CLAB Model: A Deep Learning Model for Short-term Prediction of Passenger Rental Travel Demand

Passenger travel demand prediction is an integral part of intelligent transportation systems, and accurate travel demand prediction is of great significance for vehicle scheduling. However, existing prediction methods are unable to accurately explore its potential spatiotemporal correlation and mostly ignore the impact of historical inflow on travel demand. In order to further exploit the spatiotemporal characteristics of spatiotemporal big data and improve the accuracy of the model in predicting passenger travel demand, this paper proposes a Conv-LSTM Attention BiLSTM (CLAB) model for short-time prediction of passenger rental travel demand. The attention-based Conv-LSTM module extracts spatial features and short-term temporal features of passenger travel demand at the near moment, where the attention mechanism automatically assigns different weights to discriminate the importance of demand sequences at different times. To explore long-term temporal features, two BiLSTM modules are used to extract temporal features of historical inflow sequences and temporal features of daily passenger temporal features of the demand series. Experiments are conducted using the order data of online and cruising taxis on Xiamen Island, and the results show that: (1) the CLAB model is more suitable for predicting the future 5-min short-time passenger travel demand using 30-min historical data; (2) the overall effect error of the CLAB model is lower and has better prediction results compared with the benchmark prediction model. The CLAB model is more effective than the CNN-LSTM, LSTM, BiLSTM, CNN, and ConvLSTM by 33.179%, 33.153%, 33.204%, 5.401%, and 5.914% in mean absolute error (MAE) and 34.389%, 34.423%, 34.524%, 6.772%, and 6.669% in Root Mean Square Error (RMSE), respectively; (3) the CLAB model performs better for weekday prediction with higher regularity than non-working day prediction, with best prediction for weekday morning peaks. © 2023 Journal of Geo-Information Science. All rights reserved.