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Calibration of traffic flow fundamental diagrams for network simulation applications: A two-stage clustering approach.

, , , and . ITSC, page 1348-1353. IEEE, (2016)

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Calibration of traffic flow fundamental diagrams for network simulation applications: A two-stage clustering approach., , , and . ITSC, page 1348-1353. IEEE, (2016)A robust machine learning structure for driving events recognition using smartphone motion sensors., , , and . J. Intell. Transp. Syst., 28 (1): 54-68 (January 2024)A simulation study on prioritizing connected freight vehicles at intersections for traffic flow optimization (industrial paper)., , , , , , and . SIGSPATIAL/GIS, page 53:1-53:10. ACM, (2022)μ-DDRL: A QoS-Aware Distributed Deep Reinforcement Learning Technique for Service Offloading in Fog computing Environments., , , and . CoRR, (2023)A hybrid machine-learning and optimization method to solve bi-level problems., , , and . Expert Syst. Appl., (2018)Application of an exact gradient method to estimate dynamic origin-destination demand for melbourne network., , and . ITSC, page 1945-1950. IEEE, (2016)How Many Simulation Runs are Required to Achieve Statistically Confident Results: A Case Study of Simulation-Based Surrogate Safety Measures., , , and . ITSC, page 274-278. IEEE, (2015)A solution to the road network design problem for multimodal flow., , , , and . ITSC, page 235-240. IEEE, (2016)Network-Wide Traffic State Estimation and Rolling Horizon-Based Signal Control Optimization in a Connected Vehicle Environment., , and . IEEE Trans. Intell. Transp. Syst., 23 (6): 5840-5858 (2022)Electric vehicle charging: it is not as simple as charging a smartphone (vision paper)., , , , , and . SIGSPATIAL/GIS, page 32:1-32:4. ACM, (2022)