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In order to explore the establishment method of high-resolution vehicle emission inventory and analyze the temporal and spatial variation law of vehicle greenhouse gases (GHG) emissions. This paper proposes a bottom-up method based on road network information and the real-time average interval speed of road segments. A traffic speed-flow model is proposed to predict the hourly traffic flow and the localized Motor Vehicle Emission Simulator (MOVES) is used to simulate the CO 2 , N 2 O and CH 4 emission factors. A high temporal (1 h × 1 h) and spatial (1 km × 1 km) resolution GHG emission inventory of motor vehicles in Beijing in 2018 is developed by this means. The actual emissions of CO 2 , N 2 O and CH 4 are 19,864,590, 82 and 511 t, respectively. And the total GHG emission is 19,901,933 tCO 2e combined with global warming potential (GWP). The daily GHG emissions on weekday and weekend are 55206.30 and 52817.64 tCO 2e , respectively. There are three obvious peak emission periods on the weekday, namely, the morning peak (8:00–9:00), the noon peak (11:00–12:00) and the evening peak (18:00–19:00), which contribute 11.76%, 11.84% and 12.92% of the daily emission, respectively. The 1 h × 1 h emission grid shows the spatial distribution characteristics of emissions. The areas within the Fifth Ring Road (973 km2) are only 5.93% of the total area of the city (16,410 km2) but contribute 41.53% of the total vehicle GHG emissions. This study provides detailed data support for implementing vehicle GHG emission mitigation measures. • A 1 km × 1 km vehicle greenhouse gas emission inventory in Beijing was developed. • A bottom-up method based on road network and vehicle travel speed was proposed. • A modified speed-flow model was used to predict the hourly traffic flow on road. • The temporal and spatial variation laws of vehicle GHG emissions were analyzed. • The vehicle CO 2 emission in 2018 estimated in this study was 19,901,933 tCO 2e. [ABSTRACT FROM AUTHOR]