Short-Term Traffic Forecast System of Beijing
Journal
Transportation Research Record Journal of the Transportation Research Board
Pages
116-123
Date Issued
2010-01
Date
2010-01
Author(s)
Dong, Shen
Li, Ruimin
Sun, Li-Guang
Chang, Tang-Hsien
Lu, Huapu
Abstract
A short-term, real-time system was developed to support traffic management in Beijing. The requirements of a large amount of data and unstable traffic flow are the biggest challenges to such a system. The models and software framework thus should be effective enough to face these problems. The core of such a system is the short-term traffic flow forecast model. Rapid urbanization and transportation development in Beijing have led to traffic flow patterns with some unstable characteristics. The short-term forecast model for an online system thus was designed with the fast-paced trend in mind. The model considers historical data, real-time data, and space data, and it can be updated online. Thus a combined model was developed with three submodels: discrete Fourier transform model, autoregressive model, and neighborhood regression model. Weights of each submodel were based on forecast error. Both the historical forecast error and real-time forecast error were considered. The system was built on a browser–server structure to support combined forecast models. The framework, modules, and interface of this system are introduced in this paper.
SDGs
Description
The TRB 89th Annual Meeting, Washington D.C., USA, Jan.10-14,2010.已有刊登在該會期刊。
Type
conference paper