Public bike scheduling monitoring method

Abstract

The invention discloses a public bike scheduling monitoring method. The method comprises the following steps: step 1, data acquisition, i.e., obtaining lease site data through a wireless communication mode; step 2, vehicle scheduling, i.e., work personnel in a scheduling center scheduling release sites needing scheduling at any time through controlling scheduling vehicles in a command road network; and step 3, scheduling quantity statistics, i.e., finishing bike scheduling-in and scheduling-out quantity statistics of each lease site for the scheduling personnel to use. The public bike scheduling monitoring method can effectively improve the scientific public bike scheduling management, enhances the scheduling efficiency, decreases the scheduling cost, improves the utilization rate of public bikes and alleviates the conflict of difficult bike renting and difficult bike returning.

Description

technical field [0001] The invention relates to a new dispatch management method for public bicycles, which belongs to the field of intelligent transportation. Background technique [0002] At present, the public bicycle system mainly uses the combination of manual inspection and rental point monitoring to understand the bicycle rental situation at each rental point, and uses manual experience to schedule some rental points, and the number of public bicycle scheduling is mainly completed by manual statistics. This scheduling management method has certain limitations: [0003] 1) The cost of manual inspection is high and the efficiency is low; [0004] 2) The scale of the public bicycle system is very large, and it is difficult to solve the problem of no car to borrow and no empty parking space to return at most of the other rental points for a long time. [0005] 3) Manual statistics are greatly affected by human factors, and are prone to errors. There may be false reports...

AI Technical Summary

Problems solved by technology

[0003] 1) The cost of manual inspection is high and the efficiency is low;

[0004] 2) The scale of the public bicycle system is very large. Only relying on human experience to schedule some rental points, it is difficult to solve the problem that most of the remaining rental points have no cars to borrow and no empty parking spaces to return for a long time

[0005] 3) Manual statistics are greatly affected by human factors, and are prone to errors. There may be false reports or false reports or untimely reports, etc.

[0006] As the scale of the public bicycle system continues to increase, the scheduling tasks will become more arduous, and there will be higher requirements for the efficiency and timeliness of the scheduling work. It is difficult to guarantee the service level by statistically processing data manually.

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