Multi-objective Optimization Framework for Trade-Off Among Pedestrian Delays and Vehicular Emissions at Signal-Controlled Intersections

Traffic congestion has several adverse effects on urban traffic networks. Increased travel times of vehicles, with the addition of excessive greenhouse emissions, can be listed as harmful effects. To address these issues, transportation engineers aim to reduce private car usage, reduce travel times through different control strategies, and mitigate harmful effects on urban networks. In this study, we introduce an innovative approach to optimizing traffic signal control settings. This methodology takes into account both pedestrian delays and vehicular emissions. Non-dominated sorting genetic algorithm-II and Multiobjective Artificial Bee Colony algorithms are adopted to solve the multi-objective optimization problem. The vehicular emissions are modeled through the MOVES3 emission model and integrated into the utilized microsimulation environment. Initially, the proposed framework is tested on a hypothetical test network, followed by a real-world case study. Results indicate a significant improvement in pedestrian delays and lower emissions

A bi-objective turning restriction design problem in urban road networks

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A Traffic-Aware Approach for Enabling Unmanned Aerial Vehicles (UAVs) in Smart City Scenarios

In smart cities, vehicular applications require high computation capabilities and low-latency communication. Edge computing offers promising solutions for addressing these requirements because of several features, such as geo-distribution, mobility, low latency, heterogeneity, and support for real-time interactions. To employ network edges, existing fixed roadside units can be equipped with edge computing servers. Nevertheless, there are situations where additional infrastructure units are required to handle temporary high traffic loads during public events, unexpected weather conditions, or extreme traffic congestion. In such cases, the use of flying roadside units are carried by unmanned aerial vehicles (UAVs), which provide the required infrastructure for supporting traffic applications and improving the quality of service. UAVs can be dynamically deployed to act as mobile edges in accordance with traffic events and congestion conditions. The key benefits of this dynamic approach include: 1) the potential for characterizing the environmental requirements online and performing the deployment accordingly, and 2) the ability to move to another location when necessary. We propose a traffic-aware method for enabling the deployment of UAVs in vehicular environments. Simulation results show that our proposed method can achieve full network coverage under different scenarios without extra communication overhead or delay

Control y simulación de tráfico urbano en Colombia: Estado del arte

Las condiciones actuales de la movilidad en Colombia generan interrogantes acerca de qué tan apropiadas son las estrategias de control de tráfico aplicadas en las redes urbanas del país. Con esto en mente, se plantea una revisión de las estrategias de control y plataformas de simulación de sistemas de tráfico más utilizadas en Colombia y en otras partes del mundo; con el propósito de caracterizar el nivel de desarrollo del país en el estudio e implementación de estrategias de control de tráfico urbano y, posteriormente, formular propuestas orientadas hacia la mejora de la movilidad urbana en el país./ The current mobility conditions in Colombia give place to questions about the suitability of the traffic control strategies applied on the Colombian urban networks. Therefore, a review of the control strategies and simulation platforms used in Colombia and around the world is shown. This is done to characterize the level of development of the country, in terms of research and implementation of such control strategies and, furthermore, to formulate proposals oriented towards the improvement of the Colombian urban mobility

Cost Optimization Approach for MANET using Particle Swarm Optimization

This paper present the approach require to increase the QoS of MANET network using particle swarm optimization algorithm. To improve data communication between two nodes we propose an efficient algorithm for AODV protocol using PSO where instead of suppling all default parameter with default value of AODV protocol we try to provide selective parameters with optimum value so that overall requirement of control packet get decrease that in turn result in to increase quality of service parameters of MANET. For the enhancement of reliability and reduction of cost, node speed control mechanism is implemented using PSO, The given method which is use for simulation, reduces the overall loss of data and also make transmission effective. We have also tested the performance of network by changing data rates and the speed of the node

Intelligent Advancements in Location Management and C-RAN Power-Aware Resource Allocation

The evolving of cellular networks within the last decade continues to focus on delivering a robust and reliable means to cope with the increasing number of users and demanded capacity. Recent advancements of cellular networks such as Long-Term Evolution (LTE) and LTE-advanced offer a remarkable high bandwidth connectivity delivered to the users. Signalling overhead is one of the vital issues that impact the cellular behavior. Causing a significant load in the core network hence effecting the cellular network reliability. Moreover, the signaling overhead decreases the Quality of Experience (QoE) of users. The first topic of the thesis attempts to reduce the signaling overhead by developing intelligent location management techniques that minimize paging and Tracking Area Update (TAU) signals. Consequently, the corresponding optimization problems are formulated. Furthermore, several techniques and heuristic algorithms are implemented to solve the formulated problems. Additionally, network scalability has become a challenging aspect that has been hindered by the current network architecture. As a result, Cloud Radio Access Networks (C-RANs) have been introduced as a new trend in wireless technologies to address this challenge. C-RAN architecture consists of: Remote Radio Head (RRH), Baseband Unit (BBU), and the optical network connecting them. However, RRH-to-BBU resource allocation can cause a significant downgrade in efficiency, particularly the allocation of the computational resources in the BBU pool to densely deployed small cells. This causes a vast increase in the power consumption and wasteful resources. Therefore, the second topic of the thesis discusses C-RAN infrastructure, particularly where a pool of BBUs are gathered to process the computational resources. We argue that there is a need of optimizing the processing capacity in order to minimize the power consumption and increase the overall system efficiency. Consequently, the optimal allocation of computational resources between the RRHs and BBUs is modeled. Furthermore, in order to get an optimal RRH-to-BBU allocation, it is essential to have an optimal physical resource allocation for users to determine the required computational resources. For this purpose, an optimization problem that models the assignment of resources at these two levels (from physical resources to users and from RRHs to BBUs) is formulated

Automatic Threshold Selections by exploration and exploitation of optimization algorithm in Record Deduplication

A deduplication process uses similarity function to identify the two entries are duplicate or not by setting the threshold.  This threshold setting is an important issue to achieve more accuracy and it relies more on human intervention. Swarm Intelligence algorithm such as PSO and ABC have been used for automatic detection of threshold to find the duplicate records. Though the algorithms performed well there is still an insufficiency regarding the solution search equation, which is used to generate new candidate solutions based on the information of previous solutions.  The proposed work addressed two problems: first to find the optimal equation using Genetic Algorithm(GA) and next it adopts an modified  Artificial Bee Colony (ABC) to get the optimal threshold to detect the duplicate records more accurately and also it reduces human intervention. CORA dataset is considered to analyze the proposed algorithm