A Kind of Urban Road Travel Time Forecasting Model with Loop Detectors

Guangyu Zhu, Li Wang, Peng Zhang and Kang Song

International Journal of Distributed Sensor Networks, 2016, vol. 12, issue 2, 9043835

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.

Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1155/2016/9043835 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:12:y:2016:i:2:p:9043835

DOI: 10.1155/2016/9043835

Access Statistics for this article

More articles in International Journal of Distributed Sensor Networks
Bibliographic data for series maintained by SAGE Publications ().