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High-Resolution Simulation and Prediction of Urban Private Vehicles Energy Consumption System: Agent-Based Modelling

https://doi.org/10.2139/ssrn.4775227
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inferredcites →https://doi.org/10.1016/j.apenergy.2015.07.063openalex-extracted
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inferredcites →https://doi.org/10.1016/j.energy.2018.04.164openalex-extracted
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inferredcites →https://doi.org/10.1016/j.jclepro.2018.12.099openalex-extracted
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Potential for reducing carbon emissions from urban traffic based on the carbon emission satisfaction: Case study in Shanghai
inferredcites →https://doi.org/10.1016/j.procs.2018.04.080openalex-extracted
Predicting Electric Vehicle Charging Demand using Mixed Generalized Extreme Value Models with Panel Effects
inferredcites →https://doi.org/10.1016/j.tra.2016.01.012openalex-extracted
A study on the determinants of private car ownership in China: Findings from the panel data
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An empirical evaluation of different usage pattern between car-sharing battery electric vehicles and private ones
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inferredcites →https://doi.org/10.1016/j.trf.2018.09.024openalex-extracted
Running on empty – Users’ charging behavior of electric vehicles versus traditional refueling
inferredcites →https://doi.org/10.1016/j.trpro.2017.03.035openalex-extracted
Autonomous Shared Mobility-On-Demand: Melbourne Pilot Simulation Study
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A simultaneous model of household activity participation and trip chain generation
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Impact of uncoordinated plug-in electric vehicle charging on residential power demand
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Transportation emissions scenarios for New York City under different carbon intensities of electricity and electric vehicle adoption rates
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Reliability Evaluation of Composite Power Systems: Evaluating the Impact of Full and Plug-in Hybrid Electric Vehicles
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Transportation Energy Data Book: Edition 11
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