Artificial Intelligence and Big Data Analytics

Artificial Intelligence and Big Data Analytics

Group Leader: Dr. Sanjay Ranka

Sanjay Ranka is a professor in the Department of Computer Information Science and Engineering at University of Florida. His current research interests are High Performance Computing and Artificial Intelligence for health care and transportation. He teaches courses on data science (three course curriculum), data mining and parallel computing. From 1999-2002, he was the Chief Technology Officer at Paramark (Sunnyvale, CA). At Paramark, he developed a real-time optimization service called PILOT for marketing campaigns. PILOT served more than 10 million optimized decisions a day in 2002 with a 99.99% uptime. Paramark was recognized by VentureWire/Technologic Partners as a top 100 Internet technology company in 2001 and 2002 and was acquired in 2002. He has also held positions as a tenured faculty positions at Syracuse University and as a researcher/visitor at IBM T.J. Watson Research Labs and Hitachi America Limited. He is a fellow of the IEEE and AAAS, and a past member of IFIP Committee on System Modeling and Optimization. He is an associate Editor-in-Chief of the Journal of Parallel and Distributed Computing and an associate editor for ACM Computing Surveys, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Sustainable Computing: Systems and Informatics, Knowledge and Information Systems, and International Journal of Computing. He is also an editorial board member of Applied Sciences (Compuing and Artificial Intelligence). Additionally, he is a book series editor for CRC Press for Bigdata. In the past, he has been an associate editor for IEEE Transactions on Parallel and Distributed Systems and IEEE Transactions on Computers. His work has received 12200+ citations with an h-index of 54 (based on Google Scholar).


Artificial Intelligence and Big Data Analytics have the potential for profound advancement in smart city planning, intelligent transportation operations and safety. Our preliminary work has been successful in achieving these outcomes. Our goal is to broaden the scope of our effort and relationships to improve the impact on real-world usage of technologies and software.


We live in an era of real-time gathering of information and its dissemination. The size of the digital universe is estimated to be around 50 Zettabytes today and expected to double every two years. The growth of exploitable data has the potential to trigger disruptive changes in the transportation sector and is having a profound impact in urban planning, intelligent mobility, and safety.

Vehicle loop detectors that have traditionally been deployed at intersections to detect the passage of vehicles have high deployment and maintenance costs; and are not always useful for observing the movements of pedestrians and scooters. The use of other modalities, such as video and lidar has great potential to improve accuracy and timeliness in the detection of vehicles, pedestrians, bicyclists, etc. Additionally, data can be collected from interactions of vehicles with onboard units (that mimic the behavior of future connected vehicles). As part of the I-Street Trapezium project we are collecting one or more of the above modalities at 27 intersections in Gainesville. This information is then synthesized to create a real-time city-wide traffic palette that can is used to improve traffic safety and operations and stored in a data warehouse. This data is then used by artificial intelligence and machine learning techniques for real-time incident detection, vehicle classification, space-time trajectories, near-misses and travel-time distributions of vehicles and pedestrians while maintaining individual privacy.

We are developing signalized intersection control strategies and sensor fusion algorithms for jointly optimizing vehicle trajectories and signal control for a mixture of autonomous vehicles and traditional vehicles at every intersection. This approach uses short-range communication for autonomous vehicles and radar-based detection of traditional vehicles. These strategies are currently being field tested on actual signals at the Florida DoT’s TERL facility in Tallahassee and intersections in the City of Gainesville.

We are also working on developing algorithms and software to collect and process real-time General Bikeshare Feed Specification (GBFS) data feeds to infer real-time micromobility trips. This information will enable us to conduct spatio-temporal analysis for micromobility usage, model and forecast future micromobility demand by using machine learning, and provide suggestions for short-term and long-term transportation planning.


Project TitlePIFunding SourceStatus
CPS:TTP OPTION:SYNERGY:Traffic Signal Control with Connected and Autonomous Vehicles in the Traffic StreamDr. Lily ElefteriadouNational Science FoundationActive
Extended Development and Testing of Optimized Signal Control with Autonomous and Connected VehiclesDr. Lily ElefteriadouFlorida Department of Transportation Active
SCC: Video Based Machine Learning for Smart Traffic Analysis and ManagementDr. Sanjay RankaNational Science FoundationActive
Bigdata Analytics and Artificial Intelligence for Smart IntersectionsDr. Sanjay RankaFlorida Department of Transportation Active
Data Analytics and Evaluation of the Gainesville Trapezium Connected Vehicle Signal Phasing and Timing (SPaT) Deployment ProjectDr. Sanjay RankaFlorida Department of Transportation Active
Machine Learning algorithms for Demand and Turning Movement Count PredictionDr. Sanjay RankaFlorida Department of Transportation Active
Data Management and Analytics for UF Smart TestbedDr. Sanjay RankaUnited States Department of Transportation Active
Micro-Mobility as a Solution to Reduce Urban Traffic CongestionDr. Xilei ZhaoSoutheastern Transportation Research, Innovation, Development and Education Center (STRIDE)Active
Traffic-event Unification System Highlighting Arterial RoadsDr. Sanjay RankaFlorida Department of Transportation Completed 2020
Dynamic Intersection Learning Machine Optimization Real-time EngineDr. Sanjay RankaFlorida Department of Transportation Completed 2019
Truck Taxonomy & Classification using Video and Weigh-In Motion (WIM) TechnologyDr. Sanjay RankaFlorida Department of Transportation Completed 2019
Mobility-on-Demand Transit for Smart, Sustainable CitiesDr. Xilei ZhaoSoutheastern Transportation Research, Innovation, Development and Education Center (STRIDE)Active


Photo of Lily Elefteriadou Lily Elefteriadou Director , UFTI & Barbara Goldsby Professor, Department of Civil and Coastal Engineering 352-294-7802
Photo of Anand Rangarajan Anand Rangarajan Professor, Computer & Information Science & Engineering 352.575.1759
Photo of Sanjay Ranka Sanjay Ranka Professor, Department of Computer & Information Science & Engineering 3525144213
Photo of Xiang ‘Jacob’ Yan Xiang ‘Jacob’ Yan Participant
Photo of Xilei Zhao Xilei Zhao Assistant Professor, Department of Civil and Coastal Engineering 3522947159