The prediction of parking space availability
Abstract
Intelligent Parking Systems (IPS) allow customers to select a car park according to their preferences, rapidly park their vehicle without searching for the available parking space (place) or even book their place in advance avoiding queues. IPS provides the possibility to reduce the wastage of fuel (energy) while finding a parking place and consequently reduce harmful emissions. Some systems interact with in-vehicle navigation systems and provide users with information in real-time such as free places available at a given parking lot (car park), the location and parking fees. Few of these systems, however, provide information on the forecasted utilisation at specific time. This paper describes results of a traffic survey carried out at the parking lot of supermarket and the proposal of the model predicting real-time parking space availability based on these surveyed data. The proposed model is formulated as the non-homogenous Markov chains that are used as a tool for the forecasting of parking space availability. The transition matrices are calculated for different time periods, which allow for and include different drivers’ behaviour and expectations. The proposed forecasting model is adequate for potential use by IPS with the support of different communication means such as the internet, navigation systems (GPS, Galileo etc.) and personal communication services (mobile-phones).
Keyword : intelligent parking systems, prediction, parking space availability, non-homogenous Markov chain, traffic survey
How to Cite
Brožová, H., & Růžička, M. (2020). The prediction of parking space availability. Transport, 35(5), 462-473. https://doi.org/10.3846/transport.2020.14016
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Caicedo, F.; Blazquez, C.; Miranda, P. 2012. Prediction of parking space availability in real time, Expert Systems with Applications 39(8): 7281–7290. https://doi.org/10.1016/j.eswa.2012.01.091
Caliskan, M.; Barthels, A.; Scheuermann, B.; Mauve, M. 2007. Predicting parking lot occupancy in vehicular ad hoc networks, in 2007 IEEE 65th Vehicular Technology Conference – VTC2007-Spring, 22–25 April 2007, Dublin, Ireland, 277–281. https://doi.org/10.1109/VETECS.2007.69
Caliskan, M.; Graupner, D.; Mauve, M. 2006. Decentralized discovery of free parking places, in VANET’06: Proceedings of the 3rd International Workshop on Vehicular Ad Hoc Networks, 29 September 2006, Los Angeles, CA, US, 30–39. https://doi.org/10.1145/1161064.1161070
Camero, A.; Toutouh, J.; Stolfi, D. H.; Alba, E. 2019. Evolutionary deep learning for car park occupancy prediction in smart cities, Lecture Notes in Computer Science 11353: 386–401. https://doi.org/10.1007/978-3-030-05348-2_32
Chen, N.; Wang, L.; Jia, L.; Dong, H.; Li, H. 2016. Parking survey made efficient in intelligent parking systems, Procedia Engineering 137: 487–495. https://doi.org//10.1016/j.proeng.2016.01.284
Cheng, T.; Tai, M.; Ma, Z. 2012. The model of parking demand forecast for the urban CCD, Energy Procedia 16: 1393–1400. https://doi.org/10.1016/j.egypro.2012.01.221
ČSN 73 6056. Odstavné a parkovací plochy silničních vozidel. (in Czech).
ČSN 73 6110. Projektování místních komunikací. (in Czech).
D’Aloia, M.; Rizzi, M.; Russo, R.; Notarnicola, M.; Pellicani, L. 2015. A marker-based image processing method for detecting available parking slots from UAVs, Lecture Notes in Computer Science 9281: 275–281. https://doi.org//10.1007/978-3-319-23222-5_34
De Groot, J. I. M.; Steg, L.; Dicke, M. 2008. Transportation trends from a moral perspective: value orientations, norms and reducing car use, in F. N. Gustavsson (Ed.). New Transportation Research Progress, 67–91.
Hendricks, S.; Outwater, M. 1998. Demand forecasting model for park-and-ride lots in King County, Washington, Transportation Research Record: Journal of the Transportation Research Board 1623: 80–87. https://doi.org/10.3141/1623-11
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Khaliq, A.; Van der Waerden, P.; Janssens, D.; Wetsa, G. 2019. A conceptual framework for forecasting car driver’s on-street parking decisions, Transportation Research Procedia 37: 131–138. https://doi.org/10.1016/j.trpro.2018.12.175
Klappenecker, A.; Lee, H.; Welch, J. L. 2014. Finding available parking spaces made easy, Ad Hoc Networks 12: 243–249. https://doi.org/10.1016/j.adhoc.2012.03.002
Lam, W. H. K.; Li, Z.-C.; Huang, H.-J.; Wong, S. C. 2006. Modeling time-dependent travel choice problems in road networks with multiple user classes and multiple parking facilities, Transportation Research Part B: Methodological 40(5): 368–395. https://doi.org/10.1016/j.trb.2005.05.003
Li, B.; Pei, Y.; Wu, H.; Huang, D. 2017. MADM-based smart parking guidance algorithm, PLoS One 12(12): e0188283. https://doi.org/10.1371/journal.pone.0188283
Li, P.; Li, D.; Zhang, X. 2014. CGPS: a collaborative game in parking-lot search, Advances in Intelligent Systems and Computing 250: 105–113. https://doi.org/10.1007/978-81-322-1695-7_13
Liang, W.; Zhang, Y.; Hu, J.; Wang, X. 2017. A personalized route guidance approach for urban travelling and parking to a shopping mall, in 2017 4th International Conference on Transportation Information and Safety (ICTIS), 8–10 August 2017, Banff, Canada, 319–324. https://doi.org/10.1109/ICTIS.2017.8047783
Lin, T.; Rivano, H.; Le Mouël, F. 2017. A survey of smart parking solutions, IEEE Transactions on Intelligent Transportation Systems 18(12): 3229–3253. https://doi.org//10.1109/TITS.2017.2685143
Mamandi, A.; Yousefi, S.; Atani, R. E. 2015. Game theory-based and heuristic algorithms for parking-lot search, in 2015 International Symposium on Computer Science and Software Engineering (CSSE), 18–19 August 2015, Tabriz, Iran, 1–8. https://doi.org/10.1109/CSICSSE.2015.7369235
Martolos, J.; Šindlerová, V.; Bartoš, L.; Mužík, J. 2013. Metody prognózy intenzit generované dopravy: monografie. Plzeň: EDIP. 108 s. (in Czech).
Mathur, S.; Jin, T.; Kasturirangan, N.; Chandrasekaran, J.; Xue, W.; Gruteser, M.; Trappe, W. 2010. ParkNet: drive-by sensing of road-side parking statistics, in MobiSys’10: Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, 15–18 June 2010, San Francisco, CA, US, 123–136. https://doi.org/10.1145/1814433.1814448
Oh, B.-W.; Lee, S.-Y.; Kim, M.-S.; Yang, Y.-K. 2002. Spatial applications using 4S technology for mobile environment, in IEEE International Geoscience and Remote Sensing Symposium, 24–28 June 2002, Toronto, Canada, 2444–2446. https://doi.org/10.1109/IGARSS.2002.1026572
Rada hlavního města Prahy. 2016. Pražské stavební předpisy 2016: 10. nařízení, kterým se stanovují obecné požadavky na využívání území a technické požadavky na stavby v hlavním městě Praze. 59 s. Available from Internet: https://www.praha.eu/file/2188282/narizeni_c._10.pdf (in Czech).
Richter, F.; Di Martino, S.; Mattfeld, D. C. 2014. Temporal and spatial clustering for a parking prediction service, in 2014 IEEE 26th International Conference on Tools with Artificial Intelligence, 10–12 November 2014, Limassol, Cyprus, 278–282. https://doi.org/10.1109/ICTAI.2014.49
Rong, Y.; Xu, Z.; Yan, R.; Ma, X. 2018. Du-parking: spatiotemporal big data tells you realtime parking availability, in KDD’18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 19–23 August 2018, London, UK, 646–654. https://doi.org/10.1145/3219819.3219876
Schlote, A.; King, C.; Crisostomi, E.; Shorten, R. 2014. Delaytolerant stochastic algorithms for parking space assignment, IEEE Transactions on Intelligent Transportation Systems 15(5): 1922–1935. https://doi.org/10.1109/TITS.2014.2304356
Serfozo, R. 2009. Basics of Applied Stochastic Processes. Springer. 443 p. https://doi.org/10.1007/978-3-540-89332-5
Shoup, D. C. 1999. The trouble with minimum parking requirements,Transportation Research Part A: Policy and Practice 33(7–8): 549–574. https://doi.org/10.1016/S0965-8564(99)00007-5
Stolfi, D. H.; Alba, E.; Yao, X. 2017. Predicting car park occupancy rates in smart cities, Lecture Notes in Computer Science 10268: 107–117. https://doi.org/10.1007/978-3-319-59513-9_11
Sun, M.; Li, Z.; Peng, L.; Li, H.; Fang, X. 2018. FLOPS: an efficient and high-precision prediction on available parking spaces in a long time-span, in 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 4–7 November 2018, Maui, HI, US, 2937–2942. https://doi.org/10.1109/ITSC.2018.8569435
Tilahun, S. L.; Di Marzo Serugendo, G. 2017. Cooperative multiagent system for parking availability prediction based on time varying dynamic Markov chains, Journal of Advanced Transportation 2017: 1760842. https://doi.org/10.1155/2017/1760842
US 2004/0032342. Method and System for Projecting Dynamic Parking Availability Based on an Ongoing Survey for Remote Lots with High Demand. United States Patent Application.
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, Z.; Liu, H.; Wang, X. 2003. The research on the key technologies for improving efficiency of parking guidance system, in Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems, 12–15 October 2003, Shanghai, China, 1177–1182. https://doi.org/10.1109/ITSC.2003.1252670
Zhao, Z.; Kim, J.-W.; Zhang, L. 2018. Parking data collection, storage and mining in smart city, in ICBDR 2018: Proceedings of the 2nd International Conference on Big Data Research, 27–29 October 2018, Weihai, China, 95–99. https://doi.org//10.1145/3291801.3291841
Balzano, W.; Vitale, F. 2017. DiG-Park: a smart parking availability searching method using V2V/V2I and DGP-class problem, in 2017 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA), 27–29 March 2017, Taipei, Taiwan, 698–703. https://doi.org/10.1109/WAINA.2017.104
Bosserhoff, D. 2009. Methoden zur Abschätzung des Verkehrsaufkommens durch kommerzielle Einrichtungen und andere Vorhaben der Bauleitplanung, in Mariánské Lázně 2009: Ddoprava generovaná komerčními zónami: sborník přednášek konference, 12.–13. listopadu 2009, Mariánské Lázně, Česká Republika, 23–39. (in German).
Caicedo, F. 2009. The use of space availability information in “PARC” systems to reduce search times in parking facilities, Transportation Research Part C: Emerging Technologies 17(1): 56–68. https://doi.org/10.1016/j.trc.2008.07.001
Caicedo, F.; Blazquez, C.; Miranda, P. 2012. Prediction of parking space availability in real time, Expert Systems with Applications 39(8): 7281–7290. https://doi.org/10.1016/j.eswa.2012.01.091
Caliskan, M.; Barthels, A.; Scheuermann, B.; Mauve, M. 2007. Predicting parking lot occupancy in vehicular ad hoc networks, in 2007 IEEE 65th Vehicular Technology Conference – VTC2007-Spring, 22–25 April 2007, Dublin, Ireland, 277–281. https://doi.org/10.1109/VETECS.2007.69
Caliskan, M.; Graupner, D.; Mauve, M. 2006. Decentralized discovery of free parking places, in VANET’06: Proceedings of the 3rd International Workshop on Vehicular Ad Hoc Networks, 29 September 2006, Los Angeles, CA, US, 30–39. https://doi.org/10.1145/1161064.1161070
Camero, A.; Toutouh, J.; Stolfi, D. H.; Alba, E. 2019. Evolutionary deep learning for car park occupancy prediction in smart cities, Lecture Notes in Computer Science 11353: 386–401. https://doi.org/10.1007/978-3-030-05348-2_32
Chen, N.; Wang, L.; Jia, L.; Dong, H.; Li, H. 2016. Parking survey made efficient in intelligent parking systems, Procedia Engineering 137: 487–495. https://doi.org//10.1016/j.proeng.2016.01.284
Cheng, T.; Tai, M.; Ma, Z. 2012. The model of parking demand forecast for the urban CCD, Energy Procedia 16: 1393–1400. https://doi.org/10.1016/j.egypro.2012.01.221
ČSN 73 6056. Odstavné a parkovací plochy silničních vozidel. (in Czech).
ČSN 73 6110. Projektování místních komunikací. (in Czech).
D’Aloia, M.; Rizzi, M.; Russo, R.; Notarnicola, M.; Pellicani, L. 2015. A marker-based image processing method for detecting available parking slots from UAVs, Lecture Notes in Computer Science 9281: 275–281. https://doi.org//10.1007/978-3-319-23222-5_34
De Groot, J. I. M.; Steg, L.; Dicke, M. 2008. Transportation trends from a moral perspective: value orientations, norms and reducing car use, in F. N. Gustavsson (Ed.). New Transportation Research Progress, 67–91.
Hendricks, S.; Outwater, M. 1998. Demand forecasting model for park-and-ride lots in King County, Washington, Transportation Research Record: Journal of the Transportation Research Board 1623: 80–87. https://doi.org/10.3141/1623-11
Inaba, K.; Shibui, M.; Naganawa, T.; Ogiwara, M.; Yoshikai, N. 2001. Intelligent parking reservation service on the Internet, in Proceedings 2001 Symposium on Applications and the Internet Workshops, 8–12 January 2001, San Diego, CA, US, 159–164. https://doi.org/10.1109/SAINTW.2001.998224
Khaliq, A.; Van der Waerden, P.; Janssens, D.; Wetsa, G. 2019. A conceptual framework for forecasting car driver’s on-street parking decisions, Transportation Research Procedia 37: 131–138. https://doi.org/10.1016/j.trpro.2018.12.175
Klappenecker, A.; Lee, H.; Welch, J. L. 2014. Finding available parking spaces made easy, Ad Hoc Networks 12: 243–249. https://doi.org/10.1016/j.adhoc.2012.03.002
Lam, W. H. K.; Li, Z.-C.; Huang, H.-J.; Wong, S. C. 2006. Modeling time-dependent travel choice problems in road networks with multiple user classes and multiple parking facilities, Transportation Research Part B: Methodological 40(5): 368–395. https://doi.org/10.1016/j.trb.2005.05.003
Li, B.; Pei, Y.; Wu, H.; Huang, D. 2017. MADM-based smart parking guidance algorithm, PLoS One 12(12): e0188283. https://doi.org/10.1371/journal.pone.0188283
Li, P.; Li, D.; Zhang, X. 2014. CGPS: a collaborative game in parking-lot search, Advances in Intelligent Systems and Computing 250: 105–113. https://doi.org/10.1007/978-81-322-1695-7_13
Liang, W.; Zhang, Y.; Hu, J.; Wang, X. 2017. A personalized route guidance approach for urban travelling and parking to a shopping mall, in 2017 4th International Conference on Transportation Information and Safety (ICTIS), 8–10 August 2017, Banff, Canada, 319–324. https://doi.org/10.1109/ICTIS.2017.8047783
Lin, T.; Rivano, H.; Le Mouël, F. 2017. A survey of smart parking solutions, IEEE Transactions on Intelligent Transportation Systems 18(12): 3229–3253. https://doi.org//10.1109/TITS.2017.2685143
Mamandi, A.; Yousefi, S.; Atani, R. E. 2015. Game theory-based and heuristic algorithms for parking-lot search, in 2015 International Symposium on Computer Science and Software Engineering (CSSE), 18–19 August 2015, Tabriz, Iran, 1–8. https://doi.org/10.1109/CSICSSE.2015.7369235
Martolos, J.; Šindlerová, V.; Bartoš, L.; Mužík, J. 2013. Metody prognózy intenzit generované dopravy: monografie. Plzeň: EDIP. 108 s. (in Czech).
Mathur, S.; Jin, T.; Kasturirangan, N.; Chandrasekaran, J.; Xue, W.; Gruteser, M.; Trappe, W. 2010. ParkNet: drive-by sensing of road-side parking statistics, in MobiSys’10: Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, 15–18 June 2010, San Francisco, CA, US, 123–136. https://doi.org/10.1145/1814433.1814448
Oh, B.-W.; Lee, S.-Y.; Kim, M.-S.; Yang, Y.-K. 2002. Spatial applications using 4S technology for mobile environment, in IEEE International Geoscience and Remote Sensing Symposium, 24–28 June 2002, Toronto, Canada, 2444–2446. https://doi.org/10.1109/IGARSS.2002.1026572
Rada hlavního města Prahy. 2016. Pražské stavební předpisy 2016: 10. nařízení, kterým se stanovují obecné požadavky na využívání území a technické požadavky na stavby v hlavním městě Praze. 59 s. Available from Internet: https://www.praha.eu/file/2188282/narizeni_c._10.pdf (in Czech).
Richter, F.; Di Martino, S.; Mattfeld, D. C. 2014. Temporal and spatial clustering for a parking prediction service, in 2014 IEEE 26th International Conference on Tools with Artificial Intelligence, 10–12 November 2014, Limassol, Cyprus, 278–282. https://doi.org/10.1109/ICTAI.2014.49
Rong, Y.; Xu, Z.; Yan, R.; Ma, X. 2018. Du-parking: spatiotemporal big data tells you realtime parking availability, in KDD’18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 19–23 August 2018, London, UK, 646–654. https://doi.org/10.1145/3219819.3219876
Schlote, A.; King, C.; Crisostomi, E.; Shorten, R. 2014. Delaytolerant stochastic algorithms for parking space assignment, IEEE Transactions on Intelligent Transportation Systems 15(5): 1922–1935. https://doi.org/10.1109/TITS.2014.2304356
Serfozo, R. 2009. Basics of Applied Stochastic Processes. Springer. 443 p. https://doi.org/10.1007/978-3-540-89332-5
Shoup, D. C. 1999. The trouble with minimum parking requirements,Transportation Research Part A: Policy and Practice 33(7–8): 549–574. https://doi.org/10.1016/S0965-8564(99)00007-5
Stolfi, D. H.; Alba, E.; Yao, X. 2017. Predicting car park occupancy rates in smart cities, Lecture Notes in Computer Science 10268: 107–117. https://doi.org/10.1007/978-3-319-59513-9_11
Sun, M.; Li, Z.; Peng, L.; Li, H.; Fang, X. 2018. FLOPS: an efficient and high-precision prediction on available parking spaces in a long time-span, in 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 4–7 November 2018, Maui, HI, US, 2937–2942. https://doi.org/10.1109/ITSC.2018.8569435
Tilahun, S. L.; Di Marzo Serugendo, G. 2017. Cooperative multiagent system for parking availability prediction based on time varying dynamic Markov chains, Journal of Advanced Transportation 2017: 1760842. https://doi.org/10.1155/2017/1760842
US 2004/0032342. Method and System for Projecting Dynamic Parking Availability Based on an Ongoing Survey for Remote Lots with High Demand. United States Patent Application.
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, Z.; Liu, H.; Wang, X. 2003. The research on the key technologies for improving efficiency of parking guidance system, in Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems, 12–15 October 2003, Shanghai, China, 1177–1182. https://doi.org/10.1109/ITSC.2003.1252670
Zhao, Z.; Kim, J.-W.; Zhang, L. 2018. Parking data collection, storage and mining in smart city, in ICBDR 2018: Proceedings of the 2nd International Conference on Big Data Research, 27–29 October 2018, Weihai, China, 95–99. https://doi.org//10.1145/3291801.3291841