The paper introduces CityTrajBench, a unified benchmark framework that standardizes the evaluation of city-scale vehicle trajectory generation, demonstrating that no single generation model dominates…

The paper introduces EvoXXLTraffic, an ultra-large, sensor-evolving dataset that simulates real-world road network growth, demonstrating that existing state-of-the-art traffic forecasting models fail…

The paper proposes a novel four-stage simulation framework that uses GPS-derived seasonal spatial priors and LLMs to generate demographically accurate, synthetic tourist mobility schedules for urban p…

This paper provides a comprehensive review of network optimization aspects for Connected and Autonomous Vehicles (CAVs), aiming to clarify misconceptions and outline future research directions.

The paper introduces a higher-order network framework to compare observed and simulated human mobility data, demonstrating that while synthetic data is promising, current simulation models have specif…

The paper introduces CityGen, a diffusion-based framework that enables zero-label city adaptation for autonomous driving by synthesizing city-style data conditioned on HD maps and visual prompts, sign…

The paper proposes CPGAN, a novel Generative Adversarial Network (GAN) that incorporates a collision-penalizing loss function to significantly improve the simulation of collision avoidance in dense, b…

The paper develops a trust-aware framework to model how connected vehicles adapt their routing decisions and overall traffic flow when exposed to misinformation, showing that endogenous trust provides…

AlphaTransit introduces a novel search-based planning framework that combines Monte Carlo Tree Search (MCTS) with a neural policy-value network to efficiently design high-quality, city-scale bus trans…

The paper proposes GC-MoE, a graph-conditioned Mixture of Experts framework, to improve traffic forecasting by assigning personalized, specialized forecasting experts to individual road segments.

This paper analyzes the performance and explainability requirements of evolutionary algorithms when applied to complex, real-world physics-informed optimization problems, identifying a gap between cur…

The paper introduces the Generative Response Model (GRM) to improve constrained auto-bidding by predicting future traffic and cost/value curves from a single bid multiplier, allowing for an exact, lig…

The paper develops a novel deep reinforcement learning framework, SMamba-DDPG, to accurately model vehicle-type-specific pedestrian crash avoidance behavior, finding that pedestrians react faster and…

MobEvolve introduces an agentic self-evolving heuristic system that significantly improves human mobility generation by iteratively refining its internal logic using an LLM agent, outperforming deep g…

ReasonLight is a multimodal foundation model-enhanced RL framework that enables zero-shot traffic signal control by semantically refining RL-proposed actions using heterogeneous sensor and camera data…

The paper introduces a U-Net deep learning surrogate model to accelerate Quality-Diversity optimization for urban layout design, demonstrating that this spatial approach enables highly accurate climat…

RiskFlow is a novel framework that generates realistic and safety-critical multi-agent traffic scenarios by reformulating trajectory generation as a single-pass transport problem in the action space.

The paper proposes using pseudo-sensitivities, derived from adjoint sensitivity fields, as an optimal conditioning signal in a Bernoulli flow-matching framework to significantly improve the out-of-dis…

The paper extends modular dynamic Bayesian networks (MDBNs) to model non-Markovian queues, providing the first causal metamodeling technique for such systems with significant speedup.

This paper reviews the current state of cybersecurity for EV charging infrastructure, analyzing existing machine learning countermeasures and proposing future directions to overcome data limitations i…