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ELEKTROMOBİLİTEDE TAHMİNE DAYALI MODELLEME: BİR ZAMAN SERİSİ ANALİZİ

Year 2026, Volume: 29 Issue: 1, 295 - 314, 03.03.2026

Rana Bedir Urfalı , Ersin Kaya

https://izlik.org/JA29UL67FG

Abstract

Bu çalışma, gelecekteki eğilimleri tahmin etmek amacıyla, bir ildeki elektrikli bisiklet kullanımını zaman serisi modelleri ile analiz etmektedir. Sürdürülebilir ulaşımın kritik bir parçası olan elektrikli bisikletler, kentsel hareketliliği ve talep tahminini iyileştirir; bu da elektrikli bisiklet paylaşım sistemleri yönetimi için hayati önem taşır. Tahminler ilk olarak yalnızca bisiklet kullanım verileriyle tek değişkenli modeller (ARIMA, Prophet, SSA) kullanılarak yapıldı. Ardından, sıcaklık, yağış ve rüzgar hızı gibi dış etkenleri dahil eden çok değişkenli yaklaşımlara (SARIMAX, Multivariate Prophet, MSSA) geçildi. Bu sayede veri seti, çevresel etkileri yakalamak için meteorolojik değişkenlerle birleştirildi. Model performansı RMSE ve MAE metrikleriyle değerlendirildi. Sonuçlar, doğrulukta bölgesel farklılıklar olduğunu gösterdi: ARIMA, tek değişkenli modeller arasında dört bölgede en iyisiydi, ancak SARIMAX ve Multivariate Prophet çoğu bölgede üstün tahminler üretti. Ayrıca MSSA, dış etkileri dahil etmenin faydasını vurgulayarak 13 bölgede SSA'yı sürekli olarak geride bıraktı. Genel bulgular, hava durumu verilerini entegre etmenin tahmin hassasiyetini artırdığını ve daha iyi operasyonel stratejileri desteklediğini ortaya koymaktadır. Bu yaklaşım, elektrikli bisiklet paylaşım hizmetlerini ve şehir içi ulaşım planlamasını optimize ederek daha verimli kaynak kullanımına ve yüksek kullanıcı memnuniyetine katkıda bulunmaktadır.

Keywords

Elektrikli bisiklet zaman serisi tahmini tek değişkenli modeller çok değişkenli modeller karşılaştırmalı analiz

References

  • Afriyie, J. K., Twumasi-Ankrah, S., Gyamfi, K. B., Arthur, D., & Pels, W. A. (2020). Evaluating the performance of unit root tests in single time series processes. Mathematics and Statistics, 8(6), 656-664. https://doi.org/10.13189/ms.2020.080605
  • Agarwal, A., Alomar, A., & Shah, D. (2022). On multivariate singular spectrum analysis and its variants. ACM SIGMETRICS Performance Evaluation Review, 50(1), 79-80. https://doi.org/10.1145/3547353.3526952
  • Alagade, A., & Sahu, M. (2025). Satellite-based assessment and forecasting of greenhouse gas (GHG) concentrations in Indian megacities using advanced statistical methods. Environmental Science and Pollution Research, 32(24), 15006-15024. https://doi.org/10.1007/s11356-025-36583-1
  • Alencar, V. A., Pessamilio, L. R., Rooke, F., Bernardino, H. S., & Borges Vieira, A. (2021). Forecasting the carsharing service demand using uni and multivariable models. Journal of Internet Services and Applications, 12(1), 4. https://doi.org/10.1186/s13174-021-00137-8
  • Alharbi, F. R., & Csala, D. (2022). A seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) forecasting model-based time series approach. Inventions, 7(4), 94. https://doi.org/10.3390/inventions7040094
  • Alhussan, A. A., Khafaga, D. S., Abotaleb, M., Mishra, P., & El-Kenawy, E. S. M. (2024). Global potato production forecasting based on time series analysis and advanced waterwheel plant optimization algorithm. Potato Research, 67(4), 1965-2000. https://doi.org/10.1007/s11540-024-09728-x
  • Almazrouee, A. I., Almeshal, A. M., Almutairi, A. S., Alenezi, M. R., Alhajeri, S. N., & Alshammari, F. M. (2020). Forecasting of electrical generation using prophet and multiple seasonality of holt–winters models: A case study of Kuwait. Applied Sciences, 10(23), 8412. https://doi.org/10.3390/app10238412
  • Chowdhury, M. S., & Hafsa, B. (2022). Multi-decadal land cover change analysis over Sundarbans Mangrove Forest of Bangladesh: A GIS and remote sensing based approach. Global Ecology and Conservation, 37, e02151. https://doi.org/10.1016/j.gecco.2022.e02151
  • Cocca, M., Teixeira, D., Vassio, L., Mellia, M., Almeida, J. M., & Couto da Silva, A. P. (2020). On car-sharing usage prediction with open socio-demographic data. Electronics, 9(1), 72. https://doi.org/10.3390/electronics9010072
  • Fattah, J., Ezzine, L., Aman, Z., El Moussami, H., & Lachhab, A. (2018). Forecasting of demand using ARIMA model. International journal of engineering business management, 10, 1847979018808673. https://doi.org/10.1177/1847979018808673
  • Iftikhar, H., Khan, F., Rodrigues, P. C., Alharbi, A. A., & Allohibi, J. (2025). Forecasting of inflation based on univariate and multivariate time series models: an empirical application. Mathematics, 13(7), 1121. https://doi.org/10.3390/math13071121
  • İnaç, H. (2024). PREDICTION OF DRIVING TIME OF ELECTRIC SCOOTER (E-SCOOTER) DRIVERS BY MACHINE LEARNING. Elektronik Sosyal Bilimler Dergisi, 23(91), 1041-1057. https://doi.org/10.17755/esosder.1432527
  • Jaber, A., Csonka, B., & Juhász, J. (2022). Long term time series prediction of bike sharing trips: A cast study of Budapest city. world, 1(2), 3. https://doi.org/10.1109/SCSP54748.2022.9792540
  • Jagannathan, J., & Divya, C. (2021). Time series analyzation and prediction of climate using enhanced multivariate prophet. International Journal of Engineering Trends and Technology, 69(10), 89-96. https://doi.org/10.14445/22315381/IJETT-V69I10P212
  • Komarica, J., Glavić, D., & Kaplanović, S. (2024). Predicting and analyzing electric bicycle adoption to enhance urban mobility in belgrade using ANN models. Applied Sciences, 14(19), 8965. https://doi.org/10.3390/app14198965
  • Kwarteng, S., & Andreevich, P. (2024). Comparative analysis of ARIMA, SARIMA and Prophet model in forecasting. Research & Development, 5(4), 110-120. https://doi.org/10.11648/j.rd.20240504.13
  • Patterson, K., Hassani, H., Heravi, S., & Zhigljavsky, A. (2011). Multivariate singular spectrum analysis for forecasting revisions to real-time data. Journal of Applied Statistics, 38(10), 2183-2211. https://doi.org/10.1080/02664763.2010.545371
  • Sanami, S., Mosalli, H., Yang, Y., Yeh, H. G., & Aghdam, A. G. (2025, July). Demand forecasting for electric vehicle charging stations using multivariate time-series analysis. In 2025 American Control Conference (ACC) (pp. 3461-3466). IEEE. https://doi.org/10.48550/arXiv.2502.16365
  • Subramanian, M., Cho, J., Veerappampalayam Easwaramoorthy, S., Murugesan, A., & Chinnasamy, R. (2023). Enhancing sustainable transportation: AI-driven bike demand forecasting in smart cities. Sustainability, 15(18), 13840. https://doi.org/10.3390/su151813840
  • Swari, M. H. P., Irawan, H. A., Muliawati, A., Aliansyah, Z., & Diyasa, I. G. S. M. (2025, December). Enhancing Time Series Forecasting Accuracy through Hybrid ARIMA–MLP Integration: A Case Study on E-Bicycle Sales. In 2025 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS) (pp. 967-972). IEEE. https://doi.org/10.1109/ICIMCIS68501.2025.11327149
  • Taoussi, B., Boudia, S. M., & Mazouni, F. S. (2025). Wind speed forecasting using univariate and multivariate time series models. Stochastic Environmental Research and Risk Assessment, 39(2), 547-579. https://doi.org/10.1007/s00477-024-02881-2
  • Vishnu, G., Kaliyaperumal, D., Pati, P. B., Karthick, A., Subbanna, N., & Ghosh, A. (2023). Short-term forecasting of electric vehicle load using time series, machine learning, and deep learning techniques. World Electric Vehicle Journal, 14(9), 266. https://doi.org/10.3390/wevj14090266
  • Wang, J., Peng, X., Wu, J., Ding, Y., Ali, B., Luo, Y., ... & Zhang, K. (2024). Singular spectrum analysis (SSA) based hybrid models for emergency ambulance demand (EAD) time series forecasting. IMA Journal of Management Mathematics, 35(1), 45-64. https://doi.org/10.1093/imaman/dpad019
  • Zhang, Y., Zhong, M., Geng, N., & Jiang, Y. (2017). Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China. PloS one, 12(5), e0176729. https://doi.org/10.1371/journal.pone.0176729
  • Zunic, E., Korjenic, K., Hodzic, K., & Donko, D. (2020). Application of facebook's prophet algorithm for successful sales forecasting based on real-world data. arXiv preprint arXiv:2005.07575. https://doi.org/10.5121/ijcsit.2020.12203

PREDICTIVE MODELING IN ELECTROMOBILITY: A TIME SERIES ANALYSIS

Year 2026, Volume: 29 Issue: 1, 295 - 314, 03.03.2026

Rana Bedir Urfalı , Ersin Kaya

https://izlik.org/JA29UL67FG

Abstract

This study analyzes electric bicycle usage in a province with time series models to forecast future trends. Electric bicycles, as an essential element of sustainable transportation, enhance urban mobility and reduce traffic congestion. Accurate demand forecasting is crucial for the operational planning and administration of e-bike sharing systems. In this study, forecasts were first made using only bicycle usage data with univariate time series models (ARIMA, Prophet, SSA). Next, multivariate approaches (SARIMAX, Multivariate Prophet, MSSA) incorporated external factors such as temperature, precipitation, and wind speed. The dataset, therefore, combined bicycle usage records with meteorological variables to capture environmental impacts on demand. Model performance was assessed using RMSE and MAE metrics. Results showed regional variations in accuracy: ARIMA performed best among univariate models in four regions, while SARIMAX and Multivariate Prophet produced superior forecasts in most regions. Furthermore, MSSA consistently outperformed SSA in 13 regions, highlighting the benefit of including external influences. Overall, the findings demonstrate that integrating weather data improves forecast precision and supports better operational strategies. This contributes to optimizing e-bike sharing services and urban transport planning, enabling more efficient resource use and greater user satisfaction.

Keywords

Electric bicycle time series forecasting univariate models multivariate models comparative analysis

References

  • Afriyie, J. K., Twumasi-Ankrah, S., Gyamfi, K. B., Arthur, D., & Pels, W. A. (2020). Evaluating the performance of unit root tests in single time series processes. Mathematics and Statistics, 8(6), 656-664. https://doi.org/10.13189/ms.2020.080605
  • Agarwal, A., Alomar, A., & Shah, D. (2022). On multivariate singular spectrum analysis and its variants. ACM SIGMETRICS Performance Evaluation Review, 50(1), 79-80. https://doi.org/10.1145/3547353.3526952
  • Alagade, A., & Sahu, M. (2025). Satellite-based assessment and forecasting of greenhouse gas (GHG) concentrations in Indian megacities using advanced statistical methods. Environmental Science and Pollution Research, 32(24), 15006-15024. https://doi.org/10.1007/s11356-025-36583-1
  • Alencar, V. A., Pessamilio, L. R., Rooke, F., Bernardino, H. S., & Borges Vieira, A. (2021). Forecasting the carsharing service demand using uni and multivariable models. Journal of Internet Services and Applications, 12(1), 4. https://doi.org/10.1186/s13174-021-00137-8
  • Alharbi, F. R., & Csala, D. (2022). A seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) forecasting model-based time series approach. Inventions, 7(4), 94. https://doi.org/10.3390/inventions7040094
  • Alhussan, A. A., Khafaga, D. S., Abotaleb, M., Mishra, P., & El-Kenawy, E. S. M. (2024). Global potato production forecasting based on time series analysis and advanced waterwheel plant optimization algorithm. Potato Research, 67(4), 1965-2000. https://doi.org/10.1007/s11540-024-09728-x
  • Almazrouee, A. I., Almeshal, A. M., Almutairi, A. S., Alenezi, M. R., Alhajeri, S. N., & Alshammari, F. M. (2020). Forecasting of electrical generation using prophet and multiple seasonality of holt–winters models: A case study of Kuwait. Applied Sciences, 10(23), 8412. https://doi.org/10.3390/app10238412
  • Chowdhury, M. S., & Hafsa, B. (2022). Multi-decadal land cover change analysis over Sundarbans Mangrove Forest of Bangladesh: A GIS and remote sensing based approach. Global Ecology and Conservation, 37, e02151. https://doi.org/10.1016/j.gecco.2022.e02151
  • Cocca, M., Teixeira, D., Vassio, L., Mellia, M., Almeida, J. M., & Couto da Silva, A. P. (2020). On car-sharing usage prediction with open socio-demographic data. Electronics, 9(1), 72. https://doi.org/10.3390/electronics9010072
  • Fattah, J., Ezzine, L., Aman, Z., El Moussami, H., & Lachhab, A. (2018). Forecasting of demand using ARIMA model. International journal of engineering business management, 10, 1847979018808673. https://doi.org/10.1177/1847979018808673
  • Iftikhar, H., Khan, F., Rodrigues, P. C., Alharbi, A. A., & Allohibi, J. (2025). Forecasting of inflation based on univariate and multivariate time series models: an empirical application. Mathematics, 13(7), 1121. https://doi.org/10.3390/math13071121
  • İnaç, H. (2024). PREDICTION OF DRIVING TIME OF ELECTRIC SCOOTER (E-SCOOTER) DRIVERS BY MACHINE LEARNING. Elektronik Sosyal Bilimler Dergisi, 23(91), 1041-1057. https://doi.org/10.17755/esosder.1432527
  • Jaber, A., Csonka, B., & Juhász, J. (2022). Long term time series prediction of bike sharing trips: A cast study of Budapest city. world, 1(2), 3. https://doi.org/10.1109/SCSP54748.2022.9792540
  • Jagannathan, J., & Divya, C. (2021). Time series analyzation and prediction of climate using enhanced multivariate prophet. International Journal of Engineering Trends and Technology, 69(10), 89-96. https://doi.org/10.14445/22315381/IJETT-V69I10P212
  • Komarica, J., Glavić, D., & Kaplanović, S. (2024). Predicting and analyzing electric bicycle adoption to enhance urban mobility in belgrade using ANN models. Applied Sciences, 14(19), 8965. https://doi.org/10.3390/app14198965
  • Kwarteng, S., & Andreevich, P. (2024). Comparative analysis of ARIMA, SARIMA and Prophet model in forecasting. Research & Development, 5(4), 110-120. https://doi.org/10.11648/j.rd.20240504.13
  • Patterson, K., Hassani, H., Heravi, S., & Zhigljavsky, A. (2011). Multivariate singular spectrum analysis for forecasting revisions to real-time data. Journal of Applied Statistics, 38(10), 2183-2211. https://doi.org/10.1080/02664763.2010.545371
  • Sanami, S., Mosalli, H., Yang, Y., Yeh, H. G., & Aghdam, A. G. (2025, July). Demand forecasting for electric vehicle charging stations using multivariate time-series analysis. In 2025 American Control Conference (ACC) (pp. 3461-3466). IEEE. https://doi.org/10.48550/arXiv.2502.16365
  • Subramanian, M., Cho, J., Veerappampalayam Easwaramoorthy, S., Murugesan, A., & Chinnasamy, R. (2023). Enhancing sustainable transportation: AI-driven bike demand forecasting in smart cities. Sustainability, 15(18), 13840. https://doi.org/10.3390/su151813840
  • Swari, M. H. P., Irawan, H. A., Muliawati, A., Aliansyah, Z., & Diyasa, I. G. S. M. (2025, December). Enhancing Time Series Forecasting Accuracy through Hybrid ARIMA–MLP Integration: A Case Study on E-Bicycle Sales. In 2025 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS) (pp. 967-972). IEEE. https://doi.org/10.1109/ICIMCIS68501.2025.11327149
  • Taoussi, B., Boudia, S. M., & Mazouni, F. S. (2025). Wind speed forecasting using univariate and multivariate time series models. Stochastic Environmental Research and Risk Assessment, 39(2), 547-579. https://doi.org/10.1007/s00477-024-02881-2
  • Vishnu, G., Kaliyaperumal, D., Pati, P. B., Karthick, A., Subbanna, N., & Ghosh, A. (2023). Short-term forecasting of electric vehicle load using time series, machine learning, and deep learning techniques. World Electric Vehicle Journal, 14(9), 266. https://doi.org/10.3390/wevj14090266
  • Wang, J., Peng, X., Wu, J., Ding, Y., Ali, B., Luo, Y., ... & Zhang, K. (2024). Singular spectrum analysis (SSA) based hybrid models for emergency ambulance demand (EAD) time series forecasting. IMA Journal of Management Mathematics, 35(1), 45-64. https://doi.org/10.1093/imaman/dpad019
  • Zhang, Y., Zhong, M., Geng, N., & Jiang, Y. (2017). Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China. PloS one, 12(5), e0176729. https://doi.org/10.1371/journal.pone.0176729
  • Zunic, E., Korjenic, K., Hodzic, K., & Donko, D. (2020). Application of facebook's prophet algorithm for successful sales forecasting based on real-world data. arXiv preprint arXiv:2005.07575. https://doi.org/10.5121/ijcsit.2020.12203
There are 25 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence (Other)
Journal Section Research Article
Authors

Rana Bedir Urfalı 0009-0003-9495-2482

Ersin Kaya 0000-0001-5668-5078

Submission Date November 4, 2025
Acceptance Date February 5, 2026
Publication Date March 3, 2026
IZ https://izlik.org/JA29UL67FG
Published in Issue Year 2026 Volume: 29 Issue: 1

Cite

APA Bedir Urfalı, R., & Kaya, E. (2026). PREDICTIVE MODELING IN ELECTROMOBILITY: A TIME SERIES ANALYSIS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 29(1), 295-314. https://izlik.org/JA29UL67FG