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International Journal of Intelligent Transportation Systems Research
Published in:

14-10-2024

Predicting Curb Side Parking Availability for Commercial Vehicle Loading Zones

Authors: Milan Jain, Vinay C Amatya, Amelia Bleeker, Soumya Vasisht, John T Feo, Katherine E Wolf

Published in: International Journal of Intelligent Transportation Systems Research | Issue 3/2024

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Abstract

Commercial fleet management and operations pose distinct challenges compared to regular passenger vehicles. These challenges stem from the varying sizes, shapes, and parking demands of commercial vehicles, requiring specific curbside accommodations. Despite extensive research on smart-parking management for personal vehicles, there has been limited focus on improving parking outcomes for urban freight systems. To address this gap, we have developed a framework that utilizes sensors installed in parking areas to collect occupancy information. This framework predicts parking space availability for commercial vehicles in 10-minute intervals. The current states and the predictions are relayed to the drivers in near real-time through a web-based interface, empowering them to find suitable parking spaces and reducing search time. Our framework incorporates a suite of machine-learning models for predicting curbside parking availability based on real-time sensor data from commercial vehicle loading zones. We evaluated these models in a busy commercial district in the Seattle area, focusing on prediction accuracy and real-world performance. Our study concludes that, for practical use, the convolutional neural network (CNN) model outperforms other architectures, including Spatial Temporal Graph Convolutional Networks (ST-GCN) and Transformer.
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Literature
1.
2.
3.
4.
On-street parking. https://​www.​nice.​fr/​fr/​transports-et-deplacements/​le-stationnement-sur-voirie
5.
6.
Quiñones, M., Gonazález, V., Quinoñes, L., Valdivieso, C., Yaguana, W.: Diseño de un Sistema de Aparcamiento Inteligente Usando una Red de Sensores Inalámbricos. 2015 10th Iberian Conference on Information Systems and Technologies, CISTI 2015, pp. 4–9 (2015)
7.
Nazir, N., Dowling, C., Choudhury, S., Zoepf, S., Ma, K.: Optimal, centralized dynamic curbside parking space zoning. In: 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), pp. 91–98. IEEE (2022)
8.
Diaz Ogás, M.G., Fabregat, R., Aciar, S.: Survey of smart parking systems, 6 (2020)
9.
Dutta, A., Jain, M., Khan, A., Sathanur, A.: Deep reinforcement learning to maximize arterial usage during extreme congestion (2023). arXiv:​2305.​09600
10.
Girón-Valderrama, G.D.C., Machado-León, J.L., Goodchild, A.: Commercial vehicle parking in downtown seattle: insights on the battle for the curb. Transp. Res. Rec. 2673(10), 770–780 (2019)CrossRef
11.
Wang, H., He, W.: A reservation-based smart parking system. In: 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 690–695. IEEE (2011)
12.
Tsiropoulou, E.E., Baras, J.S., Papavassiliou, S., Sinha, S.: Rfid-based smart parking management system. Cyber-Phys. Syst. 3(1–4), 22–41 (2017)CrossRef
13.
Wei, L., Wu, Q., Yang, M., Ding, W., Li, B., Gao, R.: Design and implementation of smart parking management system based on rfid and internet. In: 2012 International Conference on Control Engineering and Communication Technology, pp. 17–20. IEEE (2012)
14.
Melnyk, P., Djahel, S., Nait-Abdesselam, F.: Towards a smart parking management system for smart cities. In: 2019 IEEE International Smart Cities Conference (ISC2), pp. 542–546. IEEE (2019)
15.
Abdulkader, O., Bamhdi, A.M., Thayananthan, V., Jambi, K., Alrasheedi, M.: A novel and secure smart parking management system (spms) based on integration of wsn, rfid, and iot. In: 2018 15th Learning and Technology Conference (L &T), pp. 102–106. IEEE (2018)
16.
Joshi, Y., Gharate, P., Ahire, C., Alai, N., Sonavane, S.: Smart parking management system using rfid and ocr. In: 2015 International Conference on Energy Systems and Applications, pp. 729–734. IEEE (2015)
17.
18.
Yan, G., Yang, W., Rawat, D.B., Olariu, S.: Smartparking: a secure and intelligent parking system. IEEE Intell. Transp. Syst. Mag. 3(1), 18–30 (2011)CrossRef
19.
Srikanth, S.V., Pramod, P.J., Dileep, K.P., Tapas, S., Patil M.U., Sarat, C.B.N.: Design and implementation of a prototype smart parking (spark) system using wireless sensor networks. In: 2009 International Conference on Advanced Information Networking and Applications Workshops, pp. 401–406. IEEE (2009)
20.
Takizawa, H., Yamada, K., Ito, T.: Vehicles detection using sensor fusion. In: IEEE Intelligent Vehicles Symposium, 2004, pp. 238–243. IEEE (2004)
21.
Zhu, Z., Zhao, Y., Lu, H.: Sequential architecture for efficient car detection. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2007)
22.
Funck, S., Mohler, N., Oertel, W.: Determining car-park occupancy from single images. In: IEEE Intelligent Vehicles Symposium, 2004, pp. 325–328. IEEE (2004)
23.
Vasisht, S., Choudhury, S., Nazir, N., Zoepf, S., Dowling, C.P.: Estimating driver response rates to variable message signage at Seattle-Tacoma International Airport. Findings (2022)
24.
Nazir, N., Vasisht, S., Choudhury, S., Zoepf, S., Dowling, C.P.: Mitigating landside congestion at airports through predictive control of diversionary messages (2022). arXiv:​2209.​13837
25.
26.
Pullola, S., Atrey, P.K., El Saddik, A.: Towards an intelligent GPS-based vehicle navigation system for finding street parking lots. ICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications, (November):1251–1254 (2007)
27.
Klappenecker, A., Lee, H., Welch, J.L.: Finding available parking spaces made easy. Ad Hoc Networks 12(1), 243–249 (2014)CrossRef
28.
Zheng, Y., Rajasegarar, S., Leckie, C.: Parking availability prediction for sensor-enabled car parks in smart cities. 2015 IEEE 10th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2015, (April):1–6 (2015)
29.
Chen, X.: Parking Occupancy Prediction and Pattern Analysis (2014)
30.
Richter, F., Di Martino, S., Mattfeld, DC.: Temporal and spatial clustering for a parking prediction service. Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, 2014-Decem:278–282 (2014)
31.
Vlahogianni, E.I., Kepaptsoglou, K., Tsetsos, V., Karlaftis, M.G.: A real-time parking prediction system for smart cities. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 20(2), 192–204 (2016)CrossRef
32.
Li, B., Pei, Y., Wu, H., Huang, D.: MADM-based smart parking guidance algorithm. PLOS ONE 12(12), e0188283 (2017)CrossRef
33.
Li, J., Zhang, H., Hu, J., Cheng, W.: Curbside parking occupancy detection-dashcam-based solutions. In: 2024 25th IEEE International Conference on Mobile Data Management (MDM), pp. 219–226. IEEE (2024)
34.
Lyu, M., Ji, Y., Kuai, C., Zhang, S.: Short-term prediction of on-street parking occupancy using multivariate variable based on deep learning. J. Traffic Transp. Eng. (English Edition) 11(1), 28–40 (2024)CrossRef
35.
Liu, J., Qian, S.: Modeling multimodal curbside usage in dynamic networks. Transportation Science (2024)
36.
Zhang, H., Xia, Y., Zhong, S., Wang, K., Tong, Z., Wen, Q., Zimmermann, R., Liang, Y.: Predicting parking availability in singapore with cross-domain data: a new dataset and a data-driven approach (2024). arXiv preprint arXiv:​2405.​18910
37.
Ramirez-Rios, D.G., Kalahasthi, L.K., Holguín-Veras, J.: On-street parking for freight, services, and e-commerce traffic in us cities: a simulation model incorporating demand and duration. Transp. Res. A Policy Pract. 169, 103590 (2023)CrossRef
38.
Jaller, M., Holguín-Veras, J., Hodge, S.D.: Parking in the city: challenges for freight traffic. Transp. Res. Rec. 2379(1), 46–56 (2013)
39.
Dablanc, L., Beziat, A.: Parking for freight vehicles in dense urban centers-the issue of delivery areas in Paris. Marne la Vallee, France (2015)
40.
Muñuzuri, J., Cuberos, M., Abaurrea, F., Escudero, A.: Improving the design of urban loading zone systems. J. Transp. Geogr. 59, 1–13 (2017)CrossRef
41.
Oliveira, L.K.D., Nóbrega, R.A.D.A., Ebias, D.G., et al.: Analysis of freight trip generation model for food and beverage in Belo Horizonte (Brazil). Region J. ERSA 4(1), 17–30 (2017)
42.
Alves, R., Lima, R.D.S., Silva, K., Gomes, W., González-Calderón, C.A.: Challenges in urban logistics: a research study in são joão del rei, a historical brazilian city. Technical report (2018)
43.
Hochreiter, S., Schmidhuber, J.: Long Short-Term Memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef
44.
Hopfield, J.J.: Neurons with graded response have collective computational properties like those of two-state neurons. Proc. Natl. Acad. Sci. 81(10), 3088–3092 (1984)CrossRef
45.
Hoseinzade, E., Haratizadeh, S.: CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Systems with Applications 129, 273–285 (2019)CrossRef
46.
Wang, K., Li, K., Zhou, L., Hu, Y., Cheng, Z., Liu, J., Chen, C.: Multiple convolutional neural networks for multivariate time series prediction. Neurocomputing 360, 107–119 (2019)CrossRef
47.
Gehring, J., Auli, M., Grangier, D., Yarats, D., Dauphin, Y.N.: Convolutional sequence to sequence learning. In: International Conference on Machine Learning, pp. 1243–1252. PMLR (2017)
48.
Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting (2017). arXiv preprint arXiv:​1709.​04875
49.
Wang, M., Zheng, D., Ye, Z., Gan, Q., Li, M., Song, X., Zhou, J., Ma, C., Yu, L., Gai, Y., Xiao, T., He, T., Karypis, G., Li, J., Zhang, Z.: Deep graph library: a graph-centric, highly-performant package for graph neural networks (2019). arXiv:​1909.​01315
50.
Wu, N., Green, B., Ben, X., O’Banion, S.: Deep transformer models for time series forecasting: the influenza prevalence case (2020). arXiv:​2001.​08317
51.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)
52.
F M Awan, Y Saleem, R Minerva, and N Crespi. A comparative analysis of machine/deep learning models for parking space availability prediction. Sensors (Switzerland), 20(1), 2020
53.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035. Curran Associates, Inc. (2019)
54.
Shao, W., Zhang, Y., Guo, B., Qin, K., Chan, J., Salim, F.D.: Parking availability prediction with long short term memory model. In: Green, Pervasive, and Cloud Computing: 13th International Conference, GPC 2018, Hangzhou, China, May 11-13, 2018, Revised Selected Papers 13, pp. 124–137. Springer (2019)
55.
Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., Li, H.: T-gcn: A temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21(9), 3848–3858 (2019)CrossRef
56.
57.
Xu, B., Zhang, Y., Lu, H., Chen, Y., Chen, T., Iovine, M., Lee, M.-C., Li, Z.: AITemplate (2022)
Metadata
Title
Predicting Curb Side Parking Availability for Commercial Vehicle Loading Zones
Authors
Milan Jain
Vinay C Amatya
Amelia Bleeker
Soumya Vasisht
John T Feo
Katherine E Wolf
Publication date
14-10-2024
Publisher
Springer US
Published in
International Journal of Intelligent Transportation Systems Research / Issue 3/2024
Print ISSN: 1348-8503
Electronic ISSN: 1868-8659
DOI
https://doi.org/10.1007/s13177-024-00420-5

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