A Novel Hierarchical Hybrid Model for Short-Term Bus Passenger Flow Forecasting

Huawei Zhai,Licheng Cui,Weishi Zhang,Xiaowei Xu,Ruijie Tian

doi:10.1155/2020/7917353

Abstract

For the increasing travel demands and public transport problems, dynamically adjusting timetable or bus scheduling is necessary based on accurate real-time passenger flow forecasting. In order to get more accurate passenger flow in future, this paper proposes a novel hierarchical hybrid model based on time series model, deep belief networks (DBNs), and improved incremental extreme learning machine (Im-ELM) to forecast short-term passenger flow. The proposed model is named HTSDBNE with two modelling steps. First, referring the idea of parallelization, the hybrid model, constructed by time series model, DBN, and Im-ELM, is used to forecast short-term passenger flow in different time scales hierarchically and parallel. Second, Im-ELM is utilized to analyse the relationship of forecasting results from the first step, and the weighted outputs of Im-ELM are as the final forecasting results. Comparing with single forecasting models and typical hybrid forecasting models, the testing results indicate that HTSDBNE has better performances. The mean absolute percent error of prediction results is around 10% and fully meets the application requirements of bus operation enterprise.

Highlights

For current urban bus transport system, it faces more and more problems, such as improper arriving of buses, overcrowded or empty carriages, and so on, which cause passengers delay, bad ride experiences, and waste of transport resources. us, many enterprises try to adopt dynamically setting the timetable in real time based on the passenger flow variations and provide services in a proactive manner as opposed to a reactive manner with a predictive capability [1, 2]
For tracking the nonlinear characteristics of real passenger flow, many nonlinear methods have been introduced by researchers, such as the support vector machine (SVM) model [8], least squares support vector machine (LSSVM) model [9], fuzzy neural networks [10, 11], Bayesian network [12, 13], radial basis function neural networks (RBF-ANN) [14,15,16,17], and grey model [18,19,20]. e Journal of Advanced Transportation core idea of these nonlinear methods is to construct the nonlinear relationship between passenger flow and mine more potential information without prior knowledge [21]
In order to solve the problems mentioned above, make full use of the advantages of linear and nonlinear models, improve the universality and accuracy of the models, and reduce model complexity, this paper proposes a short-term passenger flow hierarchical hybrid forecasting model based on time series model, deep belief networks (DBNs) and incremental extreme learning machine (Im-ELM), called HTSDBNE

Summary

Introduction

For current urban bus transport system, it faces more and more problems, such as improper arriving of buses, overcrowded or empty carriages, and so on, which cause passengers delay, bad ride experiences, and waste of transport resources. us, many enterprises try to adopt dynamically setting the timetable in real time based on the passenger flow variations and provide services in a proactive manner as opposed to a reactive manner with a predictive capability [1, 2]. Short-term passenger flow prediction, the forecasting time interval not exceeding 60 minutes, is essentially important for setting the timetable in real time It is one of the most significant basics for the operation planning and decision making so as to rationally utilize transport resources, solve or ease transport problems, and provide better bus services [3, 4]. In order to solve the problems mentioned above, make full use of the advantages of linear and nonlinear models, improve the universality and accuracy of the models, and reduce model complexity, this paper proposes a short-term passenger flow hierarchical hybrid forecasting model based on time series model, DBN and Im-ELM, called HTSDBNE.

Bus Passenger Flow Statistics

Experiments and Analysis

Findings

Conclusions