Based on various types of prior information, the network sensor location problem (NSLP) optimizes the locations of sensors in a network, providing comprehensive traffic flow information with a minimum number of sensors. However, few studies have considered the accuracy of prior information, and it is obvious that prior information inconsistent with reality will affect the results of NSLP. Generally, prior information can be obtained from other flow information. When solving NSLP, prior information requires additional or specialized field flow investigations to ensure its accuracy. In general, short-term prior information is adopted to infer the flow of information of interest. The purpose of this study is, therefore, to select the optimal time interval for investigating prior information. This study analyzed the types and characteristics of prior information, and proposed a mathematical method based on Pareto Optimality to identify the optimal time interval for investigating prior information, which can not only ensure the accuracy of prior information, but also reduce the investigation cost. Taking the turning ratio as an example, the traffic flow collected by sensors located at nine intersections on Zhongshan North Road in Shanghai, China was analyzed, and the turning ratios were calculated. Using the proposed method, the optimal time interval for investigating the turning ratio was determined to be 20 to 25 min. In addition, the most representative time intervals were identified as 9:00 to 10:00 a.m. It is recommended that investigating prior information in the early morning and in the morning and evening peaks be avoided.

基于各种类型的先验信息，网络传感器位置问题（NSLP）可优化网络中传感器的位置，并以最少的传感器数量提供全面的流量信息。但是，很少有研究考虑先验信息的准确性，很明显，先验信息与现实不符会影响NSLP的结果。通常，可以从其他流信息中获得先验信息。解决NSLP时，现有信息需要其他或专门的现场流量调查以确保其准确性。通常，采用短期先验信息来推断感兴趣的信息流。因此，本研究的目的是为调查先验信息选择最佳时间间隔。本研究分析了先验信息的类型和特征，提出了一种基于帕累托最优性的数学方法，确定了先验信息的最优侦查时间间隔，既可以保证先验信息的准确性，又可以降低调查成本。以转弯比为例，分析了位于上海中山北路九个交叉路口的传感器收集到的交通流量，并计算了转弯比。使用建议的方法，确定用于研究转弯比的最佳时间间隔为20到25分钟。此外，最有代表性的时间间隔定为上午9:00到上午10:00。建议避免在清晨和早晚高峰时段调查以前的信息。