Reliable Environmental Planning at HS2 Construction Sites

Jasmin Ahluwalia, Xu Feng, Khuong An Nguyen, Matthew Tye
Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 329:1060-1062, 2026.

Abstract

High Speed 2 (HS2) is a major UK railway infrastructure project with substantial economic and transport benefits. However, its scale and operational complexity have potentially created environmental impacts. Thus, forecasting air quality and noise levels may support more informed site management. Nevertheless, existing Machine Learning models do not reveal the uncertainty of their predictions, limiting their reliability in such high-stakes environments. Therefore, we propose a state-conditioned conformal prediction (CP) framework that uses recent operational-state information to produce reliable and adaptive prediction intervals. Experiments on real-world HS2 air-quality and noise monitors show that state-conditioned calibration reduced mean interval width by up to 40.38%.

Cite this Paper

BibTeX
@InProceedings{pmlr-v329-ahluwalia26a, title = {Reliable Environmental Planning at HS2 Construction Sites}, author = {Ahluwalia, Jasmin and Feng, Xu and Nguyen, Khuong An and Tye, Matthew}, booktitle = {Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {1060--1062}, year = {2026}, editor = {Ahlberg, Ernst and Johansson, Ulf and Boström, Henrik and Carlevaro, Alberto and Hallberg Szabadváry, Johan and Carlsson, Lars}, volume = {329}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v329/main/assets/ahluwalia26a/ahluwalia26a.pdf}, url = {https://proceedings.mlr.press/v329/ahluwalia26a.html}, abstract = {High Speed 2 (HS2) is a major UK railway infrastructure project with substantial economic and transport benefits. However, its scale and operational complexity have potentially created environmental impacts. Thus, forecasting air quality and noise levels may support more informed site management. Nevertheless, existing Machine Learning models do not reveal the uncertainty of their predictions, limiting their reliability in such high-stakes environments. Therefore, we propose a state-conditioned conformal prediction (CP) framework that uses recent operational-state information to produce reliable and adaptive prediction intervals. Experiments on real-world HS2 air-quality and noise monitors show that state-conditioned calibration reduced mean interval width by up to 40.38%. } }
Endnote
%0 Conference Paper %T Reliable Environmental Planning at HS2 Construction Sites %A Jasmin Ahluwalia %A Xu Feng %A Khuong An Nguyen %A Matthew Tye %B Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2026 %E Ernst Ahlberg %E Ulf Johansson %E Henrik Boström %E Alberto Carlevaro %E Johan Hallberg Szabadváry %E Lars Carlsson %F pmlr-v329-ahluwalia26a %I PMLR %P 1060--1062 %U https://proceedings.mlr.press/v329/ahluwalia26a.html %V 329 %X High Speed 2 (HS2) is a major UK railway infrastructure project with substantial economic and transport benefits. However, its scale and operational complexity have potentially created environmental impacts. Thus, forecasting air quality and noise levels may support more informed site management. Nevertheless, existing Machine Learning models do not reveal the uncertainty of their predictions, limiting their reliability in such high-stakes environments. Therefore, we propose a state-conditioned conformal prediction (CP) framework that uses recent operational-state information to produce reliable and adaptive prediction intervals. Experiments on real-world HS2 air-quality and noise monitors show that state-conditioned calibration reduced mean interval width by up to 40.38%.
APA
Ahluwalia, J., Feng, X., Nguyen, K.A. & Tye, M.. (2026). Reliable Environmental Planning at HS2 Construction Sites. Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 329:1060-1062 Available from https://proceedings.mlr.press/v329/ahluwalia26a.html.

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