Bus arrival time estimation method and device

Abstract

The invention provides a bus arrival time estimation method and device. The method includes dividing each bus running line into a plurality of running portions, and establishing corresponded artificial neural network models respectively; training the running portions to acquire corresponded parameter values of the corresponded artificial neural network models; determining all target running portions between the current bus position and a target station, acquiring current values of the current influence factors corresponding to each target running portion, inputting the corresponded artificial neural network models, acquiring output values of the artificial neural network models corresponded to the target running portions, and acquiring times for the bus arriving the target station according to the output values. The device comprises a dividing module, a model establishing module, a first artificial neural network model processing module, a current state processing module, a second artificial neural network model processing module and a calculating module. By the aid of the method and device, the bus arrival time can be calculated more accurately.

Description

technical field [0001] The invention relates to information processing technology, in particular to a bus arrival time measurement method and device. Background technique [0002] With the development of urban intelligent transportation, the level of urban traffic operation management and service informatization has been greatly improved, but for the bus system, bus arrival prediction is still a difficult and key point, and the accurate prediction of bus arrival is directly related to bus passengers. satisfaction. [0003] At present, bus arrival prediction services have appeared in some cities. For example, electronic screens are set up next to bus stops to display the number of stations that buses of a certain line need to pass through to reach the current station. However, the number of stations the bus is away from the current station is not intuitive for the user, and the user prefers to know the waiting time, that is, the estimated time for the bus to arrive at the cu...

AI Technical Summary

Problems solved by technology

[0005] It can be seen that in the prior art, although the artificial neural network can be used to measure and calculate the arrival time of the bus, the calculation basis is mainly the historical data of the bus operation, and the dynamic operation information of the bus currently running on the road is only Used to correct the pre-calculated arrival time

At present, road operation conditions are becoming more and more complex and changeable, and the arrival time is mainly calculated based on historical data. Even after correction, the estimated time will still be inaccurate

In addition, when performing artificial neural network training in the prior art, an artificial neural network model is established and trained for a bus line, without considering the characteristics of buses that have obvious differences in different road stages during operation, which will also lead to The training result is inaccurate, which makes the final estimated bus arrival time inaccurate

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