标题:Dynamic Public Transport Passenger Flow Forecast Based on IMM Method
作者:Ma, Zhenliang; Xing, Jianping; Gao, Liang; Sha, Junchen; Wu, Yong; Wu, Yubing
通讯作者:Ma, Z
作者机构:[Ma, Zhenliang; Xing, Jianping; Gao, Liang; Sha, Junchen] Shandong Univ, Sch Informat Sci & Engn, Jinan 250100, Peoples R China.; [Wu, Yong; Wu, Yub 更多
会议名称:International Conference on Multimedia, Software Engineering and Computing
会议日期:NOV 26-27, 2011
来源:ADVANCES IN MULTIMEDIA, SOFTWARE ENGINEERING AND COMPUTING, VOL 2
出版年:2011
卷:129
页码:675-683
DOI:10.1007/978-3-642-25986-9_105
关键词:Urban public transport passenger flow forecast; IMM; DA
摘要:In this paper, an dynamic urban public transport passenger flow forecasting approach is proposed based on interact multiple model (IMM) method. The dynamic approach (DA) maximizes useful information content by assembling knowledge from correlate time sequences, and making full use of historical and real-time passenger flow data. The dynamic approach is accomplished as follows: By analyzing the source data, three correlate times sequences are constructed. The auto-regression (AR), autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA) models are selected to give predictions of the three correlate time sequence. The output of the dynamic IMM serves as the final prediction using the results from the three models. To assess the performance of different approaches, moving average, exponential smoothing. artificial neural network, ARIMA and the proposed dynamic approach are applied to the real passenger flow prediction. The results suggest that the DA can obtain a more accurate prediction than the other approaches.
收录类别:CPCI-S;EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84855222868&doi=10.1007%2f978-3-642-25986-9_105&partnerID=40&md5=52cef7c4431a915cc7371a315fe97c6b
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