Predicting Transjakarta Passengers with LSTM-BiLSTM Deep Learning Models for Smart Transportpreneurship

Abstrak

Travel pattern variations pose challenges in building a prediction model that accurately captures seasonal patterns or precision of BRT passenger numbers. An approach that integrates sophisticated prediction algorithms with high accuracy is needed to address the Transjakarta BRT passenger number prediction model problem. The proposed prediction model with the best accuracy is sought using deep learning on 8 models. The prediction model is used for short-term and long-term predictions, as well as looking for correlations in the prediction results of 13 Transjakarta corridors. The Python programming language with the Deep Learning Tensor Flow framework is run by Google Colaboratory used in the prediction simulation environment. The combination of BiLSTM-CNN was found to have the best accuracy of the evaluation value (SMAPE = 15.9387, MAPE = 0.598, and MSLE = 0.0425), although it has the longest time (134 seconds). Fluctuations in short-term predictions of passenger numbers evenly occur simultaneously across all corridors. Fluctuations in long-term predictions evenly occur simultaneously across all corridors, except in February. There is no negative correlation in the 13 prediction results and there are 8 corridors that have a close positive correlation. The prediction results can be used by transportation operators and the government to optimize resource planning and transportation policies to support sustainable community and economic mobility.