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MAC: Measuring the Impacts of Anomalies on Travel Time of Multiple Transportation Systems.

, , , , , , and . Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 3 (2): 42:1-42:24 (2019)

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coSense: Collaborative Urban-Scale Vehicle Sensing Based on Heterogeneous Fleets., , , , , , , and . Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2 (4): 196:1-196:25 (2018)Modeling Fine-Grained Human Mobility on Cellular Networks., , and . WWW (Companion Volume), page 35-37. ACM / IW3C2, (2020)CellRep: Usage Representativeness Modeling and Correction Based on Multiple City-Scale Cellular Networks., , , , , and . WWW, page 584-595. ACM / IW3C2, (2020)RISC: Resource-Constrained Urban Sensing Task Scheduling Based on Commercial Fleets., , , , and . Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 4 (2): 62:1-62:20 (2020)Prioritizing Manual Test Cases in Traditional and Rapid Release Environments., , and . ICST, page 1-10. IEEE Computer Society, (2015)MAC: Measuring the Impacts of Anomalies on Travel Time of Multiple Transportation Systems., , , , , , and . Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 3 (2): 42:1-42:24 (2019)FairCharge: A Data-Driven Fairness-Aware Charging Recommendation System for Large-Scale Electric Taxi Fleets., , , , , and . Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 4 (1): 28:1-28:25 (2020)Human Mobility Modeling on Metropolitan Scale Based on Multiple Cellphone Networks: Poster Abstract., and . IoTDI, page 321-322. ACM, (2017)MoCha: Large-Scale Driving Pattern Characterization for Usage-based Insurance., , , , , , , and . KDD, page 2849-2857. ACM, (2021)VeMo: Enabling Transparent Vehicular Mobility Modeling at Individual Levels with Full Penetration., , , , , and . MobiCom, page 11:1-11:16. ACM, (2019)