Analyzing Overnight Parking Demand Characteristics Using City-Scale Automatic Vehicle Identification Data in Shanghai, China
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 1
Abstract
Parking has become a major problem in urban traffic management, stemming from insufficient understanding of vehicle parking demand. The rapidly growing intelligent transportation system enables traffic managers to detect vehicle movement continuously on a large scale and supports individual parking behavior analysis. Basic-overnight parking demand, as the subject of this study, is the important part of parking demand. This study proposed a method to calculate overnight parking behavior based on automatic vehicle identification (AVI) data and took Shanghai as a case study to analyze overnight parking demand characteristics. After delineation of traffic analysis zones (TAZs) and reconstruction of travel paths, a vehicle state estimation model based on spatiotemporal information and the parking location model based on density-based spatial clustering of applications with noise (DBSCAN) clustering was developed to calculate overnight parking behavior. This was the first time that individual overnight parking behavior at the metropolitan level was directly calculated, instead of estimated, to master the city’s total overnight parking demand. The accuracy of overnight parking position calculation reached 85.39% in the validation experiment. This study systematically analyzed both macro urban characteristics and micro individual characteristics of overnight parking demand. Most vehicles have the characteristics of commuters in parking for 14 h overnight and being more elastic on weekends, which supports refined parking management in the temporal dimension. The spatial distribution of overnight parking demand significantly correlates with residential districts but shows severe imbalance with parking supply. The micro analysis indicates that vehicles show an obvious polarization in the tendency of overnight parking in any area. Generally, vehicles have multiple overnight parking positions but only park in one or two positions on most nights. The quantitative results are helpful to further understand urban overnight parking demand and individual overnight parking behavior, supporting accurate formulation of parking policies.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential and may only be provided with restrictions. The AVI and GPS data for Shanghai, China, used in the study are confidential. The POI data are from AMap services at https://lbs.amap.com/api/webservice/guide/api/search.
Acknowledgments
This work is supported by two grants from the State Key Program of National Natural Science of China (Grant Nos. U1811463 and U21B2090).
References
Ahmed, L. A. 2009. “A study of parking characteristics in a central part of Basrah city.” J. Eng. Sustainable Dev. 13 (1): 143–165.
Al-Sahili, K., and J. Hamadneh. 2016. “Establishing parking generation rates/models of selected land uses for Palestinian cities.” Transp. Res. Part A Policy Pract. 91 (Sep): 213–222. https://doi.org/10.1016/j.tra.2016.06.027.
Ataç, S., N. Obrenović, and M. Bierlaire. 2021. “Vehicle sharing systems: A review and a holistic management framework.” EURO J. Transp. Logist. 10 (Jan): 100033. https://doi.org/10.1016/j.ejtl.2021.100033.
Aydinoglu, A. C., and A. S. Iqbal. 2021. “Determining parking demand and locating parking areas using geographic analytics methods.” J. Urban Plann. Dev. 147 (1): 05020035. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000650.
Chen, Q., Y. Wang, and S. Pan. 2015. “Characteristics of parking in central Shanghai, China.” J. Urban Plann. Dev. 142 (3): 05015012. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000293.
Coconea, L., and E. Bellini. 2019. “Advanced traffic management systems supporting resilient smart cities.” Transp. Res. Procedia 41 (Jan): 556–558. https://doi.org/10.1016/j.trpro.2019.09.099.
Dogaroglu, B., S. P. Caliskanelli, and S. Tanyel. 2021. “Comparison of intelligent parking guidance system and conventional system with regard to capacity utilisation.” Sustainable Cities Soc. 74 (Nov): 103152. https://doi.org/10.1016/j.scs.2021.103152.
Editorial Department of China Journal of Highway and Transport. 2016. “Review on China’s traffic engineering research progress: 2016.” China J. Highway Transport 29 (6): 1–161.
Fiez, T., L. J. Ratliff, C. Dowling, and B. Zhang. 2018. “Data driven spatio-temporal modeling of parking demand.” In Proc., 2018 Annual American Control Conf. (ACC), 2757–2762. New York: IEEE.
Fu, Y., C. Wang, T.-L. Liu, and H.-J. Huang. 2021. “Parking management in the morning commute problem with ridesharing.” Res. Transp. Econ. 90 (Dec): 101037. https://doi.org/10.1016/j.retrec.2021.101037.
Gomari, S., C. Knoth, and C. Antoniou. 2021. “Cluster analysis of parking behaviour: A case study in Munich.” Transp. Res. Procedia 52 (Jan): 485–492. https://doi.org/10.1016/j.trpro.2021.01.057.
Guan, Y., Y. Wang, X. Yan, H. Guo, Y. Zhou, and R. Mishra. 2020. “A big-data-driven framework for parking demand estimation in urban central districts.” J. Adv. Transp. 2020 (Dec): 1–13. https://doi.org/10.1155/2020/8898848.
ITE (Institute of Transportation Engineers). 2019. Parking generation. Washington, DC: ITE.
Kimpton, A., D. Pojani, C. Ryan, L. Ouyang, N. Sipe, and J. Corcoran. 2020. “Contemporary parking policy, practice, and outcomes in three large Australian cities.” Prog. Plann. 153 (Nov): 100506. https://doi.org/10.1016/j.progress.2020.100506.
Li, C. 2014. “City business circle parking characteristics and parking choice research.” Master’s thesis, School of Traffic and Transportation, Chongqing Jiaotong Univ.
Li, L., Y. Jiang, Y. Zhou, and B. Wu. 2019. “Potential features of parking demand characteristics.” J. Tongji Univ. (Natural Science) 47 (4): 515–520.
Ma, X., and B. Xia. 2021. “Pattern recognition of parking demand in ancient urban areas based on spatio-temporal data fusion.” China Ancient City 35 (2): 24–30. https://doi.org/10.19924/j.cnki.1674-4144.2021.02.004.
Ministry of Public Security of the People’s Republic of China. 2014. General technical specifications for automatic recording systems of red light cameras. GA/T 496-2014. Beijing: Ministry of Public Security.
National Bureau of Statistics. 2020. ShangHai statistical yearbook 2020. Beijing: China Statistics Press.
Nie, Y., W. Yang, Z. Chen, N. Lu, L. Huang, and H. Huang. 2021. “Public curb parking demand estimation with POI distribution.” IEEE Trans. Intell. Transp. Syst. 23 (5): 4614–4624. https://doi.org/10.1109/TITS.2020.3046841.
Parmar, J., P. Das, F. Azad, S. Dave, and R. Kumar. 2020a. “Evaluation of parking characteristics: A case study of Delhi.” Transp. Res. Procedia 48 (Jan): 2744–2756. https://doi.org/10.1016/j.trpro.2020.08.242.
Parmar, J., P. Das, and S. M. Dave. 2020b. “Study on demand and characteristics of parking system in urban areas: A review.” J. Traffic Transp. Eng. 7 (1): 111–124. https://doi.org/10.1016/j.jtte.2019.09.003.
Qi, X., Y. Ji, W. Li, and S. Zhang. 2021. “Vehicle trajectory reconstruction on urban traffic network using automatic license plate recognition data.” IEEE Access 9 (Mar): 49110–49120. https://doi.org/10.1109/ACCESS.2021.3068964.
Scheiner, J., N. Faust, J. Helmer, M. Straub, and C. Holz-Rau. 2020. “What’s that garage for? Private parking and on-street parking in a high-density urban residential neighbourhood.” J. Transport Geogr. 85 (May): 102714. https://doi.org/10.1016/j.jtrangeo.2020.102714.
Shen, T., Y. Hong, M. M. Thompson, J. Liu, X. Huo, and L. Wu. 2020. “How does parking availability interplay with the land use and affect traffic congestion in urban areas? The case study of Xi’an, China.” Sustainable Cities Soc. 57 (Jun): 102126. https://doi.org/10.1016/j.scs.2020.102126.
Song, L. 2011. “Theoretical analysis and division method of traffic analysis zone.” Master’s thesis, School of Highway, Chang’an Univ.
Tian, L. 2012. “Characteristics and demand of parking facilities in Ningbo city.” Master’s thesis, School of Traffic and Transportation Engineering, Central South Univ.
Tong, C. O., S. C. Wong, and B. S. Y. Leung. 2004. “Estimation of parking accumulation profiles from survey data.” Transportation 31 (2): 183–202. https://doi.org/10.1023/B:PORT.0000016579.36253.a9.
Wang, J. J., and Q. Liu. 2014. “Understanding the parking supply mechanism in China: A case study of Shenzhen.” J. Transport Geogr. 40 (Oct): 77–88. https://doi.org/10.1016/j.jtrangeo.2014.04.019.
Xing, C. 2020. “Study on characteristics and management of on-street parking in the central area of Qiqihar.” Master’s thesis, College of Police Information Engineering, People’s Public Security Univ. of China.
Xing, Y., K. Wang, and J. J. Lu. 2020. “Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai, China.” J. Transport Geogr. 87 (Jul): 102787. https://doi.org/10.1016/j.jtrangeo.2020.102787.
Xu, L., B. Liu, and H. Zhang. 2014. “Research and application of parking demand forecasting and parking supply strategy.” Technol. Econ. Areas Commun. 16 (2): 39–43.
Xu, M. H., Y. Q. Liu, Q. L. Huang, Y. X. Zhang, and G. F. Luan. 2007. “An improved Dijkstra’s shortest path algorithm for sparse network.” Appl. Math. Comput. 185 (1): 247–254. https://doi.org/10.1016/j.amc.2006.06.094.
Yang, J., and J. Sun. 2015. “Vehicle path reconstruction using automatic vehicle identification data: An integrated particle filter and path flow estimator.” Transp. Res. Part C Emerging Technol. 58 (Sep): 107–126. https://doi.org/10.1016/j.trc.2015.07.003.
Yan-Ling, W., W. Xin, and Z. Ming-chun. 2016. “Current situation and analysis of parking problem in Beijing.” Procedia Eng. 137 (Jan): 777–785. https://doi.org/10.1016/j.proeng.2016.01.316.
Yao, W., M. Zhang, S. Jin, and D. Ma. 2021. “Understanding vehicles commuting pattern based on license plate recognition data.” Transp. Res. Part C Emerging Technol. 128 (Jul): 103142. https://doi.org/10.1016/j.trc.2021.103142.
Yu, J., and D. Yu. 2019. “Exploration and application of quantitative analysis of the contradiction between urban parking supply and demand.” In Proc., Vibrant Urban and Rural Areas for Better Living—2019 China Urban Planning Annual Conf., 409–416. Beijing: China Architecture & Building Press. https://doi.org/10.26914/c.cnkihy.2019.003400.
Zhan, X., R. Li, and S. V. Ukkusuri. 2020. “Link-based traffic state estimation and prediction for arterial networks using license-plate recognition data.” Transp. Res. Part C Emerging Technol. 117 (Aug): 102660. https://doi.org/10.1016/j.trc.2020.102660.
Zhang, J. 2005. “Research on urban parking characteristics and demand prediction.” Master’s thesis, School of Transportation, Southeast Univ.
Zheng, J. 2017. “The characteristics of parking behavior and the optimization method of supply and demand balance based on parking sharing mode in urban residential areas.” Ph.D. thesis, School of Transportation, Southeast Univ.
Zhou, Z. 2019. “Parking demand analysis and parking demand management in hospitals based on characteristics of vehicle parking.” Master’s thesis, School of Traffic and Transportation, Beijing Jiaotong Univ.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 1 • January 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Dec 29, 2021
Accepted: Aug 31, 2022
Published online: Oct 31, 2022
Published in print: Jan 1, 2023
Discussion open until: Mar 31, 2023
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