Missing traffic data caused by sensor failures or communication errors significantly hinders the efficiency of downstream tasks in Intelligent Transportation Systems (ITS), such as the critical functions of traffic monitoring and decision-making. Considering the complex distribution of missing data, it is essential to incorporate the missing features to extract dynamic spatial-temporal correlations in traffic processes. Motivated by these concerns, a novel Dynamic Spatial-Temporal Imputation Network with Missing Features (DSTMIN) is proposed to accurately impute traffic data.

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[1] Hao Li, Shiyuan Han, "For DSTMIN", IEEE Dataport, 2025. [Online]. Available: http://dx.doi.org/10.21227/mbgd-0233. Accessed: Feb. 11, 2025.
@data{mbgd-0233-25,
doi = {10.21227/mbgd-0233},
url = {http://dx.doi.org/10.21227/mbgd-0233},
author = {Hao Li; Shiyuan Han },
publisher = {IEEE Dataport},
title = {For DSTMIN},
year = {2025} }
TY - DATA
T1 - For DSTMIN
AU - Hao Li; Shiyuan Han
PY - 2025
PB - IEEE Dataport
UR - 10.21227/mbgd-0233
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Hao Li, Shiyuan Han. (2025). For DSTMIN. IEEE Dataport. http://dx.doi.org/10.21227/mbgd-0233
Hao Li, Shiyuan Han, 2025. For DSTMIN. Available at: http://dx.doi.org/10.21227/mbgd-0233.
Hao Li, Shiyuan Han. (2025). "For DSTMIN." Web.
1. Hao Li, Shiyuan Han. For DSTMIN [Internet]. IEEE Dataport; 2025. Available from : http://dx.doi.org/10.21227/mbgd-0233
Hao Li, Shiyuan Han. "For DSTMIN." doi: 10.21227/mbgd-0233