The paper presents a conditional diffusion model that generates electric vehicle battery‑current profiles conditioned on route features such as velocity and ambient temperature. Using a latent conditioning encoder and a temporal 1D U‑Net denoising backbone, the model produces realistic current trajectories that capture both the overall envelope and sharp transient events. On a dataset of 12,000 trips from nine vehicles, the model achieves a Wasserstein distance of 0.0029, outperforming direct condition injection by 89.1% in Wasserstein distance and 52.8% in MAE.
By Hemanth Neelgund Ramesh, Andr\'e Snoeck, Chyi-Fu Hong, Shijing Sun
arXiv:2606. 00572v1 Announce Type: new Abstract: Passenger count data from public transit systems reveals urban mobility patterns and is essential for planning, operation, and optimisation.
By Oluwaleke Yusuf, Adil Rasheed, Frank Lindseth
UTP-Bench is a new benchmark for uncertainty-aware travel planning that evaluates large language models on their ability to generate robust itineraries under real-world stochastic conditions. The dataset covers 504 Indian cities, incorporating attractions, restaurants, accommodations, and multi-modal transportation networks, and includes empirical delay distributions and crowd-density patterns to simulate realistic disruptions. Three new metrics—Buffer Adequacy Score, Crowd-Aware Timing Score, and Transport Delay Absorption Score—measure how well generated plans maintain robustness against transit delays and crowd variability, revealing significant gaps between state-of-the-art LLMs and human-authored itineraries.
By Etcharla Revanth Rao, Priyanshu Karmakar, Shubhojit Mallick, Manish Gupta, Shreya Ghosh, Abhik Jana
UTP‑Bench is a new benchmark that evaluates large language models on uncertainty‑aware travel planning, incorporating real‑world data from 504 Indian cities and empirical delay and crowd patterns. It introduces three metrics—Buffer Adequacy Score, Crowd‑Aware Timing Score, and Transport Delay Absorption Score—to measure how well generated itineraries remain robust under stochastic conditions. Experiments show that current LLMs lag behind human planners in buffering, delay‑aware scheduling, and crowd sensitivity.
arXiv:2607. 11349v1 Announce Type: cross Abstract: Dual-source trolleybuses alternate between overhead catenary supply and on-board battery operation, creating energy-use patterns driven by route attributes, high-frequency trajectories, and hourly weather.
By Wentao Zeng (School of Management, Foshan University, Foshan, China a School of Management, Foshan University, Foshan, China, School of Mechanical and Electrical Engineering and Automation, Foshan University, Foshan, China), Zijian Huang (School of Artificial Intelligence, South China Normal University, Guangzhou, China), Yiming Bie (School of Transportation, Jilin University, Changchun, China), Jiabin Wu (School of Management, Foshan University, Foshan, China a School of Management, Foshan University, Foshan, China), Jun Gong (Department of Civil Engineering, The University of Hong Kong, Hong Kong, China)
Dual-source trolleybuses alternate between overhead catenary supply and on-board battery operation, creating energy-use patterns driven by route attributes, high-frequency trajectories, and hourly weather. Existing models struggle to represent these heterogeneous inputs and rarely explain the causal drivers of consumption.