Proceedings ArticleDOI
An applicable short-term traffic flow forecasting method based on chaotic theory
Jianming Hu,Chun-guang Zong,Jingyan Song,Zuo Zhang,Jiang-tao Ren +4 more
- Vol. 1, pp 608-613
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TLDR
An attempt to forecast traffic flow from the viewpoint of non-linear time series based on the theory of phrase space reconstruction for traffic flow system and self-organizing Map (SOM) network is introduced to seek the near neighbor.Abstract:
Short-term traffic flow forecasting plays a very important role in urban traffic management and control. In this paper, According to the chaotic property of urban traffic flow, we compute the parameters of phrase space reconstruction for traffic flow system. Meanwhile, a local-forecasting method is introduced to predict urban road short-term traffic flow based on the theory of phrase space reconstruction. Self-organizing Map (SOM) network is introduced to seek the near neighbor. Case study using real traffic flow data from UTC-SCOOT system proves the validity of the method. The research in this paper is a significant attempt to forecast traffic flow from the viewpoint of non-linear time series.read more
Citations
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Journal ArticleDOI
A hybrid short-term traffic flow forecasting method based on spectral analysis and statistical volatility model
TL;DR: The experimental results demonstrate that the proposed method is able to unearth the underlying periodic characteristics and volatility nature of traffic flow data and show promising abilities in improving the accuracy and reliability of freeway traffic flow forecasting in multi-step ahead forecasting.
Patent
Optimizing traffic predictions and enhancing notifications
TL;DR: In this paper, travel-demand forecasting methods are described for predicting traffic volume based, at least in part, on user-entered data in the form of origin/destination data pairs, user preferences, demographic data and other types of socioeconomic data.
Journal ArticleDOI
Multiple measures-based chaotic time series for traffic flow prediction based on Bayesian theory
TL;DR: Results from numerical experiments demonstrate the improved effectiveness of the proposed Bayesian theory-based multiple measures chaotic time series prediction algorithm in terms of accuracy and timeliness for the short-term traffic flow prediction.
Journal ArticleDOI
Prediction of Traffic Flow at the Boundary of a Motorway Network
TL;DR: An adaptive prediction algorithm for the inflows into the network in regular traffic situations based on stochastic control theory is presented and shows that the algorithm provides robust predictions of traffic demand with relatively small errors for the next 30 min in a large-scale real-time environment.
Journal ArticleDOI
Multi-Dimensional traffic flow time series analysis with self-organizing maps
Yudong Chen,Yi Zhang,Jianming Hu +2 more
TL;DR: Analysis of real world traffic data shows the effectiveness of SOM in the representation and prediction of multi-dimensional traffic time series, for they can capture the nonlinear information of traffic flows data and predict traffic flows on multiple links simultaneously.
References
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Journal ArticleDOI
Dynamic prediction of traffic volume through Kalman filtering theory
TL;DR: In this article, two models employing Kalman filtering theory are proposed for predicting short-term traffic volume in Nagoya City, Japan, by taking into account data from a number of links.
Journal ArticleDOI
Traffic flow forecasting: comparison of modeling approaches
TL;DR: This research effort focused on developing traffic volume forecasting models for two sites on Northern Virginia's Capital Beltway, and found that the nonparametric regression model was easy to implement, and proved to be portable, performing well at two distinct sties.
Proceedings ArticleDOI
Traffic-flow-prediction systems based on upstream traffic
A.G. Hobeika,Chang Kyun Kim +1 more
TL;DR: Network-based model were developed to predict short term future traffic volume based on current traffic, historical average, and upstream traffic and were shown to be capable of producing reliable and accurate forecasts under congested traffic condition.
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