  • Das, S., Sun, X., Dutta, A., Zupancich, M., & Board, T. R. (2017). Twitter in Circulating Transportation Information: A Case Study on Two Cities (p. 16p). Retrieved from https://trid.trb.org/view/1438524
  • Das, S., Dixon, K. K., Avelar, R. E., Fitzpatrick, K., & Board, T. R. (2017). Using Machine Learning Techniques to Estimate Non-motorized Trips for Rural Roadways (p. 16p). Retrieved from https://trid.trb.org/view/1438537
  • Das, S., Dixon, K. K., Sun, X., Dutta, A., & Zupancich, M. (2017). Trends in Transportation Research: Exploring Content Analysis in Topics. Transportation Research Record: Journal of the Transportation Research Board, (2614), pp 27--38. https://doi.org/10.3141/2614-04
  • Dixon, K. K., Fitzpatrick, K., Avelar, R., Das, S., Texas A\&M Transportation Institute, Transportation, T. D. of, & Administration, F. H. (2017). Analysis of the Shoulder Widening Need on the State Highway System (p. 168p). Retrieved from https://static.tti.tamu.edu/tti.tamu.edu/documents/0-6840-1.pdf
  • Das, S., Brimley, B. K., Lindheimer, T., Pant, A., Texas A\&M Transportation Institute, Center for Advancing Transportation Leadership, … Technology. (2017). Safety Impacts of Reduced Visibility in Inclement Weather (p. 61p). Retrieved from http://www.atlas-center.org/wp-content/uploads/2017/04/SafetyImpacts_VisibilityWeather_FinalReport.pdf
  • Turner, S., Sener, I., Martin, M., Das, S., Shipp, E., Hampshire, R., … Administration, F. H. (2017). Synthesis of Methods for Estimating Pedestrian and Bicyclist Exposure to Risk at Areawide Levels and on Specific Transportation Facilities (p. 93p). Retrieved from https://safety.fhwa.dot.gov/ped_bike/tools_solve/fhwasa17041/fhwasa17014.pdf

2016

  • Sun, X., Das, S., Broussard, N., Road, S. N., & Transport Research Institute (VTI). (2016). Developing crash models with supporting vector machine for urban transportation planning (p. 12p). Retrieved from http://vti.diva-portal.org/smash/get/diva2:926110/FULLTEXT01.pdf
  • Das, S., Sun, X., LeBoeuf, C., & Board, T. R. (2016). Estimating Traffic Volume of Nonstate Roadways with Support Vector Regression (p. 18p). Retrieved from https://trid.trb.org/view/1393168
  • Das, S., Sun, X., & Dutta, A. (2016). Text Mining and Topic Modeling of Compendiums of Papers from Transportation Research Board Annual Meetings. Transportation Research Record: Journal of the Transportation Research Board, (2552), pp 48--56. https://doi.org/10.3141/2552-07
  • Fitzpatrick, K., Das, S., Contreras, A., Texas A\&M Transportation Institute, Center for Advancing Transportation Leadership, Safety (ATLAS Center), … Technology. (2016). Is Age a Factor in Crashes at Channelized Right-Turn Lanes? An Exploration of Potential Relationships (p. 46p). Retrieved from http://www.atlas-center.org/wp-content/uploads/2014/10/ATLAS-2016-14-Final-Research-Report-Fitzpatrick.pdf
  • Das, S., & Sun, X. (2016). Association Knowledge for Fatal Run-off-road Crashes by Multiple Correspondence Analysis. IATSS Research, 39(2), pp 146--155. https://doi.org/10.1016/j.iatssr.2015.07.001

2015

  • Das, S., Sun, X., & Board, T. R. (2015). Zero-Inflated Models for Different Severity Types in Rural Two-Lane Crashes (p. 14p). Retrieved from https://trid.trb.org/view/1337677
  • Das, S., & Sun, X. (2015). Factor Association with Multiple Correspondence Analysis in Vehicle–Pedestrian Crashes. Transportation Research Record: Journal of the Transportation Research Board, (2519), pp 95--103. https://doi.org/10.3141/2519-11
  • Sun, X., Das, S., University of Louisiana, Lafayette, Transportation, L. D. of, Development, & Administration, F. H. (2015). Developing a Method for Estimating AADT on all Louisiana Roads (p. 98p). Retrieved from http://www.ltrc.lsu.edu/pdf/2015/FR_548.pdf
  • Das, S., Sun, X., Wang, F., & LeBoeuf, C. (2015). Estimating Likelihood of Future Crashes for Crash-prone Drivers. Journal of Traffic and Transportation Engineering (English Edition), 2(3), pp 145--157. https://doi.org/10.1016/j.jtte.2015.03.003

2014

  • Das, S., Sun, X., & Board, T. R. (2014). Exploring Clusters of Contributing Factors for Single-Vehicle Fatal Crashes Through Multiple Correspondence Analysis (p. 17p). Retrieved from https://trid.trb.org/view/1288303
  • Das, S., Sun, X., & Board, T. R. (2014). Investigating the Pattern of Traffic Crashes Under Rainy Weather by Association Rules in Data Mining (p. 19p). Retrieved from https://trid.trb.org/view/1287867
  • Sun, X., Das, S., Zhang, Z., Wang, F., & LeBoeuf, C. (2014). Investigating Safety Impact of Edgelines on Narrow, Rural Two-Lane Highways by Empirical Bayes Method. Transportation Research Record: Journal of the Transportation Research Board, (2433), pp 121--128. https://doi.org/10.3141/2433-14
  • Sun, X., Das, S., University of Louisiana, Lafayette, Transportation, L. D. of, Development, & Administration, F. H. (2014). A Comprehensive Study on Pavement Edge Line Implementation (p. 71p). Retrieved from http://www.ltrc.lsu.edu/pdf/2014/FR_508.pdf

2013

  • Sun, X., Das, S., Rasel, S. K., Wang, F., & Board, T. R. (2013). Analyzing Crash-Prone Drivers in Multiple Crashes for Better Safety Educational and Enforcement Strategies (p. 13p). Retrieved from https://trid.trb.org/view/1242293
  • Das, S., Sun, X., Rasel, S., Wang, F., & Board, T. R. (2013). Investigating Safety Impact of Raised Pavement Markers on Freeways in Louisiana (p. 12p). Retrieved from https://trid.trb.org/view/1242721
  • Sun, X., Das, S., Sk, R., Fan, W., Road, S. N., & Transport Research Institute (VTI). (2013). Predicting drivers’ crash risk based on their crash history (p. 14). Retrieved from http://vti.diva-portal.org/smash/get/diva2:759067/FULLTEXT01.pdf
  • Sun, X., Das, S., University of Louisiana, Lafayette, Transportation, L. D. of, Development, Louisiana State University, Baton Rouge, & Administration, F. H. (2013). Developing Louisiana Crash Reduction Factors (p. 72p). Retrieved from http://www.ltrc.lsu.edu/pdf/2013/FR_506.pdf
  • Sun, X., Das, S., Fruge, N., Bertinot, R. L., & Magri, D. (2013). Four-Lane to Five-Lane Urban Roadway Conversions for Safety. Journal of Transportation Safety \& Security, 5(2), pp 106--117. https://doi.org/10.1080/19439962.2012.711439

2012

  • Sun, X., Das, S., Fruge, N. P., Bertinot, R. J., Magri, D., & Board, T. R. (2012). Crash Modification Factor for an Inexpensive yet Very Cost Effective Safety Improvement: Converting Undivided Four-Lane Urban Roadways to Five-Lane Roadways (p. 17p). Retrieved from https://trid.trb.org/view/1128928
  • Sun, X., Das, S., University of Louisiana, Lafayette, Transportation, L. D. of, Development, & Administration, F. H. (2012). Safety Improvement from Edge Lines on Rural Two-Lane Highways (p. 84p). Retrieved from http://www.ltrc.lsu.edu/pdf/2012/fr_487.pdf