当前位置:
X-MOL 学术
›
Comput. Aided Civ. Infrastruct. Eng.
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
Privacy‐preserving awareness in sensor deployment for traffic flow surveillance
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2025-01-08 , DOI: 10.1111/mice.13418
Ruru Hao, Shixiao Liang, Ziyang Zhai, Hang Zhou, Xin Wang, Xiaopeng Li, Tianhao Guan
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2025-01-08 , DOI: 10.1111/mice.13418
Ruru Hao, Shixiao Liang, Ziyang Zhai, Hang Zhou, Xin Wang, Xiaopeng Li, Tianhao Guan
The deployment of sensors to monitor traffic flow between origin–destination (OD) pairs, within a specified budget, remains a critical concern for both academic researchers and transportation managers. While these technologies are essential for capturing traffic data, the aspect of privacy has often been overlooked. To bridge this gap, this paper introduced the concept of privacy distance and then proposed an integer programming model to optimize traffic sensor locations by maximizing the coverage of traffic flow while taking into account the punishment brought by the risk of privacy leakage. Furthermore, to address the computational efficiency problem in large‐scale networks, a flow threshold is set to properly remove some OD pairs to balance the model tractability and computational efficiency. Two case studies of different sizes are carried out to discuss the performance. Case 1 validated the effectiveness of the model, while case 2 demonstrated its capability to handle large‐scale problems. The experimental results show that for large‐scale networks, setting a flow threshold can reduce computation time by 96% at the cost of sacrificing 12% of the OD coverage.
中文翻译:
用于交通流量监控的传感器部署中的隐私保护意识
在指定预算内部署传感器来监控起点-目的地 (OD) 对之间的交通流量,仍然是学术研究人员和运输管理人员的关键问题。虽然这些技术对于捕获流量数据至关重要,但隐私方面经常被忽视。为了弥合这一差距,本文引入了隐私距离的概念,然后提出了一种整数规划模型,通过最大化流量覆盖范围来优化流量传感器位置,同时考虑到隐私泄露风险带来的惩罚。此外,为了解决大规模网络中的计算效率问题,设置了流阈值以适当去除一些 OD 对,以平衡模型的可处理性和计算效率。进行了两个不同大小的案例研究来讨论性能。案例 1 验证了模型的有效性,而案例 2 证明了其处理大规模问题的能力。实验结果表明,对于大规模网络,设置流阈值可以将计算时间减少 96%,但代价是牺牲 12% 的 OD 覆盖率。
更新日期:2025-01-08
中文翻译:

用于交通流量监控的传感器部署中的隐私保护意识
在指定预算内部署传感器来监控起点-目的地 (OD) 对之间的交通流量,仍然是学术研究人员和运输管理人员的关键问题。虽然这些技术对于捕获流量数据至关重要,但隐私方面经常被忽视。为了弥合这一差距,本文引入了隐私距离的概念,然后提出了一种整数规划模型,通过最大化流量覆盖范围来优化流量传感器位置,同时考虑到隐私泄露风险带来的惩罚。此外,为了解决大规模网络中的计算效率问题,设置了流阈值以适当去除一些 OD 对,以平衡模型的可处理性和计算效率。进行了两个不同大小的案例研究来讨论性能。案例 1 验证了模型的有效性,而案例 2 证明了其处理大规模问题的能力。实验结果表明,对于大规模网络,设置流阈值可以将计算时间减少 96%,但代价是牺牲 12% 的 OD 覆盖率。