# Large-scale Mixed Traffic Control Using Dynamic Vehicle Routing and Privacy-Preserving Crowdsourcing

arXiv: https://arxiv.org/abs/2311.11347 · HTML: https://mlsanity.com/paper/2311.11347/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2023.

## Built on by (most cited first, 12 of 12)

- [Mixture-of-Personas Language Models for Population Simulation](https://mlsanity.com/paper/2504.05019/) (2025)
- [EnduRL: Enhancing Safety, Stability, and Efficiency of Mixed Traffic Under Real-World Perturbations Via Reinforcement Learning](https://mlsanity.com/paper/2311.12261/) (2023)
- [CARL: Congestion-Aware Reinforcement Learning for Imitation-based Perturbations in Mixed Traffic Control](https://mlsanity.com/paper/2404.00796/) (2024)
- [Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2504.04691/) (2025)
- [Learning to Change: Choreographing Mixed Traffic Through Lateral Control and Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2403.14879/) (2024)
- [Exploring Transformer-Augmented LSTM for Temporal and Spatial Feature Learning in Trajectory Prediction](https://mlsanity.com/paper/2412.13419/) (2024)
- [Analyzing Behaviors of Mixed Traffic via Reinforcement Learning at Unsignalized Intersections](https://mlsanity.com/paper/2312.05325/) (2023)
- [Neighbor-Aware Reinforcement Learning for Mixed Traffic Optimization in Large-scale Networks](https://mlsanity.com/paper/2412.12622/) (2024)
- [Optimizing Efficiency of Mixed Traffic through Reinforcement Learning: A Topology-Independent Approach and Benchmark](https://mlsanity.com/paper/2501.16728/) (2025)
- [LDGNet: A Lightweight Difference Guiding Network for Remote Sensing Change Detection](https://mlsanity.com/paper/2504.05062/) (2025)
- [Multi-Objective Reinforcement Learning for Large-Scale Mixed Traffic Control](https://mlsanity.com/paper/2512.11247/) (2025)
- [Learning to Design City-scale Transit Routes](https://mlsanity.com/paper/2512.19767/) (2025)

## Abstract (arXiv)

Controlling and coordinating urban traffic flow through robot vehicles is emerging as a novel transportation paradigm for the future. While this approach garners growing attention from researchers and practitioners, effectively managing and coordinating large-scale mixed traffic remains a challenge. We introduce an effective framework for large-scale mixed traffic control via privacy-preserving crowdsourcing and dynamic vehicle routing. Our framework consists of three modules: a privacy-protecting crowdsensing method, a graph propagation-based traffic forecasting method, and a privacy-preserving route selection mechanism. We evaluate our framework using a real-world road network. The results show that our framework accurately forecasts traffic flow, efficiently mitigates network-wide RV shortage issue, and coordinates large-scale mixed traffic. Compared to other baseline methods, our framework not only reduces the RV shortage issue up to 69.4% but also reduces the average waiting time of all vehicles in the network up to 27%.