Regression-based parking space availability prediction for the Ubike system


Numerous vehicles exist worldwide like cars, motorcycles and bicycles. Although parking for such vehicles is accessible in many places, parking problems still continually exist, such as full tons or a lack of tons. Commuters seeking a parking area expend time when areas are occupied, and resources are wasted when parking areas are empty. On the opposite hand, biking is a inexperienced vehicle in a very fuel-shortage situation and conjointly a good exercise for folks. The Ubike system could be a standard short-distance transit vehicle system in Taipei City that conjointly has the parking drawback. Thus, this study uses two common regression schemes – linear regression and support vector regression (SVR) to predict the number of bicycles in Ubike stations to determine the number of obtainable parking areas. It also uses the proportional selection technique to extend accuracy and reduce coaching time for SVR. Some evaluations are conducted to validate the feasibility of the 2 regression-based service availability prediction schemes for the Ubike system.

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