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Eco-Approach & Departure (EAD) Application in Real-World Urban Traffic

Mohammed Khairi Mohammed Khairi

Eco-Approach & Departure (EAD) Application in Real-World Urban Traffic.

Rel. Carlo Novara, Angelo Bonfitto, Michele Pagone. Politecnico di Torino, UNSPECIFIED, 2024

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A positive relationship between vehicle emissions and fuel consumption with the delays at traffic signals is shown in many empirical studies. In Urban roadways which have traffic control infrastructure (e.g., traffic lights), traffic suffers from increased delays due to idling at the traffic signals on red and increased fuel consumption and emissions due to inherent accelerations/decelerations required at the signals. To minimize delays and therefore lowering fuel consumption and emissions, however, there are a variety of strategies that are now being considered to reduce fuel consumption and carbon dioxide emissions from the transportation sector concerning the vehicle and the driver, one of which is the “eco-driving” strategy which is garnering increased interest in recent years. Eco-driving typically consists of changing a person’s driving behavior based on general (static) advice to the driver, such as driving gently, accelerating slowly, reducing high speeds and idling, etc. Taking this one-step further, it is possible to provide real time advice to drivers based on changing traffic and infrastructure conditions for even greater fuel and emission savings. Eco-driving at signalized intersections is one of the most promising solutions for fuel saving and emission reduction in urban areas. Applying this approach, drivers would effectively reduce stops and idling and avoid unnecessary acceleration and deceleration by receiving signal phase and timing (SPaT) information in advance. The Eco-Approach and Departure (EAD) application is one primary example in which drivers are guided to approach, travel through and depart from signalized intersections in an eco-friendly manner using SPaT information broadcasted by the traffic signals. With the knowledge of incoming signal information, connected vehicles can improve their fuel efficiency by following well-designed speed profiles. In an ideal connected urban area with Vehicle-to-Infrastructure (V2I) connectivity, Signal Phase and Timing (SPaT) can be broadcast to approaching vehicles so that connected vehicles adjust their speed for a timely arrival at a green light. The SPaT information can be broadcasted in many ways, directly transmitted to vehicles within range using Dedicated Short Range Communications (DSRC) technology or may become available via cellular networks provided by the traffic control center. Alternatively, inferring SPaT information is possible via on-board cameras and via crowd-sourcing. Not only using SPaT information by connected and automated vehicles improves the energy efficiency, but also, more is the penetration of these vehicles on urban roads, more is the reduction of the energy consumption of conventional vehicles as well, as conventional vehicles are more likely to smoothly follow the connected and automated vehicles, and this will therefore result in reducing the chance of stopping at intersections as well. This thesis will focus on Anticipating Signal Phase and Timing (SPaT) as the first chapter is an introduction, the second chapter provides a theoretical background of the work previously done, followed by a chapter discussing the methodology followed in this thesis. Chapter 4 is two practical applications; the first one is a simple application with a simplified model, while the second one is a real application applied on a real scenario using a high fidelity model, followed by a fifth chapter providing a brief conclusion about the framework.

Relators: Carlo Novara, Angelo Bonfitto, Michele Pagone
Academic year: 2023/24
Publication type: Electronic
Number of Pages: 72
Corso di laurea: UNSPECIFIED
Classe di laurea: New organization > Master science > LM-33 - MECHANICAL ENGINEERING
Aziende collaboratrici: Politecnico di Torino
URI: http://webthesis.biblio.polito.it/id/eprint/30453
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