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

A new networking paradigm, Vehicular Edge Computing (VEC), has been introduced in recent years to the vehicular network to augment its computing capacity. The ultimate challenge to fulfill the requirements of both communication and computation is increasingly prominent, with the advent of ever-growing modern vehicular applications. With the breakthrough of VEC, service providers directly host services in close proximity to smart vehicles for reducing latency and improving quality of service (QoS). This paper illustrates the VEC architecture, coupled with the concept of the smart vehicle, its services, communication, and applications. Moreover, we categorized all the technical issues in the VEC architecture and reviewed all the relevant and latest solutions. We also shed some light and pinpoint future research challenges. This article not only enables naive readers to get a better understanding of this latest research field but also gives new directions in the field of VEC to the other researchers.

/pdf/a-survey-on-vehicular-edge-computing-architecture-kb23flscv9.pdf

A Survey on Vehicular Edge Computing: Architecture, Applications, Technical Issues, and Future Directions

In an urban environment, intersections are critical locations in terms of road crashes and number of killed or injured people. Vehicular ad hoc networks (VANETs) can help reduce the traffic collisions at intersections by sending warning messages to the vehicles. However, the performance of VANETs should be enhanced to guarantee delivery of the messages, particularly safety messages to the destination. Data congestion control is an efficient way to decrease packet loss and delay and increase the reliability of VANETs. In this paper, a centralized and localized data congestion control strategy is proposed to control data congestion using roadside units (RSUs) at intersections. The proposed strategy consists of three units for detecting congestion, clustering messages, and controlling data congestion. In this strategy, the channel usage level is measured to detect data congestion in the channels. The messages are gathered, filtered, and then clustered by machine learning algorithms. $K$ - means algorithm clusters the messages based on message size, validity of messages, and type of messages. The data congestion control unit determines appropriate values of transmission range and rate, contention window size, and arbitration interframe spacing for each cluster. Finally, RSUs at the intersections send the determined communication parameters to the vehicles stopped before the red traffic lights to reduce communication collisions. Simulation results show that the proposed strategy significantly improves the delay, throughput, and packet loss ratio in comparison with other congestion control strategies using the proposed congestion control strategy.

Centralized and Localized Data Congestion Control Strategy for Vehicular Ad Hoc Networks Using a Machine Learning Clustering Algorithm

A survey on context-aware vehicular network applications

A decade ago, the crowdsourcing term was first coined and used to represent a method for expressing the wisdom of the crowd in accomplishing two types of tasks One type includes tasks that need human intelligence rather than machines, and the other type covers those tasks that can be accomplished with a higher time and cost efficiency using the crowd rather than employing experts The crowdsourcing process contains five modules: The first is designing incentives to mobilize the crowd to do the required task This step is followed by four modules for collecting and assuring quality and then verifying and aggregating the received information The verification and quality control can be done for the tasks, collected data and the participants by having more participants answer the same question or accepting answers only from experts to avoid errors from unreliable participants Methods of discovering topic experts are utilized to discover reliable candidates in the crowd who have relevant experience in the discussed topic Expert discovery reduces the number of needed participants per question which reduces the overall cost This work summarizes and reviews the methods used to accomplish each processing step Yet, choosing a specific method remains application dependent

Overview of the crowdsourcing process

Vehicular Ad-hoc Networks (VANETs) have attracted attention in the support of safe driving, intelligent navigation, and emergency and entertainment applications. VANET can be viewed as an intelligent component of the Transportation Systems as vehicles communicate with each other as well as with roadside base stations located at critical points of the road, such as intersections or construction sites. In this paper, we provide an overview of the context-aware processing and communication gateway associated with Vehicular Ad-hoc Network (VANET). The concept of context-awareness, the recent advances and various challenges involved in context-aware processing are discussed. Some arising ideas such as based on context ontology, relevancy, hybrid dissemination, service oriented routing are also presented. This paper further briefly describes the communication gateway in VANET which includes its functional view together with the standards and their detailed preliminary specifications applicable to VANET.

Vehicular ad-hoc Networks(VANETs): capabilities, challenges in context-aware processing and communication gateway

Intelligent transportation systems (ITS) involve various emerging technologies and applications. This paper presents a comprehensive review of recent advances on data/information fusion and context-awareness referring to ITS. Data/Information fusion is necessary to fuse the data from different sensors and thereby extract relevant information on the target sources. On the other hand, context-aware information processing provides awareness of the driving environments by deploying intelligent query processing and smart information dissemination. The fusion and context-awareness should help in improving ITS operations with better road-awareness service, traffic monitoring, vehicle detection as well as development of new methods. This paper is centered on data fusion and context aware methodologies developed recently in the areas of ITS rather than on their ITS applications. We found that the recent progresses in ITS fusion are devoted to the potential cooperative approaches providing real-time/dynamic vehicle sensing technologies, whereas the recent context awareness techniques are deploying service concepts (e.g. location aware service) and frameworks. It is believed that the newly developed advanced fusion/context-aware techniques are becoming more effective to tackle complex traffic scenarios (e.g. traffic intersection) as well as complex urban environments.

Recent Advances on Context-Awareness and Data/Information Fusion in ITS

Vehicular Ad-hoc Network (VANET) has become an active area of research due to its major role to improve vehicle and road safety, traffic efficiency, and convenience as well as comfort to both drivers and passengers. This paper thus addresses some of the attributes and challenging issues related to Vehicular Ad-hoc Networks (VANETs). A lot of VANET research work have focused on specific areas including routing, broadcasting, Quality of Service (QoS), and security. In this paper, a detailed overview of the current information gathering and data fusion capabilities and challenges in the context of VANET is presented. In addition, an overall VANET framework, an illustrative VANET scenario are provided in order to enhance safety, flow, and efficiency of the transportation system.

https://link.springer.com/content/pdf/10.1007/978-3-642-31368-4_5.pdf

Vehicular ad-hoc networks(VANETs): capabilities, challenges in information gathering and data fusion

The currently deployed prediction models for strawberry fresh produce (FP) are based on either conventional machine learning (ML) or on simple deep learning (DL) models that are mostly applied for yield prediction. In this paper, we propose more comprehensive DL models that are applied for the first time to predict strawberry yield. The strawberry price is predicted as well directly from weather input parameters and yield. The strawberry price prediction is achieved using compound DL models such as Convolutional Long Short-Term Memory Recurrent Neural Network (CNN-LSTM). It is found that by adding attention, the performance of the compound models usually improves. After utilizing an aggregated performance measure to find the best model, the Attention-CNN-LSTM model proved to be the best compared to the rest of the deployed conventional ML models as well as the compound and simple DL models. The aggregated measure shows that this model is capable of precisely predicting strawberry prices five weeks ahead while maintaining the lowest prediction error and the highest model correlation.

Lobna Nassar

Papers

Overview of the crowdsourcing process

Vehicular ad-hoc Networks(VANETs): capabilities, challenges in context-aware processing and communication gateway

Recent Advances on Context-Awareness and Data/Information Fusion in ITS

Vehicular ad-hoc networks(VANETs): capabilities, challenges in information gathering and data fusion

Prediction of Strawberry Yield and Farm Price Utilizing Deep Learning