TY - JOUR
T1 - Learning Shared Mobility-Aware Knowledge for Multiple Urban Travel Demands
AU - Wang, Qianru
AU - Guo, Bin
AU - Ouyang, Yi
AU - Cheng, Lu
AU - Wang, Liang
AU - Yu, Zhiwen
AU - Liu, Huan
N1 - Funding Information:
This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB2100800; in part by the National Science Fund for Distinguished Young Scholars under Grant 62025205; and in part by the National Natural Science Foundation of China under Grant 61772428, Grant 61725205, and Grant 62032020.
Publisher Copyright:
© 2014 IEEE.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - With the growth of Internet of Things (IoT) devices, smart travel methods, such as sharing-bike and ride-hailing become popular commuting methods. With people's growing needs and the rapid dynamics in a city environment, simply using a single travel demand for prediction may be insufficient. Alternatively, modeling multiple travel demands simultaneously can deepen our understanding toward the status of these potentially correlated demands and deploy the transportation in the city better. An important observation in this work is that multiple travel demands in a city often show common patterns, referred to as the shared mobility-aware knowledge. In addition, there are also unique patterns that characterize individual travel demand resulting in unique knowledge. To better leverage the shared and unique knowledge, we propose a novel framework (MultiST) to predict multiple spatialoral sequences (multiple travel demands) via two components that extract the shared and unique spatialoral dependencies, respectively. For the unique component, we use convolutional neural networks and gated recurrent units to embed unique knowledge. For the shared component, we design a recurrent Gaussian cell to extract temporal dependencies. Empirical results show that MultiST outperforms six state-of-the-art baseline methods and three variants of MultiST. We further visualize the temporal dependencies of the shared knowledge and discuss the practical implications.
AB - With the growth of Internet of Things (IoT) devices, smart travel methods, such as sharing-bike and ride-hailing become popular commuting methods. With people's growing needs and the rapid dynamics in a city environment, simply using a single travel demand for prediction may be insufficient. Alternatively, modeling multiple travel demands simultaneously can deepen our understanding toward the status of these potentially correlated demands and deploy the transportation in the city better. An important observation in this work is that multiple travel demands in a city often show common patterns, referred to as the shared mobility-aware knowledge. In addition, there are also unique patterns that characterize individual travel demand resulting in unique knowledge. To better leverage the shared and unique knowledge, we propose a novel framework (MultiST) to predict multiple spatialoral sequences (multiple travel demands) via two components that extract the shared and unique spatialoral dependencies, respectively. For the unique component, we use convolutional neural networks and gated recurrent units to embed unique knowledge. For the shared component, we design a recurrent Gaussian cell to extract temporal dependencies. Empirical results show that MultiST outperforms six state-of-the-art baseline methods and three variants of MultiST. We further visualize the temporal dependencies of the shared knowledge and discuss the practical implications.
KW - Gaussian model
KW - Multiple urban demands
KW - Shared knowledge
KW - Spatial - temporal prediction
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U2 - 10.1109/JIOT.2021.3115174
DO - 10.1109/JIOT.2021.3115174
M3 - Article
AN - SCOPUS:85115795580
SN - 2327-4662
VL - 9
SP - 7025
EP - 7035
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 9
ER -