Open Access

ARTICLE

An Integrated Framework of Explainable Artificial Intelligence and Large Language Models for Shared Mobility Energy Efficiency Prediction and Policy Synthesis

Saud Aljubairi¹, Mahbub Hassan², Hridoy Deb Mahin³, Md Ashequl Islam^4,*, Md Ehtesamul Haque¹, M M Hafizur Rahman⁵
1 Department of Computer Science, College of Computer Science and Information Technology (CCSIT), King Faisal University, Al Ahsa, Saudi Arabia
2 Department of Civil Engineering, Faculty of Engineering, Chulalongkorn University, Pathumwan, Bangkok, Thailand
3 Sylhet Engineering College (SEC), School of Applied Sciences & Technology, Shahjalal University of Science and Technology (SUST), Sylhet, Bangladesh
4 School of Electrical and Mechanical Engineering, College of Engineering and Information Technology, Adelaide University, Adelaide, SA, Australia
5 Department of Computer Networks & Communications, College of Computer Sciences and Information Technology (CCSIT), King Faisal University, Al Ahsa, Saudi Arabia
* Corresponding Author: Md Ashequl Islam. Email:
(This article belongs to the Special Issue: Sustainable Transport Technologies and Strategies: Impacts on Energy and Environment)

Energy Engineering https://doi.org/10.32604/ee.2026.085666

Received 15 May 2026; Accepted 10 July 2026; Published online 18 August 2026

Abstract

Urban transport in rapidly growing Asian cities represents a growing share of energy consumption and greenhouse gas emissions, yet energy efficiency determinants of coexisting shared mobility modes remain poorly quantified in tropical developing-country contexts. This study develops an integrated Explainable AI and Large Language Model framework for energy efficiency prediction and evidence-based policy synthesis across four urban mobility modes (Solo Car, Shared Ride-Hailing, Motorcycle Taxi, Electric Minibus), using a 6000-record physics-grounded synthetic dataset calibrated to Bangkok and Dhaka. A key methodological contribution, a route-group holdout protocol eliminating intra-route leakage largely unaddressed in prior trip-level transport energy studies, shows XGBoost achieving the best held-out $R^2$ of $0.731$, while naive random splitting inflated this figure by $0.08$ to $0.12$ points, confirming leakage as a substantive performance threat. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) jointly identified occupant count (mean $|\varphi |=598.98$ kJ/pax-km), road gradient ($304.69$), and load factor ($266.38$) as the dominant energy drivers, with Spearman rank-agreement of $\rho =0.29$ to $0.80$ across modes. Electric Minibus recorded a median energy intensity of $228$ kJ/pax-km, $8.2$ times more efficient than Solo Car, while ride-hailing pooling simulations projected a mean energy reduction of $38\mathrm{\%}$ on affected trips. A structured three-stage LLM synthesis chain, with a boundary-risk audit, translated these findings into mode-specific policy recommendations with explicit confidence assessments. Occupancy optimisation offers greater near-term energy reduction potential than vehicle technology substitution alone, and the framework offers a replicable methodology for transport energy analysis in data-scarce settings, with extension to further cities identified as a priority for future work.

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Keywords

Shared mobility; energy efficiency; explainable AI; SHAP; LIME; XGBoost; LLM; policy synthesis; Bangkok; Dhaka; route-group holdout; urban transport; tropical cities

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