Identifying travel patterns is a crucial task in integrated mobility as a service. To address the limitations of existing traffic travel pattern recognition algorithms, namely insufficient accuracy and real-time application demands, this paper proposed a real-time travel pattern recognition algorithm based on a self adaptive pooling enhanced attention mechanism. Based on the point cloud network, the proposed algorithm efficiently learned current and historical information by incorporating causal convolution and causal pooling, thereby achieving real-time travel pattern recognition. A self adaptive pooling enhanced attention module was further embedded into the framework model to calculate the weight map among different features, thereby enhancing the feature modeling capability. Additionally, the algorithm integrated both motion and geographical features, which effectively improved the recognition accuracy of bus and car travel patterns. Experimental results show that the proposed algorithm achieves superior accuracy. Compared with other one-stage methods, its recognition accuracy is improved by approximately 0.05. Compared with the latest two-stage models such as FPbiLSTM, the parameter count of the proposed algorithm is only 0.167 that of these models, making the proposed approach more lightweight and suitable for deployment on mobile devices.
LI Yinxiang
,
DU Wenyuan
,
XU Zhe
,
PENG Chen
,
YAN Jianqiang
. Real-Time Travel Pattern Recognition Algorithm Based on Self Adaptive Pooling Enhanced Attention Mechanism[J]. Journal of Applied Sciences, 2026
, 44(1)
: 21
-33
.
DOI: 10.3969/j.issn.0255-8297.2026.01.002
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