Multi-scale Fusion and Dynamic Adaptive Graph Bus Passenger Flow Prediction Model

Abstract

Bus passenger flow prediction is a crucial issue of public transportation planning and management. Though spatio-temporal graph convolution has shown promising results for subway passenger flow prediction, the existing spatial modeling methods based on graph convolution will bring huge spatial memory consumption for complex bus lines and larger-scale node data. Additionally, bus passenger flow is significantly influenced by immediate traffic conditions within a short time. To tackle these challenges, a multi-scale fusion and dynamic adaptive graph bus passenger flow prediction model (MFDAG) is presented. The proposed model effectively integrates passenger flow, time, and weekly information to enhance the feature dimension of the data. Moreover, it employs a dynamic adaptive graph method to learn the relationships between different stations. Furthermore, a multi-scale fusion propagation method is proposed to represent the complex spatial dependency relation, and a multi-scale convolution propagation method is designed to learn the multi-scale temporal dependency relation. The experiments are conducted by using two passenger flow datasets, and the results are compared with other traffic prediction methods. Experimental results demonstrate that the proposed bus passenger flow prediction method based on multi-scale fusion and dynamic adaptive graph exhibits higher prediction accuracy.

Keywords

• bus passenger flow prediction; graph sampling; dynamic adaptive graph; multi-scale fusion