Machine Learning Model to Forecast Demand of Boston Bike-Ride Sharing
##plugins.themes.bootstrap3.article.main##
Bike-ride sharing systems are the new generation of traditional bike rentals, where the entire process is automated. A user rents a bike from one location and returns it at another location. There are more than 500 bike-ride sharing systems around the world, consisting of more than 500,000 bikes. Bike-ride sharing systems are typically found in urban and large cities such as Boston, N.Y. City, Washington DC, Paris, Montreal, and Barcelona. Bike-ride sharing is particularly important due to their important impact on traffic, environment, and health. As popular as bike-ride sharing systems are, there is a lack of a reliable model to forecast (predict) bike rental demand daily. Lack of available bikes constitutes an inconvenience to individuals seeking a bike at a certain location and a loss of revenues for companies operating the bikes. This paper develops a Machine Learning (ML) model (algorithm) to forecast (predict) the number of bikes rented daily based on historical data. Moreover, the model overlays environmental and seasonal settings to study their impact on bike rental demand. We test our ML model using a real-life dataset obtained from a local bike-ride sharing company in the City of Boston in the state of Massachusetts in the United States. We also applied the model to historical dataset from New York City (NYC). In both cases, the model is accurate and reliable.
References
-
Eren E, Uz VE. A review on bike-sharing: The factors affecting bike-sharing demand. Sustainable Cities and Society. 2020 Mar 01; 54:101882. doi: 10.1016/j.scs.2019.101882.
Google Scholar
1
-
Tripodi A, Persia L. Impact of bike sharing system in an urban area. Advances in Transportation Studies. 2015 July; 36(35): 143–156.
Google Scholar
2
-
El-Assi W, Mahmoud MS, Habib KN. Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto. Transportation. 2017 May 01;44(3):589–613. doi: 10.1007/s11116-015-9669-z.
Google Scholar
3
-
Freund D, Henderson SG, O’Mahony E, Shmoys DB. Analytics and Bikes: Riding Tandem with Motivate to Improve Mobility. Interfaces. 2019 Oct;49(5):310-323. doi: 10.1287/inte.2019.1005.
Google Scholar
4
-
INFORMS. Increasing bike-share efficiency: Researchers from Ornell University recognized with INFORMS Daniel H. Wagner Prize [Internet]. 2018 [updated 2018 Nov 21; cited 2021 Jun 8]. Available from: https://www.informs.org/About-INFORMS/News-Room/Press-Releases/Increasing-bike-share-efficiency-Researchers-from-Cornell-University-recognized-with-INFORMS-Daniel-H.-Wagner-Prize.
Google Scholar
5
-
pandas. pandas.DataFrame [Internet]. 2008 [cited 2022 Mar 10]. Available from: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html.
Google Scholar
6
-
Zhou Q, Liao F, Mou C, Wang P. Measuring Interpretability for Different Types of Machin Learning Models. Pacific-Asia Conference on Knowledge Discovery and Data Mining; Australia, 2018.
Google Scholar
7
-
Saba T, Abunadi I, Shahzad MN, Khan AR. Machine learning techniques to detect and forecast the daily total COVID-19 infected and deaths cases under different lockdown types. Microsc Res Tech. 2021 Jul;84(7):1462-1474. doi: 10.1002/jemt.23702.
Google Scholar
8
-
Ghori KM, Abbasi AR, Awais M, Imran M, Ullah A, Szathmary. Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection. IEEE Access. 2020;8:16033-16048. doi: 10.1109/ACCESS.2019.2962510.
Google Scholar
9
-
IBM. Supervised Learning [Internet]. 2020 [cited 10 Mar 2022]. Available from: https://www.ibm.com/cloud/learn/supervised-learning.
Google Scholar
10
-
GeeksforGeerks. Python – Coefficient of Determination-R2 score [Internet]. 2020 [cited 2022 Feb 22]. Available from: https://www.geeksforgeeks.org/python-coefficient-of-determination-r2-score/.
Google Scholar
11
-
Towards Data Sceince. Ridge and Lasso Regression: L1 and L2 Regularization [Internet]. 2018 [cited 2022 Mar 10]. Available from: https://towardsdatascience.com/ridge-and-lasso-regression-a-complete-guide-with-python-scikit-learn-e20e34bcbf0b.
Google Scholar
12
-
Medium. Cross Validation in Time Series [Internet]. 2020 [cited 2022 Mar 10]. Available from: https://medium.com/@soumyachess1496/cross-validation-in-time-series-566ae4981ce4.
Google Scholar
13
Most read articles by the same author(s)
-
Zahra Karimi,
Shrikant Savant,
Abe Zeid,
Sagar Kamarthi,
Shape Recognition and Corner Points Detection in 2D Drawings Using a Machine Learning Long Short-Term Memory (LSTM) Approach , European Journal of Artificial Intelligence and Machine Learning: Vol. 3 No. 1 (2024)
Similar Articles
- Ejiofor Martins Ugwu, Onate Egerton Taylor, Nuka Dumle Nwiabu, An Improved Visual Attention Model for Automated Vehicle License Plate Number Recognition Using Computer Vision , European Journal of Artificial Intelligence and Machine Learning: Vol. 1 No. 3 (2022)
You may also start an advanced similarity search for this article.