13,903 research outputs found

    Washington START Transportation Model

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    The document describes the Washington START transportation simulation model. In particular, it provides information about the model structure, the equilibrium concept, and the data used to calibrate the model. It also briefly describes the reference scenario and the elasticity analysis. Finally, the document discusses past and potential future applications and possible directions for model extensions.transportation simulation, policy analysis, general equilibrium, travel demand, transportation network, mode of transportation

    Carbon Free Boston: Transportation Technical Report

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    Part of a series of reports that includes: Carbon Free Boston: Summary Report; Carbon Free Boston: Social Equity Report; Carbon Free Boston: Technical Summary; Carbon Free Boston: Buildings Technical Report; Carbon Free Boston: Waste Technical Report; Carbon Free Boston: Energy Technical Report; Carbon Free Boston: Offsets Technical ReportOVERVIEW: Transportation connects Boston’s workers, residents and tourists to their livelihoods, health care, education, recreation, culture, and other aspects of life quality. In cities, transit access is a critical factor determining upward mobility. Yet many urban transportation systems, including Boston’s, underserve some populations along one or more of those dimensions. Boston has the opportunity and means to expand mobility access to all residents, and at the same time reduce GHG emissions from transportation. This requires the transformation of the automobile-centric system that is fueled predominantly by gasoline and diesel fuel. The near elimination of fossil fuels—combined with more transit, walking, and biking—will curtail air pollution and crashes, and dramatically reduce the public health impact of transportation. The City embarks on this transition from a position of strength. Boston is consistently ranked as one of the most walkable and bikeable cities in the nation, and one in three commuters already take public transportation. There are three general strategies to reaching a carbon-neutral transportation system: ‱ Shift trips out of automobiles to transit, biking, and walking;1 ‱ Reduce automobile trips via land use planning that encourages denser development and affordable housing in transit-rich neighborhoods; ‱ Shift most automobiles, trucks, buses, and trains to zero-GHG electricity. Even with Boston’s strong transit foundation, a carbon-neutral transportation system requires a wholesale change in Boston’s transportation culture. Success depends on the intelligent adoption of new technologies, influencing behavior with strong, equitable, and clearly articulated planning and investment, and effective collaboration with state and regional partners.Published versio

    Towards Sustainable Mobility Indicators: Application to the Lyons Conurbation

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    This paper applies the theme of sustainable development to the case of urban transport and daily mobility of the inhabitants of a city. A set of indicators which simultaneously takes the three dimensions of sustainability––environmental, economic, and social––into account is suggested. We present here the results of exploratory research funded by Renault Automobile Manufacturers, carried out to verify the feasibility and the usefulness of elaborating such sustainable mobility indicators. Values of the economics, environmental and social indicators are presented for the Lyons case. These estimations are mainly based on the household travel survey held in this city in 1994–1995. In the end, this set of indicators should allow the comparison of different urban transport strategies within an urban area, but also between different urban contexts, and through time. The conditions of generalization of these measurements of indicators are then discussed.Trip distance ; Daily mobility ; Sustainability indicators ; Household travel survey ; Methodology ; Pollutant emissions ; Expenditures ; Global costs

    Predicting space occupancy for street paid parking

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    This dissertation discusses how to develop a prediction method for on-street parking space availability, using only historical occupancy data collected from on-street multi-space parking meters. It is analyzed how to transform the raw data into a dataset representing the occupancy and how can this information be used to detect when the parking spaces on a street are Vacant or Full. Attributes like weather conditions and holidays are added to the data, giving them more context and comprehension. After the data preparation and analysis, a prediction model is developed using machinelearning techniques that can forecast the availability of the parking spaces on a street at a specific day and on a given moment. For that, a classification method is implemented based on decision trees and neural networks, comparing both methods regarding results and development time. Particular attention is given to the algorithm parameters, to achieve the right balance between accuracy and computational time. The developed model proved effective, correctly capturing the different behavior of each street through the different weeks, and returning results useful to drivers searching for parking and to the business owners while monitoring their parking investments and returns.Esta dissertação apresenta como pode ser desenvolvido um mĂ©todo para previsĂŁo de disponibilidade de lugares de estacionamento em rua, utilizando dados histĂłricos obtidos atravĂ©s de parquĂ­metros de controlo a mĂșltiplos lugares. É analisado como os dados em bruto dos parquĂ­metros podem ser transformados num conjunto de dados que represente qual a ocupação dos lugares, e posteriormente como esta informação pode ser utilizada para detetar se o estacionamento em uma rua estĂĄ livre ou ocupado. SĂŁo adicionados tambĂ©m mais alguns atributos, como por exemplo informação sobre as condiçÔes meteorolĂłgicas ou que dias sĂŁo feriados, dando mais algum contexto e compreensĂŁo Ă  informação jĂĄ existente. ApĂłs a preparação e anĂĄlise dos dados, Ă© desenvolvido um mĂ©todo de previsĂŁo utilizando tĂ©cnicas de aprendizagem automĂĄtica de modo a que seja possĂ­vel saber qual a disponibilidade de estacionamento em uma rua, a um dia especĂ­fico e a um determinado momento. Para isso, foi implementado um mĂ©todo de classificação baseado em ĂĄrvores de decisĂŁo e redes neuronais, comparando ambos os mĂ©todos do ponto de vista dos resultados e do tempo de desenvolvimento. Foi dada especial atenção aos parĂąmetros utilizados em cada algoritmo, de modo a que haja um balanço entre a precisĂŁo e tempo de computação. O modelo desenvolvido mostrou ser eficaz, captando corretamente o comportamento de cada rua nas diferentes semanas, devolvendo resultados uteis aos condutores que procurem lugares de estacionamento e aos proprietĂĄrios do negĂłcio por lhes permitir monitorizar o desempenho dos seus investimentos em parques de estacionamento e qual o retorno

    Congestion Pricing: Long-Term Economic and Land-Use Effects

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    We employ a spatially disaggregated general equilibrium model of a regional economy that incorporates decisions of residents, firms, and developers integrated with a spatially disaggregated strategic transportation planning (START) model that features mode, time period, and route choice to evaluate economic effects of congestion pricing. First, we evaluate the long-run effects of a road-pricing policy based on the integrated model of land use, strategic transport, and regional economy (LUSTRE) and compare them with the short-term effects obtained from the START model alone. We then look at distributional effects of the policy in question and point out differences and similarities in the short run versus the long run. Finally, we analyze the mechanisms at the source of the economic and land-use effects induced by the road-pricing policy.traffic congestion, welfare analysis, CGE modeling, cordon tolls, distributional effects

    Housing and Mobility Toolkit for San Mateo County

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    Since the end of the Great Recession, San Mateo County has attracted new workers at a record rate without building anywhere near enough housing. This jobs-housing imbalance drives the cost of housing up and forces many moderate and lower-income employees and their families out of the County. A lack of access to quality affordable housing in the County and the entire Bay Area along with limited transportation options means that an increased number of employees drive in and out of the County every workday. The resultant congestion, gridlock, and long commutes along with other negative environmental, social, and economic impacts create a major concern for communities in the County and beyond. Clearly, this problem has two distinct but interrelated dimensions: housing development and transportation planning. A select group of Mineta Transportation Institute (MTI) Research Associates worked closely with representatives from the San Mateo County Home for All initiative to help address this challenge by developing a toolkit of successful case studies with a holistic approach to housing development and transportation planning

    The integrated dynamic land use and transport model MARS

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    Cities worldwide face problems like congestion or outward migration of businesses. The involved transport and land use interactions require innovative tools. The dynamic Land Use and Transport Interaction model MARS (Metropolitan Activity Relocation Simulator) is part of a structured decision making process. Cities are seen as self organizing systems. MARS uses Causal Loop Diagrams from Systems Dynamics to explain cause and effect relations. MARS has been benchmarked against other published models. A user friendly interface has been developed to support decision makers. Its usefulness was tested through workshops in Asia. This paper describes the basis, capabilities and uses of MARS
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