Intelligent Mobility

Intelligent Mobility uses emerging technology and data to connect people, places and goods across all transport modes to make movement of people and goods smarter, greener and more efficient. The intelligent mobility has a tremendous potential to affect not only the transportation and transportation infrastructure but also various other industries such as logistics, agriculture, education, real estate, entertainment. The intelligent mobility improves time usage, fuel efficiency and provides service access to people previously limited by conditions such as age or disabilities. It works as an enabler for various industries to open up new economic, environmental and social opportunities.

I work with undergraduate and graduate students and collaborate with my colleagues to tackle the research challenges in Intelligent Mobility. We make use of several simulation tools in our work, such as Veins, SUMO and OMNET++.

Dynamic initiative assignment for data collection in Transportation Systems

In this research project, we design a game-theory based incentive mechanism that dynamically assigns compensation for data collection in a target area:

https://www.nsf.gov/awardsearch/showAward?AWD_ID=1739409

In addition to the development of this mechanism, we explore modeling and simulation of various approaches for intelligent transportation system.

Market-based Approach for Intelligent Transportation System

In this research project, we explore the idea of the use of market-based methodologies to bridge the consumer to infrastructure enabled by these technologies.

Cooperative Navigation for Connected and Autonomous Vehicles

The pervasive deployment of connected and autonomous vehicle (CAV) technology will offer various opportunities for new applications. The cooperative behavior capability of CAVs is invaluable for critical operations within the transportation system.

In this project, we present an approach for cooperative CAVs to support the navigation of specialty vehicles such as first responders in the traffic. We lay out the trajectory planning problem for a specialty vehicle and formalize it in terms of the locations and accelerations of vehicles in a certain range. This problem is encoded onto a graph, where we map the safety constraints using the edge weights. We formulate the cooperative planning of the surrounding vehicles as an optimization problem, where the objective is to maximize the second largest eigenvalue of the resulting graph Laplacian. The performance of the approach is evaluated in extensive simulations.

Intelligent Mobility Education

The intelligent mobility professionals must be educated to apply their knowledge in various fields. The fundamental requirements of intelligent mobility field can be grouped in three main areas:

  • Management of licenses, policies and commercial decisions

  • Data analytics for intelligent transportation system

  • Applications of telecommunication and computing technologies

I participated in the creation of the new undergraduate concentration in Intelligent Mobility at the Department of Data Science and Business Analytics of Florida Polytechnic University. This is a unique interdisciplinary concentration bringing learning units together from transportation systems management, network science, big data analytics, Internet of Things, sustainability and economic development.

Related Publications

  • A. Chakeri, X. Wang, Q. Goss, M. I. Akbas and L. G. Jaimes. "A Platform-based Incentive Mechanism for Autonomous Vehicle Crowdsensing." In IEEE Open Journal of Intelligent Transportation Systems, doi: 10.1109/OJITS.2021.3056925., February, 2021.

  • Q. Goss, M. I. Akbas, A. Chakeri and L. G. Jaimes. "An Association Rules Learning Approach to Unsupervised Classification of Street Networks." In the IEEE SoutheastCon, March, 2020.

  • X. Wang, Q. Goss, M. I. Akbas, A. Chakeri, J. Calderon and L. G. Jaimes. "Incentive Mechanism for Vehicular Crowdsensing with Budget Constraints." In the IEEE SoutheastCon, March, 2020.

  • H. Chintakunta, M. I. Akbas. "Spectrum Analytic Approach for Cooperative Navigation of Connected and Autonomous Vehicles." In Proceedings of the ACM International Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications (DIVANet), November, 2019.

  • H. Chintakunta, M. I. Akbas. "CANSAVE: Cooperative Autonomous Navigation To Support Autonomous Emergency Vehicles". In the Intelligent Transportation Society (ITS) of America Annual Meeting, Washington DC, June, 2019.

  • M. I. Akbas, C. A. Long, S. K. Hanumanthu, E. Anderson and R. Razdan. "FPolyOS: A Simulation Platform to Explore Breakthrough Concepts in Intelligent Transportation". In Proceedings of the IEEE SoutheastCon, April, 2019.

  • Q. Goss, M. I. Akbas, L. G. Jaimes and R. Sanchez-Arias. "Street Network Generation with Adjustable Complexity Using k-Means Clustering". In Proceedings of the IEEE SoutheastCon, April, 2019.

  • L. G. Jaimes and M. I. Akbas. "Incentive Mechanisms for Mobile Crowdsourcing, Reaching Spatial and Temporal Coverage Under Budget Constraints". In the National Science Foundation (NSF) Cyber Physical Systems (CPS) PI Meeting, November, 2018.

  • M. I. Akbas and S. Taj. “Intelligent Mobility Concentration for Undergraduate Students in Data Science and Business Analytics". In the North American Conference on Industrial Engineering and Operations Management (IEOM), University of the District of Colombia (UDC), September , 2018.

  • C. Medrano-Berumen and M. I. Akbas. “MHopCAV: Multi-Hop Clustering for Autonomous Vehicle Networks”. In the Florida Conference on Recent Advances in Robotics, May, 2018.