A travel time forecasting model based on change-point detection method

3rd International Conference on Advances in Energy, Environment and Chemical Engineering

能源学;生态环境科学;化学工业

Li, Shupeng^1 ; Guang, Xiaoping^1 ; Qian, Yongsheng^1 ; Zeng, Junwei^1

School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou

730070, China^1

关键词: Autoregressive integrated moving average models;    Change point detection;    Control parameters;    First-order differentials;    Traffic management;    Travel time forecasting;    Urban road traffic;    Weight functions;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/69/1/012120/pdf
DOI  :  10.1088/1755-1315/69/1/012120

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Travel time parameters obtained from road traffic sensors data play an important role in traffic management practice. A travel time forecasting model is proposed for urban road traffic sensors data based on the method of change-point detection in this paper. The first-order differential operation is used for preprocessing over the actual loop data; a change-point detection algorithm is designed to classify the sequence of large number of travel time data items into several patterns; then a travel time forecasting model is established based on autoregressive integrated moving average (ARIMA) model. By computer simulation, different control parameters are chosen for adaptive change point search for travel time series, which is divided into several sections of similar state.Then linear weight function is used to fit travel time sequence and to forecast travel time. The results show that the model has high accuracy in travel time forecasting.