An Urban Traffic Flow Fusion Network Based on a Causal Spatiotemporal Graph Convolution Network

Traffic flow prediction is an important part of intelligent transportation systems. In recent years, most methods have considered only the feature relationships of spatial dimensions of traffic flow data, and ignored the feature fusion of spatial and temporal aspects. Traffic flow has the features of periodicity, nonlinearity and complexity. There are many relatively isolated points in the nodes of traffic flow, resulting in the features usually being accompanied by high-frequency noise. The previous methods directly used the graph convolution network for feature extraction. A polynomial approximation graph convolution network is essentially a convolution operation to enhance the weight of high-frequency signals, which lead to excessive high-frequency noise and reduce prediction accuracy to a certain extent. In this paper, a deep learning framework is proposed for a causal gated low-pass graph convolution neural network (CGLGCN) for traffic flow prediction. The full convolution structure adopted by the causal convolution gated linear unit (C-GLU) extracts the time features of traffic flow to avoid the problem of long running time associated with recursive networks. The reduction of running parameters and running time greatly improved the efficiency of the model. The new graph convolution neural network with self-designed low-pass filter was able to extract spatial features, enhance the weight of low-frequency signal features, suppress the influence of high-frequency noise, extract the spatial features of each node more comprehensively, and improve the prediction accuracy of the framework. Several experiments were carried out on two real-world real data sets. Compared with the existing models, our model achieved better results for short-term and long-term prediction.© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Machine Learning Approaches for Traffic Flow Forecasting

Intelligent Transport Systems (ITS) as a field has emerged quite rapidly in the recent years. A competitive solution coupled with big data gathered for ITS applications needs the latest AI to drive the ITS for the smart and effective public transport planning and management. Although there is a strong need for ITS applications like Advanced Route Planning (ARP) and Traffic Control Systems (TCS) to take the charge and require the minimum of possible human interventions. This thesis develops the models that can predict the traffic link flows on a junction level such as road traffic flows for a freeway or highway road for all traffic conditions. The research first reviews the state-of-the-art time series data prediction techniques with a deep focus in the field of transport Engineering along with the existing statistical and machine leaning methods and their applications for the freeway traffic flow prediction. This review setup a firm work focussed on the view point to look for the superiority in term of prediction performance of individual statistical or machine learning models over another. A detailed theoretical attention has been given, to learn the structure and working of individual chosen prediction models, in relation to the traffic flow data. In modelling the traffic flows from the real-world Highway England (HE) gathered dataset, a traffic flow objective function for highway road prediction models is proposed in a 3-stage framework including the topological breakdown of traffic network into virtual patches, further into nodes and to the basic links flow profiles behaviour estimations. The proposed objective function is tested with ten different prediction models including the statistical, shallow and deep learning constructed hybrid models for bi-directional links flow prediction methods. The effectiveness of the proposed objective function greatly enhances the accuracy of traffic flow prediction, regardless of the machine learning model used. The proposed prediction objective function base framework gives a new approach to model the traffic network to better understand the unknown traffic flow waves and the resulting congestions caused on a junction level. In addition, the results of applied Machine Learning models indicate that RNN variant LSTMs based models in conjunction with neural networks and Deep CNNs, when applied through the proposed objective function, outperforms other chosen machine learning methods for link flow predictions. The experimentation based practical findings reveal that to arrive at an efficient, robust, offline and accurate prediction model apart from feeding the ML mode with the correct representation of the network data, attention should be paid to the deep learning model structure, data pre-processing (i.e. normalisation) and the error matrices used for data behavioural learning. The proposed framework, in future can be utilised to address one of the main aims of the smart transport systems i.e. to reduce the error rates in network wide congestion predictions and the inflicted general traffic travel time delays in real-time

Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of additional travel time and increased fuel consumption. Integrating emerging technologies into transportation systems provides opportunities for improving traffic prediction significantly and brings about new research problems. In order to lay the foundation for understanding the open research challenges in traffic prediction, this survey aims to provide a comprehensive overview of traffic prediction methodologies. Specifically, we focus on the recent advances and emerging research opportunities in Artificial Intelligence (AI)-based traffic prediction methods, due to their recent success and potential in traffic prediction, with an emphasis on multivariate traffic time series modeling. We first provide a list and explanation of the various data types and resources used in the literature. Next, the essential data preprocessing methods within the traffic prediction context are categorized, and the prediction methods and applications are subsequently summarized. Lastly, we present primary research challenges in traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies (TR_C), Volume 145, 202

Multi-Spatio-temporal Fusion Graph Recurrent Network for Traffic forecasting

Traffic forecasting is essential for the traffic construction of smart cities in the new era. However, traffic data's complex spatial and temporal dependencies make traffic forecasting extremely challenging. Most existing traffic forecasting methods rely on the predefined adjacency matrix to model the Spatio-temporal dependencies. Nevertheless, the road traffic state is highly real-time, so the adjacency matrix should change dynamically with time. This article presents a new Multi-Spatio-temporal Fusion Graph Recurrent Network (MSTFGRN) to address the issues above. The network proposes a data-driven weighted adjacency matrix generation method to compensate for real-time spatial dependencies not reflected by the predefined adjacency matrix. It also efficiently learns hidden Spatio-temporal dependencies by performing a new two-way Spatio-temporal fusion operation on parallel Spatio-temporal relations at different moments. Finally, global Spatio-temporal dependencies are captured simultaneously by integrating a global attention mechanism into the Spatio-temporal fusion module. Extensive trials on four large-scale, real-world traffic datasets demonstrate that our method achieves state-of-the-art performance compared to alternative baselines

프로브 차량 자료를 이용한 도시교통 네트워크의 속도 추정 순환형 신경망 모형

학위논문(박사)--서울대학교 대학원 :공과대학 건설환경공학부,2020. 2. 고승영.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.도시교통류는 복잡성을 내재하고 있다. 이 복잡성으로 인해, 일반적으로 지역간 간선 도로 네트워크의 속도를 추정하던 모형들을 사용할 경우 여러가지 한계점이 발생하게 된다. 본 연구는 도시교통류 상의 링크에서 프로브 차량 방식으로 수집된 속도자료의 특성을 분석하고, 기존 모형의 한계점을 제시하고, 이러한 한계점에 대한 해법으로서 변형된 순환형 신경망 모형을 개발하였다. 모형 개발에 있어, 기존 모형의 한계점을 보완하기 위해, 본 연구에서는 도시교통류의 단속류적 특징에 주목하였다. 자료 분석을 통해, 본 연구에서는 단속류에서 나타나는 현상으로서 차량군의 분리와 높은 빈도의 전이상태 발생을 확인하였다. 해당 현상들을 이용하여, 본 연구에서는 각 차량군의 특징을 이용한 이용한 2단계 모형과, 교통 상태를 분리하여 적용하는 선택적 드롭아웃 방식을 제시하였다. 추가적으로, 자료의 수집에 있어 빈발하는 결측 데이터를 효과적으로 다루기 위한 능동적 대체 방식을 개발하였다. 개발 모형은 평균적으로 높은 정확도를 보일 뿐 아니라, 기존 모형들의 한계점인 특정 상황에 대한 정확도를 제고하고 추정값과 추정 대상값의 상관관계를 높이며, 자료의 주기성을 적절하게 학습할 수 있었다.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