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Symmetry 2018, 10(9), 369; https://doi.org/10.3390/sym10090369

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

1
Information Science and Technology School, Dalian Maritime University, Dalian 116026, China
2
Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR 72204, USA
3
Public Security Information Department, Liaoning Police College, Dalian 116036, China
4
Information Science and Engineering School, Shenyang Ligong University, Shenyang 110168, China
*
Author to whom correspondence should be addressed.
Received: 19 July 2018 / Revised: 22 August 2018 / Accepted: 28 August 2018 / Published: 31 August 2018
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Abstract

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. View Full-Text
Keywords: short-term prediction; Kalman filter; time series; big data technology; deep learning short-term prediction; Kalman filter; time series; big data technology; deep learning
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Zhai, H.; Cui, L.; Nie, Y.; Xu, X.; Zhang, W. A Comprehensive Comparative Analysis of the Basic Theory of the Short Term Bus Passenger Flow Prediction. Symmetry 2018, 10, 369.

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