Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2022, Cilt: 10 Sayı: 1, 35 - 41, 30.06.2022
https://doi.org/10.51354/mjen.986631

Öz

Kaynakça

  • W. Shao, Y. Zhang, B. Guo, K. Qin, J. Chan, and F. D. Salim, "Parking availability prediction with long short term memory model," in International Conference on Green, Pervasive, and Cloud Computing, 2018: Springer, pp. 124-137.
  • https://www.sfmta.com/demand-responsive-parking-pricing, Accessed: 24 August 2021.
  • https://www.melbourne.vic.gov.au/about-council/governance-transparency/open-data/Pages/on-street-parking-data.aspx, Accessed: 24 August 2021.
  • https://www.smartparking.com/latest/case-studies/city-of-westminster, Accessed: 24 August 2021.
  • E. I. Vlahogianni, K. Kepaptsoglou, V. Tsetsos, and M. G. Karlaftis, "A Real-Time Parking Prediction System for Smart Cities," Journal of Intelligent Transportation Systems, vol. 20, no. 2, pp. 192-204, 2015, doi: 10.1080/15472450.2015.1037955.
  • S. R. Yanxu Zheng, Christopher Leckie, "Parking Availability Prediction for Sensor-Enabled Car Parks in Smart Cities," 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) Singapore, 2015.
  • W. Alajali, S. Wen, and W. Zhou, "On-Street Car Parking Prediction in Smart City: A Multi-source Data Analysis in Sensor-Cloud Environment," in Security, Privacy, and Anonymity in Computation, Communication, and Storage, (Lecture Notes in Computer Science, 2017, ch. Chapter 58, pp. 641-652.
  • C. Y. Li Xiangdong, CEN Gang, Xu Zengwei, "Prediction of short-term available parking space using LSTM model," The 14th International Conference on Computer Science & Education (ICCSE 2019).
  • S. Saharan, N. Kumar, and S. Bawa, "An efficient smart parking pricing system for smart city environment: A machine-learning based approach," Future Generation Computer Systems, vol. 106, pp. 622-640, 2020, doi: 10.1016/j.future.2020.01.031.
  • F. M. Awan, Y. Saleem, R. Minerva, and N. Crespi, "A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction," Sensors (Basel), vol. 20, no. 1, Jan 6 2020, doi: 10.3390/s20010322.
  • R. M. A. Sampathkumar, Pon Harshavardhanan, S. Murugan, P. Jayarajan, V. Sivasankaran, "Majority Voting based Hybrid Ensemble Classification Approach for Predicting Parking Availability in Smart City based on IoT," 11th ICCCNT 2020.
  • S. C. Koumetio Tekouabou, E. A. Abdellaoui Alaoui, W. Cherif, and H. Silkan, "Improving parking availability prediction in smart cities with IoT and ensemble-based model," Journal of King Saud University - Computer and Information Sciences, 2020, doi: 10.1016/j.jksuci.2020.01.008.
  • L. J. J. W. Jesper C. Provoost, Sander J. van der Drift, Maurice van Keulen, Andreas Kamilaris, "Short Term Prediction of Parking Area states Using Real Time Data and Machine Learning Techniques," 2019.
  • A. A. Sergio Di Martino, "Exploiting Recurrent Patterns to Improve Scalability of Parking Availability Prediction Systems," Electronics 2020 , 9 , 838, 2020.
  • Z. Zhao, Y. Zhang, Y. Zhang, K. Ji, and H. Qi, "Neural-Network-Based Dynamic Distribution Model of Parking Space Under Sharing and Non-Sharing Modes," Sustainability, vol. 12, no. 12, 2020, doi: 10.3390/su12124864.
  • J. Liu, J. Wu, and L. Sun, "Control method of urban intelligent parking guidance system based on Internet of Things," Computer Communications, vol. 153, pp. 279-285, 2020, doi: 10.1016/j.comcom.2020.01.063.
  • M. P. L. Jamie Arjona, Josep Casanovas-Garcia, Juan José Vázquez, "Improving Parking Availability Information Using Deep Learning Techniques," 22nd EURO Working Group on Transportation Meeting, EWGT 2019, 2020.
  • J. Qiu, J. Tian, H. Chen, and X. Lu, "Prediction Method of Parking Space Based on Genetic Algorithm and RNN," in Advances in Multimedia Information Processing – PCM 2018, (Lecture Notes in Computer Science, 2018, ch. Chapter 79, pp. 865-876.
  • S. Yang, W. Ma, X. Pi, and S. Qian, "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, vol. 107, pp. 248-265, 2019, doi: 10.1016/j.trc.2019.08.010.
  • H. C. Kedi Lv, Yingda Lv, "Parking Space Predicting Algorithm Based on Recurrent Neural Network and Ensemble Learning Algorithm," Proceedings of CCIS2019.
  • Z. Zhao, Y. Zhang, and Y. Zhang, "A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale," Journal of Advanced Transportation, vol. 2020, pp. 1-12, 2020, doi: 10.1155/2020/5624586.
  • D. H. Stolfi, E. Alba, and X. Yao, "Can I Park in the City Center? Predicting Car Park Occupancy Rates in Smart Cities," Journal of Urban Technology, pp. 1-15, 2019, doi: 10.1080/10630732.2019.1586223.

Parking lot occupancy prediction using long short-term memory and statistical methods

Yıl 2022, Cilt: 10 Sayı: 1, 35 - 41, 30.06.2022
https://doi.org/10.51354/mjen.986631

Öz

In crowded city centers, drivers looking for available parking space generate extra traffic and in addition, the resulting excessive exhaust gases cause air pollution. Therefore, directing the drivers to a parking spot in an intelligent way is an important task for smart city applications. This task requires the prediction of occupancy states of parking lots which involves appropriate processing of the historical parking data. In this work, Long-Short Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) methods were applied to parking data collected from curbside parking spots of Adana, Turkey for predicting the parking lot occupancy rates of future values. The experiments were performed for making predictions with different prediction horizons that are 1 minute, 5 minutes, and 15 minutes. The performances of the methods were compared by calculating root mean squared error (RMSE) and mean absolute error (MAE) values. The experiments were performed on data from five different days. According to the results, when the prediction horizon is set to 1 minute, LSTM achieved RMSE and MAE values of 0.98 and 0.72, respectively. For the same prediction horizon, ARIMA achieved RMSE and MAE values of 0.62 and 0.35, respectively. On the other hand, LSTM achieved smaller error values for larger prediction horizons. In conclusion, it was shown that LSTM is more suitable for larger prediction horizons, however, ARIMA is better at predicting near-future values.

Kaynakça

  • W. Shao, Y. Zhang, B. Guo, K. Qin, J. Chan, and F. D. Salim, "Parking availability prediction with long short term memory model," in International Conference on Green, Pervasive, and Cloud Computing, 2018: Springer, pp. 124-137.
  • https://www.sfmta.com/demand-responsive-parking-pricing, Accessed: 24 August 2021.
  • https://www.melbourne.vic.gov.au/about-council/governance-transparency/open-data/Pages/on-street-parking-data.aspx, Accessed: 24 August 2021.
  • https://www.smartparking.com/latest/case-studies/city-of-westminster, Accessed: 24 August 2021.
  • E. I. Vlahogianni, K. Kepaptsoglou, V. Tsetsos, and M. G. Karlaftis, "A Real-Time Parking Prediction System for Smart Cities," Journal of Intelligent Transportation Systems, vol. 20, no. 2, pp. 192-204, 2015, doi: 10.1080/15472450.2015.1037955.
  • S. R. Yanxu Zheng, Christopher Leckie, "Parking Availability Prediction for Sensor-Enabled Car Parks in Smart Cities," 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) Singapore, 2015.
  • W. Alajali, S. Wen, and W. Zhou, "On-Street Car Parking Prediction in Smart City: A Multi-source Data Analysis in Sensor-Cloud Environment," in Security, Privacy, and Anonymity in Computation, Communication, and Storage, (Lecture Notes in Computer Science, 2017, ch. Chapter 58, pp. 641-652.
  • C. Y. Li Xiangdong, CEN Gang, Xu Zengwei, "Prediction of short-term available parking space using LSTM model," The 14th International Conference on Computer Science & Education (ICCSE 2019).
  • S. Saharan, N. Kumar, and S. Bawa, "An efficient smart parking pricing system for smart city environment: A machine-learning based approach," Future Generation Computer Systems, vol. 106, pp. 622-640, 2020, doi: 10.1016/j.future.2020.01.031.
  • F. M. Awan, Y. Saleem, R. Minerva, and N. Crespi, "A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction," Sensors (Basel), vol. 20, no. 1, Jan 6 2020, doi: 10.3390/s20010322.
  • R. M. A. Sampathkumar, Pon Harshavardhanan, S. Murugan, P. Jayarajan, V. Sivasankaran, "Majority Voting based Hybrid Ensemble Classification Approach for Predicting Parking Availability in Smart City based on IoT," 11th ICCCNT 2020.
  • S. C. Koumetio Tekouabou, E. A. Abdellaoui Alaoui, W. Cherif, and H. Silkan, "Improving parking availability prediction in smart cities with IoT and ensemble-based model," Journal of King Saud University - Computer and Information Sciences, 2020, doi: 10.1016/j.jksuci.2020.01.008.
  • L. J. J. W. Jesper C. Provoost, Sander J. van der Drift, Maurice van Keulen, Andreas Kamilaris, "Short Term Prediction of Parking Area states Using Real Time Data and Machine Learning Techniques," 2019.
  • A. A. Sergio Di Martino, "Exploiting Recurrent Patterns to Improve Scalability of Parking Availability Prediction Systems," Electronics 2020 , 9 , 838, 2020.
  • Z. Zhao, Y. Zhang, Y. Zhang, K. Ji, and H. Qi, "Neural-Network-Based Dynamic Distribution Model of Parking Space Under Sharing and Non-Sharing Modes," Sustainability, vol. 12, no. 12, 2020, doi: 10.3390/su12124864.
  • J. Liu, J. Wu, and L. Sun, "Control method of urban intelligent parking guidance system based on Internet of Things," Computer Communications, vol. 153, pp. 279-285, 2020, doi: 10.1016/j.comcom.2020.01.063.
  • M. P. L. Jamie Arjona, Josep Casanovas-Garcia, Juan José Vázquez, "Improving Parking Availability Information Using Deep Learning Techniques," 22nd EURO Working Group on Transportation Meeting, EWGT 2019, 2020.
  • J. Qiu, J. Tian, H. Chen, and X. Lu, "Prediction Method of Parking Space Based on Genetic Algorithm and RNN," in Advances in Multimedia Information Processing – PCM 2018, (Lecture Notes in Computer Science, 2018, ch. Chapter 79, pp. 865-876.
  • S. Yang, W. Ma, X. Pi, and S. Qian, "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, vol. 107, pp. 248-265, 2019, doi: 10.1016/j.trc.2019.08.010.
  • H. C. Kedi Lv, Yingda Lv, "Parking Space Predicting Algorithm Based on Recurrent Neural Network and Ensemble Learning Algorithm," Proceedings of CCIS2019.
  • Z. Zhao, Y. Zhang, and Y. Zhang, "A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale," Journal of Advanced Transportation, vol. 2020, pp. 1-12, 2020, doi: 10.1155/2020/5624586.
  • D. H. Stolfi, E. Alba, and X. Yao, "Can I Park in the City Center? Predicting Car Park Occupancy Rates in Smart Cities," Journal of Urban Technology, pp. 1-15, 2019, doi: 10.1080/10630732.2019.1586223.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Ercan Avşar 0000-0002-1356-2753

Yusuf Can Anar 0000-0002-2227-768X

Abdurrahman Özgür Polat 0000-0002-4922-6567

Erken Görünüm Tarihi 3 Temmuz 2022
Yayımlanma Tarihi 30 Haziran 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 10 Sayı: 1

Kaynak Göster

APA Avşar, E., Anar, Y. C., & Polat, A. Ö. (2022). Parking lot occupancy prediction using long short-term memory and statistical methods. MANAS Journal of Engineering, 10(1), 35-41. https://doi.org/10.51354/mjen.986631
AMA Avşar E, Anar YC, Polat AÖ. Parking lot occupancy prediction using long short-term memory and statistical methods. MJEN. Haziran 2022;10(1):35-41. doi:10.51354/mjen.986631
Chicago Avşar, Ercan, Yusuf Can Anar, ve Abdurrahman Özgür Polat. “Parking Lot Occupancy Prediction Using Long Short-Term Memory and Statistical Methods”. MANAS Journal of Engineering 10, sy. 1 (Haziran 2022): 35-41. https://doi.org/10.51354/mjen.986631.
EndNote Avşar E, Anar YC, Polat AÖ (01 Haziran 2022) Parking lot occupancy prediction using long short-term memory and statistical methods. MANAS Journal of Engineering 10 1 35–41.
IEEE E. Avşar, Y. C. Anar, ve A. Ö. Polat, “Parking lot occupancy prediction using long short-term memory and statistical methods”, MJEN, c. 10, sy. 1, ss. 35–41, 2022, doi: 10.51354/mjen.986631.
ISNAD Avşar, Ercan vd. “Parking Lot Occupancy Prediction Using Long Short-Term Memory and Statistical Methods”. MANAS Journal of Engineering 10/1 (Haziran 2022), 35-41. https://doi.org/10.51354/mjen.986631.
JAMA Avşar E, Anar YC, Polat AÖ. Parking lot occupancy prediction using long short-term memory and statistical methods. MJEN. 2022;10:35–41.
MLA Avşar, Ercan vd. “Parking Lot Occupancy Prediction Using Long Short-Term Memory and Statistical Methods”. MANAS Journal of Engineering, c. 10, sy. 1, 2022, ss. 35-41, doi:10.51354/mjen.986631.
Vancouver Avşar E, Anar YC, Polat AÖ. Parking lot occupancy prediction using long short-term memory and statistical methods. MJEN. 2022;10(1):35-41.

Manas Journal of Engineering 

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