基于3D卷积和LSTM编码解码的出行需求预测

doi:10.11896/jsjkx.210400022

Abstract

Abstract: Reliable regional travel demand forecasting can provide reasonable and effective suggestions for the scheduling and planning of traffic resources.However,travel forecasting is a very challenging problem,facing massive spatial-temporal big data modeling problem.And how to extract the spatial and temporal features of the data effectively has become a research hotspot of urban computing.This paper proposes a demand forecasting model based on 3D deconvolution and encoder-decoder attention mechanism (in short 3D-EDADF),which is used to predict the inflow and outflow of travel demand in urban areas at the same time.3D-EDADF model first uses 3D convolution to extract spatial-temporal correlation of data,then uses LSTM encoder-decoder to capture temporal dependence,and combines attention mechanism to describe the difference of inflow and outflow.3D-EDADF model conducts hybrid modeling on the three time-dependent features of closeness dependency,daily dependency and periodic dependency,and then weights and fuses their multi-dimensional features to obtain the final prediction result.The experiments are carried out by using real travel demand data sets.The results show that compared with baseline models,the 3D-EDADF model has the lowest overall prediction error and has better prediction performance.

Key words: 3D convolution, Attention mechanism, Encoder-decoder, Spatial-temporal big data, Travel demand prediction

CLC Number:

TP181

TENG Jian, TENG Fei, LI Tian-rui. Travel Demand Forecasting Based on 3D Convolution and LSTM Encoder-Decoder[J].Computer Science, 2021, 48(12): 195-203.

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Travel Demand Forecasting Based on 3D Convolution and LSTM Encoder-Decoder

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