Managing parking reservations to mitigate congestion in office-oriented garages

References

• Ayala, D., O. Wolfson, B. Xu, B. DasGupta, and J. Lin. 2012. “Pricing of Parking for Congestion Reduction.” Proceedings of the 20th International Conference on Advances in Geographic Information Systems, 43–51. https://doi.org/10.1145/2424321.2424328.
   Google Scholar
• Berndt, D. J., and J. Clifford. 1994. “Using Dynamic Time Warping to Find Patterns in Time Series.” Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, 359–370.
   Google Scholar
• Bock, F., K. Xia, and M. Sester. 2018. “Mapping Similarities in Temporal Parking Occupancy Behavior Based on City-Wide Parking Meter Data.” Proceedings of the ICA 1 (2018): 1–5. https://doi.org/10.5194/ica-proc-1-12-2018.
   Google Scholar
• Chaniotakis, E., and A. J. Pel. 2015. “Drivers’ Parking Location Choice Under Uncertain Parking Availability and Search Times: A Stated Preference Experiment.” Transportation Research Part A: Policy and Practice 82:228–239. https://doi.org/10.1016/j.tra.2015.10.004.
   Web of Science ®Google Scholar
• Chen, Z. B., Z. T. Xu, M. Zangui, and Y. F. Yin. 2016. “Analysis of Advanced Management of Curbside Parking.” Transportation Research Record 2567 (1): 57–66. https://doi.org/10.3141/2567-07.
   Google Scholar
• Chen, X., X. Zhou, and G. F. List. 2011. “Using Time-Varying Tolls to Optimize Truck Arrivals at Ports.” Transportation Research Part E: Logistics & Transportation Review 47 (6): 965–982. https://doi.org/10.1016/j.tre.2011.04.001.
   Web of Science ®Google Scholar
• Dogaroglu, B., and S. P. Caliskanelli. 2020. “Investigation of Car Park Preference by Intelligent System Guidance.” Research in Transportation Business & Management 37:100567. https://doi.org/10.1016/j.rtbm.2020.100567.
   Web of Science ®Google Scholar
• Feng, N., F. Zhang, J. Lin, J. Zhai, and X. Du. 2019. “Statistical Analysis and Prediction of Parking Behavior.” Network and Parallel Computing: 16th IFIP WG 103 International Conference, NPC 2019, Hohhot, China, August 23–24, 2019, Proceedings 16:93–104. Springer. https://doi.org/10.1007/978-3-030-30709-7_8.
   Google Scholar
• Fiez, T., and L. J. Ratliff. 2019. “Gaussian Mixture Models for Parking Demand Data.” IEEE Transactions on Intelligent Transportation Systems 21 (8): 3571–3580. https://doi.org/10.1109/TITS.2019.2939499.
   Web of Science ®Google Scholar
• Ganesh Sundarkumar, G., S. B. B. V. C. R. Munigety, and A. S. Arora. 2021. “A Time Series Clustering Based Approach for Construction of Real-World Drive Cycles.” Transportation Research Part D: Transport & Environment 97:102896. https://doi.org/10.1016/j.trd.2021.102896.
   Web of Science ®Google Scholar
• Gomari, S., C. Knoth, and C. Antoniou. 2021. “Cluster Analysis of Parking Behaviour: A Case Study in Munich.” Transportation Research Procedia 52:485–492. https://doi.org/10.1016/j.trpro.2021.01.057.
   Google Scholar
• Greenshields, B. D., J. Bibbins, W. Channing, and H. Miller. (1935). “A Study of Traffic Capacity”. In Highway Research Board Proceedings, 448–477. Washington, DC.
   Google Scholar
• Guan, Y., Y. Wang, X. Yan, H. Guo, and Y. Zhou. 2020. “A Big-Data-Driven Framework for Parking Demand Estimation in Urban Central Districts.” Journal of Advanced Transportation 2020:8898848. https://doi.org/10.1155/2020/8898848.
   Web of Science ®Google Scholar
• Gu, Z., Y. Li, M. Saberi, T. H. Rashidi, and Z. Liu. 2023. “Macroscopic Parking Dynamics and Equitable Pricing: Integrating Trip-Based Modeling with Simulation-Based Robust Optimization.” Transportation Research Part B: Methodological 173:354–381. https://doi.org/10.1016/j.trb.2023.05.011.
   Web of Science ®Google Scholar
• Guo, L. Y., S. Huang, J. Zhuang, and A. W. Sadek. 2013. “Modeling Parking Behavior Under Uncertainty: A Static Game Theoretic versus a Sequential Neo-Additive Capacity Modeling Approach.” Networks & Spatial Economics 13 (3): 327–350. https://doi.org/10.1007/s11067-012-9183-1.
   Web of Science ®Google Scholar
• He, H., Z. Zhang, P. Yan, and Ieee. (2018). “A Real-Time Reservation Service for Smart Parking System”. In 2018 15th International Conference on Service Systems and Service Management, Hangzhou, China.
   Google Scholar
• Hua, M. Z., X. W. Chen, S. J. Zheng, L. Cheng, and J. X. Chen. 2020. “Estimating the Parking Demand of Free-Floating Bike Sharing: A Journey-Data-Based Study of Nanjing, China.” Journal of Cleaner Production 244:118764. https://doi.org/10.1016/j.jclepro.2019.118764.
   Web of Science ®Google Scholar
• Hunt, J. D., and S. Teply. 1993. “A Nested Logit Model of Parking Location Choice.” Transportation Research Part B: Methodological 27 (4): 253–265. https://doi.org/10.1016/0191-2615(93)90035-9.
   Web of Science ®Google Scholar
• Inci, E., J. V. Ommeren, and M. Kobus. 2017. “The External Cruising Costs of Parking.” Journal of Economic Geography 17 (6): 1301–1323. https://doi.org/10.1093/jeg/lbx004.
   Web of Science ®Google Scholar
• Jain, A. K. 2010. “Data Clustering: 50 Years Beyond K-Means.” Pattern Recognition Letters 31 (8): 651–666. https://doi.org/10.1016/j.patrec.2009.09.011.
   Web of Science ®Google Scholar
• Jelen, G., V. Podobnik, and J. Babic. 2021. “Contextual Prediction of Parking Spot Availability: A Step Towards Sustainable Parking.” Journal of Cleaner Production 312:127684. https://doi.org/10.1016/j.jclepro.2021.127684.
   Web of Science ®Google Scholar
• Keogh, E. J., and M. J. Pazzani. (1999). “Scaling Up Dynamic Time Warping to Massive Datasets”. In European Conference on Principles of Data Mining and Knowledge Discovery, Prague, Czech Republic, 1–11. Springer.
   Google Scholar
• Kim, O. T. T., N. H. Tran, C. Pham, T. LeAnh, M. T. Thai, and C. S. Hong. 2020. “Parking Assignment: Minimizing Parking Expenses and Balancing Parking Demand among Multiple Parking Lots.” IEEE Transactions on Automation Science and Engineering 17 (3): 1320–1331. https://doi.org/10.1109/TASE.2019.2948200.
   Web of Science ®Google Scholar
• Lai, M., X. Cai, and Q. Hu. 2021. “Market design for commute-driven private parking lot sharing.” Transportation Research Part C: Emerging Technologies 124:102915. https://doi.org/10.1016/j.trc.2020.102915.
   Web of Science ®Google Scholar
• Le Minh Kieu, Y. O., and C. Cai. 2018. “Large-Scale Transit Market Segmentation with Spatial-Behavioural Features.” Transportation Research Part C: Emerging Technologies 90:97–113. https://doi.org/10.1016/j.trc.2018.03.003.
   Web of Science ®Google Scholar
• Levin, M. W. 2019. “Linear Program for System Optimal Parking Reservation Assignment.” Journal of Transportation Engineering, Part A: Systems 145 (12). https://doi.org/10.1061/JTEPBS.0000280.
   Web of Science ®Google Scholar
• Li, Z. C., W. J. Liu, and X. Y. Wang. 2022. “Women-Only Parking Spaces: Determinants of Parking Space Choice, Investment Decision, and a Case Study of Wuhan, China.” Transportation Research Part C-Emerging Technologies 137:103553. https://doi.org/10.1016/j.trc.2022.103553.
   Web of Science ®Google Scholar
• Liu, W., H. Yang, and Y. Yin. 2014. “Expirable Parking Reservations for Managing Morning Commute with Parking Space Constraints.” Transportation Research Part C-Emerging Technologies 44:185–201. https://doi.org/10.1016/j.trc.2014.04.002.
   Web of Science ®Google Scholar
• Liu, W., H. Yang, Y. F. Yin, and F. N. Zhang. 2014. “A Novel Permit Scheme for Managing Parking Competition and Bottleneck Congestion.” Transportation Research Part C-Emerging Technologies 44:265–281. https://doi.org/10.1016/j.trc.2014.04.005.
   Web of Science ®Google Scholar
• Mackowski, D., Y. Bai, and Y. F. Ouyang. 2015. “Parking Space Management via Dynamic Performance-Based Pricing.” Transportation Research Part C-Emerging Technologies 59:66–91. https://doi.org/10.1016/j.trc.2015.05.021.
   Web of Science ®Google Scholar
• Mahmud, S., T. Akter, and S. Hernandez. 2020. “Truck Parking Usage Patterns by Facility Amenity Availability.” Transportation Research Record 2674 (10): 749–763. https://doi.org/10.1177/0361198120937305.
   Web of Science ®Google Scholar
• Mejri, N., M. Ayari, R. Langar, and L. Saidane. 2016. “Reservation-Based Multi-Objective Smart Parking Approach for Smart Cities.”
   Google Scholar
• Ni, X.-Y., and D. Sun. 2017. “Agent-Based Modelling and Simulation to Assess the Impact of Parking Reservation System.” Journal of Advanced Transportation 2017:1–10. https://doi.org/10.1155/2017/2576094.
   Google Scholar
• Optimization, L. G. 2020. “Gurobi Optimizer Reference Manual, 2020”. 12. http://www.gurobi.com.
   Google Scholar
• Ottomanelli, M., M. Dell’orco, and D. Sassanelli. 2011. “Modelling Parking Choice Behaviour Using Possibility Theory.” Transportation Planning and Technology 34 (7): 647–667. https://doi.org/10.1080/03081060.2011.602846.
   Web of Science ®Google Scholar
• Petitjean, F., A. Ketterlin, and P. Gançarski. 2011. “A Global Averaging Method for Dynamic Time Warping, with Applications to Clustering.” Pattern Recognition 44 (3): 678–693. https://doi.org/10.1016/j.patcog.2010.09.013.
   Web of Science ®Google Scholar
• Piccioni, C., M. Valtorta, and A. Musso. 2019. “Investigating Effectiveness of On-Street Parking Pricing Schemes in Urban Areas: An Empirical Study in Rome.” Transport Policy 80:136–147. https://doi.org/10.1016/j.tranpol.2018.10.010.
   Web of Science ®Google Scholar
• Qin, H. M., X. H. Yang, Y. J. Wu, H. Z. Guan, P. F. Wang, and N. Shahinpoor. 2020. “Analysis of Parking Cruising Behaviour and Parking Location Choice.” Transportation Planning and Technology 43 (7): 717–734. https://doi.org/10.1080/03081060.2020.1805545.
   Web of Science ®Google Scholar
• Sakoe, H. 1971. “Dynamic-Programming Approach to Continuous Speech Recognition.” In 1971 Proc. the International Congress of Acoustics, Budapest, Budapest, Hungary.
   Google Scholar
• Shanker, A. P., and A. Rajagopalan. 2007. “Off-line signature verification using DTW.” Pattern Recognition Letters 28 (12): 1407–1414. https://doi.org/10.1016/j.patrec.2007.02.016.
   Web of Science ®Google Scholar
• Shoup, D. 2021. “Pricing Curb Parking.” Transportation Research Part A: Policy and Practice 154:399–412. https://doi.org/10.1016/j.tra.2021.04.012.
   Web of Science ®Google Scholar
• Shoup, D. C. 2006. “Cruising for Parking.” Transport Policy 13 (6): 479–486. https://doi.org/10.1016/j.tranpol.2006.05.005.
   Web of Science ®Google Scholar
• Tavafoghi, H., K. Poolla, and P. Varaiya. 2019. “A Queuing Approach to Parking: Modeling, Verification, and Prediction.” arXiv Preprint arXiv: 190811479. https://doi.org/10.48550/arXiv.1908.11479.
   Google Scholar
• Teodorovic, D., and P. Lucic. 2006. “Intelligent Parking Systems.” European Journal of Operational Research 175 (3): 1666–1681. https://doi.org/10.1016/j.ejor.2005.02.033.
   Web of Science ®Google Scholar
• Thorndike, R. L. 1953. “Who belongs in the Family?” Psychometrika 18 (4): 267–276. https://doi.org/10.1007/BF02289263.
   Google Scholar
• Tian, Q., L. Yang, C. L. Wang, and H. J. Huang. 2018. “Dynamic Pricing for Reservation-Based Parking System: A Revenue Management Method.” Transport Policy 71:36–44. https://doi.org/10.1016/j.tranpol.2018.07.007.
   Web of Science ®Google Scholar
• Tsai, M. T., and C. P. Chu. 2012. “Evaluating Parking Reservation Policy in Urban Areas: An Environmental Perspective.” Transportation Research Part D-Transport and Environment 17 (2): 145–148. https://doi.org/10.1016/j.trd.2011.10.006.
   Web of Science ®Google Scholar
• Van Der Goot, D. 1982. “A Model to Describe the Choice of Parking Places.” Transportation Research Part A: General 16 (2): 109–115. https://doi.org/10.1016/0191-2607(82)90003-6.
   Google Scholar
• Waheed, A., P. V. Krishna, J. Gitanjali, B. Sadoun, and M. Obaidat. 2021. “Learning Automata and Reservation Based Secure Smart Parking System: Methodology and Simulation Analysis.” Simulation Modelling Practice and Theory 106:102205. https://doi.org/10.1016/j.simpat.2020.102205.
   Web of Science ®Google Scholar
• Wang, H., R. M. Li, X. K. Wang, and P. Shang. 2020. “Effect of On-Street Parking Pricing Policies on Parking Characteristics: A Case Study of Nanning.” Transportation Research Part A-Policy and Practice 137:65–78. https://doi.org/10.1016/j.tra.2020.04.003.
   Web of Science ®Google Scholar
• Wang, S. F., Z. H. Li, and N. Xie. 2022. “A Reservation and Allocation Model for Shared-Parking Addressing the Uncertainty in drivers’ Arrival/Departure Time.” Transportation Research Part C-Emerging Technologies 135:103484. https://doi.org/10.1016/j.trc.2021.103484.
   Web of Science ®Google Scholar
• Wu, F., and W. Ma. 2022. “Clustering Analysis of the Spatio-Temporal On-Street Parking Occupancy Data: A Case Study in Hong Kong.” Sustainability 14 (13): 7957. https://doi.org/10.3390/su14137957.
   Web of Science ®Google Scholar
• Xiao, J., Y. Lou, and J. Frisby. 2018. “How Likely am I to Find Parking? - a Practical Model-Based Framework for Predicting Parking Availability.” Transportation Research Part B-Methodological 112:19–39. https://doi.org/10.1016/j.trb.2018.04.001.
   Web of Science ®Google Scholar
• Xie, M. H., S. Y. Lin, Z. H. Wu, X. Y. Zhang, and Y. Q. Wang. 2022. “Optimal Allocation and Adjustment Mechanism of Shared Parking Slots Considering Combined Parking Resources.” Transportation Letters-The International Journal of Transportation Research 15 (7): 730–741. https://doi.org/10.1080/19427867.2022.2082767.
   Web of Science ®Google Scholar
• Xue, Y. Q., L. Cheng, P. Lin, J. An, and H. Z. Guan. 2020. “Parking Space Reservation Behavior of Car Travelers from the Perspective of Bounded Rationality: A Case Study of Nanchang City, China.” Journal of Advanced Transportation 2020:13. https://doi.org/10.1155/2020/8851372.
   Web of Science ®Google Scholar
• Yan, P. Y., X. Q. Cai, D. B. Ni, F. Chu, and H. He. 2021. “Two-Stage Matching-and-Scheduling Algorithm for Real-Time Private Parking-Sharing Programs.” Computers & Operations Research 125:105083. https://doi.org/10.1016/j.cor.2020.105083.
   Web of Science ®Google Scholar
• Yang, H., W. Liu, X. Wang, and X. Zhang. 2013. “On the Morning Commute Problem with Bottleneck Congestion and Parking Space Constraints.” Transportation Research Part B: Methodological 58:106–118. https://doi.org/10.1016/j.trb.2013.10.003.
   Web of Science ®Google Scholar
• Yang, S., W. Ma, X. Pi, and S. Qian. 2019. “A Deep Learning Approach to Real-Time Parking Occupancy Prediction in Transportation Networks Incorporating Multiple Spatio-Temporal Data Sources.” Transportation Research Part C: Emerging Technologies 107:248–265. https://doi.org/10.1016/j.trc.2019.08.010.
   Web of Science ®Google Scholar
• Zhang, X., K. Pitera, and Y. Wang. 2023. “Parking Reservation Techniques: A Review of Research Topics, Considerations, and Optimization Methods.” Journal of Traffic and Transportation Engineering (English Edition) 10 (6): 1099–1117. https://doi.org/10.1016/j.jtte.2023.07.009.
   Google Scholar
• Zhao, P. F., H. Z. Guan, P. F. Wang, and H. Yan. 2021. “Evaluation of Environmental Benefits Caused by Reservation-based Shared Parking: A Case Study of Beijing, China.” IEEE Access 9:3744–3751. https://doi.org/10.1109/ACCESS.2020.3039437.
   Web of Science ®Google Scholar
• Zhao, P., H. Guan, H. Wei, and S. Liu. 2021. “Mathematical Modeling and Heuristic Approaches to Optimize Shared Parking Resources: A Case Study of Beijing, China.” Transportation Research Interdisciplinary Perspectives 9:100317. https://doi.org/10.1016/j.trip.2021.100317.
   Google Scholar
• Zhao, D., and G. P. Ong. 2021. “Geo-Fenced Parking Spaces Identification for Free-Floating Bicycle Sharing System.” Transportation Research Part A-Policy and Practice 148:49–63. https://doi.org/10.1016/j.tra.2021.03.007.
   Web of Science ®Google Scholar
• Zou, B., N. Kafle, O. Wolfson, and J. Lin. 2015. “A Mechanism Design based Approach to Solving Parking Slot Assignment in the Information Era.” Transportation Research Part B-Methodological 81:631–653. https://doi.org/10.1016/j.trb.2015.05.015.
   Web of Science ®Google Scholar