Ant-inspired Interaction Networks For Decentralized Vehicular Traffic Congestion Control

Mimicking the autonomous behaviors of animals and their adaptability to changing or foreign environments lead to the development of swarm intelligence techniques such as ant colony optimization (ACO) and particle swarm optimization (PSO) now widely used to tackle a variety of optimization problems. The aim of this dissertation is to develop an alternative swarm intelligence model geared toward decentralized congestion avoidance and to determine qualities of the model suitable for use in a transportation network. A microscopic multi-agent interaction network inspired by insect foraging behaviors, especially ants, was developed and consequently adapted to prioritize the avoidance of congestion, evaluated as perceived density of other agents in the immediate environment extrapolated from the occurrence of direct interactions between agents, while foraging for food outside the base/nest. The agents eschew pheromone trails or other forms of stigmergic communication in favor of these direct interactions whose rate is the primary motivator for the agents\u27 decision making process. The decision making process at the core of the multi-agent interaction network is consequently transferred to transportation networks utilizing vehicular ad-hoc networks (VANETs) for communication between vehicles. Direct interactions are replaced by dedicated short range communications for wireless access in vehicular environments (DSRC/WAVE) messages used for a variety of applications like left turn assist, intersection collision avoidance, or cooperative adaptive cruise control. Each vehicle correlates the traffic on the wireless network with congestion in the transportation network and consequently decides whether to reroute and, if so, what alternate route to take in a decentralized, non-deterministic manner. The algorithm has been shown to increase throughput and decrease mean travel times significantly while not requiring access to centralized infrastructure or up-to-date traffic information

An Improved Simulated Annealing Technique for Enhanced Mobility in Smart Cities

Vehicular traffic congestion is a significant problem that arises in many cities. This is due to the increasing number of vehicles that are driving on city roads of limited capacity. The vehicular congestion significantly impacts travel distance, travel time, fuel consumption and air pollution. Avoidance of traffic congestion and providing drivers with optimal paths are not trivial tasks. The key contribution of this work consists of the developed approach for dynamic calculation of optimal traffic routes. Two attributes (the average travel speed of the traffic and the roads’ length) are utilized by the proposed method to find the optimal paths. The average travel speed values can be obtained from the sensors deployed in smart cities and communicated to vehicles via the Internet of Vehicles and roadside communication units. The performance of the proposed algorithm is compared to three other algorithms: the simulated annealing weighted sum, the simulated annealing technique for order preference by similarity to the ideal solution and the Dijkstra algorithm. The weighted sum and technique for order preference by similarity to the ideal solution methods are used to formulate different attributes in the simulated annealing cost function. According to the Sheffield scenario, simulation results show that the improved simulated annealing technique for order preference by similarity to the ideal solution method improves the traffic performance in the presence of congestion by an overall average of 19.22% in terms of travel time, fuel consumption and CO2 emissions as compared to other algorithms; also, similar performance patterns were achieved for the Birmingham test scenario

Bio-inspired Computing and Smart Mobility

Por último, se aborda la predicción de plazas libres de aparcamiento utilizando técnicas de aprendizaje automático, tales como series temporales, agrupamiento, etc., incluyendo un prototipo de aplicación web. La tercera parte de esta tesis doctoral se enfoca en el diseño y evaluación de un nuevo algoritmo inspirado en la epigénesis, el Algoritmo Epigenético. Luego de la descripción del modelo en el que se basa y de sus partes, se utiliza este nuevo algoritmo para la resolución del problema de la mochila multidimensional y se comparan sus resultados con los de otros algoritmos del estado de arte. Por último se emplea también el Algoritmo Epigenético para la optimización de la arquitectura Yellow Swarm, un problema de movilidad inteligente resuelto por un nuevo algoritmo bioinspirado. A lo largo de esta tesis doctoral se han descrito los problemas de movilidad inteligente y propuesto nuevas herramientas para su optimización. A partir de los experimentos realizados se concluye que estas herramientas, basadas en algoritmos bioinspirados, son eficientes para abordar estos problemas, obteniendo resultados competitivos comparados con los del estado del arte, los cuales han sido validados estadísticamente. Esto representa un aporte científico pero también una serie de mejoras para la sociedad toda, tanto en su salud como en el aprovechamiento de su tiempo libre. Fecha de lectura de Tesis: 01 octubre 2018.Esta tesis doctoral propone soluciones a problemas de movilidad inteligente, concretamente la reducción de los tiempos de viajes en las vías urbanas, las emisiones de gases de efecto invernadero y el consumo de combustible, mediante el diseño y uso de nuevos algoritmos bioinspirados. Estos algoritmos se utilizan para la optimización de escenarios realistas, cuyo trazado urbano se obtiene desde OpenStreetMap, y que son luego evaluados en el microsimulador SUMO. Primero se describen las bases científicas y tecnológicas, incluyendo la definición y estado del arte de los problemas a abordar, las metaheurísticas que se utilizarán durante el desarrollo de los experimentos, así como las correspondientes validaciones estadísticas. A continuación se describen los simuladores de movilidad como principal herramienta para construir y evaluar los escenarios urbanos. Por último se presenta una propuesta para generar tráfico vehicular realista a partir de datos de sensores que cuentan el número de vehículos en la ciudad, utilizando herramientas incluidas en SUMO combinadas con algoritmos evolutivos. En la segunda parte se modelan y resuelven problemas de movilidad inteligente utilizando las nuevas arquitecturas Red Swarm y Green Swarm para sugerir nuevas rutas a los vehículos utilizando nodos con conectividad Wi-Fi. Red Swarm se centra en la reducción de tiempos de viajes evitando la congestión de las calles, mientras que Green Swarm está enfocado en la reducción de emisiones y consumo de combustible. Luego se propone la arquitectura Yellow Swarm que utiliza una serie de paneles LED para indicar desvíos que los vehículos pueden seguir en lugar de nodos Wi-Fi haciendo esta propuesta más accesible. Además se propone un método para genera rutas alternativas para los navegadores GPS de modo que se aprovechen mejor las calles secundarias de las ciudades, reduciendo los atascos

Design and Performance Analysis of Urban Traffic Control Systems

This study aims to investigate the design and performance of different architectures for urban traffic control with consideration of variations and uncertainties in traffic flow. The architectures, which ranging from centralised, semi-centralised to decentralised, are applied to different road networks. Both macroscopic and microscopic flow models are developed and used to calculate the performance of the systems. The macroscopic model is capable of generating essential traffic dynamics, such as traffic queues’ spillover, formation and dissipation. The control systems’ are tested under varies traffic demand levels. The results suggest that the centralised systems generally can outperform the decentralised systems, and the most benefit gained in the centralised control comes from its setting of signal offsets. On the other hand, the microscopic flow model captures the movement of each individual vehicle and drivers' rerouting behaviour with respect to traffic conditions. The test results showed that the drivers' response to the traffic condition can help a decentralised system perform as well as a centralised system. This study brings a new insight into cooperative transport management, and contributes to the state-of-the-art of urban traffic system design

VANET-enabled eco-friendly road characteristics-aware routing for vehicular traffic

There is growing awareness of the dangers of climate change caused by greenhouse gases. In the coming decades this could result in numerous disasters such as heat-waves, flooding and crop failures. A major contributor to the total amount of greenhouse gas emissions is the transport sector, particularly private vehicles. Traffic congestion involving private vehicles also causes a lot of wasted time and stress to commuters. At the same time new wireless technologies such as Vehicular Ad-Hoc Networks (VANETs) are being developed which could allow vehicles to communicate with each other. These could enable a number of innovative schemes to reduce traffic congestion and greenhouse gas emissions. 1) EcoTrec is a VANET-based system which allows vehicles to exchange messages regarding traffic congestion and road conditions, such as roughness and gradient. Each vehicle uses the messages it has received to build a model of nearby roads and the traffic on them. The EcoTrec Algorithm then recommends the most fuel efficient route for the vehicles to follow. 2) Time-Ants is a swarm based algorithm that considers not only the amount of cars in the spatial domain but also the amoumt in the time domain. This allows the system to build a model of the traffic congestion throughout the day. As traffic patterns are broadly similar for weekdays this gives us a good idea of what traffic will be like allowing us to route the vehicles more efficiently using the Time-Ants Algorithm. 3) Electric Vehicle enhanced Dedicated Bus Lanes (E-DBL) proposes allowing electric vehicles onto the bus lanes. Such an approach could allow a reduction in traffic congestion on the regular lanes without greatly impeding the buses. It would also encourage uptake of electric vehicles. 4) A comprehensive survey of issues associated with communication centred traffic management systems was carried out

Study of Group Route Optimization for IoT enabled Urban Transportation Network

Traffic congestion is always a major issue in urban planning, especially when the vehicles in the roadway keep growing and the local authorities are lack of solutions to manage or distribute the traffics in the city. Although there are several factors that may cause traffic congestion, inefficiency in traffic management is always the main issue. Additionally, the most traditional methods of resolving traffic congestion or rerouting algorithm are mainly designed for individuals’ benefits, by simply planning a driver’s route based on minimum travel time or shortest path accordingly. There is lack of consideration in group benefit or urban development. However, with the development of technologies in Internet of Things (IoT), vehicle to vehicle (V2V) or Vehicle to Infrastructure (V2I) communications, group based routing becomes achievable. Instead of optimizing the routing path for individual drivers, this paper studies how to develop a new method to provide new routing method based on vehicles’ similarities in a specific urban’s transportation environmen

DEVELOPMENT AND EVALUATION OF AN INTELLIGENT TRANSPORTATION SYSTEMS-BASED ARCHITECTURE FOR ELECTRIC VEHICLES

The rapid development of increasingly complex in-vehicle electronics now offers an unprecedented level of convenience and versatility as well as accelerates the demand for connected driving experience, which can only be achieved in a comprehensive Intelligent Transportation Systems (ITS) technology based architecture. While a number of charging and range related issues continue to impede the Electric Vehicle (EV) market growth, integrating ITS technologies with EVs has the potential to address the problems and facilitate EV operations. This dissertation presents an ITS based vehicle infrastructure communication architecture in which abundant information can be exchanged in real time through vehicle-to-vehicle and vehicle-to- infrastructure communication, so that a variety of in-vehicle applications can be built to enhance the performance of EVs. This dissertation emphasizes on developing two applications that are specifically designed for EVs. First, an Ant Colony Optimization (ACO) based routing and recharging strategy dedicated to accommodate EV trips was devised. The algorithm developed in this study seeks, in real time, the lowest cost route possible without violating the energy constraint and can quickly provide an alternate suboptimal route in the event of unexpected situations (such as traffic congestion, traffic incident and road closure). If the EV battery requires a recharge, the algorithm can be utilized to develop a charging schedule based on recharging locations, recharging cost and wait time, and to simultaneously maintain the minimum total travel time and energy consumption objectives. The author also elucidates a charge scheduling model that maximizes the net profit for each vehicle-to-grid (V2G) enabled EV owner who participates in the grid ancillary services while the energy demands for their trips can be guaranteed as well. By applying ITS technologies, the charge scheduling model can rapidly adapt to changes of variables or coefficients within the model for the purpose of developing the latest optimal charge/discharge schedule. The performance of EVs involved in the architecture was validated by a series of simulations. A roadway network in Charleston, SC was created in the simulator and a comparison between ordinary EVs and connected EVs was performed with a series of simulation experiments. Analysis revealed that the vehicle-to-vehicle and vehicle-to- infrastructure communication technology resulted in not only a reduction of the total travel time and energy consumption, but also in the reduction of the amount of the recharged electricity and corresponding cost, thus significantly relieving the concerns of range anxiety. The routing and recharging strategy also potentially allows for a reduction in the EV battery capacity, in turn reducing the cost of the energy storage system to a reasonable level. The efficiency of the charge scheduling model was validated by estimating optimal annual financial benefits and leveling the additional load from EV charging to maintain a reliable and robust power grid system. The analysis showed that the scheduling model can indeed optimize the profit which substantially offsets the annual energy cost for EV owners and that EV participants can even make a positive net profit with a higher power of the electrical circuit. In addition, the extra load distribution from the optimized EV charging operations was more balanced than that from the unmanaged EV operations. Grid operators can monitor and ease the load in real time by adjusting the prices should the load exceed the capacity. The ITS supported architecture presented in this dissertation can be used in the evolution of a new generation of EVs with new features and benefits for prospective owners. This study suggests a great promise for the integration of EVs with ITS technologies for purpose of promoting sustainable transportation system development

Congestion Propagation Based Bottleneck Identification in Urban Road Networks

© 1967-2012 IEEE. Due to the rapid development of vehicular transportation and urbanization, traffic congestion has been increasing and becomes a serious problem in almost all major cities worldwide. Many instances of traffic congestion can be traced to their root causes, the so-called traffic bottlenecks, where relief of traffic congestion at bottlenecks can bring network-wide improvement. Therefore, it is important to identify the locations of bottlenecks and very often the most effective way to improve traffic flow and relieve traffic congestion is to improve traffic situations at bottlenecks. In this article, we first propose a novel definition of traffic bottleneck taking into account both the congestion level cost of a road segment itself and the contagion cost that the congestion may propagate to other road segments. Then, an algorithm is presented to identify congested road segments and construct congestion propagation graphs to model congestion propagation in urban road networks. Using the graphs, maximal spanning trees are constructed that allow an easy identification of the causal relationship between congestion at different road segments. Moreover, using Markov analysis to determine the probabilities of congestion propagation from one road segment to another road segment, we can calculate the aforementioned congestion cost and identify bottlenecks in the road network. Finally, simulation studies using SUMO confirm that traffic relief at the bottlenecks identified using the proposed technique can bring more effective network-wide improvement. Furthermore, when considering the impact of congestion propagation, the most congested road segments are not necessarily bottlenecks in the road network. The proposed approach can better capture the features of urban bottlenecks and lead to a more effective way to identify bottlenecks for traffic improvement. Experiments are further conducted using data collected from inductive loop detectors in Taipei road network and some road segments are identified as bottlenecks using the proposed method

Data-driven flight path rerouting during adverse weather: Design and development of a passenger-centric model and framework for alternative flight path generation using nature inspired techniques

A major factor that negatively impacts flight operations globally is adverse weather. To reduce the impact of adverse weather, avoidance procedures such as finding an alternative flight path can usually be carried out. However, such procedures usually introduce extra costs such as flight delay. Hence, there exists a need for alternative flight paths that efficiently avoid adverse weather regions while minimising costs. Existing weather avoidance methods used techniques, such as Dijkstra’s and artificial potential field algorithms that do not scale adequately and have poor real time performance. They do not adequately consider the impact of weather and its avoidance on passengers. The contributions of this work include a new development of an improved integrated model for weather avoidance, that addressed the impact of weather on passengers by defining a corresponding cost metric. The model simultaneously considered other costs such as flight delay and fuel burn costs. A genetic algorithm (GA)-based rerouting technique that generates optimised alternative flight paths was proposed. The technique used a modified mutation strategy to improve global search. A discrete firefly algorithm-based rerouting method was also developed to improve rerouting efficiency. A data framework and simulation platform that integrated aeronautical, weather and flight data into the avoidance process was developed. Results show that the developed algorithms and model produced flight paths that had lower total costs compared with existing techniques. The proposed algorithms had adequate rerouting performance in complex airspace scenarios. The developed system also adequately avoided the paths of multiple aircraft in the considered airspace