Alternative Approach for Forecasting Parking Volumes

被引:9
|
作者
Lim, Hyeonsup [1 ]
Williams, Grant T. [1 ]
Abdelqader, Dua [1 ]
Amagliani, Joseph [1 ]
Ling, Ziwen [1 ]
Priester, Davis William [1 ]
Cherry, Christopher R. [1 ]
机构
[1] Univ Tennessee, 321 JD Tickle Bldg, Knoxville, TN 37996 USA
来源
WORLD CONFERENCE ON TRANSPORT RESEARCH - WCTR 2016 | 2017年 / 25卷
关键词
Parking Demand; Walking; Cruising; Generalized Cost; REQUIREMENTS; DEMAND;
D O I
10.1016/j.trpro.2017.05.360
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
Parking is an issue at the forefront of transportation planning in the core of any urban area. As public and private sectors invest in city development, particularly in the high density and mixed-use central business districts, forecasting parking volumes for multiple facilities throughout urban transformation is critical to parking supply decision-making. Most previous studies have limitations that may yield inaccurate predictions and cannot precisely analyze the impact to area parking facilities, due to model simplicity and limited data accessibility. In order to provide accurate estimation with detailed information and account for technological improvements in data availability, this study provides an alternative method by utilizing an assignment model with a generalized cost approach. This enables more detailed information of forecasting parking volumes and assessing parking accessibility with consideration of shared parking and time-of-day distribution of parking demand. A case study was conducted in downtown Knoxville, Tennessee providing a sensitivity analysis for investigating the effects of parameters in the model. The sensitivity analysis includes parking generation rates, walking speed, and cruising time. Additionally, the results of three alternative scenarios are provided to show the advantages of this model to examine the impact of parking facility development on adjacent facilities. Manual adjustments and surveys may be required to apply this model for other cities, but it would provide more useful information for their (policymakers, developers, and planners) decision making. (C) 2017 The Authors. Published by Elsevier B.V.
引用
收藏
页数:14
相关论文
共 50 条
  • [21] Forecasting with Alternative Data
    Fleder M.
    Shah D.
    Fleder, Michael (mfleder@mit.edu), 1600, Association for Computing Machinery (48): : 23 - 24
  • [22] An alternative to price forecasting
    UMA alternativa para previsão de preços
    Collares, Marcello (mcollares@fisheri.com), 2018, Assoc. Tecnica Brasileira de Celulose e Papel (79):
  • [23] Forecasting with Alternative Data
    Fleder, Michael
    Shah, Devavrat
    PROCEEDINGS OF THE ACM ON MEASUREMENT AND ANALYSIS OF COMPUTING SYSTEMS, 2019, 3 (03)
  • [24] Assessing Alternative Approaches to Setting Parking Requirements
    Engel-Yan, Joshua
    Passmore, Dylan
    ITE JOURNAL-INSTITUTE OF TRANSPORTATION ENGINEERS, 2010, 80 (12): : 30 - 34
  • [25] An Artificial Intelligence Based Forecasting in Smart Parking with IoT
    Fedchenkov, Petr
    Anagnostopoulos, Theodoros
    Zaslavsky, Arkady
    Ntalianis, Klimis
    Sosunova, Inna
    Sadov, Oleg
    INTERNET OF THINGS, SMART SPACES, AND NEXT GENERATION NETWORKS AND SYSTEMS, NEW2AN 2018, 2018, 11118 : 33 - 40
  • [26] Night parking demand forecasting based on survival analysis
    Li L.
    Gao T.
    Jiang Y.
    Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition), 2020, 50 (01): : 192 - 199
  • [27] Forecasting Groundwater Level by Artificial Neural Networks as an Alternative Approach to Groundwater Modeling
    Chitsazan, Manouchehr
    Rahmani, Gholamreza
    Neyamadpour, Ahmad
    JOURNAL OF THE GEOLOGICAL SOCIETY OF INDIA, 2015, 85 (01) : 98 - 106
  • [28] Forecasting groundwater level by artificial neural networks as an alternative approach to groundwater modeling
    Manouchehr Chitsazan
    Gholamreza Rahmani
    Ahmad Neyamadpour
    Journal of the Geological Society of India, 2015, 85 : 98 - 106
  • [29] Smart-Meter Big Data for Load Forecasting: An Alternative Approach to Clustering
    Alemazkoor, Negin
    Tootkaboni, Mazdak
    Nateghi, Roshanak
    Louhghalam, Arghavan
    IEEE ACCESS, 2022, 10 : 8377 - 8387