A bus missing data recovery method based on coupling information

Abstract

The invention provides a method for recovering bus missing driving data, which includes steps: establishing master information and selecting missing data to be recovered; establishing the recovery model data set of the same line coupling information; establishing a recovery model data set of coupling information of other lines of the same section from the station i to the station j; the coupling data fitting model being established by using a limit learning machine ELM; taking the bus main information and the corresponding model parameters coupled with the same line to calculate the corresponding output values and weights; at the same time, the bus information and the correspond model parameters of the other route being coupled at the same time, and the corresponding output values and weight being calculated; the output of each model being processed by accumulating and normalizing weights, a weighted output estimate of the line l from the station i to the station j being calculated, and obtaining missing data to be recovered based on the driving data associated before and after the same bus shift and the number of buses traveling on other routes at similar times on the same road section. According to the construction of data recovery model, the missing data of bus driving can be recovered with high precision, and the data base of bus data mining can be established.

Description

technical field [0001] The invention relates to the technical field of intelligent transportation and the technical field of data recovery, in particular to a method for recovering missing bus driving data. Background technique [0002] In recent years, most cities in China have installed positioning systems on urban buses, and can transmit real-time positioning information back to the data center to provide data services for passengers' real-time bus inquiries and bus operation management. [0003] With the improvement of the management service level of the operating company, it is necessary to analyze the data of the bus trajectory of each line, dig out its inherent characteristics, and provide a basis for operation optimization. However, in the actual bus system, affected by factors such as GPS positioning drift, signal interruption or network transmission error, there is a certain proportion of missing bus trajectory data (even more than 20% of the data is missing in som...

AI Technical Summary

Problems solved by technology

[0004] On the one hand, the bus trajectory has obvious time-varying dynamics and volatility, and traditional methods such as linear interpolation and average interpolation have large errors in the recovery of bus missing data.

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