Detecting Predominance of on-Street Parking Payment Schemes by Means of Linear Regression
Amtul Waheed1, P.Venkata Krishna2
1Amtul Waheed, Department of Computer Science Engineering, Sri Padmavati Mahila Visvavidayalam, Tirupati (A.P), India.
2P.Venkata Krishna, Department of Computer Science Engineering, Sri Padmavati Mahila Visvavidayalam, Tirupati (A.P), India.
Manuscript received on January 12, 2020. | Revised Manuscript received on January 30, 2020. | Manuscript published on March 30, 2020. | PP: 59-61 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7139038620/2020©BEIESP | DOI: 10.35940/ijrte.F7139.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: On street parking is one of the important and crucial components of urban traffic and transportation system. Allocation of parking space on street is major reason for traffic congestion. Optimizing traffic congestion and facilitating on street parking is a long stand issue. According to urban environment it is expected that car drivers prefers parking space based on road conditions, speed limit and surrounding activities and availability of parking space. The other major components to be ponder while searching parking space is payment method used while parking the car. This paper investigates car driver’s behaviors in selecting parking payment schemas, visualized data as well predicted via machine learning technique of linear regression analysis on the open data set of On-street Car Parking Meters with Location of City of Melbourne’s in the Australian.
Keywords: Smart parking, on-street parking, parking meters.
Scope of the Article: Regression and Prediction