Urban scale taxi track prediction method based on attention mechanism

Abstract

An urban scale taxi track prediction method based on an attention mechanism comprises the following steps that 1, GPS data are collected, and noise appearing in the GPS data collection process is subjected to data cleaning; step 2, map matching is carried out on the cleaned GPS data to obtain experimental data required by the model; step 3, model training; an LSTM network is adopted at an encodingend of the encoder, an encoding vector C is used as the LSTM network of the decoding end at a decoding end, an attention mechanism is applied to a hidden layer vector of the encoder, and a predictedvalue at the previous moment is used as an input value at the current moment each time and is sent into the decoder. According to the method, the embedded vector is adopted to represent the road section information in the urban area, the encoder is used for encoding the track of the taxi, the decoder containing the attention mechanism is used for predicting the track, the correlation in the tracksequence is fully mined, and the track prediction accuracy can be effectively improved.

Description

technical field [0001] The invention belongs to the field of trajectory prediction, and relates to an attention mechanism-based urban-scale taxi trajectory prediction method. Background technique [0002] For trajectory prediction, most of the earlier researches used the Markov chain-based method, that is, to make statistics on historical data and obtain the transition probability between road sections, and take the road section with the highest probability as the prediction result when predicting. However, the Markov chain is based on clustering or existing historical data, and it is impossible to predict the data with low frequency of road section combinations in historical data. Therefore, WU et al. used neural networks to give appropriate weights to different road sections. There will also be similar weights for similar road sections, and the effect of clustering can also be achieved when making predictions. This method avoids the complex process of clustering and allow...

AI Technical Summary

Problems solved by technology

However, the limitation of this method is that the road network information is known, and in the case of unknown road network information, CHOI et al. proposed a method of using historical data to train the neural network and obtain the predicted value at the next moment

However, its prediction results are only accurate to the "community", and the method of dividing the subdivisions adopted lacks basis

Images