Abstract:

In order to address the challenges of urban traffic congestion and safety, an ACLR model was proposed. By integrating ConvLSTM, attention mechanisms, and residual structures, the ACLR model effectively enhanced the extraction of spatio-temporal traffic features.The time, space and other characteristics of taxi traffic were processed respectively, and the influence of regional point of interest(POI) data on taxi traffic was mined. Additionally, a specialized learning component was incorporated to capture the impact of external factors and point-of-interest density on traffic flow. Using taxi trajectory data from Beijing, the ACLR model demonstrates superior prediction accuracy compared to other models such as the autoregressive integrated moving average (ARIMA) model, long short-term memory (LSTM), deep spatio-temporal residual networks (ST-ResNet), convolutional neural network(CNN)-ResNet-LSTM (CRL), and attentive crowd flow machines (ACFM) in urban traffic flow forecasting,which is helpful to improve the prediction performance of the model without POI density or considering POI density. The predicted value of the ACLA model is basically consistent with the real value, and it can also be in good agreement with the real value during peak hours, which effectively improves the ability to extract traffic temporal and spatial characteristics, reduces the prediction error, and optimizes the traffic flow prediction performance.

Key words: attentive convolutional long short-term memory(ConvLSTM) residual network(ResNet)(ACLR), urban taxi, traffic flow prediction, space-time characteristics, residual structure