http://hdl.handle.net/1721.1/1781 Institute-wide collaboration focusing on statistical engineering, virtual education, and the environment http://dspace.mit.edu/retrieve/704 http://hdl.handle.net/1721.1/1781 Search the Channel search http://dspace.mit.edu/simple-search http://hdl.handle.net/1721.1/1678 Title: Beyond Health and Retirement: Placing Transportation on the Aging Policy Agenda <br/> <br/>Authors: Coughlin, Joseph F. http://hdl.handle.net/1721.1/1677 Title: Modeling Traffic Flow Emissions <br/> <br/>Authors: Cappiello, Alessandra <br/> <br/>Abstract: The main topic of this thesis is the development of light-duty vehicle dynamic emission models and their integration with dynamic traffic models. Combined, these models constitute fundamental components to support the development and assessment of traffic management policies, and the optimization of their parameters, to alleviate the negative impacts of road traffic. We develop and implement a dynamic model of emissions (CO2, CO, HC, and NOx) and fuel consumption for light-duty vehicles. The model is derived from regression-based and load-based emissions modeling approaches, and effectively combines their respective advantages. The model is calibrated for two vehicle categories using FTP as well MEC01 driving cycles data. The US06 driving cycle is used to validate the estimation capabilities of the proposed model. The preliminary results indicate that the model gives reasonable results compared to actual measurements as well to results obtained with CMEM, a well-known load-based dynamic emission model. Furthermore, the results indicate that the model runs fast, and is relatively simple to calibrate. We propose a framework for the integration of dynamic emission models with nonmicroscopic dynamic traffic models, that do not estimate vehicle acceleration. A probabilistic model of acceleration is designed and implemented to link the traffic and the emission models. The model provides an experimental distribution of the accelerations for any given speed and road type. The framework is applied to integrate the dynamic emission model developed in this thesis with a mesoscopic dynamic traffic flow model. Using a hypothetical case study, we illustrate the potential of the combined models to estimate the effects of route guidance strategies, which are one of numerous examples of dynamic traffic management strategies, on traffic travel times and traffic emissions. In presence of incidents, it is shown that route guidance can reduce total travel times as well as total emissions. http://hdl.handle.net/1721.1/1676 Title: Agents to the Rescue? <br/> <br/>Authors: West, Patricia M.; Ariely, Dan; Bellman, Steve; Bradlow, Eric; Huber, Joel; Johnson, Eric; Kahn, Barbara; Little, John; Schkade, David <br/> <br/>Abstract: The advent of electronic environments is bound to have profound effects on consumer decision making. While the exact nature of these influences is only partially known it is clear that consumers could benefit from properly designed electronic agents that know individual users' preferences and can act on their behalf. An examination of the various roles agents perform is presented as a framework for thinking about the design of electronic agents. In addition, a set of goals is established that include both outcome-based measures, such as improving decision quality, as well as process measures like increasing satisfaction and developing trust. http://hdl.handle.net/1721.1/1675 Title: A Statistical Model of Vehicle Emissions and Fuel Consumption <br/> <br/>Authors: Cappiello, Alessandra; Chabini, Ismail; Nam, Edward K.; Lue, Alessandro; Zeid, Maya Abou <br/> <br/>Abstract: A number of vehicle emission models are overly simple, such as static speed-dependent models widely used in practice, and other models are sophisticated as to require excessive inputs and calculations, which can slow down computational time. We develop and implement an instantaneous statistical model of emissions (CO2, CO, HC, and NOx) and fuel consumption for light-duty vehicles, which is derived from the physical loadbased approaches that are gaining in popularity. The model is calibrated for a set of vehicles driven on standard as well as aggressive driving cycles. The model is validated on another driving cycle in order to assess its estimation capabilities. The preliminary results indicate that the model gives reasonable results compared to actual measurements as well as to results obtained with CMEM, a well-known load-based emission model. Furthermore, the results indicate that the model runs fast and is relatively simple to calibrate. The model presented can be integrated with a variety of traffic models to predict the spatial and temporal distribution of traffic emissions and assess the impact of ITS traffic management strategies on travel times, emissions, and fuel consumption.