Recommendation method, system and client for taking taxi

Abstract

The present invention provides a recommendation method, system and client for taking a taxi, which implement the effect of recommending a path for a user to conveniently and rapidly take a taxi when the taxi is difficult to take. The method comprises: presetting nodes, and acquiring a path between each two nodes; acquiring a riding recommendation weight value of any one path within a preset time period according to empirical data; storing the riding recommendation weight value; acquiring a current position of the user and current time, and acquiring the nearest node corresponding to the current position; selecting more than one path containing the nearest node; acquiring a riding recommendation weight value of the selected path within the preset time period in which the current time is; and recommending a preset number of the paths corresponding to the riding recommendation weight value. The effect of enabling the user to relatively quickly take a taxi in the recommended path is implemented, user travel is facilitated, and convenience of taking a taxi is improved.

Description

technical field [0001] The invention relates to a recommendation method, system and client for taking a taxi. Background technique [0002] For the convenience of travel, many people will choose to take a taxi, and the parking place of the taxi will not only be restricted by traffic rules, but also affected by road traffic congestion. In order to grab time to carry more passengers, taxi drivers usually Will bypass congested roads during peak hours and need to find a shortcut. Then, it becomes more difficult for passengers to take a taxi on congested roads during peak hours, which not only seriously affects the travel of passengers, but also takes a lot of time to wait for the bus; and the efficiency of taking a taxi is particularly low. [0003] The patent document with the application number 201210580716.4 discloses a method for recommending passenger points of taxis based on spatio-temporal clustering. By reading the GPS data of the taxis in the selected time period, the ...

AI Technical Summary

Problems solved by technology

[0004] The above method is aimed at the taxi terminal, and recommends places where taxi drivers often carry passengers, so that the driver can obtain customers; and the above method only determines the places with a lot of passengers, not the same as the places in the empty state. Locations where there are many taxis, therefore, it is still impossible to solve the problem of difficulty in hailing taxis for travel users

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