Fluctuation Trend and Space Prediction of Bus Passenger Flows Based on FIG-SVM

doi:10.3969/j.issn.1674-0696.2023.11.18

Journal of Chongqing Jiaotong University(Natural Science) ›› 2023, Vol. 42 ›› Issue (11): 134-140.DOI: 10.3969/j.issn.1674-0696.2023.11.18

• Transportation Infrastructure Engineering • Previous Articles

Fluctuation Trend and Space Prediction of Bus Passenger Flows Based on FIG-SVM

HE Qingling, PEI Yulong, XU Huizhi, HOU Lin

(College of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, Heilongjiang, China)

Received:2022-06-29 Revised:2022-09-20 Published:2023-11-27

基于FIG-SVM的公交客流波动趋势与空间预测

何庆龄，裴玉龙，徐慧智，侯琳

(东北林业大学土木与交通学院，黑龙江哈尔滨 150040)

作者简介:何庆龄(1994—),男,甘肃靖远人,博士研究生,主要从事智能优化算法和交通规划方面的研究。E-mail:qinglinghe@yeah.net 通信作者：裴玉龙(1961—),男,黑龙江桦川人,教授,博士,主要从事交通规划方面的研究。E-mail:peiyulong@nefu.edu.cn
基金资助:
国家重点研发计划项目（2018YFB1600902）；中央高校基本科研业务费专项资金（2572022AW62）

Abstract

Abstract: According to the fluctuation characteristics of bus passenger flows time series and the correlation between influencing factors, the bus passenger flows were divided into time windows with the length of 7 d. The time window sample data information was granularized through fuzzy theory, and the time series after granulation of three sets of fuzzy information such as LOW, R and UP were obtained. The support vector machine model was used to predict, and the fluctuation trend and spatial range of bus passenger flows were obtained. The proposed model was used to empirically study the 245 d IC data of No. 1 bus in Harbin. The results show that the proposed model has higher accuracy than ARMA and GA-BP model and can effectively predict the fluctuation trend and space of bus passenger flows.

Key words: traffic engineering; bus passenger flows; fluctuation characteristics; space prediction; support vector machine

摘要： 根据公交客流时间序列的波动特征和影响因素的相关关系，将公交客流以7 d为长度划分时间窗口，通过模糊理论将时间窗口样本数据信息粒化，得到LOW、R、UP这3组模糊信息粒化后的时间序列，使用支持向量机模型对其进行预测，获得了公交客流波动趋势与空间范围。运用该模型对哈尔滨市1路公交245 d的IC卡数据进行实证研究。结果表明：该模型相较于ARMA和GA-BP模型具有较高精度，能有效预测公交客流的波动趋势与空间。

关键词: 交通工程;公交客流;波动特征;空间预测;支持向量机

CLC Number:

U491.1+4

HE Qingling, PEI Yulong, XU Huizhi, HOU Lin. Fluctuation Trend and Space Prediction of Bus Passenger Flows Based on FIG-SVM[J]. Journal of Chongqing Jiaotong University(Natural Science), 2023, 42(11): 134-140.

何庆龄，裴玉龙，徐慧智，侯琳. 基于FIG-SVM的公交客流波动趋势与空间预测[J]. 重庆交通大学学报（自然科学版）, 2023, 42(11): 134-140.

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Fluctuation Trend and Space Prediction of Bus Passenger Flows Based on FIG-SVM

基于FIG-SVM的公交客流波动趋势与空间预测

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