Dynamic route guidance method based on multi-target Sarsa learning

Abstract

The invention provides a dynamic route guidance method based on multi-target Sarsa learning. The process includes steps: information initialization; information updating; and guidance route calculation comprising Q vector table normalization, calculation of a scalar value based on a driver preference, calculation of Boltzmann probability distribution, and selection of a next driving route for a driver in accordance with the personal preference of the driver through a roulette method until a vehicle of the driver reaches a destination. According to a traffic condition of a current traffic system, a driving route of the vehicle is optimized, the efficiency of the traffic system is improved, and the traffic jam condition is alleviated. From a practical angle, dynamic route guidance of multiple guidance targets is performed, which is in more accordance with the guidance demand in the practical life. The guidance preference of the driver is considered, the dynamic guidance route in accordance with the personal preference is provided for the driver, the acceptance rate of the guidance route is increased, the passing efficiency of the traffic system is further improved, and the traffic jam condition is alleviated.

Description

technical field [0001] The invention belongs to the technical field of intelligent transportation, and in particular relates to a dynamic path induction method based on multi-objective Sarsa learning. Background technique [0002] In recent years, with the rapid development of my country's social economy, the number of private cars has continued to rise, and the pressure on urban traffic has increased accordingly. Problems such as urban traffic congestion, congestion, and frequent traffic accidents have also become increasingly serious. In addition, as an important participant in the traffic system, drivers often have multiple induced goals at the same time and have different preferences for different goals during a journey. Whether to consider the driver's personal preference will have a great impact on the acceptance of induced information and thus affect the traffic efficiency of the traffic system. Therefore, from the perspective of alleviating traffic congestion and sa...

AI Technical Summary

Problems solved by technology

However, compared with the single-strategy method, the multi-strategy method will learn a set of optimal solutions to approach the Pareto front every time it interacts with the environment. This process requires a lot of computing time and the corresponding calculation amount is also very large.

And using a multi-strategy method in on-policy learning, the amount of calculation and the large amount of time required for the corresponding solution set make this method unsuitable for dynamic path induction systems

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