Vehicle scheduling method based on fuzzy decision

Abstract

The invention discloses a vehicle scheduling method based on a fuzzy decision. The method comprises the following steps of: S1, performing schedule filtering on candidate vehicles; S2, performing effective distance filtering on the filtered candidate vehicles; and S3, screening the vehicles which most possibly receive orders by making use of a Bayesian Belief Network. According to the method, the candidate vehicles are excluded according to objective conditions, namely, after the candidate vehicles are excluded according to the schedules of the vehicles and after the distances between the vehicles and the pick up locations are excluded, the order receiving intentions of the drivers of the rest of candidate vehicles are calculated by the Bayesian Belief Network, so that the vehicles which most possibly receive orders are filtered, and then the filtered vehicles which most possibly receive orders are polled, thus, the polling is more targeted, the probability for receiving orders is greatly improved, and the time for polling the candidate vehicles which are less likely to receive orders is saved. Large amounts of data statistics shows that after the adoption of the method provided by the invention, the candidate vehicles are polled, and the probability for receiving orders is improved by 10-15%.

Description

technical field [0001] The invention relates to a vehicle scheduling method based on fuzzy decision-making, and belongs to the technical field of vehicle scheduling. Background technique [0002] Taxi is an important means of transportation for people to go out in cities. How to quickly dispatch vehicles to meet the increasing needs of users has become a new challenge for various car platforms. At present, each car platform adopts the following method for vehicle scheduling: check which vehicles are empty on the background server, then poll the empty vehicles near the place where passengers want to board, and then determine the desired vehicle according to the results of the polling. dispatched vehicles. However, before the polling, the willingness of each vehicle driver to receive orders is not known, so the polling is often not targeted, resulting in a low probability of receiving orders, that is, a low efficiency of vehicle scheduling, wasting a lot of time for blind in...

AI Technical Summary

Problems solved by technology

However, before polling, the willingness of each vehicle driver to receive orders is not known, so polling is often not targeted, resulting in a low probability of receiving orders, which means that the efficiency of vehicle scheduling is low, wasting a lot of time for blind inquiries

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