Characteristics Analysis of Bus Stop Failure Using Automatic Vehicle Location Data

被引:8
|
作者
Li, Rui [1 ]
Xue, Xin [1 ]
Wang, Hua [2 ]
机构
[1] Hohai Univ, Coll Civil & Transportat Engn, 1 Xi Kang Rd, Nanjing 210098, Peoples R China
[2] Natl Univ Singapore, Dept Civil & Environm Engn, 21 Lower Kent Ridge Rd, Singapore 117576, Singapore
基金
中国国家自然科学基金;
关键词
TIME-ESTIMATION; TRAVEL-TIME; DWELL TIME; MODEL; RELIABILITY; PREDICTION; CAPACITY;
D O I
10.1155/2020/8863262
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Queue forming behind a bus stop on an urban street is common and a traffic bottleneck usually occurs around the bus stop area. The bus stop failure means arriving buses cannot move into the bus stop due to limited capacity but have to wait for available loading areas. It is related with the transit operation level. Traditionally, the failure rate (FR), defined as the percentage of buses that arrives at the bus stop to find all loading areas occupied, is adopted in bus capacity analysis. However, the concept of FR is unable to quantitatively analyze failure characteristics in terms of its dispersion and uncertainty over time. Therefore, in this paper, we propose a new index called failure duration rate (FDR) to evaluate the bus stop failure, which can characterize waiting time for traffic delay calculation and capacity drop estimation. The automatic vehicle location data at eight bus stops in Wujiang District Suzhou, China, over 56 working days, are used to analyze the temporal characteristics of FR and FDR. We next examined the failed service duration characteristics during peak hours at the eight bus stops. Based on these characteristics analyses, we then proposed a Distribution Fitting and Cumulative Distribution Correlation (DF-CDC) approach to explore the correlation between FDR and FR at the same cumulative distribution function levels and validated the bus stop failure performance using the cross-validation method. The analysis results revealed that (i) FR fluctuates more significant than FDR, (ii) FDR is a more robust index than FR in describing the traffic characteristics incurred by bus stop failures, and (iii) FDR performs better in failure characteristics analysis than FR.
引用
下载
收藏
页数:16
相关论文
共 50 条
  • [21] Evaluating alternative methods to estimate bus running times by archived automatic vehicle location data
    Pili, Francesco
    Olivo, Alessandro
    Barabino, Benedetto
    IET INTELLIGENT TRANSPORT SYSTEMS, 2019, 13 (03) : 523 - 530
  • [22] Passengers' Perceptions and Effects of Bus-Holding Strategy using Automatic Vehicle Location Technology
    Hanaoka, Shinya
    Qadir, Fayyaz Mahmood
    JOURNAL OF ADVANCED TRANSPORTATION, 2009, 43 (03) : 301 - 319
  • [23] Passengers' perceptions and effects of bus-holding strategy using automatic vehicle location technology
    Hanaoka, Shinya
    Qadir, Fayyaz Mahmood
    Journal of Advanced Transportation, 2009, 43 (03): : 301 - 319
  • [24] Identifying Causes of Performance Issues in Bus Schedule Adherence with Automatic Vehicle Location and Passenger Count Data
    Mandelzys, Michael
    Hellinga, Bruce
    TRANSPORTATION RESEARCH RECORD, 2010, (2143) : 9 - 15
  • [25] Assigning Bus Delay and Predicting Travel Times using Automated Vehicle Location Data
    Coghlan, Christy
    Dabiri, Sina
    Mayer, Brian
    Wagner, Mitch
    Williamson, Eric
    Eichler, Michael
    Ramakrishnan, Naren
    TRANSPORTATION RESEARCH RECORD, 2019, 2673 (03) : 624 - 636
  • [26] Measuring temporal and spatial travel efficiency for transit route system using low-frequency bus automatic vehicle location data
    Yang, Fengping
    Peng, Liqun
    Wang, Chenhao
    Bai, Yuelong
    ADVANCES IN MECHANICAL ENGINEERING, 2018, 10 (10):
  • [27] Time Reliability Measures in bus Transport Services from the Accurate use of Automatic Vehicle Location raw Data
    Barabino, Benedetto
    Di Francesco, Massimo
    Mozzoni, Sara
    QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2017, 33 (05) : 969 - 978
  • [28] Characteristics Analysis of Mixed Traffic Flow around Bus Stop under Cooperative Vehicle Infrastructure Environment
    Xue, Xin
    Cao, Yang
    Yang, Qiao
    Yu, Yan
    Li, Rui
    CICTP 2022: INTELLIGENT, GREEN, AND CONNECTED TRANSPORTATION, 2022, : 2485 - 2495
  • [29] A prescription for transit arrival/departure prediction using automatic vehicle location data
    Cathey, FW
    Dailey, DJ
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2003, 11 (3-4) : 241 - 264
  • [30] Diagnosis of Irregularity Sources by Automatic Vehicle Location Data
    Barabino, Benedetto
    Di Francesco, Massimo
    IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE, 2021, 13 (02) : 152 - 165