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%.

翻译：通过机器人车辆实现城市交通流的控制与协调正成为一种新颖的未来交通范式。尽管该方法已引起研究人员和实践者的日益关注，但有效管理和协调大规模混合交通仍是一项挑战。本文提出了一种通过隐私保护众包与动态车辆路由实现大规模混合交通控制的有效框架。该框架由三个模块组成：隐私保护众感知方法、基于图传播的交通预测方法以及隐私保护路由选择机制。我们使用真实道路网络对该框架进行了评估。结果表明，该框架能够准确预测交通流，有效缓解网络范围内的机器人车辆短缺问题，并协调大规模混合交通。与基线方法相比，该框架不仅将机器人车辆短缺问题减少了69.4%，还将网络中所有车辆的平均等待时间降低了27%。