ABSTRACT
Vehicle parking issues have been one of the biggest problems faced in urban areas, as the supply and demand for vehicles and parking spaces are getting unbalanced year by year. The traditional approach of adding more parking spaces is no longer an effective solution. A practical and intelligent solution is to predict open parking spots using machine learning (ML), which would increase the utilization of available parking spaces and alleviate traffic congestion and decrease emissions from idling vehicles. This study aims to propose a parking prediction model using support vector regression (SVR) to predict available parking spaces. The data used in training the ML model is collected using a custom object detector, which is developed using the YOLOv4 (You Only Look Once) algorithm. The result shows that the custom YOLOv4 model is able to detect and identify empty and occupied parking spaces, and the SVR prediction model can predict the number of empty parking spaces. Two additional ML algorithms, which are linear regression (LR) and decision tree regressor were applied in this project to compare the performance of the SVR prediction model.
- Q. Fu, X. Yang, and Z. Niu, 2014. Bi-level objective model of optimal parking lot recommendation based on parking guidance signs. Application Research of Computers, vol. 31, no. 10, pp. 3017–3019, 2014Google Scholar
- Mambo Malaysia, “Idling wastes fuel and money (how much you could have saved?),” Mambo Malaysia, 12-Aug-2021. [Online]. Available: https://www.mambomalaysia.com/vehicle-idling-wastes-fuel-money/. [Accessed: 12-Nov-2021].Google Scholar
- Z. Zhao and Y. Zhang, “A comparative study of parking occupancy prediction methods considering parking type and parking scale,” Journal of Advanced Transportation, 14-Feb-2020. [Online]. Available: https://www.hindawi.com/journals/jat/2020/5624586/. [Accessed: 17-Nov-2021].Google ScholarCross Ref
- V. Paidi, J. Håkansson, H. Fleyeh, and R. G. Nyberg, “Directory of open access journals,” Sustainability, 01-Mar-2022. [Online]. Available: https://doaj.org/article/d57a4c9ecc4947739c082ebdbe32a0bb. [Accessed: 14-Feb-2023].Google Scholar
- J. Fan, Q. Hu, and Z. Tang, “Predicting vacant parking space availability: An SVR method with Fruit Fly Optimisation,” IET Intelligent Transport Systems, vol. 12, no. 10, pp. 1414–1420, 2018.Google ScholarCross Ref
- J. Nyambal and R. Klein, “Automated parking space detection using Convolutional Neural Networks,” arXiv.org, 14-Jun-2021. [Online]. Available: http://arxiv.org/abs/2106.07228. [Accessed: 15-Nov-2021].Google Scholar
- J. M. Ealn Davan, T. W. Koh, D. L. Tong, and K. L. Tseu, “Anticipation of parking vacancy during peak/non-peak hours using convolutional neural network – yolov3 in university campus,” 2021 International Conference on Green Energy, Computing and Sustainable Technology (GECOST), 2021.Google ScholarCross Ref
- J. Nelson and J. Solawetz, “Responding to the controversy about yolov5,” Roboflow Blog, 04-Mar-2021. [Online]. Available: https://blog.roboflow.com/yolov4-versus-yolov5/. [Accessed: 19-Aug-2022].Google Scholar
- P. R. L. de Almeida, L. S. Oliveira, A. S. Britto, E. J. Silva, and A. L. Koerich, “PKLot – a robust dataset for parking lot classification,” Expert Systems with Applications, vol. 42, no. 11, pp. 4937–4949, 2015.Google ScholarDigital Library
- M.-R. Hsieh, Y.-L. Lin, and W. H. Hsu, “Drone-based object counting by Spatially Regularized Regional Proposal Network,” 2017 IEEE International Conference on Computer Vision (ICCV), 2017.Google ScholarCross Ref
- G. Amato, C. Vairo, C. Gennaro, F. Falchi, and F. Carrara, “CNRPARK+EXTA dataset for visual occupancy detection of parking lots,” CNR Parking Dataset - Dataset for visual occupancy detection of parking lots. [Online]. Available: http://cnrpark.it/. [Accessed: 07-Aug-2022].Google Scholar
- BraunGe, “Aerial view car detection for Yolov5,” Kaggle, 02-May-2022. [Online]. Available: https://www.kaggle.com/datasets/braunge/aerial-view-car-detection-for-yolov5. [Accessed: 07-Aug-2022].Google Scholar
- V. Praharsha, “Yolov4 model architecture,” OpenGenus IQ: Computing Expertise & Legacy, 11-Jan-2022. [Online]. Available: https://iq.opengenus.org/yolov4-model-architecture/. [Accessed: 26-Aug-2022].Google Scholar
- Techzizou, “Train a custom yolov4 object detector on Windows,” Medium, 05-Oct-2021. [Online]. Available: https://medium.com/geekculture/train-a-custom-yolov4-object-detector-on-windows-fe5332b0ca95. [Accessed: 20-Aug-2022].Google Scholar
- D. A. Swanson, “On the relationship among values of the same summary measure of error,” Review of Economics & Finance, 01-Jan-1970. [Online]. Available: https://ideas.repec.org/a/bap/journl/150301.html. [Accessed: 23-Aug-2022].Google Scholar
Index Terms
- Predicting Open Parking Space using Deep Learning and Support Vector Regression
Recommendations
Predicting truck parking occupancy using machine learning
AbstractThe logistics industry faces an increasing shortage of truck parking spots. This results in illegal parking or fatigued driving with hazardous consequences for traffic safety, as truck drivers have no insight into future availability of parking ...
Deep Learning based Street Parking Sign Detection and Classification for Smart Cities
GoodIT '21: Proceedings of the Conference on Information Technology for Social GoodSmart traffic management is essential for smart cities. Detection and classification of traffic signs is essential in autonomous driving and in helping with traffic navigation in general. Automated recognition of street parking signs, however, is a more ...
Predicting on-street parking violation rate using deep residual neural networks
Highlights- Deep learning-based on-street parking violation prediction system is proposed.
- ...
AbstractThe lack of available parking spaces can be among the most significant issues that can affect the quality of life of citizens in large cities. This has led to the development of on-street parking systems that typically ensure that ...
Comments