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Continuous transportation demand growth in recent years has led to many traffic issues in urban areas. Among the most challenging ones are traffic congestion and the associated vehicular emissions. Efficient design of traffic signal control systems can be a promising approach to address these problems. This research develops a real-time signal control system, which optimizes signal timings at an under-saturated isolated intersection by minimizing total vehicular emissions. A combination of previously introduced analytical models based on traffic flow theory has been used. These models are able to estimate time spent per driving mode (i.e., time spent accelerating, decelerating, cruising, and idling) as a function of demand, vehicle arrival times, saturation flow, and signal control parameters. Information on vehicle activity is used along with the Vehicle Specific Power (VSP) model, which estimates emission rates per time spent in each operating mode to obtain total emissions per cycle. For the evaluation of the proposed method, data from two real-world intersections of Mesogion and Katechaki Avenues located in Athens, Greece and University and San Pablo Avenues, in Berkeley, CA has been used. The evaluation has been performed through both deterministic (i.e. under the assumption of perfect information for all inputs) and stochastic (i.e. without having perfect information for some inputs) arrival tests. The results of evaluation tests have shown that the proposed emission-based signal control system reduces emissions compared to traditional vehicle-based signal control system in most cases.

A door-to-door trip may involve multiple traffic modes. For example, travelers may drive to a subway station and make a transfer to rail transit; alternatively, people may also start their trips by walking/cycling to a bus/subway station and then take transit in most of the trip. A successful eco-route planning thus should be able to cover multiple traffic modes and offer intermodal routing suggestions. Developing such a system requires to address extensive concerns. The dissertation is a building block of the multi-modal energy-efficient routing system which is being developed and tested in the simulation environment before real applications. Four submodules have been developed in the dissertation as partial fulfillment of the simulation-based system: energy consumption modeling, subway system development, on-road vehicles dynamic eco-routing, and information effect on route choice behavior. Other submodules such as pedestrian/bicycle modeling will be studied in the future. Towards the research goal, the dissertation first develops fuel consumption models for on-road vehicles. Given that gasoline light duty vehicles (LDVs) and electric vehicles were modeled in previous studies, the research effort mainly focuses on heavy duty vehicles (HDVs). Specifically, heavy duty diesel trucks (HDDTs) as well as diesel and hybrid-electric transit buses are modeled. The models are developed based on the Virginia Tech Comprehensive Power-based Fuel consumption Modeling (VT-CPFM) framework. The results demonstrate that the model estimates are highly consistent with field observations as well as the estimates of the Comprehensive Modal Emissions Model (CMEM) and MOtor Vehicle Emissions Simulator (MOVES). It is also found that the optimum fuel economy cruise speed ranges between 32 and 52 km/h for the tested trucks and between 39 and 47 km/h for the tested buses on grades varying from 0% to 8%, which is significantly lower than LDVs (60-80 km/h). The dissertation then models electric train dynamics and energy consumption in support of subway simulation system development and trip energy estimation. The dynamics model varies throttle and brake level with running speed rather than assuming constants as was done by previous studies, and the energy consumption model considers instantaneous energy regeneration. Both models can be easily calibrated using non-engine data and implemented in simulation systems and eco-transit applications. The results of the dynamics modeling demonstrate that the proposed model can adequately capture instantaneous acceleration/deceleration behavior and thus produce realistic train trajectories. The results of the energy consumption modeling demonstrate that the model produces the estimates consistent with the National Transit Database (NTD) results, and is applicable for project-level analysis given its ability in capturing the energy consumption differences associated with train, route and operational characteristics. The most suitable simulation testbed for system development is then identified. The dissertation investigates four state-of-the-art microsimulation models (INTEGRATION, VISSIM, AIMSUM, PARAMICS). Given that the car-following model within a micro-simulator controls longitudinal vehicle motion and thus determines the resulting vehicle trajectories, the research effort mainly focuses on the performance of the built-in car-following models from the energy and environmental perspective. The vehicle specific power (VSP) distributions resulting from each of the car-following models are compared to the field observations. The results demonstrate that the Rakha-Pasumarthy-Adjerid (RPA) model (implemented in the INTEGRATION software) outperforms the Gipps (AIMSUM), Fritzsche (PARAMICS) and Wiedemann (VISSIM) models in generating accurate VSP distributions and fuel consumption and emission estimates. This demonstrates the advantage of the INTEGRATION model over the other three simulation models for energy and environmental analysis. A new eco-routing model, comprehensively considering microscopic characteristics, is then developed, followed by a numerical experiment to test the benefit of the model. With the resulting eco-routing model, an on-road vehicle dynamic eco-routing system is constructed for in-vehicle navigation applications, and tested for different congestion levels. The results of the study demonstrate that the proposed eco-routing model is able to generate reasonable routing suggestions based on real-time information while at the same time differentiate eco-routes between vehicle models. It is also found that the proposed dynamic eco-routing system achieves lower network-wide energy consumption levels compared to the traditional eco-routing and travel time routing at all congestion levels. The results also demonstrate that the conventional fuel savings relative to the travel time routing decrease with the increasing congestion level; however, the electric power savings do not monotonically vary with congestion level. Furthermore, the energy savings relative to the traditional eco-routing are also not monotonically related to congestion level. In addition, network configuration is demonstrated to significantly affect eco-routing benefits. The dissertation finally investigates the potential to influence driver behavior by studying the impact of information on route choice behavior based on a real world experiment. The results of the experiment demonstrate that the effectiveness of information in routing rationality depends upon the traveler's age, preferences, route characteristics, and information type. Specifically, information effect is less evident for elder travelers. Also, the provided information may not be contributing if travelers value other considerations or one route significantly outperforms the others. The results also demonstrate that, when travelers have limited experiences, strict information is more effective than variability information, and that the faster less reliable route is more attractive than the slower more reliable route; yet the difference becomes insignificant with experiences accumulation. The results of the study will be used to enhance system design through considering route choice incentives. / Ph. D. / A door-to-door trip may involve multiple traffic modes. For example, travelers may drive to a subway station and make a transfer to rail transit; alternatively, people may also start their trips by walking/cycling to a bus/subway station and then take transit in most of the trip. A successful eco-route planning thus should be able to cover multiple traffic modes and offer intermodal routing suggestions. Developing such a system requires to address extensive concerns. The dissertation is a building block of the multi-modal energy-efficient routing system which is being developed and tested in the simulation environment before real applications. Four submodules have been developed in the dissertation as partial fulfillment of the simulation-based system: energy consumption modeling, subway system development, on-road vehicles dynamic eco-routing, and information effect on route choice behavior. Other submodules such as pedestrian/bicycle modeling will be studied in the future. Towards the research goal, the dissertation first develops fuel consumption models for on-road vehicles. Given that gasoline light duty vehicles (LDVs) and electric vehicles were modeled in previous studies, the research effort mainly focuses on heavy duty vehicles (HDVs) including heavy duty diesel trucks (HDDTs) as well as diesel and hybrid-electric transit buses. The model estimates are demonstrated to provide a good fit to field data. The dissertation then models electric train dynamics and energy consumption in support of subway simulation system development and trip energy estimation. The proposed dynamics model is able to produce realistic acceleration behavior, and the proposed energy consumption model can provide robust energy estimates that are consistent with field data. Both models can be calibrated without mechanical data and thus easily implemented in complex frameworks such as simulation systems and eco-transit applications. The most suitable simulation testbed for system development is then identified. The dissertation investigates four state-of-the-art microsimulation models (INTEGRATION, VISSIM, AIMSUM, PARAMICS). The results demonstrate that INTEGRATION outperforms the other three simulation models for energy and environmental analysis. Also, INTEGRATION is able to generate measures of effectiveness (MOEs) for electric vehicles, which makes it more competitive than the state-of-the-art counterpart. A dynamic eco-routing system is then developed in the INTEGRATION simulation environment. The built-in eco-routing model of the system comprehensively considers microscopic characteristics and is demonstrated to generate reasonable routing solutions based on real-time information while at the same time differentiate vehicle models. The system is able to provide routing suggestions for both conventional gasoline/diesel and electric vehicles. The testing results demonstrate that the proposed eco-routing system achieves network-wide energy savings compared to the traditional eco-routing and travel time routing at all tested congestion levels. Also, network configuration is demonstrated to significantly affect eco-routing benefits. The dissertation finally investigates the potential to influence driver behavior by studying the impact of information on route choice behavior based on a real world experiment. The results of the experiment demonstrate that the effectiveness of information in routing rationality depends upon the traveler’s age, preferences, route characteristics, and information type. Specifically, information effect is less evident for elder travelers. Also, the provided information may not be contributing if travelers value other considerations or one route significantly outperforms the others. The results also demonstrate that, when travelers have limited experiences, strict information is more effective than variability information, and that the faster less reliable route is more attractive than the slower more reliable route; yet the difference becomes insignificant with experiences accumulation. The results of the study will be used to enhance system design through considering route choice incentives.

Ocorrem anualmente aproximadamente 600.000 mortes de crianças com até cinco anos, no mundo. Pneumonia é a principal causa e mais de 50 por cento destas mortes são atribuídas à poluição do ar. Ela ainda é responsável pelo aumento do risco de infecções respiratórias, asma, condições neonatais adversas e anomalias congênitas. A poluição do ar também afeta o desenvolvimento cognitivo de crianças e induz o desenvolvimento de doenças crônicas na idade adulta. Entre 70 e 80 por cento da poluição do ar em nações em desenvolvimento são de origem veicular. Objetivando definir critérios baseados em medições com sensoriamento remoto para identificação de veículos automotores leves do ciclo Otto com elevadas emissões de monóxido de carbono, hidrocarbonetos ou óxido nítrico, foram utilizados os dados secundários gerados pela Remote Sensing do Brasil Ltda dos quais foram selecionados 179.142 veículos em uso da frota circulante da cidade de São Paulo com medições completas dos índices de emissão dos poluentes monóxido de carbono (CO), hidrocarbonetos (HC) e óxido nítrico (NO) e ainda velocidade e aceleração do veículo quando da medição e inclinação da pista no local escolhido para as medições. Foram ajustados modelos estatísticos da classe Generalised Additive Models for Location, Scale and Shape (GAMLSS) visando testar a influência do Tipo de Combustível, da Potência Específica do Veículo (VSP) e das Fases do Programa de Controle da Poluição do Ar por Veículos Automotores (Proconve) sobre as emissões de CO, HC e NO, medidos usando o Remote Sensing Device (RSD). As emissões foram então conceitualmente subdivididas em dois grupos: veículos com emissões normais e com emissões anormais, isso para os diversos poluentes em veículos das Fases L3, L4 e L5 que são as fases de interesse para o gerenciamento da qualidade do ar. Variáveis latentes foram definidas para indicarem as distribuições dos veículos em relação a esses grupos e Fases. O algoritmo Expectation-Maximization (EM) foi empregado para identificação dos parâmetros das distribuições. Para determinação dos valores associados aos veículos com elevadas emissões de determinado poluente e fase do Proconve, foi empregado o percentil 98 por cento da distribuição ajustada para os veículos dos grupos com emissões normais. Assim sendo, o Erro de Tipo I foi fixado em 2 por cento sendo que esse percentual foi estabelecido considerando o Erro de Tipo II, de apontar o veículo como tendo emissão normal quando na realidade trata-se de um high emitter. Através desta abordagem foram determinados os valores indicativos de veículos com elevadas emissões segundo o poluente e a Fase do Proconve. Os resultados apontaram decréscimo nas emissões de CO e de HC segundo as Fases do Proconve. Para o NO, o comportamento das emissões não acompanhou as reduções impostas pelas Fases do Proconve. Foi constatado que os veículos de 2005 a 2009, movidos exclusivamente a gasool, foram os que apresentaram as maiores emissões de NO. Diversos possíveis fatores causadores deste comportamento diferenciado do NO foram discutidos neste trabalho. Os dados de qualidade do ar detectaram aumento significativo nas concentrações ambientais de Óxidos de Nitrogênio (NOx) em 2007, quando foi monitorado este parâmetro no período de inverno, o que pode indicar a influência dos high emitters, mas necessita de estudos mais aprofundados para confirmação da causa deste comportamento. / Approximately 600,000 deaths occur worldwide annually for children up to five years of age. Pneumonia is the leading cause and more than 50 per cent of these deaths are attributed to air pollution. It is still responsible for increased risk of respiratory infections, asthma, adverse neonatal conditions and congenital anomalies. Air pollution also affects the cognitive development of children and induces future development of chronic diseases in adulthood. In order to define criteria based on remote sensing measurements to identify Otto cycle light duty vehicles (LDV) with high emissions of carbon monoxide, hydrocarbons or nitric oxide it was used secondary data produced by Remote Sensing do Brasil Ltda, from which 179,142 inuse vehicles were selected, that belongs to the city of São Paulos current fleet. All those vehicles had complete measurements of emission of carbon monoxide (CO), hydrocarbons (HC) and nitric oxide (NO), and also speed and acceleration of the vehicle during measurements, and slope of the track at the place chosen for the measurements. Statistical models of the Generalized Additive Models for Location, Scale and Shape (GAMLSS) class were adjusted to test the influence of fuel type, Vehicle Specific Power (VSP) and of the Brazilian Vehicle Emission Control Program [Proconve] phases on CO, HC and NO emissions, measured using Remote Sensing Device (RSD). The emissions were then conceptually subdivided into two groups: vehicles with normal and abnormal emission, for the various pollutants in vehicles of L3, L4 and L5 phases of Proconve, which were of interest for the air quality management. Latent variables were defined to indicate the distribution of vehicles in relation to those groups and phases. The algorithm Expectation Maximization (EM) was employed to identify all parameters of the distributions. We use the 98 per cent percentiles of the statistical distribution set, for vehicles of groups with normal emissions to determine the limit values for vehicles with high emissions of pollutants and Proconve Phase. Therefore, the Type I Error was set at 2 per cent and this percentage was established considering the Type II Error to point the vehicle as having normal emission when in fact it is a high emitter. Through this approach, the indicative values of vehicles with high emissions according to the pollutant and the Proconve Phase were determined. Results of emissions measured with the RSD technique indicated a decrease in CO and HC emissions according to the Proconve Phase. For the NO, the emissions behavior did not follow the reductions imposed by the Proconve Phases. It was found that newer vehicles year model from 2005 to 2009 exclusively gasohol-powered vehicles, were the ones that presented the highest NO emissions. Several possible causative factors of this differential behavior of NO were discussed in this study. A significant increase in the environmental concentrations of Nitrogen Oxides (NOx) was detected in 2007, when this parameter was monitored in the winter period. This may indicate the influence of the high emitter vehicles, but it requires a more in-depth cause-effect study for confirmation of this behavior.

Ocorrem anualmente aproximadamente 600.000 mortes de crianças com até cinco anos, no mundo. Pneumonia é a principal causa e mais de 50 por cento destas mortes são atribuídas à poluição do ar. Ela ainda é responsável pelo aumento do risco de infecções respiratórias, asma, condições neonatais adversas e anomalias congênitas. A poluição do ar também afeta o desenvolvimento cognitivo de crianças e induz o desenvolvimento de doenças crônicas na idade adulta. Entre 70 e 80 por cento da poluição do ar em nações em desenvolvimento são de origem veicular. Objetivando definir critérios baseados em medições com sensoriamento remoto para identificação de veículos automotores leves do ciclo Otto com elevadas emissões de monóxido de carbono, hidrocarbonetos ou óxido nítrico, foram utilizados os dados secundários gerados pela Remote Sensing do Brasil Ltda dos quais foram selecionados 179.142 veículos em uso da frota circulante da cidade de São Paulo com medições completas dos índices de emissão dos poluentes monóxido de carbono (CO), hidrocarbonetos (HC) e óxido nítrico (NO) e ainda velocidade e aceleração do veículo quando da medição e inclinação da pista no local escolhido para as medições. Foram ajustados modelos estatísticos da classe Generalised Additive Models for Location, Scale and Shape (GAMLSS) visando testar a influência do Tipo de Combustível, da Potência Específica do Veículo (VSP) e das Fases do Programa de Controle da Poluição do Ar por Veículos Automotores (Proconve) sobre as emissões de CO, HC e NO, medidos usando o Remote Sensing Device (RSD). As emissões foram então conceitualmente subdivididas em dois grupos: veículos com emissões normais e com emissões anormais, isso para os diversos poluentes em veículos das Fases L3, L4 e L5 que são as fases de interesse para o gerenciamento da qualidade do ar. Variáveis latentes foram definidas para indicarem as distribuições dos veículos em relação a esses grupos e Fases. O algoritmo Expectation-Maximization (EM) foi empregado para identificação dos parâmetros das distribuições. Para determinação dos valores associados aos veículos com elevadas emissões de determinado poluente e fase do Proconve, foi empregado o percentil 98 por cento da distribuição ajustada para os veículos dos grupos com emissões normais. Assim sendo, o Erro de Tipo I foi fixado em 2 por cento sendo que esse percentual foi estabelecido considerando o Erro de Tipo II, de apontar o veículo como tendo emissão normal quando na realidade trata-se de um high emitter. Através desta abordagem foram determinados os valores indicativos de veículos com elevadas emissões segundo o poluente e a Fase do Proconve. Os resultados apontaram decréscimo nas emissões de CO e de HC segundo as Fases do Proconve. Para o NO, o comportamento das emissões não acompanhou as reduções impostas pelas Fases do Proconve. Foi constatado que os veículos de 2005 a 2009, movidos exclusivamente a gasool, foram os que apresentaram as maiores emissões de NO. Diversos possíveis fatores causadores deste comportamento diferenciado do NO foram discutidos neste trabalho. Os dados de qualidade do ar detectaram aumento significativo nas concentrações ambientais de Óxidos de Nitrogênio (NOx) em 2007, quando foi monitorado este parâmetro no período de inverno, o que pode indicar a influência dos high emitters, mas necessita de estudos mais aprofundados para confirmação da causa deste comportamento. / Approximately 600,000 deaths occur worldwide annually for children up to five years of age. Pneumonia is the leading cause and more than 50 per cent of these deaths are attributed to air pollution. It is still responsible for increased risk of respiratory infections, asthma, adverse neonatal conditions and congenital anomalies. Air pollution also affects the cognitive development of children and induces future development of chronic diseases in adulthood. In order to define criteria based on remote sensing measurements to identify Otto cycle light duty vehicles (LDV) with high emissions of carbon monoxide, hydrocarbons or nitric oxide it was used secondary data produced by Remote Sensing do Brasil Ltda, from which 179,142 inuse vehicles were selected, that belongs to the city of São Paulos current fleet. All those vehicles had complete measurements of emission of carbon monoxide (CO), hydrocarbons (HC) and nitric oxide (NO), and also speed and acceleration of the vehicle during measurements, and slope of the track at the place chosen for the measurements. Statistical models of the Generalized Additive Models for Location, Scale and Shape (GAMLSS) class were adjusted to test the influence of fuel type, Vehicle Specific Power (VSP) and of the Brazilian Vehicle Emission Control Program [Proconve] phases on CO, HC and NO emissions, measured using Remote Sensing Device (RSD). The emissions were then conceptually subdivided into two groups: vehicles with normal and abnormal emission, for the various pollutants in vehicles of L3, L4 and L5 phases of Proconve, which were of interest for the air quality management. Latent variables were defined to indicate the distribution of vehicles in relation to those groups and phases. The algorithm Expectation Maximization (EM) was employed to identify all parameters of the distributions. We use the 98 per cent percentiles of the statistical distribution set, for vehicles of groups with normal emissions to determine the limit values for vehicles with high emissions of pollutants and Proconve Phase. Therefore, the Type I Error was set at 2 per cent and this percentage was established considering the Type II Error to point the vehicle as having normal emission when in fact it is a high emitter. Through this approach, the indicative values of vehicles with high emissions according to the pollutant and the Proconve Phase were determined. Results of emissions measured with the RSD technique indicated a decrease in CO and HC emissions according to the Proconve Phase. For the NO, the emissions behavior did not follow the reductions imposed by the Proconve Phases. It was found that newer vehicles year model from 2005 to 2009 exclusively gasohol-powered vehicles, were the ones that presented the highest NO emissions. Several possible causative factors of this differential behavior of NO were discussed in this study. A significant increase in the environmental concentrations of Nitrogen Oxides (NOx) was detected in 2007, when this parameter was monitored in the winter period. This may indicate the influence of the high emitter vehicles, but it requires a more in-depth cause-effect study for confirmation of this behavior.

On-road vehicles are a major source of transportation carbon dioxide (CO2) greenhouse gas emissions in all the developed countries, and in many of the developing countries in the world. Similarly, several criteria air pollutants are associated with transportation, e.g., carbon monoxide (CO), nitrogen oxides (NOx), and particulate matter (PM). The need to accurately quantify transportation-related emissions from vehicles is essential. Transportation agencies and researchers in the past have estimated emissions using one average speed and volume on a long stretch of roadway. With MOVES, there is an opportunity for higher precision and accuracy. Integrating a microscopic traffic simulation model (such as VISSIM) with MOVES allows one to obtain precise and accurate emissions estimates. The new United States Environmental Protection Agency (USEPA) mobile source emissions model, MOVES2010a (MOVES) can estimate vehicle emissions on a second-by-second basis creating the opportunity to develop new software ―VIMIS 1.0‖ (VISSIM/MOVES Integration Software) to facilitate the integration process. This research presents a microscopic examination of five key transportation parameters (traffic volume, speed, truck percentage, road grade and temperature) on a 10-mile stretch of Interstate 4 (I- 4) test bed prototype; an urban limited access highway corridor in Orlando, Florida. iv The analysis was conducted utilizing VIMIS 1.0 and using an advanced custom design technique; D-Optimality and I-Optimality criteria, to identify active factors and to ensure precision in estimating the regression coefficients as well as the response variable. The analysis of the experiment identified the optimal settings of the key factors and resulted in the development of Micro-TEM (Microscopic Transportation Emissions MetaModel). The main purpose of Micro-TEM is to serve as a substitute model for predicting transportation emissions on limited access highways in lieu of running simulations using a traffic model and integrating the results in an emissions model to an acceptable degree of accuracy. Furthermore, significant emission rate reductions were observed from the experiment on the modeled corridor especially for speeds between 55 and 60 mph while maintaining up to 80% and 90% of the freeway‘s capacity. However, vehicle activity characterization in terms of speed was shown to have a significant impact on the emission estimation approach. Four different approaches were further examined to capture the environmental impacts of vehicular operations on the modeled test bed prototype. First, (at the most basic level), emissions were estimated for the entire 10-mile section ―by hand‖ using one average traffic volume and average speed. Then, three advanced levels of detail were studied using VISSIM/MOVES to analyze smaller links: average speeds and volumes (AVG), second-bysecond link driving schedules (LDS), and second-by-second operating mode distributions (OPMODE). This research analyzed how the various approaches affect predicted emissions of CO, NOx, PM and CO2. v The results demonstrated that obtaining accurate and comprehensive operating mode distributions on a second-by-second basis improves emission estimates. Specifically, emission rates were found to be highly sensitive to stop-and-go traffic and the associated driving cycles of acceleration, deceleration, frequent braking/coasting and idling. Using the AVG or LDS approach may overestimate or underestimate emissions, respectively, compared to an operating mode distribution approach. Additionally, model applications and mitigation scenarios were examined on the modeled corridor to evaluate the environmental impacts in terms of vehicular emissions and at the same time validate the developed model ―Micro-TEM‖. Mitigation scenarios included the future implementation of managed lanes (ML) along with the general use lanes (GUL) on the I-4 corridor, the currently implemented variable speed limits (VSL) scenario as well as a hypothetical restricted truck lane (RTL) scenario. Results of the mitigation scenarios showed an overall speed improvement on the corridor which resulted in overall reduction in emissions and emission rates when compared to the existing condition (EX) scenario and specifically on link by link basis for the RTL scenario. The proposed emission rate estimation process also can be extended to gridded emissions for ozone modeling, or to localized air quality dispersion modeling, where temporal and spatial resolution of emissions is essential to predict the concentration of pollutants near roadways