Multiple measures-based chaotic time series for traffic flow prediction based on Bayesian theory
出版年份 2016 全文链接
标题
Multiple measures-based chaotic time series for traffic flow prediction based on Bayesian theory
作者
关键词
Traffic flow, Chaos theory, Phase space reconstruction, Bayesian estimation, Multi-measure time series, RBF neural network
出版物
NONLINEAR DYNAMICS
Volume 85, Issue 1, Pages 179-194
出版商
Springer Nature
发表日期
2016-02-25
DOI
10.1007/s11071-016-2677-5
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Lattice hydrodynamic model based delay feedback control of vehicular traffic flow considering the effects of density change rate difference
- (2015) Yongfu Li et al. Communications in Nonlinear Science and Numerical Simulation
- Extended-State-Observer-Based Double-Loop Integral Sliding-Mode Control of Electronic Throttle Valve
- (2015) Yongfu Li et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Existence of steady solutions for micropolar electrorheological fluid flows
- (2015) F. Ettwein et al. NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS
- Evaluating the energy consumption of electric vehicles based on car-following model under non-lane discipline
- (2015) Yongfu Li et al. NONLINEAR DYNAMICS
- An extended car-following model with consideration of the electric vehicle’s driving range
- (2015) Tie-Qiao Tang et al. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
- Nonlinear multivariate time–space threshold vector error correction model for short term traffic state prediction
- (2015) Tao Ma et al. TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
- A Multilane Traffic Flow Model Accounting for Lane Width, Lane-Changing and the Number of Lanes
- (2014) Tie-Qiao Tang et al. NETWORKS & SPATIAL ECONOMICS
- Non-lane-discipline-based car-following model considering the effects of two-sided lateral gaps
- (2014) Yongfu Li et al. NONLINEAR DYNAMICS
- A new car-following model with consideration of inter-vehicle communication
- (2014) Tieqiao Tang et al. NONLINEAR DYNAMICS
- New Bayesian combination method for short-term traffic flow forecasting
- (2014) Jian Wang et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections
- (2014) Jia Zheng Zhu et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- On the stability analysis of microscopic traffic car-following model: a case study
- (2013) Yongfu Li et al. NONLINEAR DYNAMICS
- Analysis of traffic dynamics on a ring road-based transportation network by means of 0–1 test for chaos and Lyapunov spectrum
- (2013) Blaž Krese et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Comparing traffic flow time-series under fine and adverse weather conditions using recurrence-based complexity measures
- (2012) Eleni I. Vlahogianni et al. NONLINEAR DYNAMICS
- A new car-following model accounting for varying road condition
- (2012) Tieqiao Tang et al. NONLINEAR DYNAMICS
- A comparison of different Bayesian design criteria for setting up stated preference studies
- (2012) Jie Yu et al. TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
- Short-term traffic speed forecasting hybrid model based on Chaos–Wavelet Analysis-Support Vector Machine theory
- (2012) Jin Wang et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Modeling and simulation for microscopic traffic flow based on multiple headway, velocity and acceleration difference
- (2010) Yongfu Li et al. NONLINEAR DYNAMICS
- Statistical methods versus neural networks in transportation research: Differences, similarities and some insights
- (2010) M.G. Karlaftis et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Investigation of temporal freeway traffic patterns in reconstructed state spaces
- (2007) Lawrence W. Lan et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started