A Comprehensive Comparative Analysis of the Basic Theory of the Short Term Bus Passenger Flow Prediction

• Published in: Symmetry (Basel) Vol. 10; no. 9; p. 369
• Main Authors: Zhai, Huawei, Cui, Licheng, Nie, Yu, Xu, Xiaowei, Zhang, Weishi
• Format: Journal Article
• Language: English
• Published: MDPI AG 01.09.2018
• Subjects: big data technology; deep learning; Kalman filter; short-term prediction; time series
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym10090369

In order to meet the real-time public travel demands, the bus operators need to adjust the timetables in time. Therefore, it is necessary to predict the variations of the short-term passenger flow. Under the help of the advanced public transportation systems, a large amount of real-time data about passenger flow is collected from the automatic passenger counters, automatic fare collection systems, etc. Using these data, different kinds of methods are proposed to predict future variations of the short-term bus passenger flow. Based on the properties and background knowledge, these methods are classified into three categories: linear, nonlinear and combined methods. Their performances are evaluated in detail in the major aspects of the prediction accuracy, the complexity of training data structure and modeling process. For comparison, some long-term prediction methods are also analyzed simply. At last, it points that, with the help of automatic technology, a large amount of data about passenger flow will be collected, and using the big data technology to speed up the data preprocessing and modeling process may be one of the directions worthy of study in the future.