DeepFR: A trajectory prediction model based on deep feature representation

Qin, Wanting; Tang, Jun; Lao, Songyang

doi:10.1016/j.ins.2022.05.019

Cited by 15 publications

(3 citation statements)

References 34 publications

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“…The traditional Convolutional Neural Network (CNN) model and Word Turn Vector (Word2vec) method, as well as Node Turn Vector (Node2vec) method, are popular ways to extract features. A Depth Feature Representation (DeepFR) model is proposed by Qin et al [24] and a representation learning applied in complex space is identified by Hu [25] to realize the trajectory prediction. Road network interface and road similarity are also considered in the representation learning method by Sun [26], and a comprehensive representation is provided for the input of trajectory prediction.…”

Section: Trajectory Feature Fusionmentioning

confidence: 99%

“…Collaborative prediction based on the attention mechanism [54] is conducted in various types of research to realize trajectory prediction. such as horizontal and vertical attention mechanisms [55], social relations encoder-based attention mechanisms [56], and sub-attention mechanisms [24]. These attention mechanisms increase the weights of significant features to improve computing efficiency and interpretability in trajectory prediction.…”

Section: Trajectory Prediction Model Based On Deep Learningmentioning

confidence: 99%

“…MSE represents the expected value of the square of the difference between the predicted value and the real value. It is a performance function to measure the performance of a network, as shown in the formula (24).…”

Section: Msementioning

confidence: 99%

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KGCN‐LSTM: A graph convolutional network considering knowledge fusion of point of interest for vehicle trajectory prediction

Chen

Fan

Qian

et al. 2023

IET Intelligent Trans Sys

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Urban vehicle trajectory prediction positively alleviates traffic congestion, avoids traffic accidents, and optimizes the urban transportation system. Since taxi trajectories are influenced by the driving intention, it is significant to consider the Points of Interest (POI) as the spatial features for trajectory prediction. A Knowledge Graph Convolutional Network Long Short-Term Memory (KGCN-LSTM) model is proposed here to improve the accuracy and robustness of trajectory prediction. POI information is considered as the prior-knowledge of the trajectory by the Graph Convolutional Network (GCN). Under multiple comparison experiments, Shopping POI gains the highest positive effect weight of 15% in holidays, and Hospital POI gains the highest weight of 16% in working days. In holidays, higher accuracy and robustness are achieved compared with benchmarks when performing the KGCN-LSTM model with POI of shopping, food, life service, scenic spots, and entertainment classes, while the performance is not improved with the rest of the POI classes. In working days, higher accuracy and stronger robustness are achieved compared with benchmarks when performing the KGCN-LSTM model with POI of hospital, life service, and exercise. While the performance is not improved with the rest of the POI classes.

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Section: Trajectory Feature Fusionmentioning

confidence: 99%