Detection of the traffic peak periods for a city has many advantages not only in terms of the traffic operation but also in urban transportation planning. In the planning, future travel demand is analyzed for peak and off-peak periods separately and transportation networks are designed based on these analyses. However, the traffic peak periods may shift in time because of changes in people's activities. Therefore, the detection and update of these periods periodically provide to improve urban transportation planning. The aim of this research is to present a new approach to determine traffic peak periods in order to utilize for use in transportation planning. In order to achieve this goal, the inductive loop detector data are obtained for a city and cleaned. Regular traffic flow patterns (without days that include unexpected incidents) of the whole city are produced, and peak periods are determined with these data. The Facebook Prophet software, which is a forecasting procedure, is presented as a new approach to determine traffic peak periods based on changepoint detection. In order to compare the results, experts' opinion, which is the conventional method, and the k-medoids clustering methods are applied. In conclusion, it is seen that the suggested new approach facilitates so much the accurate determination of traffic peak periods for urban transportation planning.

检测城市的交通高峰时段不仅在交通运营方面而且在城市交通规划中都有很多优势。在规划中，将分别针对高峰和非高峰时段的未来旅行需求进行分析，并根据这些分析设计交通网络。但是，由于人们活动的变化，交通高峰期可能会随时间变化。因此，定期检测和更新这些时段可以改善城市交通规划。这项研究的目的是提出一种确定交通高峰期的新方法，以用于交通规划。为了实现此目标，获取了城市的感应式环路检测器数据并进行了清理。产生了整个城市的规则交通流模式（没有包括意外事件在内的几天），并根据这些数据确定了高峰时段。作为一种预测程序，Facebook Prophet软件作为一种基于变更点检测来确定交通高峰时段的新方法而被提出。为了比较结果，采用了专家意见，这是常规方法，并且采用了k-medoids聚类方法。总之，可以看出，所建议的新方法极大地促进了城市交通规划中交通高峰期的准确确定。为了比较结果，采用了专家意见，这是常规方法，并且采用了k-medoids聚类方法。总之，可以看出，所建议的新方法极大地促进了城市交通规划中交通高峰期的准确确定。为了比较结果，采用了专家意见，这是常规方法，并且采用了k-medoids聚类方法。总之，可以看出，所建议的新方法极大地促进了城市交通规划中交通高峰期的准确确定。