Interpretable deep learning for traffic prediction: Revealing the role of urban morphology in spatially heterogeneous cities

^*Corresponding author for this work

Department of Computer Science, Data Science, and Engineering, New York University Shanghai, Pudong, Shanghai, China

Research output: Contribution to journal › Article › peer-review

Original language	English
Journal	Journal of Transport Geography
DOIs	https://doi.org/10.1016/j.jtrangeo.2026.104702
Publication status	Published - Jun 2026

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@article{20cbe136440e409990677f36e3ba5f12,

title = "Interpretable deep learning for traffic prediction: Revealing the role of urban morphology in spatially heterogeneous cities",

author = "Jiawei Tong and Guangyu Wang and Shuihua Wang and John Moraros",

year = "2026",

month = jun,

doi = "10.1016/j.jtrangeo.2026.104702",

language = "English",

journal = "Journal of Transport Geography",

issn = "0966-6923",

}

TY - JOUR

T1 - Interpretable deep learning for traffic prediction: Revealing the role of urban morphology in spatially heterogeneous cities

AU - Tong, Jiawei

AU - Wang, Guangyu

AU - Wang, Shuihua

AU - Moraros, John

PY - 2026/6

Y1 - 2026/6

U2 - 10.1016/j.jtrangeo.2026.104702

DO - 10.1016/j.jtrangeo.2026.104702

M3 - Article

SN - 0966-6923

JO - Journal of Transport Geography

JF - Journal of Transport Geography

ER -