プライバシー保護型クラウドソーシングと動的経路選択による大規模混在交通制御
Large-scale Mixed Traffic Control Using Dynamic Vehicle Routing and Privacy-Preserving Crowdsourcing
ロボット車両を含む都市交通を対象に、プライバシーを保護したクラウドセンシングとグラフ伝播による交通予測、経路選択を組み合わせ、大規模混在交通を効率的に制御するフレームワークを提案した。
著者: Dawei Wang, Weizi Li, Jia Pan
分類: cs.RO
原文アブストラクト
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%.
