Customized bus co-hiring optimization method considering bilateral matching

Abstract

The invention relates to a customized bus co-hiring optimization method considering bilateral matching, comprising the following steps of determining the elements of the customized bus co-hiring bilateral matching, wherein the elements include passengers, vehicles, road condition information and stops; determining objective functions including a system optimal objective function, a passenger optimal objective function and an operator optimal objective function; determining the bilateral matching co-hiring types; obtaining the customized bus co-hiring optimization model of the bilateral matching; generating a preference list according to the optimization model; and determining a bilateral stable matching algorithm. The method of the invention can ensure that the interests of both passengers and operators are satisfied. The optimization of the invention can enable passengers to find a suitable customized bus to deliver themselves to the destinations, and the customized bus can also findthe most suitable passenger to pick up, so as to avoid the reduction of revenue caused by the restriction of detour or vehicle capacity saturation.

Description

technical field [0001] The invention relates to a bus sharing optimization method. In particular, it involves a customized bus sharing optimization method that considers bilateral matching and is applied to transportation scheduling and management. Background technique [0002] In recent years, with the continuous development of Internet technology, transportation methods have also become more abundant. Relying on the Internet and smartphone apps, new travel modes such as online car-hailing and shared bicycles have begun to emerge. This has brought a big impact on traditional public transportation, especially regular buses. Due to road congestion during morning and evening peak hours, poor punctuality and comfort, travelers have shifted to rail transit and other rising modes of transportation. According to statistics, in 2017, the full load rate of buses in Tianjin during peak hours was only 61.5%, far from the requirement of 80% full load rate during peak hours, resulting...

AI Technical Summary

Problems solved by technology

[0005] From the above, it can be seen that there is no research on the mutual selection between operators and passengers during the operation of customized buses. With the widespread popularity of customized buses and the gradual increase in passenger sharing, customized bus operators need to Consider which passengers to choose to serve during the operation of the line, and passengers will also choose a customized bus suitable for travel according to the information of the bus

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