Analysis of trip generation rates in residential commuting based on mobile phone signaling data

被引:16
|
作者
Shi, Fei [1 ]
Zhu, Le [1 ]
机构
[1] Nanjing Univ, Nanjing, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Trip rate; signaling data; commuting trip; trip generation; trip production; trip attraction; HANDBOOK; SCHOOL; MODEL;
D O I
10.5198/jtlu.2019.1431
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
In this paper, mobile phone signaling data are first processed to extract information such as the trip volume and spatial distribution from the starting point to the termination point. This information is then used to identify the residential and employment locations of users. Next, multiple Thiessen polygons based on cell towers are aggregated into Traffic Analysis Zones (TAZs) to minimize differences between the actual cell tower coverage and the theoretical coverage. Then, based on TAZ cluster analysis involving transport accessibility and commuting population density, multiple stepwise regression is applied to obtain the commuting trip production rates and attraction rates for overall residential land and each subdivided housing type during the peak morning hours. The obtained commuting trip generation rates can be directly applied to local transport analysis models. This paper suggests that as information and data sharing continue, mobile phone signaling data will become increasingly important for use in future trip rate research.
引用
收藏
页码:201 / 220
页数:20
相关论文
共 50 条
  • [1] Urban Traffic Commuting Analysis Based on Mobile Phone Data
    Dong, Honghui
    Ding, Xiaoqing
    MingchaoWu
    Shi, Yan
    Jia, Limin
    Qin, Yong
    Chu, Lianyu
    [J]. 2014 IEEE 17TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2014, : 611 - 616
  • [2] Utilizing Mobile Phone Signaling Data for Trip Mode Identification
    Chen, Xiaoxu
    Yang, Chao
    Xu, Xiangdong
    [J]. CICTP 2019: TRANSPORTATION IN CHINA-CONNECTING THE WORLD, 2019, : 4588 - 4599
  • [3] Methods for Inferring Route Choice of Commuting Trip From Mobile Phone Network Data
    Sakamanee, Pitchaya
    Phithakkitnukoon, Santi
    Smoreda, Zbigniew
    Ratti, Carlo
    [J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2020, 9 (05)
  • [4] Borrowing residential trip generation rates
    Miller, JS
    Hoel, LA
    Goswami, AK
    Ulmer, JM
    [J]. JOURNAL OF TRANSPORTATION ENGINEERING, 2006, 132 (02) : 105 - 113
  • [5] Analysis of Public Transit Trip Chain of Commuters Based on Mobile Phone Data and GPS Data
    Zhou, Li
    Ji, Yuxiong
    Wang, Yizhe
    [J]. 2017 4TH INTERNATIONAL CONFERENCE ON TRANSPORTATION INFORMATION AND SAFETY (ICTIS), 2017, : 635 - 639
  • [6] Urban Road Traffic State Identification Based on Mobile Phone Signaling Data and Commuting Travel Identification
    Long, Zhen
    Lu, Zhenbo
    Wang, Yulu
    Ji, Xiaohui
    [J]. CICTP 2020: TRANSPORTATION EVOLUTION IMPACTING FUTURE MOBILITY, 2020, : 39 - 49
  • [7] Bus Trip OD Identification Based on Mobile Phone Data
    Yu, Yong-Bo
    Hou, Jia
    [J]. Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology, 2021, 21 (02): : 65 - 72
  • [8] Modelling trip generation using mobile phone data: A latent demographics approach
    Bwambale, Andrew
    Choudhury, Charisma F.
    Hess, Stephane
    [J]. JOURNAL OF TRANSPORT GEOGRAPHY, 2019, 76 : 276 - 286
  • [9] A Models Comparison to Estimate Commuting Trips Based on Mobile Phone Data
    Pinheiro, Carlos A. R.
    Van Vlasselaer, Veronique
    Baesens, Bart
    Evsukoff, Alexandre G.
    Silva, Moacyr A. H. B.
    Ebecken, Nelson F. F.
    [J]. SOFTWARE ENGINEERING IN INTELLIGENT SYSTEMS (CSOC2015), VOL 3, 2015, 349 : 35 - 44
  • [10] Estimation of Trip Generation Rates for Residential Areas in Jordan
    Al-Masaeid, Hashem R.
    Fayyad, Sanaa S.
    [J]. JORDAN JOURNAL OF CIVIL ENGINEERING, 2018, 12 (01) : 162 - 172