Biography
Agnimitra Sengupta is an Assistant Professor of Civil Engineering in the School of Science, Engineering, and Technology at Penn State Harrisburg. He earned his M.S. and Ph.D. in Civil Engineering from Penn State University in 2023, specializing in transportation systems. His research focuses on traffic safety and operations, infrastructure management, and the application of artificial intelligence and machine learning in transportation systems. His work emphasizes data-driven methods for traffic prediction and uncertainty quantification, video-based analytics for roadway safety improvement, and infrastructure condition assessment using advanced sensing technologies. Dr. Sengupta has contributed to several funded research projects supported by the National Cooperative Highway Research Program, the U.S. Department of Transportation, and the Pennsylvania Department of Transportation. He is actively engaged in professional service, currently serving as a member of the Transportation Research Board Standing Committee on Testing and Evaluation of Transportation Structures and as a panel member for a National Cooperative Highway Research Program project.
Research Interests
- Traffic safety and operations
- Infrastructure management
- Statistical and econometric modeling
- Machine learning and artificial intelligence
Publications
Sengupta A. and Guler S.I. (2025). Deep Learning-based spatial translation of traffic prediction using Newell’s theory. ASCE Journal of Transportation Engineering, Part A: Systems. 151 (7).
Hoxha E., Feng J., Sengupta A., Kirakosian D., He Y., Shang B., Gjinofci A. and Xiao J. (2025) Contrastive learning for robust defect mapping in concrete slabs using impact echo. Construction and Building Materials, 461, DOI: 10.1016/j.conbuildmat.2024.139829
Papakonstantinou K., Guler S.I., Gayah V., Bhattacharya A., Saifullah M., Sengupta A. and Lu M. (2024). Quantifying the impact of data unavailability, inaccuracies, and uncertainty on deterioration modeling and infrastructure asset management. CIAMTIS US DOT Region 3 University Transportation Center, Final Report CIAM-UTC-REG41
Sengupta A., Mondal S., Das A. and Guler S.I. (2024). A Bayesian approach to quantifying uncertainties and improving generalizability in traffic prediction models. Transportation Research Part C: Emerging Technologies, 162, DOI: 10.1016/j.trc.2024.1045855. Sengupta A., Guler S.I., Gayah V. and Warchol S. (2024).
Evaluating the reliability of automatically generated pedestrian and bicycle crash surrogates. Accident Analysis and Prevention, 203, DOI:10.1016/j.aap.2024.107614
Savolainen P., Gates T., Johari M., Bamney A., Jashami H., Gupta N.,Abatan A., Donnell E., Guler S.I., Sengupta A., Smaglik E., Gooch J. and Hallmark S. (2024). NCHRP Report 1081: Acceleration, deceleration, and stopping sight distance criteria for geometric design of highways and streets. NCHRP Research Project 15-75, DOI: 10.17226/27490
Prajapati A., Sengupta A. and Guler S.I. (2023). Accounting for traffic dynamics in pavement maintenance scheduling. Transportation Research Record, DOI: 10.1177/03611981231206176
Sengupta A., Azari H., Guler S.I. and Shokouhi P. (2023). Validating a physics-based automatic classification scheme for impact echo signals on data using a concrete slab with known defects. Transportation Research Record, DOI:10.1177/03611981231173649
Mahmud A., Sengupta A. and Gayah V. (2023). Crash classification by manner of collision: A comparative study. Transportation Letters: The International Journal of Transportation Research. DOI:10.1080/19427867.2023.2175419
Sengupta A., Mondal S., Guler S.I. and Shokouhi P. (2022). A hybrid hidden Markov model and time-frequency approach to Impact Echo signal classification. Journal of Nondestructive Evaluation. 41(69). DOI:10.1007 s10921-022-00901-1
Sengupta A., Gayah V. and Donnell E.T. (2021). Examining the impacts of crash data aggregation on SPF estimation. Accident Analysis and Prevention. 160. DOI: 10.1016/j.aap.2021.106313
Sengupta A., Guler S.I. and Shokouhi P. (2021). Interpreting impact echo data to predict condition rating of concrete bridge decks: A machine-learning approach. ASCE Journal of Bridge Engineering. 26(8). DOI: 10.1061/(ASCE)BE.1943-5592.0001744
Education
Ph.D, Civil Engineering, Pennsylvania State University
M.S., Civil Engineering, Pennsylvania State University
B.E., Construction Engineering, Jadavpur University