Forecast and Early Warning of Regional Bus Passenger Flow Based on Machine Learning
Wusheng Liu, Qian Tan, Wei Wu
Abstract
This paper mainly forecasts the short-term passenger flow of regional bus stations based on the integrated circuit (IC) card data of bus stations and puts forward an early warning model for regional bus passenger flow. Firstly, the bus stations were aggregated into virtual regional bus stations. Then, the short-term passenger flow of regional bus stations was predicted by the machine learning (ML) method of support vector machine (SVM). On this basis, the early warning model for regional bus passenger flow was developed through the capacity analysis of regional bus stations. The results show that the prediction accuracy of short-term passenger flow could be improved by replacing actual bus stations with virtual regional bus stations because the passenger flow of regional bus stations is more stable than that of a single bus station. The accurate prediction and early warning of regional bus passenger flow enable urban bus dispatchers to maintain effective control of urban public transport, especially during special and large-scale activities.