Qinglong Lu

Qinglong Lu is a Research Associate and a Ph.D. candidate at Technical University of Munich (TUM) since June 2021. His research focuses on mobility pattern analysis, transportation system resilience evaluation, ride-sharing services, and DTA model calibration.

Qinglong holds a bachelor’s degree in Traffic Engineering from Sun Yat-sen University (SYSU), China, and a Master of Science in Transportation Systems from Technical University of Munich (TUM), Germany.

Other links: Google Scholar    Github    ResearchGate

Education

Jun 2021 -    
Present
Ph.D. Candidate
Technical University of Munich (TUM)
Munich, Germany
Oct 2018 -    
Oct 2020
M.Sc. in Transportation Systems
Technical University of Munich (TUM)
Munich, Germany
Sep 2014 -    
Jun 2018
B.E. in Traffic Engineering
Sun Yat-sen University (SYSU)
Guangzhou, China

Teaching

Summer semester: Statistical Learning and Data Analytics for Transportation Systems

Winter semester: Special Topic on Model Calibration

Supervising

Selected Publications

Journal publications

Lyu, C., Lu, Q. L., Wu, X., & Antoniou, C. (2024). Tucker factorization-based tensor completion for robust traffic data imputation. Transportation Research Part C: Emerging Technologies160, 104502.

Lu, Q. L., Qurashi, M., & Antoniou, C. (2024). A two-stage stochastic programming approach for dynamic OD estimation using LBSN data. Transportation Research Part C: Emerging Technologies158, 104460.

Lu, Q. L., Mahajan, V., Lyu, C., & Antoniou, C. (2024). Analyzing the impact of fare-free public transport policies on crowding patterns at stations using crowdsensing data. Transportation Research Part A: Policy and Practice179, 103944.

Lu, Q. L., Qurashi, M., & Antoniou, C. (2023). Simulation-based policy analysis: The case of urban speed limits. Transportation Research Part A: Policy and Practice, 175, 103754.

Lu, Q. L., Qurashi, M., & Antoniou, C. (2022). A ridesplitting market equilibrium model with utility-based compensation pricingTransportation, 1-36.

Qurashi, M., Lu, Q. L., Cantelmo, G., & Antoniou, C. (2022). Dynamic demand estimation on large scale networks using Principal Component Analysis: The case of non-existent or irrelevant historical estimatesTransportation Research Part C: Emerging Technologies136, 103504.

Lu, Q. L., Qurashi, M., Varesanovic, D., Sodnik, J., & Antoniou, C. (2021). Exploring the influence of automated driving styles on network efficiencyTransportation Research Procedia52, 380-387.

Lu, Q. L. (2020). A structural equations approach for modeling the endogeneity of lane-mean speeds considering the downstream speedsEuropean Journal of Transport and Infrastructure Research20(4), 252-265.

Conference contributions

Yang, N., Lu, Q. L., Lyu, C., & Antoniou, C. (2024). Transfer learning for transportation system resilience patterns prediction using floating car data. In 103th TRB Annual Meeting 2024.

Lu, Q. L., Sun, W., Dai, J., Schmöcker, J.D., & Antoniou, C. (2023). Surrogate modeling for recovery measure optimization to improve traffic resilience. In the 9th International Symposium on Transport Network Resilience 2023.

Lu, Q. L., Qurashi, M., & Antoniou, C. (2023). A Two-Stage Stochastic Programming approach for Dynamic OD Estimation. In 102th TRB Annual Meeting 2023.

Lu, Q. L., Qurashi, M., & Antoniou, C. (2022). A Stochastic Programming Method for OD Estimation Using LBSN Check-In Data. In 4th Symposium on Management of Future Motorway and Urban Traffic Systems.

Lu, Q. L., Yang, K., & Antoniou, C. (2021). Crash risk analysis for the mixed traffic flow with human-driven and connected and autonomous vehicles. In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) (pp. 1233-1238). IEEE.

Qurashi, M., Lu, Q., Cantelmo, G., & Antoniou, C. (2021). PC-SPSA: Exploration and assessment of different historical data--set generation methods for enhanced DTA model calibration. In 9th Symposium of the European Association for Research in Transportation (hEART2020).

Qurashi, M., Lu, Q., Cantelmo, G., & Antoniou, C. (2020). PC-SPSA: Implementation assessment and exploration of different historical data-set generation methods for enhanced DTA model calibration. In 3rd Symposium on Management of Future Motorway and Urban Traffic Systems.

Gao, J., Lu, Q. L., & Cai, M. (2020). Quantifying privacy vulnerability under linkage attack across multi-source individual mobility dataIn 99th Transportation Research Board (TRB) Annual Meeting.