Abstract
Urban road travel time is an important parameter to reflect the traffic flow state. Besides, it is one of the important parameters for the traffic management department to formulate guidance measures, provide traffic information service, and improve the efficiency of the detectors group. Therefore, it is crucial to improve the forecast accuracy of travel time in traffic management practice. Based on the analysis of the change-point and the ARIMA model, this paper constructs a model for the massive data collected by loop detectors to forecast travel time parameters. Firstly, the preprocessing algorithm for the data of loop detectors is given, and the calculating model of the travel time is studied. Secondly, a change-point detection algorithm is designed to classify the sequence of large number of travel time data items into several patterns. Then, this paper establishes a forecast model to forecast travel time in different patterns using the improved ARIMA model. At last, the model is verified by simulation and the verification results of several groups of examples show that the model has high accuracy and practicality.
Highlights
Urban road travel time is an important parameter to reflect the state of traffic flow of a road [1, 2]
Mori et al give a thorough classification of the methods for travel time forecasting and they divide the forecasting model into naive model, traffic flow model, data model, and hybrid model [6]
(2) The pattern partition of travel time series based on change-point analysis and setting up the forecasting model based on ARIMA
Summary
Urban road travel time is an important parameter to reflect the traffic flow state. It is one of the important parameters for the traffic management department to formulate guidance measures, provide traffic information service, and improve the efficiency of the detectors group. It is crucial to improve the forecast accuracy of travel time in traffic management practice. Based on the analysis of the change-point and the ARIMA model, this paper constructs a model for the massive data collected by loop detectors to forecast travel time parameters. A change-point detection algorithm is designed to classify the sequence of large number of travel time data items into several patterns. The model is verified by simulation and the verification results of several groups of examples show that the model has high accuracy and practicality
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