標題: Negative Binomial Additive Models for Short-Term Traffic Flow Forecasting in Urban Areas
作者: Daraghmi, Yousef-Awwad
Yi, Chih-Wei
Chiang, Tsun-Chieh
資訊工程學系
Department of Computer Science
關鍵字: Additive models;autocorrelation;multivariate;negative binomial (NB);overdispersion;seasonal patterns;short-term forecast;spatial correlation
公開日期: 1-四月-2014
摘要: Parallel, coordinated, and network-wide traffic management requires accurate and efficient traffic forecasting models to support online, real-time, and proactive dynamic control. Forecast accuracy is impacted by a critical characteristic of traffic flow, i.e., overdispersion. Efficiency depends on the time complexity of forecasting algorithms. Therefore, this paper proposes a novel spatiotemporal multivariate forecasting model that is based on the negative binomial additive models (NBAMs). Negative binomial is utilized to handle overdispersion, and additive models are used to efficiently smooth nonlinear spatial and temporal variables. To evaluate the model, it is applied to real-world data collected from Taipei City and compared with other forecasting models. The results indicate that the proposed model is an accurate and efficient approach in forecasting traffic flow in urban context where flow is overdispersed, autocorrelated, and influenced by upstream and downstream roads as well as the daily seasonal patterns, namely, low-, moderate-, and high-traffic seasons.
URI: http://dx.doi.org/10.1109/TITS.2013.2287512
http://hdl.handle.net/11536/24266
ISSN: 1524-9050
DOI: 10.1109/TITS.2013.2287512
期刊: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume: 15
Issue: 2
起始頁: 784
結束頁: 793
顯示於類別:期刊論文


文件中的檔案:

  1. 000334584800029.pdf