1,553 research outputs found

    Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data

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    The file attached to this record is the author's final peer reviewed version.Current traffic management systems in urban networks require real-time estimation of the traffic states. With the development of in-vehicle and communication technologies, connected vehicle data has emerged as a new data source for traffic measurement and estimation. In this work, a machine learning-based methodology for signal phase and timing information (SPaT) which is highly valuable for many applications such as green light optimal advisory systems and real-time vehicle navigation is proposed. The proposed methodology utilizes data from connected vehicles travelling within urban signalized links to estimate the queue tail location, vehicle accumulation, and subsequently, link outflow. Based on the produced high-resolution outflow estimates and data from crossing connected vehicles, SPaT information is estimated via correlation analysis and a machine learning approach. The main contribution is that the single-source proposed approach relies merely on connected vehicle data and requires neither prior information such as intersection cycle time nor data from other sources such as conventional traffic measuring tools. A sample four-leg intersection where each link comprises different number of lanes and experiences different traffic condition is considered as a testbed. The validation of the developed approach has been undertaken by comparing the produced estimates with realistic micro-simulation results as ground truth, and the achieved simulation results are promising even at low penetration rates of connected vehicles

    Connected and Automated Vehicles in Urban Transportation Cyber-Physical Systems

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    Understanding the components of Transportation Cyber-Physical Systems (TCPS), and inter-relation and interactions among these components are key factors to leverage the full potentials of Connected and Automated Vehicles (CAVs). In a connected environment, CAVs can communicate with other components of TCPS, which include other CAVs, other connected road users, and digital infrastructure. Deploying supporting infrastructure for TCPS, and developing and testing CAV-specific applications in a TCPS environment are mandatory to achieve the CAV potentials. This dissertation specifically focuses on the study of current TCPS infrastructure (Part 1), and the development and verification of CAV applications for an urban TCPS environment (Part 2). Among the TCPS components, digital infrastructure bears sheer importance as without connected infrastructure, the Vehicle-to-Infrastructure (V2I) applications cannot be implemented. While focusing on the V2I applications in Part 1, this dissertation evaluates the current digital roadway infrastructure status. The dissertation presents a set of recommendations, based on a review of current practices and future needs. In Part 2, To synergize the digital infrastructure deployment with CAV deployments, two V2I applications are developed for CAVs for an urban TCPS environment. At first, a real-time adaptive traffic signal control algorithm is developed, which utilizes CAV data to compute the signal timing parameters for an urban arterial in the near-congested traffic condition. The analysis reveals that the CAV-based adaptive signal control provides operational benefits to both CVs and non-CVs with limited data from 5% CVs, with 5.6% average speed increase, and 66.7% and 32.4% average maximum queue length and stopped delay reduction, respectively, on a corridor compared to the actuated coordinated scenario. The second application includes the development of a situation-aware left-turning CAV controller module, which optimizes CAV speed based on the follower driver\u27s aggressiveness. Existing autonomous vehicle controllers do not consider the surrounding driver\u27s behavior, which may lead to road rage, and rear-end crashes. The analysis shows that the average travel time reduction for the scenarios with 600, 800 and 1000 veh/hr/lane opposite traffic stream are 61%, 23%, and 41%, respectively, for the follower vehicles, if the follower driver\u27s behavior is considered by CAVs

    Design and modeling of a stair climber smart mobile robot (MSRox)

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    Reduced Fuel Emissions through Connected Vehicles and Truck Platooning

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    Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication enable the sharing, in real time, of vehicular locations and speeds with other vehicles, traffic signals, and traffic control centers. This shared information can help traffic to better traverse intersections, road segments, and congested neighborhoods, thereby reducing travel times, increasing driver safety, generating data for traffic planning, and reducing vehicular pollution. This study, which focuses on vehicular pollution, used an analysis of data from NREL, BTS, and the EPA to determine that the widespread use of V2V-based truck platooning—the convoying of trucks in close proximity to one another so as to reduce air drag across the convoy—could eliminate 37.9 million metric tons of CO2 emissions between 2022 and 2026

    Dynamic Vehicular Routing in Urban Environments

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    Traffic congestion is a persistent issue that most of the people living in a city have to face every day. Traffic density is constantly increasing and, in many metropolitan areas, the road network has reached its limits and cannot easily be extended to meet the growing traffic demand. Intelligent Transportation System (ITS) is a world wide trend in traffic monitoring that uses technology and infrastructure improvements in advanced communication and sensors to tackle transportation issues such as mobility efficiency, safety, and traffic congestion. The purpose of ITS is to take advantage of all available technologies to improve every aspect of mobility and traffic. Our focus in this thesis is to use these advancements in technology and infrastructure to mitigate traffic congestion. We discuss the state of the art in traffic flow optimization methods, their limitations, and the benefits of a new point of view. The traffic monitoring mechanism that we propose uses vehicular telecommunication to gather the traffic information that is fundamental to the creation of a consistent overview of the traffic situation, to provision real-time information to drivers, and to optimizing their routes. In order to study the impact of dynamic rerouting on the traffic congestion experienced in the urban environment, we need a reliable representation of the traffic situation. In this thesis, traffic flow theory, together with mobility models and propagation models, are the basis to providing a simulation environment capable of providing a realistic and interactive urban mobility, which is used to test and validate our solution for mitigating traffic congestion. The topology of the urban environment plays a fundamental role in traffic optimization, not only in terms of mobility patterns, but also in the connectivity and infrastructure available. Given the complexity of the problem, we start by defining the main parameters we want to optimize, and the user interaction required, in order to achieve the goal. We aim to optimize the travel time from origin to destination with a selfish approach, focusing on each driver. We then evaluated constraints and added values of the proposed optimization, providing a preliminary study on its impact on a simple scenario. Our evaluation is made in a best-case scenario using complete information, then in a more realistic scenario with partial information on the global traffic situation, where connectivity and coverage play a major role. The lack of a general-purpose, freely-available, realistic and dependable scenario for Vehicular Ad Hoc Networks (VANETs) creates many problems in the research community in providing and comparing realistic results. To address these issues, we implemented a synthetic traffic scenario, based on a real city, to evaluate dynamic routing in a realistic urban environment. The Luxembourg SUMO Traffic (LuST) Scenario is based on the mobility derived from the City of Luxembourg. The scenario is built for the Simulator of Urban MObiltiy (SUMO) and it is compatible with Vehicles in Network Simulation (VEINS) and Objective Modular Network Testbed in C++ (OMNet++), allowing it to be used in VANET simulations. In this thesis we present a selfish traffic optimization approach based on dynamic rerouting, able to mitigate the impact of traffic congestion in urban environments on a global scale. The general-purpose traffic scenario built to validate our results is already being used by the research community, and is freely-available under the MIT licence, and is hosted on GitHub

    Assessment of a signalized cross intersection optimized by GPS data through V2I connection

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    [ES] El objetivo principal de este trabajo es analizar la influencia de los errores de datos de posicionamiento de vehículos conectados en la transmisión de la información a los controladores de una intersección en cruz semaforizada. Teniendo en cuenta que la llegada del 5G permite la transmisión de datos masivos de una manera eficiente, esta investigación se centra en la comparación de la precisión de los datos de geolocalización de dispositivos que podrían hallarse en vehículos conectados, como son: (i) dispositivos GPS de alta precisión (errores de posicionamiento despreciables); (ii) dispositivos GPS convencionales (errores de posicionamiento mayores que un metro); y (iii) dispositivos GPS incorporados en teléfonos móviles (errores de posicionamiento total mayores). Para ello, se han llevado a cabo tres simulaciones de una hora en el software de microsimulación VISSIM para los siguientes escenarios de tráfico propuestos: (1) bajo nivel de demanda; (2) aproximación a la congestión de la intersección; y (3) intersección sobresaturada. Para evaluar y comparar la influencia de la precisión de los dispositivos GPS bajo los diferentes escenarios se han empleado dos controladores, que son la longitud de cola de vehículos en la intersección y el número de vehículos que acceden a la intersección por cada ramal.[EN] The main objective of this work is to analyze the influence of the positioning data errors of connected vehicles in the transmission of information to the controllers of a signalized cross intersection. Taking into account that the arrival of 5G allows the transmission of massive data in an efficient way, this research focuses on comparing the accuracy of the geolocation data of devices that could be found in connected vehicles such as (i) High Accurate GPS devices (negligible positioning errors); (ii) Standard GPS devices (positioning errors greater than one meter); and GPS incorporated in mobile phones (major total positioning errors). For this, three one-hour simulations have been carried out in the VISSIM microsimulation software for the following proposed traffic scenarios: (1) low demand level; (2) approach to congestion of the intersection; and (3) over-saturated intersection. To evaluate and compare the influence of the accuracy of GPS devices in different scenarios, two controllers have been used, which are the queue length of vehicles at the intersection and the number of vehicles that access the intersection at each approach.Pino Verona, HD. (2020). Análisis de una intersección en cruz semaforizada optimizada a partir de datos de GPS mediante conexión V2I. http://hdl.handle.net/10251/142944TFG
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