Integrated macroscopic traffic flow, emission, and fuel consumption model for control purposes

Zegeye, S. K.; Schutter, Bart De; Hellendoorn, J.; Breunesse, E. A.; Hegyi, Andreas

doi:10.1016/j.trc.2013.01.002

Cited by 140 publications

(55 citation statements)

References 21 publications

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“…The energy consumption functional here is obtained as an adaptation to the macroscopic case of the one in [17], assuming that the vehicles in the traffic network are all equipped with an electric motor (analogous metrics like the VT-macro [11] could be used in the case of vehicles with combustion engines). It is recalled that for a single vehicle the model is written as:…”

Section: Energymentioning

confidence: 99%

“…Within the objective of combining simplicity of the macroscopic traffic models with the accuracy of microscopic energy consumption models, one of the main problems is how to reconstruct acceleration profiles from macroscopic variables. In [11] [12], the authors approximate accelerations from the velocities returned by the second order model (METANET), by considering the speed difference between successive time steps, and plug them into the fuel consumption model VT-micro.…”

Section: Introductionmentioning

confidence: 99%

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Urban traffic Eco-Driving: A macroscopic steady-state analysis

Nunzio

Canudas‐de‐Wit²,

Moulin

2014

2014 European Control Conference (ECC)

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Abstract-The problem of traveling at maximum energy efficiency (Eco-Driving) is addressed for urban traffic networks at macroscopic level. The scope of this paper is the analysis of the steady-state behavior of the system, given certain boundary flows conditions fixed by traffic lights timings, and in presence of a traffic control policy based on variable speed limits. The formal study is carried out on a two-cells variable length model adapted to the urban setup from previous works on highway traffic [1] [2]. Informative traffic metrics, aimed at assessing traffic and vehicles performance in terms of traveling time, infrastructure utilization and energy consumption, are then defined and adapted to the new macroscopic traffic model. If congestion in a road section does not spill back or vanish, the system is stable and many different equilibrium points can be reached via variable speed limits. Efficient operation points and traffic conditions are identified as a trade-off between optimization of global traffic energy consumption, traveling time and infrastructure utilization.

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Section: Energymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Urban traffic Eco-Driving: A macroscopic steady-state analysis

Nunzio

Canudas‐de‐Wit²,

Moulin

2014

2014 European Control Conference (ECC)

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“…Particularly, traffic systems are very complex, nonlinear, and are affected by many external and internal factors: vehicle dynamics with respect to the infrastructure and driver's behaviour increase their complexity and stochasticity, thus making it difficult to capture all the traffic phenomena with a single model (Zegeye et al 2013). There are three main types of traffic models: macroscopic, mesoscopic and microscopic models.…”

Section: Road Traffic Emissions Assessment Modelsmentioning

confidence: 99%

“…However, cycle variable and modal models can only be fed by microscopic transport models (Smit et al 2010a) Several examples of the joint application of traffic and emission models can be found in the literature e.g. : Zegeye et al (2013) provide a general framework to integrate macroscopic transport models, specifically METANET1 with microscopic emissions models (VT-Micro), and present an empirical analysis of the error and the computation time based on calibrated models of the Dutch A12 freeway. Osorio & Nanduri (2015) combine a microsimulation traffic model with an emission model to evaluate traffic emissions and air quality in a dense neighborhood.…”

Section: Road Traffic Emissions Assessment Modelsmentioning

confidence: 99%

“…E4 in Stockholm (Nissan & Koutsopoulosb 2011) Very few examples have been reported on the impact of VSL on emissions. A case study in The Netherlands based on model predictive control, showed over 35% total emission reduction (Zegeye et al 2013). An empirical estimation in Barcelona using the difference-in-differences method comparing the use of variable speed limits and a reduced fixed speed limit showed that reducing the speed limit to 80 km/h caused a 1.7-3.2% increase in NO X (Bel & Rosell 2013).…”

Section: Speed Management Strategiesmentioning

confidence: 99%

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Traffic solutions for greener and cleaner cities

Prada¹

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Acuerda otorgarle la calificación de: ___________________________________________________ Madrid, ___ de _________________de 2017. EL PRESIENTE LOS VOCALES EL SECRETARIOThis page has been intentionally left blank AGRADECIMIENTOSSon muchas las personas que con su cariño, apoyo y confianza, han hecho posible la realización de esta tesis doctoral.En primer lugar me gustaría agradecer a mi director, Andrés Monzón, el haber confiado en mí, permitirme formar parte de la familia de TRANSyT y compartir conmigo su pasión por este trabajo. Gracias por tu ayuda y cariño durante estos años y por tu comprensión y apoyo en momentos difíciles.En segundo lugar quiero agradecer a Cristina Valdés su dedicación durante estos años, sus sabios consejos y su manera de hacer han sido de gran ayuda a lo largo de la tesis así como su cariño y apoyo.También quiero agradecer a tod@s los compañer@s de TRANSyT su vitalidad y energía que hacen que venir a trabajar cada día sea llevadero incluso en los momentos más complicados. Andrea, Sara y Natalie gracias por estar ahí siempre y para todo; Guille, Andrés y Vicky por esas numerosas charlas queriendo cambiar el mundo y en las que siempre surge alguna idea nueva y motivadora; Carmen, Rocío, Maria Eugenia y Belén por esos maravillosos consejos; Yang mil gracias por tus aportaciones y comentarios a la tesis, es un placer trabajar contigo; Patri mil gracias por el apoyo tecnológico, a mis compis de proyectos (Ascimeros, ICT y Eco-traffic) y de despachos varios porque he aprendido un montón de cosas trabajando con vosotros; a mis compis de comida porque es el mejor momento del día y de nuevo a todo@s los que formáis o habéis formado parte de TRANSyT, Juan, Laura, Paola, Thais, Álvaro, Neus, Ángela, Carlos, Fernando, Javier, Emilio, María, Khalid y un largo etcétera, gracias por crear este maravilloso ambiente de trabajo.A mi familia, a todos y cada uno de ellos, por confiar en mí y ayudarme a mantener la ilusión en todo momento. A mis padres y mi hermano por su cariño y apoyo incondicional durante todo este proceso, a mis suegros por cuidar a Martín y permitirme trabajar tranquila sabiendo que estaba en buena compañía. A Tomás, por todo lo anterior, pero también por no dejar de hacerme preguntas incomodas (aún a riesgo de sufrir represalias) que me han obligado a replantearme el enfoque de la tesis en varias ocasiones y a invertir tiempo en explicar mi trabajo para que se entienda mejor.Y gracias a ti, por ser un apoyo constante y un ejemplo a seguir, por confiar (aunque siempre basándote en hechos objetivos: P), por tu constante insistencia en que relativice los problemas y aprecie lo que realmente es importante (ha tenido que llegar Martín para que el mensaje termine de calar), por ayudarme a descubrir y potenciar mis fortalezas y por estar, porque siempre estabas y, aunque ya no estés, seguirás estando. TQ ABSTRACTCities world-wide suffer from serious air quality problems and are main contributors to climate change by consuming about two-thirds of the world´s overall energy and emitting an est...

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Connectivity and Automation as Enablers for Energy-Efficient Driving and Road Traffic Management

Othman

Nunzio

Sciarretta

et al. 2021

Handbook of Climate Change Mitigation and Adaptation

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In the member countries of the Organization for Economic Cooperation and Development (OECD), projections show that the improved energy efficiency in transportation may lead to a net decline of about 2% in energy use until 2040, thus outpacing the predicted increase of vehicle-miles traveled (VMT). However, in OECD-Europe, transportation still represents the biggest source of carbon emissions, contributing by about 25% to the total CO2 emissions, with cars and vans representing more than two thirds of this share. The situation is even more alarming in non-OECD countries, where the transportation energy demand is expected to rise by 64% until 2040. The shift that we are witnessing toward the adoption of connected and automated vehicles (CAVs) is going to be perhaps, the most disruptive since the early days of automobiles and could revolutionize movement of people and goods. This level of connectivity and autonomy will transform transportation in several dimensions with important societal and economic impacts: improved safety, increased comfort, time saving potential, and more efficient road utilization are among the most widely discussed positive impacts of CAVs. However, the potential energy efficiency benefits of these technologies remain uncertain. From a single-vehicle efficiency perspective, research suggests that lightweight, low-speed, autonomous vehicles have the potential to achieve fuel economy an order of magnitude higher than current cars. Yet, at system-wide level, current estimates suggest that the total energy consumption impacts can range from a 90% decrease to a 200% increase in fuel consumption as compared to a projected 2050 baseline energy consumption. The paradigm that traffic congestion mitigation should reduce CO2 emissions is yet to be proved. Therefore, interest in transportation regulation problems with explicit environmental considerations is growing. This work takes a more in-depth look at increased opportunities for energy-efficient driving with energy-oriented traffic management and CAVs deployment. In particular, the focus will be put on the road traffic control strategies in urban networks using connectivity to enable variable speed limits and traffic light adaptive control, as well as the energy-saving opportunities that arise for individual CAVs by anticipating future road geometry, traffic conditions, and interactions with neighboring vehicles.

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Integrated macroscopic traffic flow, emission, and fuel consumption model for control purposes

Cited by 140 publications

References 21 publications

Urban traffic Eco-Driving: A macroscopic steady-state analysis

Urban traffic Eco-Driving: A macroscopic steady-state analysis

Traffic solutions for greener and cleaner cities

Connectivity and Automation as Enablers for Energy-Efficient Driving and Road Traffic Management

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