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Vehicle Travel Destination Prediction Method Based on Multi-source Data
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DOI：10.1007/s42154-021-00136-2.png)
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Research on vehicle travel destinations mostly only consider vehicle trajectory data and ignore the influence of other multi-source data, such as weather, time, and points of interest (POI). This study proposes a destination prediction method based on multi-source data, and a multi-input neural network model is established. In terms of the coding of vehicle trajectory data, a GeoHash to vector (Geo2vec) model is proposed to realize the characterization of the trajectory. As for the coding of temporal features, a cyclic coding model is proposed based on trigonometric functions. For the coding of POI, an origin-destination POI matrix (OD-POI) model is proposed based on the state transition probability. Experimental results show that in terms of the average distance and root-mean-square distance deviations, Geo2vec reveals reductions of 4.51% and 5.63% compared to word to vector (Word2vec), and cyclic encoding shows reductions of 6.35% and 6.67% compared to label encoding; further, the method of OD-POI state transition probability is reduced by 5.85% and 6.4%, and the model based on multi-source data is 17.29% and 17.65% lower than the model based on trajectory data only. Finally, the cyclic encoding is reduced by 48.60% in the data dimension compared to one-hot encoding. Accurate destination prediction will help improve the efficiency of automotive human-computer interaction
Keyword:
Vehicle trajectory
Multi-source data
Destination prediction
Deep learning
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IF:
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论文数:
363
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1.0K
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