Yu Zhang, Ph.D. » Leadership

Dr. Yu Zhang is the Program Director of Advance Air Mobility at CUTR. She is a Professor with the Department of Civil and Environmental Engineering and serves as the Director of National Institute for Congestion Reduction, a USDOT National University Transportation Center. In aviation research, Dr. Zhang has led about twenty projects sponsored by USDOT, FAA, FDOT, and TRB, focusing on air traffic management, performance evaluation of NextGen initiatives, and integrated airspace with new entrants (electric vertical take-off and landing vehicles, unmanned aerial systems). She has led a series of pioneering studies on Advanced Air Mobility (AAM), including integrated network design and demand estimation, automated pre-departure flight planning, environmental impact analysis of AAM, simulation platform development for performance analysis of AAM system, etc. View more on this site.

AAM Research Projects and Publications:

• Evaluating the Impacts of Emerging Advanced Air Mobility on the Transportation System in the Tampa Bay Region
  • Sponsored by Florida Department of Transportation.
  • The objective of this proposed project is to introduce emerging passenger AAM in statewide demand modeling, develop scenario generator for future passenger AAM settings, to evaluate the the impacts of emerging passenger AAM in the Tampa Bay Region. The impacts could include operational impacts, such as mileage travelled, average speed in the roadway system, total travel time, air pollutant emissions, and transportation equity.
• Integrated Network Design and Demand Estimation of on-Demand Urban Air Mobility. Engineering
  • Sponsored by Center for Transportation Environmental and Community Health (CTECH), USDOT Tier 1 UTC
  • This study examines the network design of UAM on-demand service, with a particular focus on the use of integer programmingand a solution algorithm to determine the optimal locations of vertiports, user allocation to vertiports, and vertiport access- and egress-mode choices while considering the interactions between vertiport locations and potential UAM travel demand.

• • Wu, Z. & Y. Zhang* (2021). Integrated Network Design and Demand Estimation of on-Demand Urban Air Mobility. Engineering, Volume 7, Issue 4, April 2021, Pages 473-487, https://doi.org/10.1016/j.eng.2020.11.007.

• Automated Flight Planning of High-Density Urban Air Mobility
  • Sponsored by Amazon Research Award
  • This study proposes an Automated Flight Planning System (AFPS) that includes a Low-Altitude Airspace Management System (LAMS), automatically generating an airpath network with the capability of avoiding obstacles and obstructions in low-altitude airspace, and a Low-Altitude Traffic Management System (LTMS), detecting and resolving potential conflicts with flight level assignment deconfliction strategy.

• • Tang, H., Y. Zhang*, V. Mohmoodian, & H. Charkhgard (2021). Automated Flight Planning of High-Density Urban Air Mobility. Transportation Research Part C: Emerging Technologies, Volume 131, October 2021, https://doi.org/10.1016/j.trc.2021.103324.

• Environmental impact analysis of on-demand urban air mobility: A case study of the Tampa Bay Area
  • Sponsored by Center for Transportation Environmental and Community Health (CTECH), USDOT Tier 1 UTC
  • This study proposes a method of comparing air pollutantemissions from using ground transportation versus switching to multimodal UAM, including greenhouse gases and other air pollutants (NO_x, SO₂, PM₅).

• • Zhao P., J. Post, Z. Wu*, W. Du & Y Zhang* (2022), Environmental impact analysis of on-demand urban air mobility: A case study of the Tampa Bay Area, Transportation Research Part D: Transport and Environment, Volume 110, 2022, 103438, ISSN 1361-9209, https://doi.org/10.1016/j.trd.2022.103438.

• Freeway Incident Detection and Management using Unmanned Aircraft Systems
  • Sponsored by National Institute for Congestion Reduction (NICR), USDOT National UTC
  • This study tests traffic data collection with drone platform with RGB and thermal sensors, develops learning algorithms for object detection and incident identification, explores solutions to expedite the clearance of incidents for reducing non-recurrent congestion on freeway corridors.
• Collision Free 4D Path Planning for Multiple UAVs Based on Spatial Refined Voting Mechanism and PSO Approach

• • Yang Liu”, Yu Zhang, Xuejun Zhang, Xiangmin Guan, Daniel Delahaye (2019). Collision Free 4D Path Planning for Multiple UAVs Based on Spatial Refined Voting Mechanism and PSO Approach, Chinese Journal of Aeronautics, 32(6), June 2019, pps 1504-1519, https://doi.org/10.1016/j.cja.2019.03.026.

• Safety Assessment and Risk Estimation for Unmanned Aerial Vehicles Operating in National Airspace System
  • Yang Liu”, Xuejun Zhang, Yu Zhang, Xiangmin Guan, and Daniel Delahaye (2018), Safety Assessment and Risk Estimation for Unmanned Aerial Vehicles Operating in National Airspace System”, Journal of Advanced Transportation, Vol 2018, Article ID 4731585, 11 pages, https://doi.org/10.1155/2018/4731585.

Other Selected Aviation Research and Publications:

• Leveraging Deep Leaning in Understanding the Impact of Convective Weather to Airfield Efficiency (Phase I and II), Sponsored by FAA NextGen Office.
• Florida Airport Sustainability Tracking/Monitoring System, Sponsored by FDOT Aviation and Spaceports Office
• Florida Aviation Activity Forecast Methodologies and Tools Development, Sponsored by FDOT Aviation and Spaceports Office.
• ​Measuring Metroplex Performance in Convective Weather, Sponsored by FAA NextGen Office.
• Verification and Recommended Improvement to International Guidance on Performance Metrics, Sponsored by FAA ATO.
• ​ACRP 02-50, Deriving Benefits from Alternative Aircraft-Taxi Systems, Sponsored by Transportation Research Board Airport Cooperative Research Program.
• Airport Users’ Perception towards “Remote and Virtual” Control Towers at Small Airports, Sponsored by Transportation Research Board Airport Cooperative Research Program Graduate Research Award.
• The Role of Air Cargo in Tampa Bay Regional Goods Movement, Sponsored by Florida Department of Transportation. 
• ACRP 02-38, Guidebook for Energy Facilities Compatibility with Airports and Airspace (Institutional PI), Sponsored by Transportation Research Board Airport Cooperative Research Program.
• Research on FAA Performance Indicators, Sponsored by Federal Aviation Administration (FAA) Air Traffic Organization (ATO).
• Performance Metrics Development and Analysis Support (PI), Sponsored by Federal Aviation Administration (FAA) Air Traffic Organization (ATO).

1.  Y. Ma, W. Du, J. Chen, Y. Zhang, Y. Lv and X. Cao (2022). “A Spatiotemporal Neural Network Model for Estimated-Time-of-Arrival Prediction of Flights in a Terminal Maneuvering Area,” in IEEE Intelligent Transportation Systems Magazine, Jan. 5, 2022. doi: 10.1109/MITS.2021.3132766.
2. Sun, F., H. Liu and Y. Zhang, “Integrated Aircraft and Passenger Recovery With Enhancements in Modeling, Solution Algorithm, and Intermodalism,” in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 9046-9061, July 2022, doi: 10.1109/TITS.2021.3090329.
3. Wang, Y., & Y. Zhang (2021). Prediction of Runway Configurations and Airport Acceptance Rates for Multi-Airport System Using Gridded Weather Forecast, Transportation Research Part C: Emerging Technologies, Vol. 125, April 2021, https://doi.org/10.1016/j.trc.2021.103049.
4. Hu, R., J. Zhu, Y. Zhang, J. Zhang”, and F. Witlox (2020). Spatial characteristics of aircraft CO2 emissions at different airports: Some evidence from China, Transportation Research Part D: Transport and Environment. 85(2020), https://doi.org/10.1016/j.trd.2020.102435.
5. Li Y., Y. Zhang, L. Wang, X. Guan (2020). Research on potential ground risk regions of aircraft crashes based on ADS-B flight tracking data and GIS, Journal of Transportation Safety and Security, April 2020, DOI: 10.1080/19439962.2020.1754981.
6. Zhang, J., P. Zhao, Y. Zhang, X. Dai, and D. Sui (2020), Criteria Selection and Multi-Objective Optimization of Aircraft Landing Problem, Journal of Air Transport Management, Vol. 82, 2020, https://doi.org/10.1016/j.jairtraman.2019.101734.
7. Du, W., M. Zhang, Y Zhang, X. Cao, J. Zhang (2018), Delay Causality Network in Air Transport Systems, Transportation Research Part E: Logistics and Transportation Review, Volume 118, October 2018, Pages 466-476. [delay_causality_network_in__air_transport_systems.pdf]
8. Ding W., Y Zhang, M. Hansen (2018), Downstream Impact of Flight Rerouting, Transportation Research Part C: Emerging Technologies, Vol. 88 March 2018, pages 176-186. [downstream_impact.pdf]
9. Sun, X., W. Dai, Y. Zhang and S. Wandelt (2017), Finding P-Hub Median Locations: An empirical study on problems and solution techniques, Journal of Advanced Transportation, vol. 2017, Article ID 9387302, 23 pages, 2017. doi:10.1155/2017/9387302. [finding_p-hub_median.pdf]
10. Zhang, Y., and Q. Wang (2017), Methods for Determining Unimpeded Aircraft Taxiing Time and Evaluating Airport Taxiing Performance, Chinese Journal of Aeronautics, 30(2), April 2017, Pages 523 537. [methods_for_determining_uimpeded.pdf]

Dr. Yu Zhang is the Program Director of Advance Air Mobility at CUTR. She is a Professor with the Department of Civil and Environmental Engineering and serves as the Director of National Institute for Congestion Reduction, a USDOT National University Transportation Center. In aviation research, Dr. Zhang has led about twenty projects sponsored by USDOT, FAA, FDOT, and TRB, focusing on air traffic management, performance evaluation of NextGen initiatives, and integrated airspace with new entrants (electric vertical take-off and landing vehicles, unmanned aerial systems). She has led a series of pioneering studies on Advanced Air Mobility (AAM), including integrated network design and demand estimation, automated pre-departure flight planning, environmental impact analysis of AAM, simulation platform development for performance analysis of AAM system, etc. View more on this site.

AAM Research Projects and Publications:

• Evaluating the Impacts of Emerging Advanced Air Mobility on the Transportation System in the Tampa Bay Region
  • Sponsored by Florida Department of Transportation.
  • The objective of this proposed project is to introduce emerging passenger AAM in statewide demand modeling, develop scenario generator for future passenger AAM settings, to evaluate the the impacts of emerging passenger AAM in the Tampa Bay Region. The impacts could include operational impacts, such as mileage travelled, average speed in the roadway system, total travel time, air pollutant emissions, and transportation equity.
• Integrated Network Design and Demand Estimation of on-Demand Urban Air Mobility. Engineering
  • Sponsored by Center for Transportation Environmental and Community Health (CTECH), USDOT Tier 1 UTC
  • This study examines the network design of UAM on-demand service, with a particular focus on the use of integer programmingand a solution algorithm to determine the optimal locations of vertiports, user allocation to vertiports, and vertiport access- and egress-mode choices while considering the interactions between vertiport locations and potential UAM travel demand.

• • Wu, Z. & Y. Zhang* (2021). Integrated Network Design and Demand Estimation of on-Demand Urban Air Mobility. Engineering, Volume 7, Issue 4, April 2021, Pages 473-487, https://doi.org/10.1016/j.eng.2020.11.007.

• Automated Flight Planning of High-Density Urban Air Mobility
  • Sponsored by Amazon Research Award
  • This study proposes an Automated Flight Planning System (AFPS) that includes a Low-Altitude Airspace Management System (LAMS), automatically generating an airpath network with the capability of avoiding obstacles and obstructions in low-altitude airspace, and a Low-Altitude Traffic Management System (LTMS), detecting and resolving potential conflicts with flight level assignment deconfliction strategy.

• • Tang, H., Y. Zhang*, V. Mohmoodian, & H. Charkhgard (2021). Automated Flight Planning of High-Density Urban Air Mobility. Transportation Research Part C: Emerging Technologies, Volume 131, October 2021, https://doi.org/10.1016/j.trc.2021.103324.

• Environmental impact analysis of on-demand urban air mobility: A case study of the Tampa Bay Area
  • Sponsored by Center for Transportation Environmental and Community Health (CTECH), USDOT Tier 1 UTC
  • This study proposes a method of comparing air pollutantemissions from using ground transportation versus switching to multimodal UAM, including greenhouse gases and other air pollutants (NO_x, SO₂, PM₅).

• • Zhao P., J. Post, Z. Wu*, W. Du & Y Zhang* (2022), Environmental impact analysis of on-demand urban air mobility: A case study of the Tampa Bay Area, Transportation Research Part D: Transport and Environment, Volume 110, 2022, 103438, ISSN 1361-9209, https://doi.org/10.1016/j.trd.2022.103438.

• Freeway Incident Detection and Management using Unmanned Aircraft Systems
  • Sponsored by National Institute for Congestion Reduction (NICR), USDOT National UTC
  • This study tests traffic data collection with drone platform with RGB and thermal sensors, develops learning algorithms for object detection and incident identification, explores solutions to expedite the clearance of incidents for reducing non-recurrent congestion on freeway corridors.
• Collision Free 4D Path Planning for Multiple UAVs Based on Spatial Refined Voting Mechanism and PSO Approach

• • Yang Liu”, Yu Zhang, Xuejun Zhang, Xiangmin Guan, Daniel Delahaye (2019). Collision Free 4D Path Planning for Multiple UAVs Based on Spatial Refined Voting Mechanism and PSO Approach, Chinese Journal of Aeronautics, 32(6), June 2019, pps 1504-1519, https://doi.org/10.1016/j.cja.2019.03.026.

• Safety Assessment and Risk Estimation for Unmanned Aerial Vehicles Operating in National Airspace System
  • Yang Liu”, Xuejun Zhang, Yu Zhang, Xiangmin Guan, and Daniel Delahaye (2018), Safety Assessment and Risk Estimation for Unmanned Aerial Vehicles Operating in National Airspace System”, Journal of Advanced Transportation, Vol 2018, Article ID 4731585, 11 pages, https://doi.org/10.1155/2018/4731585.

Other Selected Aviation Research and Publications:

• Leveraging Deep Leaning in Understanding the Impact of Convective Weather to Airfield Efficiency (Phase I and II), Sponsored by FAA NextGen Office.
• Florida Airport Sustainability Tracking/Monitoring System, Sponsored by FDOT Aviation and Spaceports Office
• Florida Aviation Activity Forecast Methodologies and Tools Development, Sponsored by FDOT Aviation and Spaceports Office.
• ​Measuring Metroplex Performance in Convective Weather, Sponsored by FAA NextGen Office.
• Verification and Recommended Improvement to International Guidance on Performance Metrics, Sponsored by FAA ATO.
• ​ACRP 02-50, Deriving Benefits from Alternative Aircraft-Taxi Systems, Sponsored by Transportation Research Board Airport Cooperative Research Program.
• Airport Users’ Perception towards “Remote and Virtual” Control Towers at Small Airports, Sponsored by Transportation Research Board Airport Cooperative Research Program Graduate Research Award.
• The Role of Air Cargo in Tampa Bay Regional Goods Movement, Sponsored by Florida Department of Transportation. 
• ACRP 02-38, Guidebook for Energy Facilities Compatibility with Airports and Airspace (Institutional PI), Sponsored by Transportation Research Board Airport Cooperative Research Program.
• Research on FAA Performance Indicators, Sponsored by Federal Aviation Administration (FAA) Air Traffic Organization (ATO).
• Performance Metrics Development and Analysis Support (PI), Sponsored by Federal Aviation Administration (FAA) Air Traffic Organization (ATO).

1.  Y. Ma, W. Du, J. Chen, Y. Zhang, Y. Lv and X. Cao (2022). “A Spatiotemporal Neural Network Model for Estimated-Time-of-Arrival Prediction of Flights in a Terminal Maneuvering Area,” in IEEE Intelligent Transportation Systems Magazine, Jan. 5, 2022. doi: 10.1109/MITS.2021.3132766.
2. Sun, F., H. Liu and Y. Zhang, “Integrated Aircraft and Passenger Recovery With Enhancements in Modeling, Solution Algorithm, and Intermodalism,” in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 9046-9061, July 2022, doi: 10.1109/TITS.2021.3090329.
3. Wang, Y., & Y. Zhang (2021). Prediction of Runway Configurations and Airport Acceptance Rates for Multi-Airport System Using Gridded Weather Forecast, Transportation Research Part C: Emerging Technologies, Vol. 125, April 2021, https://doi.org/10.1016/j.trc.2021.103049.
4. Hu, R., J. Zhu, Y. Zhang, J. Zhang”, and F. Witlox (2020). Spatial characteristics of aircraft CO2 emissions at different airports: Some evidence from China, Transportation Research Part D: Transport and Environment. 85(2020), https://doi.org/10.1016/j.trd.2020.102435.
5. Li Y., Y. Zhang, L. Wang, X. Guan (2020). Research on potential ground risk regions of aircraft crashes based on ADS-B flight tracking data and GIS, Journal of Transportation Safety and Security, April 2020, DOI: 10.1080/19439962.2020.1754981.
6. Zhang, J., P. Zhao, Y. Zhang, X. Dai, and D. Sui (2020), Criteria Selection and Multi-Objective Optimization of Aircraft Landing Problem, Journal of Air Transport Management, Vol. 82, 2020, https://doi.org/10.1016/j.jairtraman.2019.101734.
7. Du, W., M. Zhang, Y Zhang, X. Cao, J. Zhang (2018), Delay Causality Network in Air Transport Systems, Transportation Research Part E: Logistics and Transportation Review, Volume 118, October 2018, Pages 466-476. [delay_causality_network_in__air_transport_systems.pdf]
8. Ding W., Y Zhang, M. Hansen (2018), Downstream Impact of Flight Rerouting, Transportation Research Part C: Emerging Technologies, Vol. 88 March 2018, pages 176-186. [downstream_impact.pdf]
9. Sun, X., W. Dai, Y. Zhang and S. Wandelt (2017), Finding P-Hub Median Locations: An empirical study on problems and solution techniques, Journal of Advanced Transportation, vol. 2017, Article ID 9387302, 23 pages, 2017. doi:10.1155/2017/9387302. [finding_p-hub_median.pdf]
10. Zhang, Y., and Q. Wang (2017), Methods for Determining Unimpeded Aircraft Taxiing Time and Evaluating Airport Taxiing Performance, Chinese Journal of Aeronautics, 30(2), April 2017, Pages 523 537. [methods_for_determining_uimpeded.pdf]