In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

Highway Capacity Manual改訂の動向--テイラ-教授の講演より

527 articles related to imbalanced data and rare events are reviewed.Viewing reviewed papers from both technical and practical perspectives.Summarizing existing methods and corresponding statistics by a new taxonomy idea.Categorizing 162 application papers into 13 domains and giving introduction.Some opening questions are discussed at the end of this manuscript. Rare events, especially those that could potentially negatively impact society, often require humans decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.

Learning from class-imbalanced data

Accurate and real-time traffic forecasting plays an important role in the intelligent traffic system and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an “open” scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time. To capture the spatial and temporal dependences simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is combined with the graph convolutional network (GCN) and the gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures for capturing spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data for capturing temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://www.github.com/lehaifeng/T-GCN .

T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction

Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain. Traffic forecasting is one canonical example of such learning task. The task is challenging due to (1) complex spatial dependency on road networks, (2) non-linear temporal dynamics with changing road conditions and (3) inherent difficulty of long-term forecasting. To address these challenges, we propose to model the traffic flow as a diffusion process on a directed graph and introduce Diffusion Convolutional Recurrent Neural Network (DCRNN), a deep learning framework for traffic forecasting that incorporates both spatial and temporal dependency in the traffic flow. Specifically, DCRNN captures the spatial dependency using bidirectional random walks on the graph, and the temporal dependency using the encoder-decoder architecture with scheduled sampling. We evaluate the framework on two real-world large scale road network traffic datasets and observe consistent improvement of 12% - 15% over state-of-the-art baselines.

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Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

The rear-end collision is one of the main types of accident, and it brings unnecessary casualties and property losses. Aiming at reducing the rear-end collision, some researchers focus on the front collision avoidance system. Generally, the rear-end collision avoidance system detects risk situation based on the information collected by radar or camera. When the following car is in risk of crash, the system will inform or warn the driver that the current speed is not safe, and help drivers to decelerate or brake. With the development of vehicle-to-vehicle communication (V2V), a new way to develop the rear-end collision avoidance system is put forward. By using the connected vehicles, the system can collect information and share message through the communicate equipment. In this paper, the authors introduce a collision avoidance system which is based on the vehicle-to-vehicle communication. This rear-end collision avoidance is with some driver behavior by using a car-following model based on risk perception. At last, the authors provide some experimental date about collision avoidance, and demonstrate the feasibility of this system in risk detection and crash avoidance.

A rear-end collision avoidance system of connected vehicles

Simulative investigation on head injuries of electric self-balancing scooter riders subject to ground impact

Are electric self-balancing scooters safe in vehicle crash accidents?

In view of the high traffic accident rate at intersection, a vehicle trajectory collision warning system based on Vehicle Infrastructure Integration (VII) system is proposed in order to improve the traffic safety. After analyzing the system function, physical and logical framework of the system has been designed in detail. The trajectory prediction method is based on vehicle to vehicle (V2V) wireless communication. And vehicle collision can be detected in real time by the collision detection algorithm proposed in this paper. Furthermore, time to collision (TTC) as collision risk indicator is calculated to judge the vehicular collision, and the system is able to make a warning to driver according to the value of TTC.

Vehicle collision warning system and collision detection algorithm based on vehicle infrastructure integration

Most existing studies on routing guidance only paid attention to the average path travel time, which failed to consider travel time reliability (TTR) preferences by different travelers. In this study, a moment-matching-based hybrid genetic algorithm (MHGA) is proposed to search the reliable shortest path (RSP) in stochastic road networks with link correlations. First, the goodness-of-fit results based on field data reveal that lognormal distributions are more appropriate for characterizing link travel times. The impact of topological distance (measured by the number of links) and road type on link correlations is also scrutinized. Then, a moment-matching method (MOM) is utilized to determine the parameters of the approximate path travel time distribution (TTD) by accounting for link correlations. A local search algorithm is designed to improve the search ability of the path finding algorithm. In view of travelers’ risk tolerance, the algorithm enables the provision of personalized routing guidance for individual travelers. Furthermore, to support path finding applications in a large-scale network, heuristic constraints are imposed to help reduce the computational workload and accelerate the convergence speed of the search process. Finally, numerical case studies based on synthetic networks and a real road network in Beijing are presented, and the results help demonstrate that the algorithm has good potential to solve RSP searching problems in a large-scale network with desirable efficiency.

Yunpeng Wang

Papers

A rear-end collision avoidance system of connected vehicles

Simulative investigation on head injuries of electric self-balancing scooter riders subject to ground impact

Are electric self-balancing scooters safe in vehicle crash accidents?

Vehicle collision warning system and collision detection algorithm based on vehicle infrastructure integration

The α-reliable path problem in stochastic road networks with link correlations: A moment-matching-based path finding algorithm