Commuting private car dynamic car-sharing recommendation method

Abstract

The invention discloses a commuting private car dynamic car-sharing recommendation method. The commuting private car dynamic car-sharing recommendation method comprises steps that license plates are input, and data is automatically identified, and is preprocessed to generate a car trajectory set; based on the urban vehicle trajectory set, by combining with the high repeatability and the periodicity of the time-space rules of the commuting private cars, a workday commuting private car identification algorithm is designed; by combining with the home-work addresses, the commuting time windows, and the workday dynamic influence factors (weather, accidents, and traffic)of the commuting private cars, the key characteristics affecting the car-sharing of the commuting private cars are extracted; based on the space-time characteristics of the commuting cars, the commuting cars having the similar commuting time-space rules are matched with each other, and by combining with dynamic influence factors affecting car-sharing wills of drivers, the dynamic factors are effectively combined with car-sharing demands of users in a time-space dimension, and therefore the dynamic car-sharing matching based on the wills of the users is realized, a long-term stable car-sharing mechanism is provided for the commuting private cars, and then the number of the private cars in the morning and evening rush hours is obviously reduced.

Description

technical field [0001] The invention relates to the field of intelligent transportation systems, in particular to a dynamic carpooling recommendation method for commuter private cars. Background technique [0002] With the rapid development of location-based services and mobile Internet technologies, carpooling services have become a new sharing economy model to alleviate urban traffic congestion. [0003] In the field of carpooling recommendation, there are two problems with traditional algorithms: First, most of the existing methods have a single goal of meeting passengers’ real-time carpooling needs, that is, how to reduce passengers’ waiting time, and have not effectively solved the problem of competition among carpooling drivers. And the problem of increasing the use of private cars. Therefore, existing carpooling methods have the potential to cause more serious road congestion during morning and evening peak hours. Second, most of the existing carpooling schemes rely...

AI Technical Summary

Problems solved by technology

[0003] In the field of carpooling recommendation, there are two problems with traditional algorithms: First, most of the existing methods have a single goal of meeting passengers’ real-time carpooling needs, that is, how to reduce passengers’ waiting time, and have not effectively solved the problem of competition among carpooling drivers. And the problem of increasing the use of private cars

As a result, existing carpooling methods have the potential to cause more severe road congestion during morning and evening peak hours

Second, most of the existing carpooling schemes rely on large-scale crowd movement data such as GPS trajectories and mobile phone signaling. The trajectories are rich but semantic information is lacking, and the influence of dynamic factors such as weather, holidays, major events, and traffic accidents on the carpooling scheme has not been effectively considered. Significant influence

Therefore, it is difficult to provide users with personalized dynamic carpooling solutions

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