78 citations · 78 across the 6 of their papers we have counts for
6 papers
SimSkill: A Self-Evolving LLM Agent for Skill and Knowledge Accumulation in Traffic Simulation
Qi Liu, Qinzheng Wang, Can Li +2
Cumulative culture enables humans to preserve, reuse, and extend knowledge and skills across experiences and generations. Inspired by this principle, we introduce \textit{SimSkill}…
Climate-resilient electric vehicle charging infrastructure for sustainable cities: An interpretable causal-ensemble framework for preventive maintenance and low-carbon mobility
Cande Lian, Wentao Zeng, Jiabin Wu +2
Reliable electric vehicle (EV) charging infrastructure is a cornerstone of sustainable, low-carbon cities, yet urban climate stress such as extreme heat, heavy precipitation, and h…
Inter-Stop Energy Prediction and Causal Driver Quantification for Dual-Source Trolleybuses via a Time-Aware Tabular Deep Learning Architecture
Wentao Zeng, Zijian Huang, Yiming Bie +2
Dual-source trolleybuses alternate between overhead catenary supply and on-board battery operation, creating energy-use patterns driven by route attributes, high-frequency trajecto…
Exploring the potential resource integration under passenger-freight shared mobility: collaborative optimization of multi-type bus scheduling and dynamic vehicle capacity allocation for urban-rural bus routes
Jiabin Wu, Zijian Huang, Linhong Wang +3
Under the global background of developing urban-rural travel patterns, traditional urban-rural public transport systems are generally faced with the serious challenges of passenger…
GPT-4 Enhanced Multimodal Grounding for Autonomous Driving: Leveraging Cross-Modal Attention with Large Language Models
Haicheng Liao, Huanming Shen, Zhenning Li +4
In the field of autonomous vehicles (AVs), accurately discerning commander intent and executing linguistic commands within a visual context presents a significant challenge. This p…
Reinforcement learning for traffic signal control in hybrid action space
Haoqing Luo, sheng jin
The prevailing reinforcement-learning-based traffic signal control methods are typically staging-optimizable or duration-optimizable, depending on the action spaces. In this paper,…