American Society of Civil Engineers


The Advanced Nonparametric Model for Short-Term Traffic Volume Forecasting


by Xiaofa Shi, (School of Transportation Engineering, Tongji University, China, 201804. E-mail: xfshi999@163.com) and Qianli Ren, (School of Transportation Engineering, Tongji University, China, 201804. E-mail: ren7565531@163.com)
Section: Intelligent Transportation Systems, pp. 1442-1453, (doi:  http://dx.doi.org/10.1061/41186(421)143)

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Document type: Conference Proceeding Paper
Part of: ICCTP 2011: Towards Sustainable Transportation Systems
Abstract: Short-term traffic flow forecasting is an important part of the research field of intelligent transportation systems. Although K-NN based nonparametric regression has been used in short-term traffic volume forecasting for a long time, some questions still exist, especially where the database is too large and hard to search. In order to improve the accuracy and computing speed of the proposed algorithm, this paper uses a new method called MW model to solve this problem and to improve this advanced method. Results show that the model was verified accurate and effective.


ASCE Subject Headings:
Traffic volume
Traffic flow
Forecasting