@inproceedings{10.1145/3557915.3560969,
title = {A Plug-in Memory Network for Trip Purpose Classification},
author = {Suxing Lyu and Tianyang Han and Yuuki Nishiyama and Kaoru Sezaki and Takahiko Kusakabe},
url = {https://doi.org/10.1145/3557915.3560969},
doi = {10.1145/3557915.3560969},
isbn = {9781450395298},
year = {2022},
date = {2022-11-01},
urldate = {2022-11-01},
booktitle = {Proceedings of the 30th International Conference on Advances in Geographic Information Systems},
publisher = {Association for Computing Machinery},
address = {Seattle, Washington},
series = {SIGSPATIAL '22},
abstract = {Trip purpose plays a critical role in reflecting human mobility behavior. However, it is relatively difficult to determine. With the rapid growth of urban mobility and big mobile data, utilizing these data for trip purpose classification has been a long-term objective to enhance travel demand and behavior models used in urban planning. Although studies on this topic have been extensively conducted, most past research preferred relying on traveler attributes or long-term travel histories to achieve accurate results. These data could be privacy sensitive and often do not satisfy real-world scenarios. This study addresses the problem of classifying trip purpose by only space activity information to avoid privacy conflict. 1) External memories are collected from factorized components based on the non-negative Tucker decomposition scheme. 2) These memories are extended by the cross-attention mechanism to achieve feature augmentation. 3) Subsequently, a novel concept called "latent mode alignment" is proposed. By leveraging the linear characteristics of external memories, geographic contextual latent modes are represented and matched with travel activities; this procedure is called älignment." 4) The gate mechanism controls the eventual outputs for update. The proposed plug-in memory network (PMN), combined with baseline models, effectively outperforms the original settings. Moreover, combination models are validated with strong tolerance through missing data tests, which are common and problematic in real-world scenarios. The proposed PMN is a plug-and-play design that is easy to combine with newly developed classification models, and other memory collection methods can be expected.},
keywords = {Human mobility, matrix factorization, memory network, trip purpose},
pubstate = {published},
tppubtype = {inproceedings}
}