Proactive pricing strategies for on-street parking management with physics-informed neural networks
Abstract
Effective pricing is important for on-street parking management and proactive parking pricing is an innovative strategy to achieve optimal parking utilization. For proactive parking pricing, accurately predicting parking occupancy and deriving the price elasticity of parking demand are necessary. In recent years, there have been an increasing number of studies applying big data technology for parking-occupancy prediction. However, existing research has not incorporated economic knowledge into modeling, thus preventing application of the price elasticity of parking demand. In this study, proactive pricing strategies are proposed to adjust on-street parking prices which involve a parking-occupancy prediction model and a price-optimization method. Physics-informed neural networks are employed to achieve accurate prediction of parking occupancy and calculation of parking price elasticity. An elasticity-occupancy parking-management strategy is proposed for on-street parking management which leverages parking occupancy and price elasticity to guide pricing interventions. A case study shows that the parking-occupancy prediction model can make accurate predictions and derive the price elasticity of parking demand. Proactive parking pricing enables drivers to plan their trips in advance, allowing parking occupancy within an optimal range.
Keyword : on-street parking management, parking pricing, parking-occupancy prediction, physics-informed neural network, price elasticity, proactive pricing
How to Cite
Li, J., Dong, Y., Wang, Q., & Liu, C. (2024). Proactive pricing strategies for on-street parking management with physics-informed neural networks. International Journal of Strategic Property Management, 28(5), 320–333. https://doi.org/10.3846/ijspm.2024.22233
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Millard‐Ball, A., Weinberger, R. R., & Hampshire, R. C. (2013). Comment on pierce and shoup: Evaluating the impacts of performance-based parking. Journal of the American Planning Association, 79, 330–336. https://doi.org/10.1080/01944363.2014.918481
Millard‐Ball, A., Weinberger, R. R., & Hampshire, R. C. (2014). Is the curb 80% full or 20% empty? Assessing the impacts of San Francisco’s parking pricing experiment. Transportation Research Part A: Policy and Practice, 63, 76–92. https://doi.org/10.1016/j.tra.2014.02.016
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Pierce, G., & Shoup, D. (2013). Getting the prices right. Journal of the American Planning Association, 79(1), 67–81. https://doi.org/10.1080/01944363.2013.787307
Qian, Z. S., & Rajagopal, R. (2013). Optimal parking pricing in general networks with provision of occupancy information. Procedia - Social and Behavioral Sciences, 80, 779–805. https://doi.org/10.1016/j.sbspro.2013.05.042
Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686–707. https://doi.org/10.1016/j.jcp.2018.10.045
Rong, Y., Xu, Z., Yan, R., & Ma, X. (2018). Du-parking: Spatio-temporal big data tells you realtime parking availability. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 646–654), London, United Kingdom. https://doi.org/10.1145/3219819.3219876
Saharan, S., Bawa, S., & Kumar, N. (2020a). Dynamic pricing techniques for intelligent transportation system in smart cities: A systematic review. Computer Communications, 150, 603–625. https://doi.org/10.1016/j.comcom.2019.12.003
Saharan, S., Kumar, N., & Bawa, S. (2020b). An efficient smart parking pricing system for smart city environment: A machine-learning based approach. Future Generation Computer Systems, 106, 622–640. https://doi.org/10.1016/j.future.2020.01.031
Shoup, D. (2006). Cruising for parking. Transport Policy, 13(6), 479–486. https://doi.org/10.1016/j.tranpol.2006.05.005
Shoup, D. (2018). Cashing out employer-paid parking. In D. Shoup (Ed.), Parking and the city (pp. 403–412). Routledge. https://doi.org/10.4324/9781351019668-46
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Tibshirani, R. (1997). The Lasso method for variable selection in the Cox model. Statistics in Medicine, 16(4), 385–395. 3.0.CO;2-3> https://doi.org/10.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3
Vaca, E., & Kuzmyak, J. R. (2005). Chapter 13 – Parking pricing and fees. In Traveler response to transportation system changes handbook. The National Academies Press. https://doi.org/10.17226/23415
Vlahogianni, E. I., Kepaptsoglou, K. L., Tsetsos, V., & Karlaftis, M. G. (2016). A real-time parking prediction system for smart cities. Journal of Intelligent Transportation Systems, 20, 192–204. https://doi.org/10.1080/15472450.2015.1037955
Xiao, J., Lou, Y., & Frisby, J. (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
Yan, X., Levine, J., & Marans, R. (2019). The effectiveness of parking policies to reduce parking demand pressure and car use. Transport Policy, 73, 41–50. https://doi.org/10.1016/j.tranpol.2018.10.009
Yang, S., Ma, W., Pi, X., & Qian, S. (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
Yu, B., Yin, H., & Zhu, Z. (2018, July 13–19). Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (pp. 3634–3640), Stockholm, Sweden. https://doi.org/10.24963/ijcai.2018/505
Yuan, T., Neto, W. B., Esteve Rothenberg, C., Obraczka, K., Barakat, C., & Turletti, T. (2020). Machine learning for next‐generation intelligent transportation systems: A survey. Transactions on Emerging Telecommunications Technologies, 33, Article e4427. https://doi.org/10.1002/ett.4427
Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175. https://doi.org/10.1016/S0925-2312(01)00702-0
Chung, J., Gülçehre, Ç., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. ArXiv, abs/1412.3555. https://arxiv.org/abs/1412.3555
Concas, S., & Nayak, N. (2012, January 22–26). A meta-analysis of parking pricing elasticity [Paper presentation]. Transportation Research Board 91st Annual Meeting, Washington DC, United States.
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. https://doi.org/10.1207/s15516709cog1402_1
Fabusuyi, T., & Hampshire, R. C. (2018). Rethinking performance based parking pricing: A case study of SFpark. Transportation Research Part A: Policy and Practice, 115, 90–10. https://doi.org/10.1016/j.tra.2018.02.001
Fan, J., Hu, Q., Xu, Y., & Tang, Z. (2022). Predicting vacant parking space availability: A long short-term memory approach. IEEE Intelligent Transportation Systems Magazine, 14(2), 129–143. https://doi.org/10.1109/mits.2020.3014131
Feng, Y., Xu, Y., Hu, Q., Krishnamoorthy, S., & Tang, Z. (2022). Predicting vacant parking space availability zone-wisely: A hybrid deep learning approach. Complex & Intelligent Systems, 8(5), 4145–4161. https://doi.org/10.1007/s40747-022-00700-1
Friesen, M., & Mingardo, G. (2020). Is parking in Europe ready for dynamic pricing? A reality check for the private sector. Sustainability, 12(7), Article 2732. https://doi.org/10.3390/su12072732
Harvey, A. C., & Shephard, N. (1993). 10 structural time series models. In G. S. Maddala, C. R. Rao, & H. D. Vinod (Eds.), Handbook of statistics (Vol. 11, pp. 261–302). Elsevier. https://doi.org/10.1016/s0169-7161(05)80045-8
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Hong, S., Shin, H. J., Choi, J., & Park, N. (2022, October 17–21). Prediction-based one-shot dynamic parking pricing. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 748–757), Atlanta GA USA. https://doi.org/10.1145/3511808.3557421
Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359–366. https://doi.org/10.1016/0893-6080(89)90020-8
Ismail, M. H., Razak, T. R., Gining, R. A., Fauzi, S. S., & Abdul-Aziz, A. (2021). Predicting vehicle parking space availability using multilayer perceptron neural network. IOP Conference Series: Materials Science and Engineering, 1176(1), Article 012035. https://doi.org/10.1088/1757-899x/1176/1/012035
Jelen, G., Podobnik, V., & Babic, J. (2021). Contextual prediction of parking spot availability: A step towards sustainable parking. Journal of Cleaner Production, 312, Article 127684. https://doi.org/10.1016/j.jclepro.2021.127684
Kamarianakis, Y., & Prastacos, P. (2003). Forecasting traffic flow conditions in an urban network: Comparison of multivariate and univariate approaches. Transportation Research Record: Journal of the Transportation Research Board, 1857(1), 74–84. https://doi.org/10.3141/1857-09
Kelly, J. A., & Clinch, J. P. (2009). Temporal variance of revealed preference on-street parking price elasticity. Transport Policy, 16(4), 193–199. https://doi.org/10.1016/j.tranpol.2009.06.001
Kotb, A. O., Shen, Y., Zhu, X., & Huang, Y. (2016). iParker—A new smart car-parking system based on dynamic resource allocation and pricing. IEEE Transactions on Intelligent Transportation Systems, 17(9), 2637–2647. https://doi.org/10.1109/TITS.2016.2531636
Larson, R. C., & Sasanuma, K. (2010). Congestion pricing: A parking queue model. Journal of Industrial and Systems Engineering, 4, 1–17.
Li, J., Qu, H., & You, L. (2023). An integrated approach for the near real-time parking occupancy prediction. IEEE Transactions on Intelligent Transportation Systems, 24(4), 3769–3778. https://doi.org/10.1109/tits.2022.3230199
Mackowski, D., Bai, Y., & Ouyang, Y. (2015). Parking space management via dynamic performance-based pricing. Transportation Research Procedia, 7, 170–191. https://doi.org/10.1016/j.trpro.2015.06.010
Maternini, G., Ferrari, F., & Guga, A. (2017). Application of variable parking pricing techniques to innovate parking strategies. The case study of Brescia. Case Studies on Transport Policy, 5(2), 425–437. https://doi.org/10.1016/j.cstp.2017.03.010
Millard‐Ball, A., Weinberger, R. R., & Hampshire, R. C. (2013). Comment on pierce and shoup: Evaluating the impacts of performance-based parking. Journal of the American Planning Association, 79, 330–336. https://doi.org/10.1080/01944363.2014.918481
Millard‐Ball, A., Weinberger, R. R., & Hampshire, R. C. (2014). Is the curb 80% full or 20% empty? Assessing the impacts of San Francisco’s parking pricing experiment. Transportation Research Part A: Policy and Practice, 63, 76–92. https://doi.org/10.1016/j.tra.2014.02.016
Ostermeijer, F., Koster, H., Nunes, L., & van Ommeren, J. (2022). Citywide parking policy and traffic: Evidence from Amsterdam. Journal of Urban Economics, 128, Article 103418. https://doi.org/10.1016/j.jue.2021.103418
Ottosson, D. B., Chen, C., Wang, T., & Lin, H. (2013). The sensitivity of on-street parking demand in response to price changes: A case study in Seattle, WA. Transport Policy, 25, 222–232. https://doi.org/10.1016/j.tranpol.2012.11.013
Pierce, G., & Shoup, D. (2013). Getting the prices right. Journal of the American Planning Association, 79(1), 67–81. https://doi.org/10.1080/01944363.2013.787307
Qian, Z. S., & Rajagopal, R. (2013). Optimal parking pricing in general networks with provision of occupancy information. Procedia - Social and Behavioral Sciences, 80, 779–805. https://doi.org/10.1016/j.sbspro.2013.05.042
Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686–707. https://doi.org/10.1016/j.jcp.2018.10.045
Rong, Y., Xu, Z., Yan, R., & Ma, X. (2018). Du-parking: Spatio-temporal big data tells you realtime parking availability. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 646–654), London, United Kingdom. https://doi.org/10.1145/3219819.3219876
Saharan, S., Bawa, S., & Kumar, N. (2020a). Dynamic pricing techniques for intelligent transportation system in smart cities: A systematic review. Computer Communications, 150, 603–625. https://doi.org/10.1016/j.comcom.2019.12.003
Saharan, S., Kumar, N., & Bawa, S. (2020b). An efficient smart parking pricing system for smart city environment: A machine-learning based approach. Future Generation Computer Systems, 106, 622–640. https://doi.org/10.1016/j.future.2020.01.031
Shoup, D. (2006). Cruising for parking. Transport Policy, 13(6), 479–486. https://doi.org/10.1016/j.tranpol.2006.05.005
Shoup, D. (2018). Cashing out employer-paid parking. In D. Shoup (Ed.), Parking and the city (pp. 403–412). Routledge. https://doi.org/10.4324/9781351019668-46
Smola, A., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14, 199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88
Taylor, S. J., & Letham, B. (2018). Forecasting at scale. The American Statistician, 72(1), 37–45. https://doi.org/10.1080/00031305.2017.1380080
Tibshirani, R. (1997). The Lasso method for variable selection in the Cox model. Statistics in Medicine, 16(4), 385–395. 3.0.CO;2-3> https://doi.org/10.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3
Vaca, E., & Kuzmyak, J. R. (2005). Chapter 13 – Parking pricing and fees. In Traveler response to transportation system changes handbook. The National Academies Press. https://doi.org/10.17226/23415
Vlahogianni, E. I., Kepaptsoglou, K. L., Tsetsos, V., & Karlaftis, M. G. (2016). A real-time parking prediction system for smart cities. Journal of Intelligent Transportation Systems, 20, 192–204. https://doi.org/10.1080/15472450.2015.1037955
Xiao, J., Lou, Y., & Frisby, J. (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
Yan, X., Levine, J., & Marans, R. (2019). The effectiveness of parking policies to reduce parking demand pressure and car use. Transport Policy, 73, 41–50. https://doi.org/10.1016/j.tranpol.2018.10.009
Yang, S., Ma, W., Pi, X., & Qian, S. (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
Yu, B., Yin, H., & Zhu, Z. (2018, July 13–19). Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (pp. 3634–3640), Stockholm, Sweden. https://doi.org/10.24963/ijcai.2018/505
Yuan, T., Neto, W. B., Esteve Rothenberg, C., Obraczka, K., Barakat, C., & Turletti, T. (2020). Machine learning for next‐generation intelligent transportation systems: A survey. Transactions on Emerging Telecommunications Technologies, 33, Article e4427. https://doi.org/10.1002/ett.4427
Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175. https://doi.org/10.1016/S0925-2312(01)00702-0