Traffic light control design approaches: a systematic literature review

To assess different approaches to traffic light control design, a systematic literature review was conducted, covering publications from 2006 to 2020. The review’s aim was to gather and examine all studies that looked at road traffic and congestion issues. As well, it aims to extract and analyze protruding techniques from selected research articles in order to provide researchers and practitioners with recommendations and solutions. The research approach has placed a strong emphasis on planning, performing the analysis, and reporting the results. According to the results of the study, there has yet to be developed a specific design that senses road traffic and provides intelligent solutions. Dynamic time intervals, learning capability, emergency priority management, and intelligent functionality are all missing from the conventional design approach. While learning skills in the adaptive self-organization strategy were missed. Nonetheless, the vast majority of intelligent design approach papers lacked intelligent fear tires and learning abilities

Evolutionary Computation Applied to Urban Traffic Optimization

At the present time, many sings seem to indicate that we live a global energy and environmental crisis. The scientific community argues that the global warming process is, at least in some degree, a consequence of modern societies unsustainable development. A key area in that situation is the citizens mobility. World economies seem to require fast and efficient transportation infrastructures for a significant fraction of the population. The non-stopping overload process that traffic networks are suffering calls for new solutions. In the vast majority of cases it is not viable to extend that infrastructures due to costs, lack of available space, and environmental impacts. Thus, traffic departments all around the world are very interested in optimizing the existing infrastructures to obtain the very best service they can provide. In the last decade many initiatives have been developed to give the traffic network new management facilities for its better exploitation. They are grouped in the so called Intelligent Transportation Systems. Examples of these approaches are the Advanced Traveler Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS). Most of them provide drivers or traffic engineers the current traffic real/simulated situation or traffic forecasts. They may even suggest actions to improve the traffic flow. To do so, researchers have done a lot of work improving traffic simulations, specially through the development of accurate microscopic simulators. In the last decades the application of that family of simulators was restricted to small test cases due to its high computing requirements. Currently, the availability of cheap faster computers has changed this situation. Some famous microsimulators are MITSIM(Yang, Q., 1997), INTEGRATION (Rakha, H., et al., 1998), AIMSUN2 (Barcelo, J., et al., 1996), TRANSIMS (Nagel, K. & Barrett, C., 1997), etc. They will be briefly explained in the following section. Although traffic research is mainly targeted at obtaining accurate simulations there are few groups focused at the optimization or improvement of traffic in an automatic manner â not dependent on traffic engineers experience and âartâ. O pe n A cc es s D at ab as e w w w .ite ch on lin e. co

From cellular attractor selection to adaptive signal control for traffic networks

The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains

A Comprehensive Approach to WSN-Based ITS Applications: A Survey

In order to perform sensing tasks, most current Intelligent Transportation Systems (ITS) rely on expensive sensors, which offer only limited functionality. A more recent trend consists of using Wireless Sensor Networks (WSN) for such purpose, which reduces the required investment and enables the development of new collaborative and intelligent applications that further contribute to improve both driving safety and traffic efficiency. This paper surveys the application of WSNs to such ITS scenarios, tackling the main issues that may arise when developing these systems. The paper is divided into sections which address different matters including vehicle detection and classification as well as the selection of appropriate communication protocols, network architecture, topology and some important design parameters. In addition, in line with the multiplicity of different technologies that take part in ITS, it does not consider WSNs just as stand-alone systems, but also as key components of heterogeneous systems cooperating along with other technologies employed in vehicular scenarios

Dual-battery empowered green cellular networks

With awareness of the potential harmful effects to the environment and climate change, on-grid brown energy consumption of information and communications technology (ICT) has drawn much attention. Cellular base stations (BSs) are among the major energy guzzlers in ICT, and their contributions to the global carbon emissions increase sustainedly. It is essential to leverage green energy to power BSs to reduce their on-grid brown energy consumption. However, in order to furthest save on-grid brown energy and decrease the on-grid brown energy electricity expenses, most existing green energy related works only pursue to maximize the green energy utilization while compromising the services received by the mobile users. In reality, dissatisfaction of services may eventually lead to loss of market shares and profits of the network providers. In this research, a dual-battery enabled profit driven user association scheme is introduced to jointly consider the traffic delivery latency and green energy utilization to maximize the profits for the network providers in heterogeneous cellular networks. Since this profit driven user association optimization problem is NP-hard, some heuristics are presented to solve the problem with low computational complexity. Finally, the performance of the proposed algorithm is validated through extensive simulations. In addition, the Internet of Things (IoT) heralds a vision of future Internet where all physical things/devices are connected via a network to promote a heightened level of awareness about our world and dramatically improve our daily lives. Nonetheless, most wireless technologies utilizing unlicensed bands cannot provision ubiquitous and quality IoT services. In contrast, cellular networks support large-scale, quality of service guaranteed, and secured communications. However, tremendous proximal communications via local BSs will lead to severe traffic congestion and huge energy consumption in conventional cellular networks. Device-to-device (D2D) communications can potentially offload traffic from and reduce energy consumption of BSs. In order to realize the vision of a truly global IoT, a novel architecture, i.e., overlay-based green relay assisted D2D communications with dual batteries in heterogeneous cellular networks, is introduced. By optimally allocating the network resource, the introduced resource allocation method provisions the IoT services and minimizes the overall energy consumption of the pico relay BSs. By balancing the residual green energy among the pico relay BSs, the green energy utilization is maximized; this furthest saves the on-grid energy. Finally, the performance of the proposed architecture is validated through extensive simulations. Furthermore, the mobile devices serve the important roles in cellular networks and IoT. With the ongoing worldwide development of IoT, an unprecedented number of edge devices imperatively consume a substantial amount of energy. The overall IoT mobile edge devices have been predicted to be the leading energy guzzler in ICT by 2020. Therefore, a three-step green IoT architecture is proposed, i.e., ambient energy harvesting, green energy wireless transfer and green energy balancing, in this research. The latter step reinforces the former one to ensure the availability of green energy. The basic design principles for these three steps are laid out and discussed. In summary, based on the dual-battery architecture, this dissertation research proposes solutions for the three aspects, i.e., green cellular BSs, green D2D communications and green devices, to hopefully and eventually actualize green cellular access networks, as part of the ongoing efforts in greening our society and environment

Comparison of Radio Frequency and Visible Light Propagation Channels for Vehicular Communications

Recent research has shown that both radio and visible light waves can be used to enable communications in highly dynamic vehicular environments. However, the roles of these two technologies and how they interact with each other in future vehicular communication systems remain unclear. Understanding the propagation characteristics is an essential step in investigating the benefits and shortcomings of each technology. To this end, we discuss salient properties of radio and visible light propagation channels, including radiation pattern, path loss modeling, noise and interference, and channel time variation. Comparison of these properties provides an important insight that the two communication channels can complement each other’s capabilities in terms of coverage and reliability, thus better satisfying the diverse requirements of future cooperative intelligent transportation systems