Privacy-preserving awareness in sensor deployment for traffic flow surveillance

Abstract

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.