Estimating the Capacity of a Curbside Bus Stop with Multiple Berths Using Probabilistic Models

被引:2
|
作者
Luo, Tian [1 ]
Yang, Jingshuai [2 ]
机构
[1] Lanzhou Inst Technol, Sch Automobile Engn, 1st Gongjiaping East Rd, Lanzhou 730050, Peoples R China
[2] Changan Univ, Sch Automobile, Middle Sect Naner Huan Rd, Xian 710064, Peoples R China
来源
TEHNICKI VJESNIK-TECHNICAL GAZETTE | 2020年 / 27卷 / 05期
关键词
blockage probability; bus stop and berth; capacity; logistics; performance analysis; public transit;
D O I
10.17559/TV-20181025170011
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Capacity estimation of a curbside bus stop is essential to evaluation of its operation, reliability and performance. Arrival buses and served buses will form an overflow queue and an interlocking queue in loading areas with high frequencies. Therefore, bus stop blockage may reduce the stop capacity. The capacity of a bus stop is modelled as a function of the blockage probability, the arrival of buses, and the service time, while considering the no-overtaking principle and allowable-overtaking principle. This study aims to estimate the capacity, minimum arrival time and maximum service time based on the blockage probability and number of berths. The results indicate that congestion can be effectively alleviated by increasing the number of berths when the demand for loaded buses is low due to the significantly changing probability threshold for a NO stop. A congestion and stopping principle is important when multiple bus routes converge at the same bus stop. By combination with an actual case, an optimal overtaking principle is obtained using a computer program written in the MATLAB environment. The developed methodology can be practically applied to determine the loading principle and designated stopping berths for multi-route buses.
引用
收藏
页码:1597 / 1606
页数:10
相关论文
共 50 条
  • [41] Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings
    Athreya, Arjun P.
    Bruckl, Tanja
    Binder, Elisabeth B.
    Rush, A. John
    Biernacka, Joanna
    Frye, Mark A.
    Neavin, Drew
    Skime, Michelle
    Monrad, Ditlev
    Iyer, Ravishankar K.
    Mayes, Taryn
    Trivedi, Madhukar
    Carter, Rickey E.
    Wang, Liewei
    Weinshilboum, Richard M.
    Croarkin, Paul E.
    Bobo, William, V
    NEUROPSYCHOPHARMACOLOGY, 2021, 46 (07) : 1272 - 1282
  • [42] Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings
    Arjun P. Athreya
    Tanja Brückl
    Elisabeth B. Binder
    A. John Rush
    Joanna Biernacka
    Mark A. Frye
    Drew Neavin
    Michelle Skime
    Ditlev Monrad
    Ravishankar K. Iyer
    Taryn Mayes
    Madhukar Trivedi
    Rickey E. Carter
    Liewei Wang
    Richard M. Weinshilboum
    Paul E. Croarkin
    William V. Bobo
    Neuropsychopharmacology, 2021, 46 : 1272 - 1282
  • [43] Estimating power demand shaving capacity of buildings on an urban scale using extracted demand response profiles through machine learning models
    Yu, Xinran
    Ergan, Semiha
    APPLIED ENERGY, 2022, 310
  • [44] Co-Estimating State of Charge and Capacity of Automotive Lithium-Ion Batteries Under Deep Degradation Using Multiple Estimators
    Yoo, Min Young
    Lee, Jung Heon
    Lee, Hyunjoon
    Choi, Joo-Ho
    Huh, Jae Sung
    Sung, Woosuk
    APPLIED SCIENCES-BASEL, 2024, 14 (20):
  • [45] Unified Approach for Estimating Axial-Load Capacity of Concrete-Filled Double-Skin Steel Tubular Columns of Multiple Shapes Using Nonlinear FE Models and Artificial Neural Networks
    Joo, Mohammad Rafiq
    Sofi, Fayaz Ahmad
    PRACTICE PERIODICAL ON STRUCTURAL DESIGN AND CONSTRUCTION, 2023, 28 (02)
  • [46] Estimating Schedule-Based Assignment Models for High-Speed Rail (HSR) Services Using Multiple Data Sources
    Silvestri, Fulvio
    Montino, Tommaso
    Mariano, Pietro
    SOCIOECONOMIC IMPACTS OF HIGH-SPEED RAIL SYSTEMS, IW-HSR 2023, 2024, : 149 - 172
  • [47] Estimating Grassland Biophysical Parameters in the Cantabrian Mountains Using Radiative Transfer Models in Combination with Multiple Endmember Spectral Mixture Analysis
    Fernandez-Guisuraga, Jose Manuel
    Gonzalez-Perez, Ivan
    Reguero-Vaquero, Ana
    Marcos, Elena
    REMOTE SENSING, 2024, 16 (23)
  • [48] Predicting Structural Deterioration Condition of Individual Storm-Water Pipes Using Probabilistic Neural Networks and Multiple Logistic Regression Models
    Tran, H. D.
    Perera, B. J. C.
    Ng, A. W. M.
    JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 2009, 135 (06) : 553 - 557
  • [49] Automatic iterative segmentation of multiple sclerosis lesions using Student's t mixture models and probabilistic anatomical atlases in FLAIR images
    Freire, Paulo G. L.
    Ferrari, Ricardo J.
    COMPUTERS IN BIOLOGY AND MEDICINE, 2016, 73 : 10 - 23
  • [50] Estimating Transit Route OD Flow Matrices from APC Data on Multiple Bus Trips Using the IPF Method with an Iteratively Improved Base: Method and Empirical Evaluation
    Ji, Yuxiong
    Mishalani, Rabi G.
    McCord, Mark R.
    JOURNAL OF TRANSPORTATION ENGINEERING, 2014, 140 (05)