research

Dr. Zhang's research interest focuses on applying mathematical optimization, control theory, simulation analysis, machine learning, and on-road vehicle testing to address safety, congestion, energy, environment, and resilience issues of critical civil infrastructure systems in Smart Cities, such as intelligent transportation systems (ITS), connected and automated vehicles (CAV), cyber-physical systems (CPS), freight logistics and supply chain systems, and interdependent large-scale networked infrastructure systems.

*student co-author

Data-Driven Optimization and Control of Connected and Automated Vehicles (CAVs)

1. Choobchian, P., Roscoe, G., Dick, T., Zou, B., Work, D., Zhang, K., Wang, Y., Hung, Y-C.,  2023. "Leveraging connected vehicle platooning technology to improve the efficiency and effectiveness of train fleeting under moving blocks", Transportation Research Part C: Emerging Technologies. 148, March 2023. DOI: https://doi.org/10.1016/j.trc.2023.104026 NEW

2. *Tan, Y., Zhang, K. 2022. Real-Time Distributed Cooperative Adaptive Cruise Control Model Considering Time Delays and Actuator Lag. Transportation Research Record. May, 2022. NEW

3. *Zhao, S., Zhang, K. 2021. Online Predictive Connected and Automated Eco-Driving on Signalized Arterials Considering Traffic Control Devices and Road Geometry Constraints under Uncertain Traffic Conditions. Transportation Research Part B: Methodological, 145, pp 80-117. 

4. *Zhao, S., Zhang, K. 2020. A Distributionally Robust Stochastic Optimization-based Model Predictive Control with Distributionally Robust Chance Constraints for Cooperative Adaptive Cruise Control under Uncertain Traffic Conditions. Transportation Research Part B: Methodological, 138, pp144-178. 

5. *Jalooli, A., Zhang, K., Song, M. A Novel Clustering Scheme for Heterogeneous Vehicular Networks. The 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, June 7-11, 2020.

6. *Lyu, Q. Zhang, K. A Best-Case Rosenthal Equilibrium based Coordination Mechanism for N-Person Online Routing Games of Connected and Automated Vehicles. The 98th Transportation Research Board Annual Meeting, January 2019, Washington, D.C., USA.

7. *Zhao, S. Zhang, K. A Data-Driven Optimization based Model Predictive Control for Real-Time Connected and Automated Driving on Arterials under Uncertain Traffic Conditions. The 98th Transportation Research Board Annual Meeting, January 2019, Washington, D.C., USA.

8. *Zhao, S., Zhang, K. A data-driven Model Predictive Control framework for robust Cooperative Adaptive Cruise Control using mobile sensing data. The 97th Transportation Research Board Annual Meeting, January 7-11, 2018, in Washington, D.C.

Big Data Analytics of Advanced Traffic Sensing Data from Mobile and Crowdsourced Sensors

1. *Zhao, S., Zhang, K. 2019. A Distributionally Robust Optimization Approach to Reconstructing Missing Locations and Paths using High-Frequency Trajectory Data. Transportation Research Part C, Vol 102, pp.316-335.

2. *Zhao, S., Zhang, K. A data-driven dynamic route choice model under uncertainty using connected vehicle trajectory data. The 97th Transportation Research Board Annual Meeting, January 7-11, 2018, in Washington, D.C.

3. *Zhao, S., Zhang, K., Brooks, C., Banach, D., Aden, S. A comprehensive overview of improving traffic flow observability using UAVs as mobile sensors. The 97th Transportation Research Board Annual Meeting, January 7-11, 2018, in Washington, D.C.

4. *Zhao, S., Zhang, K.  2017. Observing Individual Dynamic Choices of Activity Chains from Location-based Crowdsourced Data. Transportation Research Part C, Vol 85, pp.1-22.

5. Zhao, S., Zhang, K. 2017. A Data-Driven Optimization Model to Observe Individual Dynamic Choices of Activity-Travel-Path using Connected Vehicles as Mobile Sensors. The 96th Annual Meeting of Transportation Research Board, Washington D.C., 2017.

6. Zhao, S., Zhang, K. 2017. Observing Space-Time Queueing Dynamics at a Signalized Intersection using Vehicles as Mobile Sensors. The 96th Annual Meeting of Transportation Research Board, Washington D.C., 2017.

Interdependency and Resiliency between Smart Grid and Intelligent Transportation Systems (plug-in electric vehicles)

1. *Tang, Y., *Zhao, S., Ten, C.-W., Zhang, K., Logenthiran, T. 2020. Establishment of Enhanced Load Modeling by Correlating with Occupancy Information.  IEEE Transactions on Smart Grid, Vol. 11, No. 2, pp.1702-1713.  

2. Jafari, M., Gauchia, A., Zhao, S., Zhang, K. Gauchia, L. 2018. EV Battery Cycle Aging Investigation in Real-World Daily Driving and V2G Services, IEEE Transactions on Transportation Electrification, Vol 4 (1), pp 122-134.

3. Tang, Y., Zhao, S., Ten, C.-T., Zhang, K., 2017. Enhancement of Distribution Load Modeling Using Statistical Hybrid Regression. Proc. IEEE PES Conference on Innovative Smart Grid Technologies, Apr. 23-26, 2017, Washington, DC, USA.

4. Xiong, J., Zhang, K., Guo, Y., Su, W., 2015. Investigate the Impacts of PEV Charging Facilities on Integrated Electric Distribution System and Electrified Transportation System. IEEE Transactions on Transportation Electrification, Vol 1 (2), pp.178-187.

5. Jafari, M., Gauchia, A., Zhang, K., Gauchia, L., 2015. Simulation and Analysis of the Effect of Real-World Driving Styles in an EV Battery Performance and Aging. IEEE Transactions on Transportation Electrification. Vol 1 (4) pp 391-401. 

6. Gauchia, A., Jafari, M., Zhang, K., Gauchia, L., 2015. REV-Cycle: A MATLAB-based Tool for Large-Data Analysis of Real-Life Driving Cycles for Electric Vehicles. 2015 IEEE International Transportation Electrification Conference and Expo (ITEC), Detroit, MI, June 14-17, 2015.

7. Su, W., Wang, J., Zhang, K., Huang, A. Q., 2014. Model Predictive Control-based Power Dispatch for Distribution System Considering Plug-in Electric Vehicle Uncertainty. Electric Power Systems Research Vol 106, pp29-35.

8. Xiong, J., Zhang, K., Liu, X., Su, W., 2014. Investigating the Impact of Plug-in Electric Vehicle Charging on Power Distribution Systems with the Integrated Modeling and Simulation of Transportation Network, 2014 IEEE Transportation Electrification Conference and Expo (Asia-Pacific), Beijing, CHINA, August 31-September 3, 2014.

9. Su, W., Wang, J., Zhang, K., Chow, M., 2012. Framework for Investigating the Impact of PHEV Charging on Power Distribution and Transportation Networks. The 38th Annual Conference of the IEEE Industrial Electronics Society, Montreal, Canada, 2012.

10. Karbowski, D., Zhang, K., 2012. Evaluation of Energy Consumption of Vehicles along a Stretch of Congested Freeway. The 91st Annual Meeting of Transportation Research Board, Washington D.C. 2012.

Transportation Network Equilibrium and Optimization

1. Auld, J., Hope, M., Ley, H., Sokolov, V., Xu, B., Zhang, K., 2016. POLARIS: Agent-based Modeling Framework Development and Implementation for Integrated Travel Demand and Network and Operations Simulations. Transportation Research Part C, Vol 64, pp. 101-116.

2. Hamdar, S., Zhang, K., Zeeshan, M. K., 2016. A Dynamic Coordinated Control System for Emergency Evacuation: Exploration and Assessment. ASCE Natural Hazards Review. ASCE Natural Hazards Review, Vol 17 (2). 

3. Auld, J., Hope, M. B., Ley, H., Sokolov, V., Xu, B., Zhang, K., 2014. POLARIS: An Integrated Agent-Based Simulation Model of Activity Travel Behavior and Network Operations. 5th TRB Conference on Innovations in Travel Demand Modeling, Baltimore, MD, April 27-31, 2014.

4. Lu, C.-C., Zhou, X., Zhang, K., 2013. Dynamic Origin-Destination Demand Flow Estimation under Congested Traffic Conditions. Transportation Research Part C, Vol 34, pp.16-37.

5. Zhang, K., Mahmassani, H. S., Lu, C.-C., 2013. Dynamic Pricing, Heterogeneous Users and Perception Error: Probit-Based Bi-Criterion Dynamic Stochastic User Equilibrium Assignment. Transportation Research Part C, Vol. 27, pp. 189-204.

6. Zhang, K. Zhou, X., Lu, C.-C., A Novel Integer Program Formulation for the Dynamic Traffic Assignment Problem. The 92nd Annual Meeting of Transportation Research Board, Washington D.C. 2013.

7. Zhang, K., Mahmassani, H. S., Vovsha, P., 2011. Integrated Nested Logit Mode Choice and Dynamic Network Micro-Assignment Model Platform to Support Congestion and Pricing Studies: The New York metropolitan Case. The 90th TRB Annual Meeting, January 2011, Washington, D.C., U.S.A.

8. Verbas, I. O., Mahmassani, H. S., Zhang, K., 2011. Time-Dependent Origin-Destination Demand Estimation: Challenges and Methods for Large-Scale Networks with Multiple Vehicle Classes. Transportation Research Record, No. 2263, pp.45-56.

9. Jiang, L., Mahmassani, H. S., Zhang, K., 2011. Congestion Pricing, Heterogeneous Users and Travel Time Reliability: Multi-Criterion Dynamic User Equilibrium Model and Efficient Implementation for Large-Scale Networks. Transportation Research Record, No.2252, pp.58-67.

10. Lu, C.-C., Zhang, K. 2011. A Robust Optimization Approach for System Optimal Dynamic Traffic Assignment with Demand Uncertainty. The 90th TRB Annual Meeting, January 2011, Washington, D.C., U.S.A.

11. Zhang, K., Mahmassani, H. S., Lu, C.-C., Probit-based Time-Dependent Stochastic User Equilibrium: Reformulation and Solution Algorithm. The 88th TRB Annual Meeting, Jan. 2009, Washington D.C.

12. Zhang, K., Mahmassani, H. S., Lu, C.-C., 2008. A Probit-Based Time-Dependent Stochastic User Equilibrium Traffic Assignment Model. Transportation Research Record, No. 2085, pp. 86-94.

13. Zhou, X., Mahmassani, H. S., Zhang, K., 2008. Dynamic Micro-Assignment Modeling Approach for Integrated Multimodal Urban Corridor Management. Transportation Research Part C, Vol. 16, pp. 167-186. (Quarter's Hottest Article for Transportation Research Part C: Top1 for two quarters: January to March, and April to June, 2008; Top 10 for three quarters: July, 2008 – March, 2009)

14. Zhang, K., Mahmassani, H. S., Integrated Multimodal Urban Corridor Management: The Potential Role of Bus Rapid Transit. Transport Chicago Conference, Jun. 6th, 2008.

Traffic Flow Theory and Large-Scale Network Traffic Simulation

1. *Hung, Y.-C., Zhang, K., 2022. Impact of Cooperative Adaptive Cruise Control on Traffic Stability. Transportation Research Record, June 2022. NEW

2. Zhu, J., Zhang, K., 2015. A Bayesian Adaptive Inference Approach to Estimating Heterogeneous Gap Acceptance Functions. The 94th Annual Meeting of Transportation Research Board, Washington D.C. 2015.

3. Hope, M. B., Auld, J., Sokolov, V., Xu, B., Ley, H., Zhang, K., 2014. POLARIS: Advanced Computational Methodologies for Real-Time Transportation Simulation, 5th TRB Conference on Innovations in Travel Demand Modeling, Baltimore, MD, April 27-31, 2014.

4. Wang, L., Mao, B., Chen, S., Zhang, K., Mixed Flow Simulation at Urban Intersections: Computational Comparisons between Conflict-Point Detection and Cellular Automata Models. CSO, vol. 2, pp.100-104, 2009 International Joint Conference on Computational Sciences and Optimization (CSO 2009).

5. Wang, L., Zhang, K., Mao, B., Chen, C., Continuous Simulation-Based Conflict-Point Detection Model for Heterogeneous Mixed Traffic Flows in an Urban Intersection. The 88th TRB Annual Meeting, Jan. 2009, Washington D.C.

6. Wang, L., Mao, B., Chen, S., Zhang, K., A P2P Computational Grid-Based Parallel Traffic Micro-simulation Model for Large Scale Transportation Networks. CSO, vol. 2, pp.95-99, 2009 International Joint Conference on Computational Sciences and Optimization (CSO 2009).

7. Shao, C., Zhang, K., Xu J., 2003. Distributed and Parallel Algorithm of Dynamic Traffic Assignment based on Computer Simulation. Journal of Transportation Engineering and Information, Vol. 1, pp 56-62.

   1. Zhang, K., Shao, C., Wang, L., 2002. Study of Dynamic Traffic Assignment Based on Distributed Parallel Algorithm. Journal of Northern Jiaotong University, Vol. 26, No. 5, pp 57-61. (In Chinese)

Multimodal Freight Logistics and Supply Chain Systems

1. *Ko, S., Lautala, P., Zhang, K. Data-driven Analysis of Short-distance Freight Rail Transportation Potential: A Case Study of Log Movements in the U.S. Lake Superior Region. The 100th Transportation Research Board (TRB) Annual Meeting, Washington, D.C., January 2021. 

2. *Ko, S., Lautala, P., Zhang, K. Data-driven Study on the Log Movements for the Upper Midwest: Impact of Rail Car Fleet Size on Freight Storage and Car Idling. The 99th Transportation Research Board (TRB) Annual Meeting, Washington, D.C., January 2020.

3. Yang, D., Zhang, K., 2015. Optimizing Multi-Layer Merge-In-Transit Supply Chains. The 94th Annual Meeting of Transportation Research Board, Washington D.C. 2015.

4. Zhang, K., Nair, R., Mahmassani, H. S., Miller-Hooks,  E. D., Arcot, V. C., Kuo, A., Dong, J., Lu, C.-C., 2008 Application and Validation of a Dynamic Freight Simulation-Assignment Model to a Large-Scale Intermodal Rail Network: the Pan-European Case. Transportation Research Record, No. 2066, pp. 9-20.

5. Nair, R., Miller-Hooks, E. D., Mahmassani, H. S., Arcot, V. C., Kuo, A., Zhang, K., Kozuki, A., Ludvigsen, J., 2008. From Sea to Shining Sea: The Market Potential for International Rail-Based Intermodal Services in Europe. Transportation Research Record, No. 2066, pp. 21-30.

6. Kuo, A., Miller-Hooks, E. D., Zhang, K., Mahmassani, H. S., 2008. Train Slot Cooperation in Multicarrier, International Rail-Based Intermodal Freight Transport. Transportation Research Record, No. 2043, pp. 31-40.

7. Miller-Hooks, E. D., Mahmassani, H. S., Nair, R., Zhang,  K., Arcot,  V. C., Kuo, A., Lu,  C.-C., Dong, J., Kozuki, A., Assessing Service Design Options and Strategies for Overcoming Barriers in the Reorient Intermodal Freight Transport Corridor. Proceedings of the European Transport Conference. The Netherlands, October 17-19, 2007. Available online: www.etcproceedings.org.

8. Mahmassani, H. S., Zhang, K., Dong, J., Lu, C.-C., Arcot, V. C., Miller-Hooks, E. D., 2007. A Dynamic Network Simulation-Assignment Platform for Multi-Product Intermodal Freight Transportation Analysis. Transportation Research Record, No. 2032, pp. 9-16.