基于IC卡与GPS数据的公交通勤出行特征分析

doi:10.3969/j.issn.1674-0696.2021.10.20

重庆交通大学学报（自然科学版） ›› 2021, Vol. 40 ›› Issue (10): 171-177.DOI: 10.3969/j.issn.1674-0696.2021.10.20

• 交通+大数据人工智能 • 上一篇

基于IC卡与GPS数据的公交通勤出行特征分析

李淑庆，刘耀鸿，邱豪基

(重庆交通大学交通运输学院，重庆 400074)

收稿日期:2021-05-10 修回日期:2021-08-20 发布日期:2021-10-29
作者简介:李淑庆(1963—)，男，四川西充人，教授，主要从事交通运输规划与管理及交通安全方面的研究。E-mail：SQL999888@126.com 通信作者：刘耀鸿(1997—)，男，贵州毕节人，硕士研究生，主要从事交通运输规划与管理方面的研究。E-mail：1415257200@qq.com
基金资助:
国家重点研发计划项目(2018YFB1601000)

Commuting Characteristics of Public Transport Based on IC Card and GPS Data

LI Shuqing, LIU Yaohong, QIU Haoji

(School of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China)

Received:2021-05-10 Revised:2021-08-20 Published:2021-10-29

摘要/Abstract

摘要： 公交通勤人群是公交客流的重要组成部分，公交通勤出行特征是制定公交优先发展战略、公交规划、优化与运营管理的数据基础。针对传统以“车”为研究对象的碎片化公交通勤出行特征分析方法，基于公交IC卡与GPS数据，从公交乘客“人”角度出发，系统分析提出了公交出行链特征、区间客流不均衡系数、职住平衡水平及线路黏性系数4个维度指标参数计算方法。以重庆市主城区连续5个工作日47条代表性线路的公交乘客为例，对通勤出行特征进行分析，结果表明：重庆市主城区通勤客流换乘次数较少、长线公交客流需求较大、职住平衡水平较高、约1/4的通勤出行者对线路选择具有较强黏性。

关键词: 交通工程；通勤乘客；IC卡数据；GPS数据；预处理；通勤特征

Abstract: The public transport commuter is an important part of public transport passenger flow. The characteristics of public transport commuting travel are the data basis for formulating public transport priority development strategy, public transport planning and optimization as well as operation management. Aiming at the traditional analysis method of fragmented bus commuter travel characteristics with “car” as the research object, the calculation method of four dimensional index parameters such as bus travel chain characteristics, section passenger flow imbalance coefficient, job-residence balance level and line viscosity coefficient was systematically analyzed and put forward, which was based on bus IC card and GPS data and from the perspective of bus passengers “people”. Taking the bus passengers of 47 representative lines in the main urban area of Chongqing for 5 consecutive working days as an example, the characteristics of commuting trips were analyzed. The results show that the commuting passenger flow in the main urban area of Chongqing has less transfer times, larger demand for long-distance bus passenger flow and higher level of job-residence balance; and about a quarter of commuters have strong stickiness to route selection.

Key words: traffic engineering; commute passenger; IC card data; GPS data; pre-processing; commute characteristics

中图分类号:

U491.2+6

李淑庆，刘耀鸿，邱豪基. 基于IC卡与GPS数据的公交通勤出行特征分析[J]. 重庆交通大学学报（自然科学版）, 2021, 40(10): 171-177.

LI Shuqing, LIU Yaohong, QIU Haoji. Commuting Characteristics of Public Transport Based on IC Card and GPS Data[J]. Journal of Chongqing Jiaotong University(Natural Science), 2021, 40(10): 171-177.

参考文献

［1］ FOELL S, PHITHAKKITNUKOON S, VELOSO M, et al. Regularity of public transport usage: A case study of bus rides in lisbon, portugal［J］. Journal of Public
Transportation, 2016, 19(4): 10-10.
［2］ LI Guangxia, YU Liang, NG W S, et al. Predicting home and work locations using public transport smart card data by spectral analysis［C］∥2015 IEEE 18th
International Conference on Intelligent Transportation Systems. Canary Islands, Spain: IEEE, 2015: 2788-2793.
［3］王月玥. 基于多源数据的公共交通通勤出行特征提取方法研究［D］. 北京: 北京工业大学, 2014.
WANG Yueyue.Research on Feature Extraction Method of Public Transport Commuting Based on Multi-Source Data［D］. Beijing: Beijing University of Technology, 2014.
［4］李莹. 公交乘客出行特征及其图谱分析［D］. 广州：华南理工大学, 2017.
LI Ying.The Travel Characteristics and Spectral Analysis of Bus Passengers［D］. Guangzhou:South China University of Technology, 2017.
［5］罗霞, 李树超, 刘硕智, 等. 基于AVL和IC卡数据的公交通勤特性研究［J］. 计算机仿真, 2020, 37(6): 111-116,256.
LUO Xia, LI Shuchao, LIU Shuozhi, et al. Research on commuting characteristics of bus based on AVL and IC card data［J］. Computer Simulation, 2020, 37 (6): 111-116 ,
256.
［6］文婧, 王星, 连欣. 北京市居民通勤特征研究——基于千余份问卷调查的分析［J］.人文地理,2012,27(5):62-68.
WEN Jing, WANG Xing, LIAN Xin.A study on commuting characteri-stics in Beijing — Based on over 1 000 questionnaires ［J］. Human Geography, 2012, 27 (5): 62-68.
［7］梁泉, 翁剑成, 林鹏飞, 等. 基于个体出行图谱的公共交通通勤行为辨别方法研究［J］. 交通运输系统工程与信息, 2018, 18(2): 100-107.
LIANG Quan, WENG Jiancheng, LIN Pengfei, et al. Public transport commuter identification based on individual travel graph［J］. Journal of Transportation Systems
Engineering and Information Technology, 2018, 18 (2): 100-107.
［8］曹晨, 甄峰, 汪侠,等. 基于结构方程模型的南京市就业者通勤行为特征对健康的影响研究［J］. 地理科学进展, 2020, 39(12): 2043-2053.
CAO Chen, ZHEN Feng, WANG Xia, et al. Study on the influence of commuting behavior characteristics of urban residents on health in Nanjing city based on structural
equation model［J］. Progress in Geography, 2020, 39 (12): 2043-2053.
［9］翁小雄, 吕攀龙. 基于GBDT算法的地铁IC卡通勤人群识别［J］. 重庆交通大学学报(自然科学版), 2019, 38(5): 8-12.
WENG Xiaoxiong, LYU Panlong.Subway IC card commuter crowd identification based on GBDT algorithm［J］. Journal of Chongqing Jiaotong University(Natural Science),
2019, 38 (5): 8-12.
［10］吕攀龙. 基于多源数据的公交通勤出行特征挖掘与分析［D］. 广州: 华南理工大学, 2019.
LYU Panlong.Mining and Analysis of Public Transportation Commute Travel Characteristics Based on Multi-source Data［D］. Guangzhou: South China University of
Technology, 2019.
［11］周向栋. 基于IC卡和GPS数据的公交区间不确定性需求预测［D］.长沙: 长沙理工大学,2018.
ZHOU Xiangdong.Uncertain Transit Demand Forecasting Based on IC Card and GPS Data［D］. Changsha: Changsha University of Technology, 2018.
［12］李杨. 基于智能公交数据的超长公交线路特征分析及优化判别方法研究［D］. 成都: 西南交通大学, 2019.
LI Yang.Research on Feature Analysis and Optimum Discrimination Method of Long-Distance Bus Line Based on Intelligent Bus Data［D］. Chengdu: Southwest Jiaotong
University, 2019.
［13］冉江宇, 付凌峰, 阚长城, 等. 基于通勤大数据的城市职住分离度研究——《2020年全国主要城市通勤监测报告》核心指标分析［J］. 城市交通, 2020, 18(5): 10-17.
RAN Jiangyu, FU Lingfeng, KAN Changcheng,et al. Urban job-residence separation based on big commuting data: Analysis of core indicators in the 2020 monitoring
report on national commuting report for major cities in China［J］. Urban Transport of China, 2020, 18 (5): 10-17.
［14］ ZHOU Xingang, YEH A G, LI Weifeng, et al. A commuting spectrum analysis of the jobs-housing balance and self-containment of employ-ment with mobile phone
location big data［J］. Environment and Planning B: Urban Analytics and City Science, 2018, 45(3): 434-451.
［15］ KIM J, CORCORAN J, PAPAMANOLIS M. Route choice stickiness of public transport passengers: Measuring habitual bus ridership beha-viour using smart card data
［J］.Transportation Research Part C: Emerging Technologies, 2017, 83(11): 146-164.
［16］北京公共交通控股(集团)有限公司. 城市公共交通运营调度管理［M］. 北京: 人民交通出版社股份有限公司, 2016: 129-130.
Beijing Public Transportation Corporation. Urban Public Transportation Operation and Dispatch Management［M］. Beijing: China Communi-cations Press Co.,Ltd., 2016:
129-130.

[1]	史天亮，王文光. 信息观和粒子群算法在城市交通拥堵中的研究[J]. 重庆交通大学学报（自然科学版）, 2021, 40(04): 19-25.
[2]	马艳丽，朱洁玉，张宿峰，张鹏. 考虑能见度的无信号交叉口行人与车辆冲突识别[J]. 重庆交通大学学报（自然科学版）, 2021, 40(03): 16-21.
[3]	胡立伟1，孟玲1，祁首铭2，张亚平2,罗振武1. 基于城市交通拥塞的区域路网服务退化指数研究[J]. 重庆交通大学学报（自然科学版）, 2018, 37(12): 92-97.
[4]	孔祥夫，杨家文. 基于对数正态分布的出行时长可靠性计算[J]. 重庆交通大学学报（自然科学版）, 2017, 36(3): 84-89.
[5]	孙连超，邵毅明，颜雪丽，雷星. 基于TCT的公路交通事故黑点鉴别方法研究[J]. 重庆交通大学学报（自然科学版）, 2012, 31(1): 63-67.
[6]	崔丽珍，于洤. 基于ZigBee的无线交通同步控制系统的设计与实现[J]. 重庆交通大学学报（自然科学版）, 2012, 31(1): 83-85.

基于IC卡与GPS数据的公交通勤出行特征分析

Commuting Characteristics of Public Transport Based on IC Card and GPS Data

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 6

编辑推荐

Metrics