UTC RAVE Alert

Center for Urban Informatics and Progress Publications

Papers

Published 2021-present:

  • Nguyen, T. T., Nguyen, H. H., Sartipi, M., & Fisichella, M. (2024). "LaMMOn: Language model combined graph neural network for multi-target multi-camera tracking in online scenarios." Machine Learning, 1-27. https://doi.org/10.1007/s10994-024-06592-1
  • T. T. Zhang, Yi Ge, Anjiang Chen, Mina Sartipi, P. J. Jin, “Hash-based Gaussian Mixture Model for Roadside LiDAR Smart Infrastructure Application,” In IEEE Transactions on Intelligent Transportation Systems. 10.1109/TITS.2024.3434749.
  • T. T. Zhang, P. J. Jin, S. T. McQuade, A. Bayen and B. Piccoli, “Car-Following Models: A Multidisciplinary Review,” in IEEE Transactions on Intelligent Vehicles, doi: 10.1109/TIV.2024.3409468.
  • Tianya Terry Zhang, Peter J. Jin, Benedetto Piccoli, and Mina Sartipi, "Deep spatial-temporal embedding for vehicle trajectory validation and refinement," Computer-Aided Civil and Infrastructure Engineering, 1–19, https://doi.org/10.1111/mice.13160.
  • S. Khaleghian, H. Neema, M. Sartipi, T. Tran, R. Sen and A. Dubey, "Calibrating Real-World City Traffic Simulation Model Using Vehicle Speed Data," 2023 IEEE International Conference on Smart Computing (SMARTCOMP), Nashville, TN, USA, 2023, pp. 303-308, doi: 10.1109/SMARTCOMP58114.2023.00076.
  • S. Khaleghian, T. Tran, J. Cho, A. Harris, and M. Sartipi, "Electric Vehicle Identification in Low-Sampling Non-Intrusive Load Monitoring Systems Using Machine Learning," 2023 IEEE International Smart Cities Conference (ISC2).
  • T. Tran, S. Khaleghian, J. Zhao and M. Sartipi, "SIMCal: A High-Performance Toolkit For Calibrating Traffic Simulation," in 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 2022 pp. 2895-2902. doi: 10.1109/BigData55660.2022.10021057.
  • Sen, Rishav, Bharati, Alok Kumar, Khaleghian, Seyedmehdi, Ghosal, Malini, Wilbur, Michael, Tran, Toan, Pugliese, Philip, Sartipi, Mina, Neema, Himanshu, and Dubey, Abhishek. E-Transit-Bench: Simulation Platform for Analyzing Electric Public Transit Bus Fleet Operations. United States: N. p., 2022. Web. doi:10.1145/3538637.3539586.
  • R. Sen et al., "BTE-Sim: Fast Simulation Environment For Public Transportation," 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 2022, pp. 2886-2894, doi: 10.1109/BigData55660.2022.10020973.
  • A. Gammaa, S. Khaleghian, T. Tran, M. Sartipi, "Improving Vanet Simulation Channel Model in an Urban Environment Calibration Using Resal-World Communication Data," 102st Transportation Research Board Annual Meeting, January 2023.
  • TT Nguyen, HH Nguyen, M Sartipi, M Fisichella, "Multi-Vehicle Multi-Camera Tracking with Graph-Based Tracklet Features", IEEE Transactions on Multimedia, 2023.
  • TT Nguyen, HH Nguyen, M Sartipi, M Fisichella, "Real-Time Multi-Vehicle Multi-Camera Tracking with Graph-Based Tracklet Features". Transportation Research Record, 2023.
  • T. V. Tran and M. Sartipi, "Revisiting Pixel-based Traffic Signal Controls using Reinforcement Learning with World Models", The Workshop on Artificial Intelligence for Social Good at The 37th AAAI conference on artificial intelligence, February 2023.
  • A. Gamma, S. Khaleghian, T. V. Tran, and M. Sartipi, "Improving Vehicular Ad hoc Network Simulation Channel Model in an Urban Environment via Calibration Using Real-world Communication Data". Transportation Research Board 102nd Annual Meeting, No. TRBAM-23-04679, January 2023.
  • L. Phan, J. Zhao, J. Roland, M. Baker, M. Sartipi, "Visualization and Collaboration Platform for Vehicular Crash Hot Spot Prediction in Chattanooga, TN", 102nd Transportation Research Board Annual Meeting, No. TRBAM-23-00530, January 2023.
  • J. Zhao, A. Harris, and M. Sartipi, "Quality Assessment of Large-scale Vehicle and Pedestrian Trajectories at Intersections", Transportation Research Record, January 2023.
  • T. V. Tran, S. Khaleghian, J. Zhao, and M. Sartipi, "SIMCal: A High-Performance Toolkit For Calibrating Traffic Simulation", 2022 IEEE International Conference on Big Data (Big Data), December 2022.
  • R. Sen, T. V. Tran, S. Khaleghian, P. Pugliese, M. Sartipi, H. Neema, and A. Dubey, "BTE-Sim: Fast Simulation Environment For Public Transportation", 2022 IEEE International Conference on Big Data (Big Data), December 2022.
  • A. Saroj, T. V. Tran, A. Guin, M. Hunter, and M. Sartipi, "Optimizing Traffic Controllers along the MLK Smart Corridor Using Reinforcement Learning and Digital Twin", 2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence, October 2022.
  • J. Zhao and M. Sartipi, "Automatic Identification of Anomalous Driving Events from Trajectory Data", The 25th IEEE International Conference on Intelligent Transportation Systems (ITSC), October 2022.
  • T. V. Tran and M. Sartipi, "Neuroevolution for transportation applications". The 11th International Workshop on Urban Computing at ACM SIGKDD conference on knowledge discovery and data mining, August 2022.
  • R. Sen, A. K. Bharati, S. Khaleghian, M. Ghosal, M. Wilbur, T. V. Tran, P. Pugliese, M. Sartipi, H. Neema, and A. Dubey, "E-Transit-Bench: Simulation Platform for Analyzing Electric Public Transit Bus Fleet Operations", ACM International Conference on Future Energy Systems, June 2022.
  • M. Rahmati, J. Cho, N. Fell and M. Sartipi, "Developing Prediction Models for 30-Day Readmission after Stroke among Medicare Beneficiaries", IEEE SoutheastCon 2022, April 2022.
  • M. Mansouri, J. Roland, M. Rahmati, M., M. Sartipi, and G. Wilkerson, “A predictive paradigm for identifying elevated musculoskeletal injury risks after sport-related concussion,” Journal of Sports Orthopaedics and Traumatology. January, 2022. 
  • J. Rolandh, L. Phan , T. -N. Doan, M. Sartipi, O. Osama, K. Comstock, "A Comparison of Logistic Regression and Long Short-Term Memory for Vehicular Crash Hotspot Prediction in Chattanooga,TN". 101st Transportation Research Board Annual Meeting, No. TRBAM-22-02211, January 2022. 
  • Seyedmehdi Khaleghian, Himanshu Neema, Mina Sartipi, and Abhishek Dubey, “Calibration of Microscopic and Mesoscopic Traffic Simulation Model of a Large Scale Network Based on the Real World Speed Data". 101st Transportation Research Board Annual Meeting, No. TRBAM-22-01032, , January 2022. 
  • T. V. Tran, T. -N. Doan and M. Sartipi, "TSLib: A Unified Traffic Signal Control Framework Using Deep Reinforcement Learning and Benchmarking," 2021 IEEE International Conference on Big Data (Big Data), 2021, pp. 1739-1747, doi: 10.1109/BigData52589.2021.9671993.

Previously Published:

  • Cristina Del-Real, Chandra Ward & Mina Sartipi (2021) What do people want in a smart city? Exploring the stakeholders’ opinions, priorities and perceived barriers in a medium-sized city in the United States, International Journal of Urban Sciences, DOI: 10.1080/12265934.2021.1968939
  • Jin Cho, Krystal Place, Rebecca Salstrand, Monireh Rahmat, Misagh Mansouri, Nancy Fell, Mina Sartipi, "Developing a Predictive Tool for Hospital Discharge Disposition of Patients Poststroke with 30-Day Readmission Validation", Stroke Research and Treatment, vol. 2021, Article ID 5546766, 9 pages, 2021. https://doi.org/10.1155/2021/5546766
  • J. Roland, P. D. Way, C. Firat, T. -N. Doan, M. Sartipi, “Modeling and predicting vehicle accident occurrence in Chattanooga, Tennessee,” Elsevier Journal, Accident Analysis & Prevention, Volume 149, 2021, 105860, ISSN 0001-4575.
  • Way, Peter, Jeremiah Roland, Mina Sartipi, and Osama Osman. Spatio-Temporal Accident Prediction: Effects of Negative Sampling on Understanding Network-Level Accident Occurrence. No. TRBAM-21-02286. 2021.
  • Way, Peter, Jeremiah Roland, Mina Sartipi, and Osama Osman. "Spatio-Temporal Crash Prediction: Effects of Negative Sampling on Understanding Network-Level Crash Occurrence." Transportation Research Record (2021): 0361198121991836.
  • Mansouri M., Roland J., Nukala S., Cho J., Sartipi M. (2020) The Heavy Lifting Treatment Helper (HeaLTH) Algorithm: Streamlining the Clinical Trial Selection Process. In: Nichols J., Verastegui B., Maccabe A.., Hernandez O., Parete-Koon S., Ahearn T. (eds) Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI. SMC 2020. Communications in Computer and Information Science, vol 1315. Springer, Cham. https://doi.org/10.1007/978-3-030-63393-6_37
  • M. Fadul, D. Reising, and M Sartipi, “Identification of OFDM-based Radios under Rayleigh Fading using RF-DNA and Deep Learning,” IEEE Access, 2021.
  • M. Mansouri, J. Roland, S. Nukala, J. Cho, and M. Sartipi, “The Heavy Lifting Treatment Helper (HeaLTH) Algorithm: Streamlining the Clinical Trial Selection Process.” In Smoky Mountains Computational Sciences and Engineering Conference (pp. 542-552). Springer, Cham, 2020.
  • L.T. Phan, T. Doan, M. Sartipi. "Understanding the Effect of COVID-19 on Fuel Consumption of Public Transportation: The case study of Chattanooga, TN", IEEE BigData 2020
  • A. Alharin , Y. Patel, T. Doan, and M. Sartipi. "Data Analysis and Visualization of Traffic in Chicago with Size and Landuse-Aware Vehicle to Buildings Assignment." In Smoky Mountains Computational Sciences and Engineering Conference, pp. 518-529. Springer, Cham, 2020.
  • Y. Patel, C. Firat, T. Childers and M. Sartipi, "Ridership Prediction Of New Bus Routes At Stop Level By Modelling Socio-economic Data Using Supervised Machine Learning Methods", Transportation Research Board, Jan. 2021.
  • Peter D. Way, Jeremiah Roland, Osama Osman, Mina Sartipi, "Spatio-Temporal Accident Prediction: Effects of Negative Sampling on Understanding Network-Level Accident Occurrence," Transportation Research Record, 2021.
  • A. Alharin, T. Doan and M. Sartipi, "Reinforcement Learning Interpretation Methods: A Survey," IEEE Access, vol. 8, pp. 171058-171077, Oct. 2020.
  • P. Way, J. Roland, M. Sartipi, “On the Nature of Negative Sampling: How Non-Accident Data Helps us Understand Accident Occurrence”, Submitted to International Conference on Machine Learning and Data Mining, 2020.
  • T. Doan, L. Phan, and M. Sartipi. “Bus Fuel Consumption Problem: An in-depth Analysis and Prediction.” UrbComp ’20: The 9th SIGKDD International Workshop on Urban Computing, Aug. 24, 2020.
2019 and previous
  • B. Allen, A. Harris, J. Cho, Z. Hu, M. Sartipi, K. Place, R. Salstrand, H. True, and N. Fell, "Functional Measurement Post-stroke via Mobile Application and Body-Worn Sensor Technology", Journal of Health, Aug. 2019.
  • J. Cho, A. Alharin, Z. Hu, N. Fell, and M. Sartipi, "Predicting Post-stroke Hospital Discharge Disposition Using Interpretable Machine Learning Approach", the Proc. of IEEE Big Data Conference, Dec. 2019. 
  • J. Roland, P. Way, and M. Sartipi, “Studying the effects of weather and roadway geometrics on daily accident occurrence using a multilayer perceptron model,” Proc. of the Fourth Workshop on International Science of Smart City Operations and Platforms Engineering, SCOPE 2019, Association for Computing Machinery, p. 49-53, 2019.
  • A. Harris, J. Stovall, M. Sartipi. “MLK Smart Corridor: An Urban Testbed for Smart City Applications,” the Proc. of IEEE Big Data Conference, Dec. 2019. 
  • J. Stovall, A. O’Grady, M. Sartipi, “Scalable Object Tracking for Smart Cities,” the Proc. of IEEE Big Data Conference, Dec. 2019.
  • A. Harris and M. Sartipi, “Data integration platform for smart and connected cities,” in Proceedings of the Fourth Workshop on International Science of Smart City Operations and Platforms Engineering (SCOPE ’19), Association for Computing Machinery, New York, NY, USA, pp. 30–34. 
  • J. Cho, M. Sartipi, M. Fathollahzadeh, P. Tabares-Velasco, and S. Mohagheghi, "Residential Building A/C Load Analysis Using Deep Learning for Demand Response Management". Submitted to ACM Transactions on Data Science, 2019.
  • J. Cho, Z. Hu, and M. Sartipi, "Non-Intrusive A/C Load Disaggregation Using Deep Learning,"  Proc. of IEEE PES T&D Conference, Apr. 2018.
  • R.L Thompson, Z. HU, J. Cho, J. Stovall, M. Sartipi, “Enhancing Driver Awareness Using See-Through Technology,” SAE Technical Paper 2018-01-0611, Apr. 2018.
  • J. Cho, Z. Hu, and M. Sartipi, "A/C Load Forecasting Using Deep Learning," the Proc. of International Conference on CSCI, Dec. 2017.
  • R. Thompson, Z. Hu, J. Cho, A. Harris, J. Stovall, M. Sartipi, "See-Through Technology Using V2X Communication," the Proc. of ACM Mid-Southeast, Nov. 2017.
  • J. Cho, Z. Hu, N. Fell, G. Heath, R. Qayyum, and M. Sartipi, “Hospital Discharge Disposition of Stroke Patients in the State of Tennessee,” Journal of the Southern Medical Association, Sep. 2017.
  • J. Cho, Z. Hu, and M. Sartipi, "Post-stroke Discharge Disposition Prediction using Deep Learning," the Proc. of IEEE Southeastcon, Mar. 2017.
  • B. Williams, B. Allen, Z. Hu, H. True, J. Cho, A. Harris, N. Fell, and M. Sartipi, "Real-Time Fall Risk Assessment Using Functional Reach Test," The International Journal of Telemedicine and Applications, Jan. 2017.
  • A. Harris, H. True, Z. Hu, J. Cho, N. Fell, and M. Sartipi, “Fall Recognition using Wearable Technologies and Machine Learning Algorithms,” the Proc. of IEEE Big Data Conference, Dec. 2016.
  • Z. Hu, S. Mohagheghi, and M. Sartipi, “Flexible Data Acquisition, Compression, and Reconstruction in Advanced Metering Infrastructure”, in Proc. of Power Systems Conference, Mar. 2016.
  • B. Williams, B. Allen, H. True, N. Fell, D. Levine, and M. Sartipi, “A Real-time, Mobile Timed Up and Go System”, in Proc. of IEEE Body Sensor Networks Conference, Jun.  2015.
  • Z. Hu, S. Mohagheghi, and M. Sartipi, “Efficient Data Acquisition in Advanced Meter Infrastructure”, in Proc. of IEEE Power and Energy Society, Jul. 2015. 
  • N. Fell, K. Lowry, E. Smith, B. Wade, H. True, B. Allen, and M. Sartipi, “Validation of the Functional Reach Test in a Mobile Platform: A Pilot Study with Subjects Post-Acute Stroke”, Mobile Health in Rehabilitation, Boston University, Oct. 2014.
  • B. Allen, R. Derveloy, N. Fell, W. Gasior, G. Yu, and M. Sartipi, “Telemedicine Assessment of Fall Risk Using Wireless Sensors,” in Proc. of IEEE International Conference on Sensor and Ad Hoc Communications and Networks, June 2014.
  • B. Allen, R. Derveloy, K. Lowry, H. Handley, N. Fell, W. Gasior, G. Yu and M. Sartipi, “Evaluation of Fall Risk for Post-Stroke Patients Using Bluetooth Low-Energy Wireless Sensor”, in Proc. of IEEE Globecom, Dec. 2013.
  • M. Sartipi, “On the Rate-Distortion Performance of Compressive Sensing in Wireless Sensor Networks”, in Proc. of International Conference on Computing, Networking and Communications, Jan. 2013.
  • M. Sartipi, “Low-Complexity Distributed Compression in Wireless Sensor Networks”, in Proc. IEEE Data Compression Conference, Mar. 2012.
  • P. Ramchandara, M. Sartipi, “Compressive Sensing Based Imaging via Belief Propagation”, IEEE Asilomar Conference on Signals, Systems, and Computer, Oct. 2011.
  • R. Fletcher, M. Sartipi, “Energy-efficient data acquisition in wireless sensor networks using compressed sampling”, in Proc. IEEE Data Compression Conference, March 2011.
  • L. Yang, M. Sartipi, M. McNeely, “Usable Protection to Healthcare Application”, in Proc. of ACM Workshop on Cyber Security and Information Intelligence Research, Jan. 2011.
  • M. Sartipi, “LDPC Codes for Information Embedding and Lossy Distributed Source Coding”, Proc. of IEEE Data Compression Conference, Apr.2010.
  • M. Sartipi and J. Patterson, “TinyTermite: A Secure Routing Algorithm on Intel Mote 2 Sensor Network Platform,” Proc. of the twenty-First Conference on Innovative Applications of Artificial Intelligence (IAAI-09), Jul. 2009.
  • M. Sartipi, F. Fekri, “Lossy Distributed Source Coding using LDPC, IEEE Communications Letters, Volume 13, Issue 2, pp. 136-138, Jun. 2008. 
  • M. Sartipi, B. N. Vellambi R, N. Rahnavard, F. Fekri,” DSCM: An Energy Efficient Multicast Protocol for Wireless Sensor Networks Using Distributed Source Coding,“ Proc. of IEEE Infocom, Apr.2008.
  • F. Delgosha, M. Sartipi, and F. Fekri, “Construction of Two-dimensional Paraunitary Filter Banks over Fields of Characteristic Two and their Connections to Error-Control Coding,” IEEE Transactions on Circuits and Systems I, Volume 55, Issue 10, pp. 3095-53109, Nov. 2008.
  • M. Sartipi, F. Fekri, “Distributed Source Coding using Short to Moderate Rate-Compatible LDPC Codes: The Entire Slepian-Wolf Rate Region,” IEEE Transactions on Communications, Volume 56, Issue 3, pp. 400-411, Mar. 2008.
  • M. Sartipi, F. Delgosha, F. Fekri, “Two-Dimensional Half-Rate Codes Using two-Variable Finite-Field Filter Banks,’’ IEEE Transactions on Signal Processing, Volume 55, Issue 12, pp. 5846-5853, Dec. 2007. 
  • F. Fekri, M. Sartipi, R. M. Mersereau, R. W. Schafer, “Convolutional Codes Using Finite-Field Wavelets; Time-Varying Codes and more,’’ IEEE Transactions on Signal Processing, Volume 53, Issue 5, pp.1881-1896, May 2005.
  • M. Sartipi, F. Fekri, “Distributed Source Coding in Wireless Sensor Networks Using LDPC Coding: a Non-Uniform Framework,’’ Proc. of IEEE Data Compression Conference, pp. 477 – 477, Mar. 2005.
  • M. Sartipi, F. Fekri, “Distributed Source Coding in Wireless Sensor Networks Using LDPC coding: The entire Slepian-Wolf Rate Region,’’ Proc. of IEEE Wireless Communications and Networking Conference, pp. 1939-1944, Mar. 2005.
  • M. Sartipi, F. Fekri, “Source and Channel Coding in Wireless Sensor Networks Using LDPC Codes,’’ Proc. of IEEE Communications Society Conference on Sensor Communications and Networks, pp. 309-316, Oct. 2004. 
  • M. Sartipi, F. Fekri, “Two-Dimensional Error Correcting Codes Using Finite-Field Wavelets,’’ Proc. of IEEE Information Theory Workshop, pp. 22-29, Oct. 2004.
  • M. Sartipi, F. Fekri, “Low-Density Parity-Check Codes Based on Cyclotomic Cosets and Their Extension by Latin-Square Matrices,’’ Proc. of Forty-First Annual Allerton Conference on Communication, Control and Computing, Oct. 2003.

Center for Urban Informatics and Progress

Center for Urban Informatics and Progress

Department Hours
Monday - Friday: 8:00 am-5:00 pm