Publications

Peer-Reviewed Journal Publications

1. Khan, M. N., & Das, S. Advancing traffic safety through the safe system approach: A systematic review. Accident Analysis & Prevention, 199, 107518, 2024. https://doi.org/10.1016/j.aap.2024.107518 

2. Khan, M. N., Das, S., & Liu, J. Predicting pedestrian-involved crash severity using inception-v3 deep learning model. Accident Analysis & Prevention, 197, 107457, 2024. https://doi.org/10.1016/j.aap.2024.107457 

3. Liu, J., Das, S., & Khan, M. N. Decoding the impacts of contributory factors and addressing social disparities in crash frequency analysis. Accident Analysis & Prevention, 194, 107375, 2024. https://doi.org/10.1016/j.aap.2023.107375 

4. Khan, M. N., Das, A., & Ahmed, M. M. Prediction of Truck-Involved Crash Severity on a Rural Mountainous Freeway Using Transfer Learning with ResNet-50 Deep Neural Network. Journal of Transportation Engineering, Part A: Systems, 150(2), 04023131, 2024. https://doi.org/10.1061/JTEPBS.TEENG-7304 

5. Khan, M. N., and M. M. Ahmed. Machine and Deep Learning Techniques for Daytime Fog Detection in Real-Time Using In-Vehicle Vision System Utilizing the SHRP2 Naturalistic Driving Study Data. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2677, No. 1, 2023, pp. 995–1011. https://doi.org/10.1177/03611981221103236

6. Das, A., Khan, M. N., M. M. Ahmed. Deep Learning Approach for Detecting Lane Change Maneuvers Using SHRP2 Naturalistic Driving Data. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2677, No. 1, 2023, pp. 907–928. https://doi.org/10.1177%2F03611981221103229

7. Khan, M. N., and M. M. Ahmed. Khan. A novel deep learning approach to predict crash severity in adverse weather on rural mountainous freeway. Journal of Transportation Safety & Security, 15(8), 795-825, 2023. https://doi.org/10.1080/19439962.2022.2129891

8. Das, S., Sheykhfard, A., Liu, J., & Khan, M. N. Understanding non-motorists' views on automated vehicle safety through Bayesian network analysis and latent Dirichlet allocation. International Journal of Transportation Science and Technology, 2023. https://doi.org/10.1016/j.ijtst.2023.06.002 

9. Das, S., Dutta, A., Tamakloe, R., & Khan, M. N. Analyzing the time-varying patterns of contributing factors in work zone-related crashes. Journal of Transportation Safety & Security, 2023. https://doi.org/10.1080/19439962.2023.2246020 

10. Ali, E. M., Khan, M. N., and M. M. Ahmed, Real-Time Snowy Weather Detection Based on Machine Vision and Vehicle Kinematics: A Non-Parametric Data Fusion Analysis Protocol. Journal of Safety Research, Vol. 83, 2022, pp. 163–180. https://doi.org/10.1016/j.jsr.2022.08.013

11. Ahmed, M.M., Khan, M. N., A. Das, and S. Dadvar. Global Lessons Learned from Naturalistic Driving Studies to Advance Traffic Safety and Operation Research: A Systematic Review. Accident Analysis and Prevention, Vol. 167, No. 106568, 2022. https://doi.org/10.1016/j.aap.2022.106568.

12. Das, A., Khan, M. N., M. M. Ahmed, and S. S. Wulff. Cluster Analysis and Multi-level Modeling Approach for Investigating the Impact of Rain on Lane-Changing Behavior Utilizing Naturalistic Driving Data. Journal of Transportation Safety & Security, Vol. 14, No. 12, 2022, pp. 995–1011. https://doi.org/10.1080/19439962.2022.2069896

13. Khan, M. N., and M. M. Ahmed. Weather and Surface Condition Detection based on Road-Side Webcams: Application of Pre-trained Convolutional Neural Network. International Journal of Transportation Science and Technology, Vol. 11, No. 3, 2022, pp.468–483. https://doi.org/10.1016/j.ijtst.2021.06.003.

14. Khan, M. N., A. Das, M. M. Ahmed, and S. S. Wulff. Multilevel Weather Detection Based on Images: A Machine Learning Approach with Histogram of Oriented Gradient and Local Binary Pattern Based Features. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, Vol. 25, No. 5, 2021, pp. 513–532. https://doi.org/10.1080/15472450.2021.1944860

15. Khan, M. N., and M. M. Ahmed. Development of a Novel Convolutional Neural Network Architecture Named RoadweatherNet for Trajectory-Level Weather Detection using SHRP2 Naturalistic Driving Data. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2675, No. 9, 2021, pp. 1016–1030. https://doi.org/10.1177/03611981211005470

16. Gaweesh, S. M., Khan, M. N., and M. M. Ahmed. Development of a Novel Framework for Hazardous Materials Placard Recognition System to Conduct Commodity Flow Studies Using Artificial Intelligence AlexNet Convolutional Neural Network. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2675, No. 11, 2021, pp. 1357–1371. https://doi.org/10.1177/03611981211026653

17. Khan, M. N., and M. M. Ahmed. Trajectory-Level Fog Detection Based on in-Vehicle Video Camera with TensorFlow Deep Learning Utilizing SHRP2 Naturalistic Driving Data. Accident Analysis and Prevention, Vol. 142, No. 105521, 2020. https://doi.org/10.1016/j.aap.2020.105521

18.  Khan, M. N., A. Das, and M. M. Ahmed. Non-Parametric Association Rules Mining and Parametric Ordinal Logistic Regression for an In-Depth Investigation of Driver Speed Selection Behavior in Adverse Weather Using SHRP2 Naturalistic Driving Study Data. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2674, No. 11, 2020, pp. 101–119. https://doi.org/10.1177/0361198120941509

19. Das, A., Khan, M. N., and M. M. Ahmed. Detecting Lane Change Maneuvers Using SHRP2 Naturalistic Driving Data: A Comparative Study Machine Learning Techniques. Accident Analysis and Prevention, Vol. 142, No. 105578, 2020. https://doi.org/10.1016/j.aap.2020.105578

20. Das, A., Khan, M. N., and M. M. Ahmed. Nonparametric Multivariate Adaptive Regression Splines Models for Investigating Lane-Changing Gap Acceptance Behavior Utilizing Strategic Highway Research Program 2 Naturalistic Driving Data. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2674, No. 5, 2020, pp. 223–238. https://doi.org/10.1177/0361198120914293

21. Khan, M. N., and M. M. Ahmed. Snow Detection Using In-Vehicle Video Camera with Texture-Based Image Features Utilizing K-Nearest Neighbor, Support Vector Machine, and Random Forest. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2673, No. 8, 2019, pp. 221–232. https://doi.org/10.1177/0361198119842105

22. Khan, M. N., A. Ghasemzadeh, and M. M. Ahmed. Investigating the Impact of Fog on Freeway Speed Selection Using the SHRP2 Naturalistic Driving Study Data. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2672, No. 16, 2018, pp. 93–104. https://doi.org/10.1177/0361198118774748.

Peer-Reviewed Conference Proceedings

1. Khan, M. N., Das, S., & Liu, J. Investigating Seasonal Variability Patterns in Motorcycle Crash Injury Types Using Association Rules Mining. Proceeding of the 103rd  Transportation Research Board Annual Meeting, 2024.

2. Khan, M. N., & Das, S. A Systematic Review on Safe System Approach and its Applications in Highway Safety. Proceeding of the 103rd  Transportation Research Board Annual Meeting, 2024.

3. Khan, M. N., Das, S., & Benton, R., Jalayer M., & Hasan, A. Uncovering Motorcycle Crash Severity Patterns through Association Rules Mining  . Proceeding of the 103rd  Transportation Research Board Annual Meeting, 2024.

4. Khan, M. N., Das, S., & Liu, J. Uncovering Motorcycle Crash Severity Patterns through Association Rules Mining . Proceeding of the 103rd  Transportation Research Board Annual Meeting, 2024.

5. Liu, J., Das, S., & Khan, M. N. Spatial Analysis of Geographical Disparities in Pedestrian Safety . Proceeding of the 103rd  Transportation Research Board Annual Meeting, 2024.

6. Das, S., Liu, J., Khan, M. N., & Mills D. Harnessing Explainable Artificial Intelligence to Navigate Rural Roadway Congestion Measures. Proceeding of the 103rd  Transportation Research Board Annual Meeting, 2024.

7. Jafari, M., Das, S., Tmakloe, R., Khan, M. N., & Hossain, A. Uncovering Individual Heterogeneity in Pedestrian Crash Severity with Mixed Logit Models. Proceeding of the 103rd  Transportation Research Board Annual Meeting, 2024.

8. Khan, M. N., A. Das, and M. M. Ahmed. Prediction of Truck-Involved Crash Severity on Rural Mountainous Freeway Using Transfer Learning with ResNet-50 Deep Neural Network. Proceeding of the 102nd Transportation Research Board Annual Meeting, 2023.

9. Das, A., Khan, M. N., and M. M. Ahmed. Evaluating the Impact of Cooperative Automated Transportation on Work Zone Safety and Operation Using Weather-Sensitive Microsimulation Approach. Proceeding of the 102nd Transportation Research Board Annual Meeting, 2023.

10. Mohamed, A., M. M. Ahmed, and Khan, M. N. Real-Time Detection of Blowing Snow on Rural Mountainous Freeway Using Convolutional Neural Networks. Proceeding of the 102nd Transportation Research Board Annual Meeting, 2023.

11. Khan, M. N., A. Das, and M. M. Ahmed. Development and Assessment of a Weather-Responsive Variable Speed Limit Algorithm for Rural Freeways. ASCE International Conference on Transportation & Development (ICTD 2022), 2022.

12. Khan, M. N., A. Das, and M. M. Ahmed. Image-based Rain Detection with Local Binary Pattern-Based Features Using Machine Learning. ASCE International Conference on Transportation & Development (ICTD 2022), 2022.

13. Das, A., Khan, M. N., and M. M. Ahmed. Estimation of Macroscopic Traffic Parameters Under Adverse Weather Conditions: A Case Study in Wyoming. ASCE International Conference on Transportation & Development (ICTD 2022), 2022.

14. Khan, M. N. and M. M. Ahmed. Prediction of Crash Severity in Adverse Weather on Rural Mountainous Freeway Using ResNet Deep Learning. ASCE International Conference on Transportation & Development (ICTD 2022), 2022.

15. Khan, M. N., and M. M. Ahmed. A Machine Learning Approach for Surface Condition Detection from Webcam Leveraging Color, Texture, and Local Binary Pattern Based Image Features. Proceeding of the 101st Transportation Research Board Annual Meeting, 2022.

16. Khan, M. N., and M. M. Ahmed. Machine and Deep Learning Techniques for Daytime Fog Detection in Real-Time Using In-Vehicle Vision System Utilizing the SHRP2 Naturalistic Driving Study Data. Proceeding of the 101st Transportation Research Board Annual Meeting, 2022.

17. Das, A., Khan, M. N., M. M. Ahmed. A Deep Learning Approach for Detecting Lane Change Maneuvers Using SHRP2 Naturalistic Driving Data. Proceeding of the 101st Transportation Research Board Annual Meeting, 2022.

18. Khan, M. N., and M. M. Ahmed. Weather and Surface Condition Detection Using Road-Side Webcams: Application of Pre-Trained Convolutional Neural Network. Proceeding of the 100th Transportation Research Board Annual Meeting, 2021.

19. Khan, M. N., and M. M. Ahmed. WeatherNet: Development of a Novel Convolutional Neural Network Architecture for Trajectory-Level Weather Detection Using SHRP2 Naturalistic Driving Data. Proceeding of the 100th Transportation Research Board Annual Meeting, 2021.

20. Gaweesh, S. M., Khan, M. N., and M. M. Ahmed. Development of a Novel Framework for Hazardous Materials Placard Recognition System to Conduct Commodity Flow Studies Using Artificial Intelligence AlexNet Convolutional Neural Network. Proceeding of the 100th Transportation Research Board Annual Meeting, 2021.

21. Khan, M. N., A. Das, and M. M. Ahmed. In-Depth Investigation of Driver Speed Selection Behavior in Adverse Weather Using SHRP2 Naturalistic Driving Study Data: Non-Parametric Association Rules Mining and Parametric Ordinal Logistic Regression Approach. Proceeding of the 99th Transportation Research Board Annual Meeting, 2020.

22. Khan, M. N., and M. M. Ahmed. Fog Detection based on Images with Neural Network using TensorFlow Utilizing the SHRP2 Naturalistic Driving Study Data. Accepted for Presentation at the 99th Transportation Research Board Annual Meeting, 2020.

23. Das, A., Khan, M. N., and M. M. Ahmed. Nonparametric Multivariate Adaptive Regression Splines Models for Investigating Lane-Changing Gap Acceptance Behavior Utilizing SHRP2 Naturalistic Driving Data. Proceeding of the 99th Transportation Research Board Annual Meeting, 2020.

24. Das, A., Khan, M. N., M. M. Ahmed, and S. S. Wulff. Evaluating the Impact of Rain on Lane-Changing Behavior Using Naturalistic Driving Data: Cluster Analysis and Multi-Level Modeling Approach. Proceeding of the 99th Transportation Research Board Annual Meeting, 2020.

25. Das, A., Khan, M. N., and M. M. Ahmed. Detection of Lane Change Maneuvers Using the SHRP2 Naturalistic Driving Study Data: A Machine Learning Approach. Transportation Research Record: Proceeding of the 99th Transportation Research Board Annual Meeting, 2020.

26. Ali, E. M., Khan, M. N., and M. M. Ahmed, Real-Time Snowy Weather Detection Based on Machine Vision and Vehicle Kinematics: A Non-Parametric Data Fusion Analysis Protocol. Proceeding of the 99th Transportation Research Board Annual Meeting, 2020.

27. Khan, M. N., and M. M. Ahmed, In-Vehicle Snow Detection System with Grey Level Co-occurrence Matrix and Local Binary Pattern Based Features Using the SHRP2 Naturalistic Driving Study Video Data: A Machine Learning Approach. Proceeding of  the 98th Annual Meeting of the Transportation Research Board, 2019.

28. Khan, M. N., A. Das., A. Ghasemzadeh, and M. M. Ahmed, M. Real-Time Weather Detection System with Local Binary Pattern based Features using Artificial Neural Network and Random Forest: An Unsupervised Learning Approach. Road Safety and Simulation (RSS) Conference, 2019.

29. Das, A., Khan, M. N., Ahmed, M., Ghasemzadeh, A., and S. Gaweesh. Structural Equation Modeling Approach for Investigating Driver Behavior in Adverse Weather Conditions using Trajectory-level SHRP2 Naturalistic Driving Data. Road Safety and Simulation (RSS) Conference, 2019.

30. Khan, M. N., Ghasemzadeh, A., and Ahmed, M. Investigating the Impact of Fog on Freeway Speed Selection Using the SHRP2 Naturalistic Driving Study Data. Proceeding of  the 97th Annual Meeting of the Transportation Research Board, 2018.

Technical Reports

1. Das, S., Tsapakis, I., Khan, M. N., Liu, J., Mills, D., Miller, M., ... & Qi, Y. Leveraging Artificial Intelligence (AI) Techniques to Detect, Forecast, and Manage Freeway Congestion: Technical Report (No. FHWA/TX-23/0-7131-R1). Texas A&M Transportation Institute, 2023.

2. Ahmed, M.M., A. Das, M. N. Khan, B. Hammit, and E. Ali. Driver Performance and Behavior in Adverse Weather Conditions: Microsimulation and Variable Speed Limit Implementation of the SHRP2 Naturalistic Driving Study Results. Final Report WY-2105F, Wyoming Department of Transportation, 2022. 

3. Ahmed, M.M., R. K. Young, A. Ghasemzadeh, B. Hammit, E. Ali, M. N. Khan, A. Das, and H. Eldeeb. Implementation of SHRP2 Results within the Wyoming Connected Vehicle Variable Speed Limit System: Phase 2 Early Findings Report and Phase 3 Proposal. Wyoming Department of Transportation, 2017.

4. Ahmed, M. M., A. Ghasemzadeh, B. Hammit, M. N. Khan, A. Das, E. Ali, R. K. Young, and H. Eldeeb. Driver Performance and Behavior in Adverse Weather Conditions: An Investigation Using the SHRP2 Naturalistic Driving Study Data-Phase 2. Final Report WY-18/05F, Wyoming Department of Transportation, 2018.