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For car-following models, the car-following characteristics differ depending on the vehicle type, such as passenger cars, motorcycles, and trucks. Therefore, constructing a model for each category is essential. To that end, various modeling methods have been proposed; however, herein, we particularly focused on the long short-term memory (LSTM), which is the best method for forecasting long-term time-series data.[1, 2] The objective of this study was to construct a car-following model for each vehicle category using the LSTM and to evaluate the model accuracy for each vehicle category. In this study, US-101 and I-80 data provided by the next-generation simulation (NGSIM), which is based on natural traffic flow data, were used. In the NGSIM, only car-following situations were selected as car-following data, and these were classified into the vehicle type: motorcycles, passenger cars, and trucks.