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Macroscopic Characteristics of Mixed Traffic Flow with Deep Reinforcement Learni

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1 SIGNALFIRST DETECTED 27 March 2026
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27 Mar
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Other feeds and industry voices
8 Apr
SignalClaw: LLM-Guided Evolutionary Synthesis of Interpretable Traffic Signal Control Skills

arXiv:2604.05535v1 Announce Type: new Abstract: Traffic signal control TSC requires strategies that are both effective and interpretable for deployment, yet reinforcement learning

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20 Mar
Single Agent Robust Deep Reinforcement Learning for Bus Fleet Control

arXiv:2508.20784v2 Announce Type: replace Abstract: Bus bunching remains a challenge for urban transit due to stochastic traffic and passenger demand. Traditional solutions rely

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7 Jul
Explainable Reinforcement Learning for Adaptive Traffic Signal Control

arXiv:2607.03703v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a powerful paradigm for adaptive traffic signal control. However, in safety-critical

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20 Mar
Enactor: From Traffic Simulators to Surrogate World Models

arXiv:2603.18266v1 Announce Type: cross Abstract: Traffic microsimulators are widely used to evaluate road network performance under various ``what-if" conditions. However, the

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3 Jul
Autonomous discovery of traffic laws with AI traffic scientists

arXiv:2607.01639v1 Announce Type: new Abstract: Universal traffic laws describe recurrent patterns in congestion, mobility and driving behavior across cities, providing a

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10 Aug
Vehicle routing problem using deep reinforcement learning - A case study about truck planning in the industry

arXiv:2608.06668v1 Announce Type: new Abstract: As an important component of the supply chain industry, transportation has experienced rapid development in the past decade with

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1 May
Autonomous Traffic Signal Optimization Using Digital Twin and Agentic AI for Real-Time Decision-Making

arXiv:2604.27753v1 Announce Type: new Abstract: This article outlines a new framework of traffic light optimization through a digital twin of the transport infrastructure, managed

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22 May
Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty

arXiv:2605.20255v1 Announce Type: cross Abstract: Simulation-based testing of self-driving cars (SDCs) typically relies on scripted or simplified pedestrian models that do not

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