Dynamic Modeling for Intelligent Transportation System Applications

Special Issue on Dynamic Modeling for Intelligent Transportation System Applicationspostprin

Advanced impact integration platform for cooperative road use

In order to improve networks efficiency, a considerable number of studies has been addressing the potential of eco-friendly assignment solutions as alternative approaches to reduce emissions and/or fuel use. So far the majority of studies generally assumes that the most eco-friendly solutions are the ones that minimize the absolute amount of emissions produced along a certain trip. In this work a platform based on both empirical GPS data and microscopic simulation models of traffic, emissions, noise, and road safety was developed to examine in depth 4 routes of an origin-destination pair over a Portuguese city. In addition to the integrated externalities assessment based on state of the art techniques, a novelty of this work was the preliminary inclusion of social criteria in defining sustainable assignment solutions. This paper provides new insights about sustainable traffic management issues and addresses multiple novel route choice indicators. Specifically we found that the relative variation of the individual costs and total pollution produced among 4 routes varies to a factor of 1.4 while the variation of the potentially exposed population ranges up to a factor of 10. The main results confirm the need to take into account real-time urban activity patterns in order to effectively implement sustainable traffic management measures

Exploring multiple eco-routing guidance strategies in a commuting corridor

The introduction of eco-routing systems has been suggested as a promising strategy to reduce carbon dioxide emissions and criteria pollutants. The objective of this study is to scrutinize the impacts of an eco-routing guidance system on emissions through the use of a case study in a commuting corridor. This research aims at assessing the potential environmental benefits in terms of different pollutant emissions. Simultaneously, it addresses the extent of variations in system travel time that each eco-routing strategy implies. The methodology consists of three distinct phases. The first phase corresponded to the adjustment of a micro simulation platform of traffic and emissions with empirical data previously collected. Secondly, volume-emission-functions (VEF) were developed based on the integrated modelling structure. Finally, different scenarios of traffic flow optimization were performed at the network level based on a simplified assignment procedure. The results show that if the traffic assignment is performed with the objective of minimize overall impacts, total system environmental damage costs can be reduced up to 9% with marginal oscillations in total system travel time. However, if drivers are advised based on their own emissions minimization, total system emissions may be higher than under the standard user equilibrium flow pattern. Specifically, environmentally friendly navigation algorithms focused on individual goals may tend to do divert traffic to roads with less capacity affecting the performance of the remaining traffic. This case study brings new insights about the difficulties and potentials of implementing such systems

Eco-Mobility-on-Demand Service with Ride-Sharing

Connected Automated Vehicles (CAV) technologies are developing rapidly, and one of its more popular application is to provide mobility-on-demand (MOD) services. However, with CAVs on the road, the fuel consumption of surface transportation may increase significantly. Travel demands could increase due to more accessible travel provided by the flexible service compared with the current public transit system. Trips from current underserved population and mode shift from walking and public transit could also increase travel demands significantly. In this research, we explore opportunities for the fuel-saving of CAVs in an urban environment from different scales, including speed trajectory optimization at intersections, data-drive fuel consumption model and eco-routing algorithm development, and eco-MOD fleet assignment. First, we proposed a speed trajectory optimization algorithm at signalized intersections. Although the optimal solution can be found through dynamic programming, the curse of dimensionality limits its computation speed and robustness. Thus, we propose the sequential approximation approach to solve a sequence of mixed integer optimization problems with quadratic objective and linear constraints. The speed and acceleration constraints at intersections due to route choice are addressed using a barrier method. In this work, we limit the problem to a single intersection due to the route choice application and only consider free flow scenarios, but the algorithm can be extended to multiple intersections and congested scenarios where a leading vehicle is included as a constraint if an intersection driver model is available. Next, we developed a fuel consumption model for route optimization. The mesoscopic fuel consumption model is developed through a data-driven approach considering the tradeoff between model complexity and accuracy. To develop the model, a large quantity of naturalistic driving data is used. Since the selected dataset doesn’t contain fuel consumption data, a microscopic fuel consumption simulator, Autonomie, is used to augment the information. Gaussian Mixture Regression is selected to build the model due to its ability to address nonlinearity. Instead of selected component number by cross-validation, we use the Bayesian formulation which models the indicator of components as a random variable which has Dirichlet distribution as prior. The model is used to estimate fuel consumption cost for routing algorithm. In this part, we assume the traffic network is static. Finally, the fuel consumption model and the eco-routing algorithm are integrated with the MOD fleet assignment. The MOD control framework models customers’ travel time requirements are as constraints, thus provides flexibility for cost function design. At the current phase, we assume the traffic network is static and use offline calculated travel time and fuel consumption to assign the fleet. To rebalance the idling vehicles, we developed a traffic network partition algorithm which minimizing the expected travel time within each cluster. A Model Predictive Control (MPC) based algorithm is developed to match idling fleet distribution with the demand distribution. A traffic simulator using Simulation of Urban MObility (SUMO) and calibrated using data from the Safety Pilot Model Deployment (SPMD) database is used to evaluate the MOD system performance. This dissertation shows that if the objective function of fleet assignment is not designed properly, even if ride-sharing is allowed, the fleet fuel consumption could increase compared with the baseline where personal vehicles are used for travel.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153446/1/xnhuang_1.pd

Plataforma de informação de tráfego para redução de consumos e emissões

Doutoramento em Engenharia MecânicaApesar das recentes inovações tecnológicas, o setor dos transportes continua a exercer impactes significativos sobre a economia e o ambiente. Com efeito, o sucesso na redução das emissões neste setor tem sido inferior ao desejável. Isto deve-se a diferentes fatores como a dispersão urbana e a existência de diversos obstáculos à penetração no mercado de tecnologias mais limpas. Consequentemente, a estratégia “Europa 2020” evidencia a necessidade de melhorar a eficiência no uso das atuais infraestruturas rodoviárias. Neste contexto, surge como principal objetivo deste trabalho, a melhoria da compreensão de como uma escolha de rota adequada pode contribuir para a redução de emissões sob diferentes circunstâncias espaciais e temporais. Simultaneamente, pretende-se avaliar diferentes estratégias de gestão de tráfego, nomeadamente o seu potencial ao nível do desempenho e da eficiência energética e ambiental. A integração de métodos empíricos e analíticos para avaliação do impacto de diferentes estratégias de otimização de tráfego nas emissões de CO2 e de poluentes locais constitui uma das principais contribuições deste trabalho. Esta tese divide-se em duas componentes principais. A primeira, predominantemente empírica, baseou-se na utilização de veículos equipados com um dispositivo GPS data logger para recolha de dados de dinâmica de circulação necessários ao cálculo de emissões. Foram percorridos aproximadamente 13200 km em várias rotas com escalas e características distintas: área urbana (Aveiro), área metropolitana (Hampton Roads, VA) e um corredor interurbano (Porto-Aveiro). A segunda parte, predominantemente analítica, baseou-se na aplicação de uma plataforma integrada de simulação de tráfego e emissões. Com base nesta plataforma, foram desenvolvidas funções de desempenho associadas a vários segmentos das redes estudadas, que por sua vez foram aplicadas em modelos de alocação de tráfego. Os resultados de ambas as perspetivas demonstraram que o consumo de combustível e emissões podem ser significativamente minimizados através de escolhas apropriadas de rota e sistemas avançados de gestão de tráfego. Empiricamente demonstrou-se que a seleção de uma rota adequada pode contribuir para uma redução significativa de emissões. Foram identificadas reduções potenciais de emissões de CO2 até 25% e de poluentes locais até 60%. Através da aplicação de modelos de tráfego demonstrou-se que é possível reduzir significativamente os custos ambientais relacionados com o tráfego (até 30%), através da alteração da distribuição dos fluxos ao longo de um corredor com quatro rotas alternativas. Contudo, apesar dos resultados positivos relativamente ao potencial para a redução de emissões com base em seleções de rotas adequadas, foram identificadas algumas situações de compromisso e/ou condicionantes que devem ser consideradas em futuros sistemas de eco navegação. Entre essas condicionantes importa salientar que: i) a minimização de diferentes poluentes pode implicar diferentes estratégias de navegação, ii) a minimização da emissão de poluentes, frequentemente envolve a escolha de rotas urbanas (em áreas densamente povoadas), iii) para níveis mais elevados de penetração de dispositivos de eco-navegação, os impactos ambientais em todo o sistema podem ser maiores do que se os condutores fossem orientados por dispositivos tradicionais focados na minimização do tempo de viagem. Com este trabalho demonstrou-se que as estratégias de gestão de tráfego com o intuito da minimização das emissões de CO2 são compatíveis com a minimização do tempo de viagem. Por outro lado, a minimização de poluentes locais pode levar a um aumento considerável do tempo de viagem. No entanto, dada a tendência de redução nos fatores de emissão dos poluentes locais, é expectável que estes objetivos contraditórios tendam a ser minimizados a médio prazo. Afigura-se um elevado potencial de aplicação da metodologia desenvolvida, seja através da utilização de dispositivos móveis, sistemas de comunicação entre infraestruturas e veículos e outros sistemas avançados de gestão de tráfego.Despite recent technological innovations, transportation sector is still producing significant impacts on the economy and environment. In fact, the success in reducing transportation emissions has been lower than desirable due to several factors such as the urban sprawl and several barriers to the market penetration of cleaner technologies. Therefore, the “Europe 2020” strategy has emphasised the relevance of improving the efficiency in the transportation networks through the better use of the existing infrastructures. In this context, the main objective of this thesis is increasing the understanding of how proper route choices can contribute to reduce emissions output over different spatial and temporal contexts. Simultaneously, it is intended to evaluate the potential of different traffic management strategies in terms of traffic performance and energy/environmental efficiency. The integration of empirical and analytical methods to assess the impact of different traffic optimization strategies on CO2 emissions and local pollutants constitutes one the main contributions of this work. This thesis has been divided in two main parts. The first is predominantly empirical, using field data as the main source of information. Using GPS equipped vehicles, empirical data for approximately 13200 km of road coverage have been collected to estimate energy and emissions impacts of route choice in three different scenarios: a medium-sized urban area (Aveiro), a metropolitan area (Hampton Roads, VA) and an intercity corridor (Oporto-suburban area). The second part, predominantly analytical, is essentially based on the output of traffic simulators and optimization models. The analytical component was based on the capability of microscopic traffic models to generate detailed emissions information and to generate link-based performance functions. Then, different traffic management strategies were tested to evaluate road networks in terms of traffic performance and emissions. Both outcomes of the empirical and analytical approaches have demonstrated that fuel use and emissions impacts can also be significantly reduced through appropriate route choices and advanced traffic management systems. The empirical assessment of route choice impacts has shown that both during off peak and peak periods, the selection of an appropriate route can lead to significant emissions reduction. Depending on the location, potential emissions savings of CO2 up to 25% and local pollutants up to 60% were found. The analytical approach has demonstrated that it is possible to significantly reduce system environmental costs (30%) by modifying traffic flow distribution along a corridor with 4 alternative routes. However, despite the positive results in terms of the potential for emissions reduction based on appropriate route choices, a number of important trade-offs that need to be considered in future implementations of eco-routing systems. Among these trade-offs it is worth noting that: i) different pollutants may lead to different ecorouting strategies, ii) the minimization of pollutants emissions often involves choosing urban routes (densely populated), iii) for higher penetration levels of eco-routing devices considering local pollutants, system environmental impacts can be higher than if drivers were guided under the traditional devices focused on travel time. With this research, it has been demonstrated that road traffic management strategies focused on minimizing CO2 emissions and fuel consumption can be compatible with the minimization of system travel time. On the other hand the minimization of local pollutants may lead to considerable increases in travel time. However, given the trend rate of reduction in the emissions factors of local pollutants, it is expected that such trade-offs would tend to be minimized in medium term. Thus, the developed methodology has great potential for further real life application, either through the use of nomadic devices, infrastructures to vehicle communication or different advanced traffic management systems