Public bicycle station supply and demand prediction method based on Markov chain

Abstract

The invention discloses a public bicycle station supply and demand prediction method based on a Markov chain. A balance stable equation relevant to station importance is established by constructing a transfer probability matrix of borrowed and returned vehicle by using the bicycle lease data of a public bicycle lease station terminal in order to predict the daily borrowing and returning demands of a station. The method has the positive effects that the classical method of Markov chain on probability statistics is applied in combination with the practical problem of a public bicycle lease station, and a practicable vehicle borrowing and returning supply and demand prediction method is provided, so that theoretical guidance is provided for the specific pile construction problem and balance scheduling problem of the public bicycle station. The method has a good industrial application prospect.

Description

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AI Technical Summary

Benefits of technology

Inventors have developed an improved process called predictive modelling (PM) which allows us to better match statistical models from different sources such as social media data and vehicle usage patterns. By combining these techniques into one framework we aimed towards improving the accuracy of estimating car needs during peak hours by providing guidelines on how much space should go around each location within the park when renting them without overloading other areas nearby. Overall, this technology helps optimize traffic management systems while ensuring fair allocation between available spaces.

Problems solved by technology

There exist technical problem addressed in this patented text relating to improving the allocation of public bike lanings (BLS) within urban areas like streets during peak hours when many passengers want their owners' vehicles instead waiting long distances before getting back into oneself homes. Current solutions involve either relying heavily upon previous estimates made about how well they were actually going up against new prices or requiring more frequent trips than necessary, leading to poorer utilities and increased cost. Additionally, current models require significant time investment and infrastructure resources, making them impracable targets for implementation.

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