Safety-oriented planning of expressway truck service areas based on driver demand

Funding This study was supported by the National Natural Science Foundation of China (51978522).Peer reviewedPublisher PD

Application of the wavelet analysis to research the traffic flow intensity

The relevance of the work is the specific properties of the wavelet analysis, which make it possible to identify not only the amplitude-scale (frequency) characteristics of the time series under consideration, but also the evolution of these characteristics during the observation time. As a result of the study, it is advisable to identify those indicators of the intensity of traffic flow that may turn out to be indicators of possible problematic situations (congestion, traffic accidents, etc.). It is advisable to use them in the future when regulating and controlling traffic on the basis of processing information about traffic flows that comes from stationary video recording complexes of traffic violations. The object of study is a road with intensive one-way traffic, equipped with a software and hardware complex that allows measuring the characteristics of the flow of motor transport. The subject of the study is the daily intensity of the flow of cars. The purpose of this study is to identify patterns in the indicators evolution obtained using wavelet analysis as a result of processing of the time series of the car traffic intensity on the road network. As a theoretical and methodological approach, the wavelet transforms using the MHat wavelet, and the Morlet wavelet is used. The approach used by the authors allowed us to establish the correspondence of some characteristics obtained during the wavelet analysis with the evolution of the traffic flow intensity function during the daily observation time, which is the scientific novelty of the study. The wavelet analysis of the data of the video surveillance software and hardware complexes obtained during the day allowed us to construct time dependences of amplitude-scale (frequency) indicators of the car traffic intensity on the road connecting the central and rear areas of the city of Perm. As a result of the study of time series, experimental three-dimensional distributions of wavelet images, scalograms, skeletons and scalegrams of the function of the daily intensity of the traffic flow were obtained. An explanation of the characteristic features of the obtained dependencies and their relationship with the initial function of the traffic flow intensity is proposed. The practical significance lies in obtaining amplitude-scale (frequency) characteristics as a result of wavelet analysis of the traffic intensity using MHat and Morlet wavelets, which is of practical interest from the point of view of predicting the movement of vehicles, controlling the operation of traffic lights, monitoring the operation of equipment, etc. The direction of further research is to obtain, process, analyze and generalize the results of performing amplitude-scale wavelet analysis for time series of traffic flow intensity on parts of the road network with different vehicle traffic intensity

A new ramp metering control algorithm for optimizing freeway travel times

"In many cities around the world traffic congestion has been increasing faster than can be dealt with by new road construction. To resolve this problem traffic management devices and technology such as ramp meters are increasingly being utilized."--leaf 1.Masters of Information Technolog

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

베이지안 네트워크를 활용한 교통상태의 확률론적 예측

학위논문 (박사)-- 서울대학교 대학원 공과대학 건설환경공학부, 2017. 8. 고승영.Traffic state prediction is an important issue in traffic operations. One of the main purposes of traffic operations is to prevent flow breakdown. Therefore, it is necessary to perform traffic state predictions that reflects the stochastic process of traffic flow. However, traffic state transition is affected complexly and simultaneously by many factors, which lead to a lack of understanding and accurate prediction. Meanwhile, the Bayesian network is a methodology that not only is suitable for a problem with uncertainty but also can improve the understanding of a problem. Also, it is possible to derive fair probability with incomplete information, which allows the analysis of various situations. In this study, we developed a traffic state prediction model using the Bayesian network to reflect dynamic and stochastic traffic flow characteristics. In order to improve the structure of the Bayesian network, which has been used simply in transportation problems, we proposed a modeling procedure using mixture of Gaussians (MOGs). Also, spatially extended variables were used to consider the spatiotemporal evolution of traffic flow pattern. In particular, traffic state identification was performed by estimating the link speed in order to consider the spatial propagation of congestion. In the performance evaluation, the Bayesian network has better performance than logistic regression and has the same level of performance as artificial neural network based on machine learning. Also, by performing sensitivity analyses, we provided the understanding of traffic state prediction and the guidelines for model improvement. Therefore, the Bayesian network developed in this study can be considered as a traffic state prediction model with good prediction accuracy and provides insights for traffic state prediction.Chapter 1. Introduction 1 1.1 Research background and purpose 1 1.2 Research scope and procedure 4 Chapter 2. Literature Review 8 2.1 Characteristics of traffic state 8 2.2 Traffic state estimation and prediction 14 2.3 Bayesian network 37 2.4 Originality of this research 41 Chapter 3. Data Collection and Preparation 46 3.1 Data collection and validity check 46 3.2 Traffic state identification 47 3.3 Data Description 63 Chapter 4. Bayesian Network Modeling 66 4.1 Modeling procedure 66 4.2 Description of interface mechanism 69 4.3 Module design 74 4.4 Eliciting the structure 81 4.5 Verification 81 4.6 Parameter learning 85 Chapter 5. Model Evaluation 87 5.1 Evaluation results 87 5.2 Comparison with other methodologies 92 5.3 Sensitivity analysis 104 Chapter 6. Conclusions 127 6.1 Summary 127 6.2 Guidelines for traffic state prediction 128 6.3 Limitations of the study 129 6.4 Applications and future research 130 References 135Docto

Temporospatial Context-Aware Vehicular Crash Risk Prediction

With the demand for more vehicles increasing, road safety is becoming a growing concern. Traffic collisions take many lives and cost billions of dollars in losses. This explains the growing interest of governments, academic institutions and companies in road safety. The vastness and availability of road accident data has provided new opportunities for gaining a better understanding of accident risk factors and for developing more effective accident prediction and prevention regimes. Much of the empirical research on road safety and accident analysis utilizes statistical models which capture limited aspects of crashes. On the other hand, data mining has recently gained interest as a reliable approach for investigating road-accident data and for providing predictive insights. While some risk factors contribute more frequently in the occurrence of a road accident, the importance of driver behavior, temporospatial factors, and real-time traffic dynamics have been underestimated. This study proposes a framework for predicting crash risk based on historical accident data. The proposed framework incorporates machine learning and data analytics techniques to identify driving patterns and other risk factors associated with potential vehicle crashes. These techniques include clustering, association rule mining, information fusion, and Bayesian networks. Swarm intelligence based association rule mining is employed to uncover the underlying relationships and dependencies in collision databases. Data segmentation methods are employed to eliminate the effect of dependent variables. Extracted rules can be used along with real-time mobility to predict crashes and their severity in real-time. The national collision database of Canada (NCDB) is used in this research to generate association rules with crash risk oriented subsequents, and to compare the performance of the swarm intelligence based approach with that of other association rule miners. Many industry-demanding datasets, including road-accident datasets, are deficient in descriptive factors. This is a significant barrier for uncovering meaningful risk factor relationships. To resolve this issue, this study proposes a knwoledgebase approximation framework to enhance the crash risk analysis by integrating pieces of evidence discovered from disparate datasets capturing different aspects of mobility. Dempster-Shafer theory is utilized as a key element of this knowledgebase approximation. This method can integrate association rules with acceptable accuracy under certain circumstances that are discussed in this thesis. The proposed framework is tested on the lymphography dataset and the road-accident database of the Great Britain. The derived insights are then used as the basis for constructing a Bayesian network that can estimate crash likelihood and risk levels so as to warn drivers and prevent accidents in real-time. This Bayesian network approach offers a way to implement a naturalistic driving analysis process for predicting traffic collision risk based on the findings from the data-driven model. A traffic incident detection and localization method is also proposed as a component of the risk analysis model. Detecting and localizing traffic incidents enables timely response to accidents and facilitates effective and efficient traffic flow management. The results obtained from the experimental work conducted on this component is indicative of the capability of our Dempster-Shafer data-fusion-based incident detection method in overcoming the challenges arising from erroneous and noisy sensor readings