3,041 research outputs found
Belief Propagation and Bethe approximation for Traffic Prediction
We define and study an inference algorithm based on "belief propagation" (BP)
and the Bethe approximation. The idea is to encode into a graph an a priori
information composed of correlations or marginal probabilities of variables,
and to use a message passing procedure to estimate the actual state from some
extra real-time information. This method is originally designed for traffic
prediction and is particularly suitable in settings where the only information
available is floating car data. We propose a discretized traffic description,
based on the Ising model of statistical physics, in order to both reconstruct
and predict the traffic in real time. General properties of BP are addressed in
this context. In particular, a detailed study of stability is proposed with
respect to the a priori data and the graph topology. The behavior of the
algorithm is illustrated by numerical studies on a simple traffic toy model.
How this approach can be generalized to encode superposition of many traffic
patterns is discussed.Comment: Inria Report, 29 pages, 7 figure
Spatial and Temporal Analysis of Traffic States on Large Scale Networks
International audienceWe propose a set of methods aiming at extracting large scale features of road traffic, both spatial and temporal, based on local traffic indexes computed either from fixed sensors or floating car data. The approach relies on traditional data mining techniques like clustering or statistical analysis and is demonstrated on data artificially generated by the mesoscopic traffic simulator Metropolis. Results are compared to the output of another approach that we propose, based on the belief-propagation (BP) algorithm and an approximate Markov random field (MRF) encoding on the data. In particular, traffic patterns identified in the clustering analysis correspond in some sense to the fixed points obtained in the BP approach. The identification of latent macroscopic variables and their dynamical behavior is also obtained and the way to incorporate these in the MRF is discussed as well as the setting of a general approach for traffic reconstruction and prediction based on floating car data
Statistical Traffic State Analysis in Large-scale Transportation Networks Using Locality-Preserving Non-negative Matrix Factorization
Statistical traffic data analysis is a hot topic in traffic management and
control. In this field, current research progresses focus on analyzing traffic
flows of individual links or local regions in a transportation network. Less
attention are paid to the global view of traffic states over the entire
network, which is important for modeling large-scale traffic scenes. Our aim is
precisely to propose a new methodology for extracting spatio-temporal traffic
patterns, ultimately for modeling large-scale traffic dynamics, and long-term
traffic forecasting. We attack this issue by utilizing Locality-Preserving
Non-negative Matrix Factorization (LPNMF) to derive low-dimensional
representation of network-level traffic states. Clustering is performed on the
compact LPNMF projections to unveil typical spatial patterns and temporal
dynamics of network-level traffic states. We have tested the proposed method on
simulated traffic data generated for a large-scale road network, and reported
experimental results validate the ability of our approach for extracting
meaningful large-scale space-time traffic patterns. Furthermore, the derived
clustering results provide an intuitive understanding of spatial-temporal
characteristics of traffic flows in the large-scale network, and a basis for
potential long-term forecasting.Comment: IET Intelligent Transport Systems (2013
A review of travel time estimation and forecasting for advanced traveler information systems
Providing on line travel time information to commuters has become an important issue for
Advanced Traveler Information Systems and Route Guidance Systems in the past years, due
to the increasing traffic volume and congestion in the road networks. Travel time is one of
the most useful traffic variables because it is more intuitive than other traffic variables such as
flow, occupancy or density, and is useful for travelers in decision making.
The aim of this paper is to present a global view of the literature on the modeling of travel
time, introducing crucial concepts and giving a thorough classification of the existing tech-
niques. Most of the attention will focus on travel time estimation and travel time prediction,
which are generally not presented together. The main goals of these models, the study areas
and methodologies used to carry out these tasks will be further explored and categorized
ํ๋ก๋ธ ์ฐจ๋ ์๋ฃ๋ฅผ ์ด์ฉํ ๋์๊ตํต ๋คํธ์ํฌ์ ์๋ ์ถ์ ์ํํ ์ ๊ฒฝ๋ง ๋ชจํ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ณต๊ณผ๋ํ ๊ฑด์คํ๊ฒฝ๊ณตํ๋ถ,2020. 2. ๊ณ ์น์.Urban traffic flows are characterized by complexity. Due to this complexity, limitations arise when using models that have commonly been using to estimate the speed of arterial road networks. This study analyzes the characteristics of the speed data collected by the probe vehicle method in links on the urban traffic flow, presents the limitations of existing models, and develops a modified recurrent neural network model as a solution to these limitations. In order to complement the limitations of existing models, this study focused on the interrupted flow characteristics of urban traffic. Through data analysis, we verified the separation of platoons and high-frequency transitions as phenomena in interrupted flow. Using these phenomena, this study presents a two-step model using the characteristics of each platoon and the selected dropout method that applies traffic conditions separately. In addition, we have developed an active imputation method to deal with frequent missing data in data collection effectively. The developed model not only showed high accuracy on average, but it also improved the accuracy of certain states, which is the limitation of the existing models, increased the correlation between the estimated value and the estimated target value, and properly learned the periodicity of the data.๋์๊ตํต๋ฅ๋ ๋ณต์ก์ฑ์ ๋ด์ฌํ๊ณ ์๋ค. ์ด ๋ณต์ก์ฑ์ผ๋ก ์ธํด, ์ผ๋ฐ์ ์ผ๋ก ์ง์ญ๊ฐ ๊ฐ์ ๋๋ก ๋คํธ์ํฌ์ ์๋๋ฅผ ์ถ์ ํ๋ ๋ชจํ๋ค์ ์ฌ์ฉํ ๊ฒฝ์ฐ ์ฌ๋ฌ๊ฐ์ง ํ๊ณ์ ์ด ๋ฐ์ํ๊ฒ ๋๋ค. ๋ณธ ์ฐ๊ตฌ๋ ๋์๊ตํต๋ฅ ์์ ๋งํฌ์์ ํ๋ก๋ธ ์ฐจ๋ ๋ฐฉ์์ผ๋ก ์์ง๋ ์๋์๋ฃ์ ํน์ฑ์ ๋ถ์ํ๊ณ , ๊ธฐ์กด ๋ชจํ์ ํ๊ณ์ ์ ์ ์ํ๊ณ , ์ด๋ฌํ ํ๊ณ์ ์ ๋ํ ํด๋ฒ์ผ๋ก์ ๋ณํ๋ ์ํํ ์ ๊ฒฝ๋ง ๋ชจํ์ ๊ฐ๋ฐํ์๋ค. ๋ชจํ ๊ฐ๋ฐ์ ์์ด, ๊ธฐ์กด ๋ชจํ์ ํ๊ณ์ ์ ๋ณด์ํ๊ธฐ ์ํด, ๋ณธ ์ฐ๊ตฌ์์๋ ๋์๊ตํต๋ฅ์ ๋จ์๋ฅ์ ํน์ง์ ์ฃผ๋ชฉํ์๋ค. ์๋ฃ ๋ถ์์ ํตํด, ๋ณธ ์ฐ๊ตฌ์์๋ ๋จ์๋ฅ์์ ๋ํ๋๋ ํ์์ผ๋ก์ ์ฐจ๋๊ตฐ์ ๋ถ๋ฆฌ์ ๋์ ๋น๋์ ์ ์ด์ํ ๋ฐ์์ ํ์ธํ์๋ค. ํด๋น ํ์๋ค์ ์ด์ฉํ์ฌ, ๋ณธ ์ฐ๊ตฌ์์๋ ๊ฐ ์ฐจ๋๊ตฐ์ ํน์ง์ ์ด์ฉํ ์ด์ฉํ 2๋จ๊ณ ๋ชจํ๊ณผ, ๊ตํต ์ํ๋ฅผ ๋ถ๋ฆฌํ์ฌ ์ ์ฉํ๋ ์ ํ์ ๋๋กญ์์ ๋ฐฉ์์ ์ ์ํ์๋ค. ์ถ๊ฐ์ ์ผ๋ก, ์๋ฃ์ ์์ง์ ์์ด ๋น๋ฐํ๋ ๊ฒฐ์ธก ๋ฐ์ดํฐ๋ฅผ ํจ๊ณผ์ ์ผ๋ก ๋ค๋ฃจ๊ธฐ ์ํ ๋ฅ๋์ ๋์ฒด ๋ฐฉ์์ ๊ฐ๋ฐํ์๋ค. ๊ฐ๋ฐ ๋ชจํ์ ํ๊ท ์ ์ผ๋ก ๋์ ์ ํ๋๋ฅผ ๋ณด์ผ ๋ฟ ์๋๋ผ, ๊ธฐ์กด ๋ชจํ๋ค์ ํ๊ณ์ ์ธ ํน์ ์ํฉ์ ๋ํ ์ ํ๋๋ฅผ ์ ๊ณ ํ๊ณ ์ถ์ ๊ฐ๊ณผ ์ถ์ ๋์๊ฐ์ ์๊ด๊ด๊ณ๋ฅผ ๋์ด๋ฉฐ, ์๋ฃ์ ์ฃผ๊ธฐ์ฑ์ ์ ์ ํ๊ฒ ํ์ตํ ์ ์์๋ค.Chapter 1. Introduction 1
1.1. Study Background and Purpose 1
1.2. Research Scope and Procedure 8
Chapter 2. Literature Review 11
2.1. Data Estimation 11
2.2. Traffic State Handling 17
2.3. Originality of This Study 20
Chapter 3. Data Collection and Analysis 22
3.1. Terminology 22
3.2. Data Collection 23
3.3. Data Analysis 26
Chapter 4. Model Development 54
4.1. Basic Concept of the Model 54
4.2. Model Development 58
Chapter 5. Result and Findings 72
5.1. Estimation Accuracy of Developed Models 72
5.2. Correlation Analysis of Developed Model 77
5.3. Periodicity Analysis for Developed Models 81
5.4. Accuracy Analysis by Traffic State 86
5.5. Summary of the Result 92
Chapter 6. Conclusion 94
6.1. Summary 94
6.2. Limitation of the Study 95
6.3. Applications and Future Research 96
Appendix 98
Bibliography 119Docto
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