基于阶梯收费刷卡数据的公交下车站点算法优化与实证评估
CSTR:
作者:
作者单位:

1.西南交通大学 交通运输与物流学院,四川 成都 611756;2.重庆市交通规划研究院,重庆 401147

作者简介:

杨 飞(1980—),男,教授,博士生导师,工学博士,主要研究方向为交通大数据与智能交通。 E-mail: yangfei_traffic@163.com

通讯作者:

郭煜东(1994—),男,博士生,主要研究方向为交通大数据。E-mail: guoyudong_traffic@163.com

中图分类号:

U491.1

基金项目:

国家自然科学基金(52072313);国家重点研发计划(50908195);重庆规划和自然资源局项目(KJ-2021007)


Optimization and Empirical Evaluation of Passenger Leaving Station in Bus Based on Gradient Charge Swiping Card Data
Author:
Affiliation:

1.School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China;2.Chongqing Transport Planning Institute, Chongqing 401147, China

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    摘要:

    以某市连续5日全天公交阶梯收费刷卡数据、公交出行GPS(global positioning system)数据及公交站点数据为基础,结合出行链算法与随机森林网络,构建了一套公交下车站点融合分析模型。在模型中,首先匹配GPS数据与公交站点数据,确定不同时刻的公交到站信息,再以乘客上车站点位置、出行频率、活动空间、下车点用地类型分布、下车概率为输入,识别乘客下车站点,最终下车站点推算率提升至100%,全样本有效率达76.2%,相比现有基于出行链的方法,识别有效率提升37%。

    Abstract:

    This paper proposed a fusion analysis model for leaving station identification, based on the 5-day continuous (24 h) bus travel data, bus travel GPS data, and bus stop data of a certain city, in combination with the travel chain method and random forest network. In the model, the GPS data and bus stop data were first matched to determine the bus arrival information at different moments, then the passenger boarding location, travel frequency, activity scope, land use around the leaving station, and the probability of getting off the bus were applied as inputs to identify the passenger leaving station. The final calculation rate is improved to 100%, and the full sample efficiency rate reaches 76.2%. Compared with the existing methods based on the bus travel chain, the recognition efficiency is improved by 37%.

    参考文献
    [1] 中华人民共和国国务院. 国务院关于城市优先发展公共交通的指导意见(国发〔2012〕64号)[EB/OL]. [2013-01-15]. http://www.gov. cn/zhengce/content/2013-01/05/content_3346.htm.
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    [9] 杨鑫. 基于IC卡数据的公交客流智能推断方法研究[D]. 北京: 北京邮电大学, 2019.
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    [11] ASSEMI B, ALSGER A, MOGHADDAM M, et al. Improving alighting stop inference accuracy in the trip chaining method using neural networks[J]. Public Transport, 2020, 12(1): 89.
    [12] 胡继华,邓俊,黄泽. 结合出行链的公交IC卡乘客下车站点判断概率模型[J]. 交通运输系统工程与信息, 2014, 14(2): 62.
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杨飞,姜海航,郭煜东,刘健国,周涛.基于阶梯收费刷卡数据的公交下车站点算法优化与实证评估[J].同济大学学报(自然科学版),2022,50(3):320~327

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  • 收稿日期:2021-12-16
  • 在线发布日期: 2022-04-11
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