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and Transportation","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# VED (Vehicle Energy Dataset)\nA novel large-scale database for fuel and energy use of diverse vehicles in real-world.\n\nVED captures GPS trajectories of vehicles along with their timeseries data of fuel, energy, speed, and auxiliary power usage, and the data was collected through onboard OBD-II loggers from Nov, 2017 to Nov, 2018.\nThe fleet consists of total 383 personal cars (264 gasoline vehicles, 92 HEVs, and 27 PHEV/EVs) in Ann Arbor, Michigan, USA. \nDriving scenarios range from highways to traffic-dense downtown area in various driving conditions and seasons. \nIn total, VED accumulates approximately 374,000 miles. \n\nA number of examples were presented in the paper to demonstrate how VED can be utilized for vehicle energy and behavior studies. Potential research opportunities include data-driven vehicle energy consumption modeling, driver behavior modeling, machine and deep learning, calibration of traffic simulators, optimal route choice modeling, prediction of human driver behaviors, and decision making of self-driving cars.\n\nLink to the paper: \n[Vehicle Energy Dataset (VED), A Large-scale Dataset for Vehicle Energy Consumption Research](https://doi.org/10.1109/TITS.2020.3035596)\\\n**Geunseob (GS) Oh**, David J. LeBlanc, Huei Peng\\\nIEEE Transactions on Intelligent Transportation Systems (T-ITS), 2020.\\\nThe paper is also available on [Arxiv](https://arxiv.org/pdf/1905.02081.pdf).\n\n\nContact: gsoh@umich.edu.\n\nGS Oh, Ph.D. Candidate, University of Michigan.\n\n\n\n## Files\nVED consists of Dynamic Data (time-stamped naturalistic driving records of 383 vehicles) and Static Data (Vehicle parameters for the 383 vehicles)\n\nDynamic Data: \"VED_DynamicData.7z\" contains a number of \"VED_mmddyy_week.csv\" files\n- Includes a week worth dynamic data, for mmddyy ~ (mmddyy + 7 days)\n- Columns represent:\n\tDayNum,\tVehId,\tTrip,\tTimestamp(ms),\tLatitude[deg],\tLongitude[deg],\tVehicle Speed[km/h],\tMAF[g/sec],\tEngine RPM[RPM],\tAbsolute Load[%],\tOutside Air Temperature[DegC],\tFuel Rate[L/hr],\tAir Conditioning Power[kW],\tAir Conditioning Power[Watts],\tHeater Power[Watts],\tHV Battery Current[A],\tHV Battery SOC[%],\tHV Battery Voltage[V],\tShort Term Fuel Trim Bank 1[%],\tShort Term Fuel Trim Bank 2[%],\tLong Term Fuel Trim Bank 1[%],\tLong Term Fuel Trim Bank 2[%]\n- Notes:\n\tEach combination of VehID, Trip is unique.\n\tDayNum represents elapsed days since a reference date. (DayNum 1 = Nov, 1st, 2017, 00:00:00, DayNum 1.5 = Nov, 1st, 2017, 12:00:00)\n\tFor the details, refer to [the VED paper](https://arxiv.org/abs/1905.02081)\n\t\n\t\nStatic Data: \"VED_Static_Data_ICE\u0026HEV.xlsx\", and \"VED_Static_Data_PHEV\u0026EV.xlsx\"\n- Includes parameters of all 383 vehicles (264 gasoline vehicles, 92 HEVs, and 27 PHEV/EVs)\n\t- There are 3 pure EV vehicles in the dataset. 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