A physics-constrained deep learning approach for dynamic origin-destination estimation using link counts

@article{xianwei2026,
  title        = {{A physics-constrained deep learning approach for dynamic origin-destination estimation using link counts}},
  author       = {Xianwei Peng et al.},
  journal      = {Transportmetrica A: Transport Science},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/23249935.2026.2616045},
}

TY  - JOUR
TI  - A physics-constrained deep learning approach for dynamic origin-destination estimation using link counts
AU  - al., Xianwei Peng et
JO  - Transportmetrica A: Transport Science
PY  - 2026
ER  -

Xianwei Peng et al. (2026). A physics-constrained deep learning approach for dynamic origin-destination estimation using link counts. *Transportmetrica A: Transport Science*. https://doi.org/https://doi.org/10.1080/23249935.2026.2616045

Xianwei Peng et al.. "A physics-constrained deep learning approach for dynamic origin-destination estimation using link counts." *Transportmetrica A: Transport Science* (2026). https://doi.org/https://doi.org/10.1080/23249935.2026.2616045.

A physics-constrained deep learning approach for dynamic origin-destination estimation using link counts
Xianwei Peng et al. · Transportmetrica A: Transport Science · 2026
https://doi.org/https://doi.org/10.1080/23249935.2026.2616045