Urban transportation emissions mitigation: Coupling high-resolution vehicular emissions and traffic models for traffic signal optimization

Osorio, Carolina; Nanduri, Kanchana

doi:10.1016/j.trb.2014.12.007

Cited by 93 publications

(44 citation statements)

References 27 publications

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“…The emissions model was based on a model developed by Int Panis et al (2006), and has been adopted by Osorio and Nanduri (2015). The model utilizes instantaneous velocity and accelerations derived from the GPS dataset to compute emissions.…”

Section: Microscopic Emissions Modelmentioning

confidence: 99%

“…This paper presents a novel methodology for making instantaneous emission modeling compatible with microscopic traffic patterns (measured on a second by second basis). Previous studies have focused on the microscopic traffic simulation coupled with microscopic emissions modeling (Int Panis et al, 2006) or a combination of macroscopic and microscopic traffic simulation combined with microscopic emissions modeling (Osorio and Nanduri, 2015). However, to the authors knowledge, a study investigating measured microscopic vehicle movement (measured on a second by second basis using GPS) in tandem with microscopic emissions modeling has not been completed successfully for a substantially sized vehicle fleet and study domain, rather have been limited to small ad hoc deployments.…”

Section: Modeled Emissions Taxi Fleetmentioning

confidence: 99%

“…In particular, the emissions caused by acceleration and deceleration of vehicles were modeled based on microscopic traffic simulation model integrated with an instantaneous emission model. The functions developed by Int Panis et al (2006) were incorporated into a study addressing optimum mitigation strategies for urban transportation emissions by Osorio and Nanduri (2015) where a combination of macroscopic and microscopic traffic simulators and emissions models were employed.…”

Section: Introductionmentioning

confidence: 99%

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Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model

Nyhan

Sobolevsky

Kang

et al. 2016

Atmospheric Environment

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h i g h l i g h t sWe present a novel method for predicting air pollution emissions using transport data. Study uses measured microscopic transport data and a microscopic emissions model. GPS data from over 15,000 vehicles were analyzed to quantify speeds and accelerations. CO 2 , NO x , VOCs and PM were modeled in high spatio-temporal resolution. Highly localized areas of elevated emissions were identified. a b s t r a c t Air pollution related to traffic emissions pose an especially significant problem in cities; this is due to its adverse impact on human health and well-being. Previous studies which have aimed to quantify emissions from the transportation sector have been limited by either simulated or coarsely resolved traffic volume data. Emissions inventories form the basis of urban pollution models, therefore in this study, Global Positioning System (GPS) trajectory data from a taxi fleet of over 15,000 vehicles were analyzed with the aim of predicting air pollution emissions for Singapore. This novel approach enabled the quantification of instantaneous drive cycle parameters in high spatio-temporal resolution, which provided the basis for a microscopic emissions model. Carbon dioxide (CO 2 ), nitrogen oxides (NO x ), volatile organic compounds (VOCs) and particulate matter (PM) emissions were thus estimated. Highly localized areas of elevated emissions levels were identified, with a spatio-temporal precision not possible with previously used methods for estimating emissions. Relatively higher emissions areas were mainly concentrated in a few districts that were the Singapore Downtown Core area, to the north of the central urban region and to the east of it. Daily emissions quantified for the total motor vehicle population of Singapore were found to be comparable to another emissions dataset. Results demonstrated that highresolution spatio-temporal vehicle traces detected using GPS in large taxi fleets could be used to infer highly localized areas of elevated acceleration and air pollution emissions in cities, and may become a complement to traditional emission estimates, especially in emerging cities and countries where reliable fine-grained urban air quality data is not easily available. This is the first study of its kind to investigate

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Section: Microscopic Emissions Modelmentioning

confidence: 99%

Section: Modeled Emissions Taxi Fleetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model

Nyhan

Sobolevsky

Kang

et al. 2016

Atmospheric Environment

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“…As regards the supply strategies some researchers investigated the significance of the correlation between signalised junctions and emissions/fuel consumption ( [9]) whilst others ( [12]; [18]; [11]; [13]) developed multi-criteria optimisation frameworks using these indicators as alternative criteria.…”

Section: Introductionmentioning

confidence: 99%

Comparing Signal Setting Design Methods Through Emission and Fuel Consumption Performance Indicators

Luca

Pace

Memoli

et al. 2016

Advances in Intelligent Systems and Computing

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In order to address the Signal Setting Design at urban level two main approaches may be pursued: the coordination and the synchronisation approaches depending on the steps considered for the optimisation of decision variables (two steps vs one step). Furthermore, in terms of objective functions mono-criterion or multi-criteria may be adopted. In this paper the coordination approach is implemented considering the multicriteria optimisation at single junctions and mono-criterion optimisation at network level whereas the synchronisation is implemented considering the mono-criterion optimisation. The main purpose of the paper is the evaluation of the performances of two strategies not only considering indicators such as the total delay, the queue length etc. but also considering other indicators such as the emissions and the fuel consumption. The methodological framework is composed by three stages: i) the decision variables (green timings and offsets) computation through optimisation methods; ii) the implementation of optimal signal settings in a microscopic traffic flow simulator (Simulation of Urban MObility"-SUMO); iii) the estimation of emissions and f u e l consumption indicators.

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“…: 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. Alam et al (2014) investigate the effectiveness to reduce emissions of traffic signal control and variable message sign using a microscopic transport model and an emissions model.…”

Section: Road Traffic Emissions Assessment Modelsmentioning

confidence: 99%

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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Urban transportation emissions mitigation: Coupling high-resolution vehicular emissions and traffic models for traffic signal optimization

Cited by 93 publications

References 27 publications

Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model

Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model

Comparing Signal Setting Design Methods Through Emission and Fuel Consumption Performance Indicators

Traffic solutions for greener and cleaner cities

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