TY - JOUR
T1 - Coupled application of generative adversarial networks and conventional neural networks for travel mode detection using GPS data
AU - Li, Linchao
AU - Zhu, Jiasong
AU - Zhang, Hailong
AU - Tan, Huachun
AU - Du, Bowen
AU - Ran, Bin
N1 - Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/6
Y1 - 2020/6
N2 - Inferring travel modes of travelers in the city is important to transportation planning and infrastructure design. Based on the distribution of travel modes, transportation engineers could provide some proper strategies to reduce traffic congestion and air pollution. With advanced sensing techniques, it is possible to collect high-resolution GPS trajectory data of travelers and we can infer travel modes using some popular machine learning methods. One of the difficult tasks facing the application of machine learning especially deep learning in travel mode detection is the lack of large, labeled dataset, because to label the trajectory data is expensive and time-consuming. Moreover, samples of different travel modes are always unbalanced. Accordingly, in this paper, we take advantage of the generative model and the Convolutional Neural Networks (CNN) to develop a hybrid travel modes detection model using less labeled trajectory data. Our key contribution is the utilization of a generative adversarial network (GAN) to artificially create some training samples in such a way that it not only increases the required sample size but balances the dataset to improve the accuracy of the detection model. Furthermore, CNN is applied to extract deep features of trajectory data, and then to classify the travel modes. The results show that the highest accuracy (86.70%) can be achieved by the proposed model. In particular, the proposed method can improve the detection accuracy of bus and driving modes because it can solve the small sample size problem. Moreover, the large sample size can provide an opportunity to develop some advanced deep learning models in future studies.
AB - Inferring travel modes of travelers in the city is important to transportation planning and infrastructure design. Based on the distribution of travel modes, transportation engineers could provide some proper strategies to reduce traffic congestion and air pollution. With advanced sensing techniques, it is possible to collect high-resolution GPS trajectory data of travelers and we can infer travel modes using some popular machine learning methods. One of the difficult tasks facing the application of machine learning especially deep learning in travel mode detection is the lack of large, labeled dataset, because to label the trajectory data is expensive and time-consuming. Moreover, samples of different travel modes are always unbalanced. Accordingly, in this paper, we take advantage of the generative model and the Convolutional Neural Networks (CNN) to develop a hybrid travel modes detection model using less labeled trajectory data. Our key contribution is the utilization of a generative adversarial network (GAN) to artificially create some training samples in such a way that it not only increases the required sample size but balances the dataset to improve the accuracy of the detection model. Furthermore, CNN is applied to extract deep features of trajectory data, and then to classify the travel modes. The results show that the highest accuracy (86.70%) can be achieved by the proposed model. In particular, the proposed method can improve the detection accuracy of bus and driving modes because it can solve the small sample size problem. Moreover, the large sample size can provide an opportunity to develop some advanced deep learning models in future studies.
KW - Classification
KW - Deep learning
KW - Trajectory data
KW - Transportation planning
UR - http://www.scopus.com/inward/record.url?scp=85083756064&partnerID=8YFLogxK
U2 - 10.1016/j.tra.2020.04.005
DO - 10.1016/j.tra.2020.04.005
M3 - Article
AN - SCOPUS:85083756064
SN - 0965-8564
VL - 136
SP - 282
EP - 292
JO - Transportation Research Part A: Policy and Practice
JF - Transportation Research Part A: Policy and Practice
ER -