Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Da, Longchao"'
Autor:
Chen, Tiejin, Shirke, Prithvi, Chakravarthi, Bharatesh, Vaghela, Arpitsinh, Da, Longchao, Lu, Duo, Yang, Yezhou, Wei, Hua
This paper introduces SynTraC, the first public image-based traffic signal control dataset, aimed at bridging the gap between simulated environments and real-world traffic management challenges. Unlike traditional datasets for traffic signal control
Externí odkaz:
http://arxiv.org/abs/2408.09588
Heatwaves pose significant health risks, particularly due to prolonged exposure to high summer temperatures. Vulnerable groups, especially pedestrians and cyclists on sun-exposed sidewalks, motivate the development of a route planning method that inc
Externí odkaz:
http://arxiv.org/abs/2407.13689
The Large language models (LLMs) have showcased superior capabilities in sophisticated tasks across various domains, stemming from basic question-answer (QA), they are nowadays used as decision assistants or explainers for unfamiliar content. However
Externí odkaz:
http://arxiv.org/abs/2407.00994
Autor:
Chen, Tiejin, Da, Longchao, Zhou, Huixue, Li, Pingzhi, Zhou, Kaixiong, Chen, Tianlong, Wei, Hua
The privacy concerns associated with the use of Large Language Models (LLMs) have grown recently with the development of LLMs such as ChatGPT. Differential Privacy (DP) techniques are explored in existing work to mitigate their privacy risks at the c
Externí odkaz:
http://arxiv.org/abs/2403.04124
Traffic simulation is an essential tool for transportation infrastructure planning, intelligent traffic control policy learning, and traffic flow analysis. Its effectiveness relies heavily on the realism of the simulators used. Traditional traffic si
Externí odkaz:
http://arxiv.org/abs/2402.06127
Autor:
Da, Longchao, Liou, Kuanru, Chen, Tiejin, Zhou, Xuesong, Luo, Xiangyong, Yang, Yezhou, Wei, Hua
Transportation has greatly benefited the cities' development in the modern civilization process. Intelligent transportation, leveraging advanced computer algorithms, could further increase people's daily commuting efficiency. However, intelligent tra
Externí odkaz:
http://arxiv.org/abs/2401.00211
In practice, it is essential to compare and rank candidate policies offline before real-world deployment for safety and reliability. Prior work seeks to solve this offline policy ranking (OPR) problem through value-based methods, such as Off-policy e
Externí odkaz:
http://arxiv.org/abs/2312.11551
Numerous solutions are proposed for the Traffic Signal Control (TSC) tasks aiming to provide efficient transportation and mitigate congestion waste. In recent, promising results have been attained by Reinforcement Learning (RL) methods through trial
Externí odkaz:
http://arxiv.org/abs/2308.14284
Traffic signal control (TSC) is a complex and important task that affects the daily lives of millions of people. Reinforcement Learning (RL) has shown promising results in optimizing traffic signal control, but current RL-based TSC methods are mainly
Externí odkaz:
http://arxiv.org/abs/2307.12388
This paper introduces a library for cross-simulator comparison of reinforcement learning models in traffic signal control tasks. This library is developed to implement recent state-of-the-art reinforcement learning models with extensible interfaces a
Externí odkaz:
http://arxiv.org/abs/2211.10649