Motorway traffic management systems are useful for improving the traffic conditions on urban motorways. One of the most common motorway traffic management systems are variable speed limit systems. These systems adapt the speed limits based on the prevailing traffic conditions measured by roadside detectors and recommended or compulsory speed limits are shown on variable message signs installed on gantries over the road.Thesystems consist of three parts; the control algorithm used to determine which speed limit to be displayed, a method for estimating the traffic conditions to be used as input for the control algorithm and the infrastructure for application of the variable speed limits. The goal of the systems is often to increase safety or efficiency. Recent development in the field of connected vehicles have opened up for a new type of data source, as the status of a connected vehicle and its surroundings can be communicated at arbitrary locations. Hence, by the use of connected vehicles in variable speed limit systems there is a potential of reducing the amount of roadside equipment. It is even possible to control the connected vehicles towards the current speed limit without the use of variable message signs. This allows for the application of variable speed limits at arbitrary locations. The aim of this thesis is to examine how connected vehicles can be used to improve the efficiency of variable speed limit systems. The thesis contribute with new and improved methods using connected vehicles in all three parts of a variable speed limit system. The suggested methods are evaluated by microscopic traffic simulation. The overall conclusion is that the use of connected vehicles in variable speed limit systems can contribute to improvements in traffic efficiency compared to existing systems. The six papers included in the thesis can be summarized as follows. First, it is shown that traditional variable speed limit systems can be effective for improving the traffic conditions on the motorway and the results can be comparable to more costly alterations by reconstruction of the infrastructure to increase the capacity. Next, the usefulness of connected vehicles for application and control of the speed limits in an existing variable speed limit system is investigated. It is concluded that the design of the control algorithm and the accuracy of the estimated traffic conditions have a great effect on the final outcome of the system. The design of the control algorithm is then examined by evaluation of a number of control algorithms with respect to safety, efficiency and environmental impacts. The main benefits and drawbacks of the algorithms are highlighted and desirable characteristics to include when designing a control algorithm are identified. In two studies, methods making use of connected vehicles for estimating the traffic conditions are proposed. The results show that connected vehicles are useful for improving the accuracy of the estimated traffic conditions through the inclusion of more detailed information and information at locations where detector measurements are not available. Finally, a variable speed limit system is proposed in which connected vehicles play a central role in the estimation of the traffic conditions, as well as in the control algorithm and for application of the speed limit. The system is shown to be useful for improving traffic efficiency during an incident at an arbitrary location along the controlled road.

Autonomous vehicles create new opportunities for innovative intelligent traffic systems. Variable speed limits, which is a speed management systems that can adjust the speed limit according to traffic condition or predefined speed control algorithm on different road segments, can be better implemented with the cooperation of autonomous vehicles. These compliant vehicles can automatically follow speed limits. However, non-compliant vehicles will attempt to pass the moving bottleneck created by the compliant vehicle. This project builds a multi class cell transmission model to represent the relation between traffic flow parameters. This model can calculate flows of both compliant and non-compliant vehicles. An algorithm is proposed to calculate variable speed limits for each cell of the cell transmission model. This control algorithm is designed to reduce the stop-and-go behavior of vehicles at traffic signals. Simulation is used to test the effects of VSLs on an example network. The result shows that VSL is effective at reducing the energy consumption of the whole system and reduce the likelihood of crash occurrence.

This proceedings book features papers presented at the International Conference on New Technologies, Development and Application, held at the Academy of Sciences and Arts of Bosnia and Herzegovina in Sarajevo on 25th–27th June 2020. It covers a wide range of future technologies and technical disciplines, including complex systems such as Industry 4.0; patents in Industry 4.0; robotics; mechatronics systems; automation; manufacturing; cyber-physical and autonomous systems; sensors; networks; control; energy and renewable energy sources; automotive and biological systems; vehicular networking and connected vehicles; effectiveness and logistics systems; smart grids; nonlinear systems; power; social and economic systems; education; and IoT. The book focuses on the Fourth Industrial Revolution “Industry 4.0,” in which implementation will improve many aspects of human life in all segments and lead to changes in business paradigms and production models. Further, new business methods are emerging, transforming production systems, transport, delivery, and consumption, which need to be monitored and implemented by every company involved in the global market.

Variable Speed Limit (VSL) systems are an innovative way of managing traffic using Intelligent Transportation Systems (ITS). Unlike the traditional static speed limit signs, VSLs are speed limit signs with a capability of changing regulatory speed limits according to changing real time weather and traffic conditions. VSLs are primarily used to reduce traffic congestion and improve safety along roadways. Wyoming weather is highly variable and often severe, in the absence of recommended speed limits for such conditions drivers select their own safe driving speed leading to high variance among vehicles. This high speed variance, results in high crash rates and frequent road closures. Currently Wyoming Department of Transportation (WYDOT) using an interim manual protocol to deploy speed limits on VSL corridors. A new automated self-learning methodology for deploying VSL is described in this dissertation. As a first step in this project, weather variables that have a significant effect on vehicle speeds during bad weather conditions were identified using a robust regression statistical procedure. Second, an automated control strategy was designed that uses real time weather and observed speed variables. Third, a statistical model called decision trees was used in control strategy to enhance the performance of the automated control strategy. Fourth, a self-learning component is added to the control strategy to address concerns about the high variability of different storm events in this region. The proposed automated self-learning algorithm was tested on previous storm event datasets and results from the simulation indicate that the new system is more efficient than the current manual protocol of deploying speed limits. Finally, the transferability of the proposed control strategy was evaluated by applying the self-learning control strategy that was developed based on the storm data available at the Elk Mountain corridor to the storm data available at Laramie-Cheyenne corridor.

This book is a remarkable collection of chapters covering a wide domain of topics related to artificial intelligence and its applications to the real world. The conference attracted a total of 494 submissions from many academic pioneering researchers, scientists, industrial engineers, and students from all around the world. These submissions underwent a double-blind peer-reviewed process. Of the total submissions, 176 submissions have been selected to be included in these proceedings. It is difficult to imagine how artificial intelligence has become an inseparable part of our life. From mobile phones, smart watches, washing machines to smart homes, smart cars, and smart industries, artificial intelligence has helped to revolutionize the whole globe. As we witness exponential growth of computational intelligence in several directions and use of intelligent systems in everyday applications, this book is an ideal resource for reporting latest innovations and future of AI. Distinguished researchers have made valuable studies to understand the various bottlenecks existing in different arenas and how they can be overcome with the use of intelligent systems. This book also provides new directions and dimensions of future research work. We hope that readers find the volume interesting and valuable.

An intelligent transportation system (ITS) offers considerable opportunities for increasing the safety, efficiency, and predictability of traffic flow and reducing vehicle emissions. Sensors (or detectors) enable the effective gathering of arterial and controlled-access highway information in support of automatic incident detection, active transportation and demand management, traffic-adaptive signal control, and ramp and freeway metering and dispatching of emergency response providers. As traffic flow sensors are integrated with big data sources such as connected and cooperative vehicles, and cell phones and other Bluetooth-enabled devices, more accurate and timely traffic flow information can be obtained. The book examines the roles of traffic management centers that serve cities, counties, and other regions, and the collocation issues that ensue when multiple agencies share the same space. It describes sensor applications and data requirements for several ITS strategies; sensor technologies; sensor installation, initialization, and field-testing procedures; and alternate sources of traffic flow data. The book addresses concerns related to the introduction of automated and connected vehicles, and the benefits that systems engineering and national ITS architectures in the US, Europe, Japan, and elsewhere bring to ITS. Sensor and data fusion benefits to traffic management are described, while the Bayesian and Dempster–Shafer approaches to data fusion are discussed in more detail. ITS Sensors and Architectures for Traffic Management and Connected Vehicles suits the needs of personnel in transportation institutes and highway agencies, and students in undergraduate or graduate transportation engineering courses.