Spatio-temporal parking occupancy forecasting integrating parking sensing records and street-level images
International Journal of Applied Earth Observation and Geoinformation - Tập 118 - Trang 103290 - 2023
Tài liệu tham khảo
Agatonovic-Kustrin, 2000, Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research, J. Pharm. Biomed. Anal., 22, 717, 10.1016/S0731-7085(99)00272-1
Avşar, 2022, Parking lot occupancy prediction using long short-term memory and statistical methods, MANAS J. Eng., 10, 35, 10.51354/mjen.986631
Bengio, 1994, Learning long-term dependencies with gradient descent is difficult, IEEE Trans. Neural Netw., 5, 157, 10.1109/72.279181
Chao, 2022, Time-domain characteristics of residential parking and SEM-BL integration model of parking method choice behaviour, Math. Probl. Eng., 2022, 10.1155/2022/5164257
Chen, 2022, Assessing the effects of convolutional neural network architectural factors on model performance for remote sensing image classification: An in-depth investigation, Int. J. Appl. Earth Obs. Geoinf., 112
Cho, 2014
Chung, 2014
Feng, 2019, Statistical analysis and prediction of parking behavior, 93
Gong, 2020, Data-driven agent-based model of intra-urban activities, 160
Gong, 2020, Extracting activity patterns from taxi trajectory data: A two-layer framework using spatio-temporal clustering, Bayesian probability and Monte Carlo simulation, Int. J. Geogr. Inf. Sci., 34, 1210, 10.1080/13658816.2019.1641715
Gong, 2021
Gong, 2022, Spatio-temporal travel volume prediction and spatial dependencies discovery using GRU, GCN and Bayesian probabilities, 130
Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735
Ketkar, 2017
Kipf, 2016
Kumar, 2018, Predictive analytics: a review of trends and techniques, Int. J. Comput. Appl., 182, 31
Lee, 2017, Cruising for parking: New empirical evidence and influential factors on cruising time, J. Transp. Land Use, 10, 931, 10.5198/jtlu.2017.1142
Liu, 2019, Machine learning and deep learning methods for intrusion detection systems: A survey, Appl. Sci., 9, 4396, 10.3390/app9204396
Mohamad, 2007, The rise of the private car in Kuala Lumpur, Malaysia, IATSS Res., 31, 69, 10.1016/S0386-1112(14)60185-0
Ostrom, 1990
Paidi, 2022, Short-term prediction of parking availability in an open parking lot, J. Intell. Syst., 31, 541
Qin, 2015, Urban rail transit pricing strategies for mitigating traffic congestion: a case study of ningbo, 1952
Qureshi, 2007, Urban transport and sustainable transport strategies: A case study of karachi, Pakistan, Tsinghua Sci. Technol., 12, 309, 10.1016/S1007-0214(07)70046-9
Sherstinsky, 2020, Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network, Physica D, 404, 10.1016/j.physd.2019.132306
Shi, 2009, Correlation and regression analysis, Ann. Allergy Asthma Immunol., 103, S35, 10.1016/S1081-1206(10)60820-4
Shoup, 2006, Cruising for parking, Transp. Policy, 13, 479, 10.1016/j.tranpol.2006.05.005
Slavova, 2022, Predicting truck parking occupancy using machine learning, Procedia Comput. Sci., 201, 40, 10.1016/j.procs.2022.03.008
Tamrazian, 2015, Where is my parking spot? Online and offline prediction of time-varying parking occupancy, Transp. Res. Rec., 2489, 77, 10.3141/2489-09
Wang, 2022, Parking in inner versus outer city spaces: Spatiotemporal patterns of parking problems and their associations with built environment features in Brisbane, Australia, J. Transp. Geogr., 98, 10.1016/j.jtrangeo.2021.103261
Wang, 2015, Beijing passenger car travel survey: implications for alternative fuel vehicle deployment, Mitig. Adaptation Strategies Glob. Change, 20, 817, 10.1007/s11027-014-9609-9
Yang, 2019, A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources, Transp. Res. C, 107, 248, 10.1016/j.trc.2019.08.010
Ye, 2020, Assessment of the economic and social impact of shared parking in residential areas, Information, 11, 411, 10.3390/info11090411
Yu, 2019, A review of recurrent neural networks: LSTM cells and network architectures, Neural Comput., 31, 1235, 10.1162/neco_a_01199
Yu, 2017, Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks, Sensors, 17, 1501, 10.3390/s17071501
Yue, 2012, Exploratory calibration of a spatial interaction model using taxi GPS trajectories, Comput. Environ. Urban Syst., 36, 140, 10.1016/j.compenvurbsys.2011.09.002
Zhang, 2022, Migratable urban street scene sensing method based on vision language pre-trained model, Int. J. Appl. Earth Obs. Geoinf., 113
Zhao, 2020, T-gcn: A temporal graph convolutional network for traffic prediction, IEEE Trans. Intell. Transp. Syst., 21, 3848, 10.1109/TITS.2019.2935152