Improving Transportation Emissions Modeling By Integrating Ground Counts With Travel Demand Model Forecasts
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Improving Transportation Emissions Modeling By Integrating Ground Counts With Travel Demand Model Forecasts
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Author : Kuo-Shian Lin
language : en
Publisher:
Release Date : 1997
Improving Transportation Emissions Modeling By Integrating Ground Counts With Travel Demand Model Forecasts written by Kuo-Shian Lin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with categories.
Statewide Travel Forecasting Models
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Author : Alan J. Horowitz
language : en
Publisher: Transportation Research Board
Release Date : 2006
Statewide Travel Forecasting Models written by Alan J. Horowitz and has been published by Transportation Research Board this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Traffic estimation categories.
TRB's National Cooperative Highway Research Program (NCHRP) Synthesis 358: Statewide Travel Forecasting Models examines statewide travel forecasting models designed to address planning needs and provide forecasts for statewide transportation, including passenger vehicle and freight movements. The report explores the types and purposes of models being used, integration of state and urban models, data requirements, computer needs, resources (including time, funding, training, and staff), limitations, and overall benefits. The report includes five case studies, two that focus on passenger components, two on freight components, and one on both passenger and freight.
Disaggregating Period Based Travel Demand Model Volumes Into Hourly Volumes
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Author : Jaimee Dawn Hicks
language : en
Publisher:
Release Date : 1999
Disaggregating Period Based Travel Demand Model Volumes Into Hourly Volumes written by Jaimee Dawn Hicks and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with categories.
Modeling And Forecasting The Impact Of Major Technological And Infrastructural Changes On Travel Demand
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Author : Feras El Zarwi
language : en
Publisher:
Release Date : 2017
Modeling And Forecasting The Impact Of Major Technological And Infrastructural Changes On Travel Demand written by Feras El Zarwi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
The transportation system is undergoing major technological and infrastructural changes, such as the introduction of autonomous vehicles, high speed rail, carsharing, ridesharing, flying cars, drones, and other app-driven on-demand services. While the changes are imminent, the impact on travel behavior is uncertain, as is the role of policy in shaping the future. Literature shows that even under the most optimistic scenarios, society's environmental goals cannot be met by technology, operations, and energy system improvements only - behavior change is needed. Behavior change does not occur instantaneously, but is rather a gradual process that requires years and even generations to yield the desired outcomes. That is why we need to nudge and guide trends of travel behavior over time in this era of transformative mobility. We should focus on influencing long-range trends of travel behavior to be more sustainable and multimodal via effective policies and investment strategies. Hence, there is a need for developing policy analysis tools that focus on modeling the evolution of trends of travel behavior in response to upcoming transportation services and technologies. Over time, travel choices, attitudes, and social norms will result in changes in lifestyles and travel behavior. That is why understanding dynamic changes of lifestyles and behavior in this era of transformative mobility is central to modeling and influencing trends of travel behavior. Modeling behavioral dynamics and trends is key to assessing how policies and investment strategies can transform cities to provide a higher level of connectivity, attain significant reductions in congestion levels, encourage multimodality, improve economic and environmental health, and ensure equity. This dissertation focuses on addressing limitations of activity-based travel demand models in capturing and predicting trends of travel behavior. Activity-based travel demand models are the commonly-used approach by metropolitan planning agencies to predict 20-30 year forecasts. These include traffic volumes, transit ridership, biking and walking market shares that are the result of large scale transportation investments and policy decisions. Currently, travel demand models are not equipped with a framework that predicts long-range trends in travel behavior for two main reasons. First, they do not entail a mechanism that projects membership and market share of new modes of transport into the future (Uber, autonomous vehicles, carsharing services, etc). Second, they lack a dynamic framework that could enable them to model and forecast changes in lifestyles and transport modality styles. Modeling the evolution and dynamic changes of behavior, modality styles and lifestyles in response to infrastructural and technological investments is key to understanding and predicting trends of travel behavior, car ownership levels, vehicle miles traveled (VMT), and travel mode choice. Hence, we need to integrate a methodological framework into current travel demand models to better understand and predict the impact of upcoming transportation services and technologies, which will be prevalent in 20-30 years. The objectives of this dissertation are to model the dynamics of lifestyles and travel behavior through: " Developing a disaggregate, dynamic discrete choice framework that models and predicts long-range trends of travel behavior, and accounts for upcoming technological and infrastructural changes." Testing the proposed framework to assess its methodological flexibility and robustness." Empirically highlighting the value of the framework to transportation policy and practice. The proposed disaggregate, dynamic discrete choice framework in this dissertation addresses two key limitations of existing travel demand models, and in particular: (1) dynamic, disaggregate models of technology and service adoption, and (2) models that capture how lifestyles, preferences and transport modality styles evolve dynamically over time. This dissertation brings together theories and techniques from econometrics (discrete choice analysis), machine learning (hidden Markov models), statistical learning (Expectation Maximization algorithm), and the technology diffusion literature (adoption styles). Throughout this dissertation we develop, estimate, apply and test the building blocks of the proposed disaggregate, dynamic discrete choice framework. The two key developed components of the framework are defined below. First, a discrete choice framework for modeling and forecasting the adoption and diffusion of new transportation services. A disaggregate technology adoption model was developed since models of this type can: (1) be integrated with current activity-based travel demand models; and (2) account for the spatial/network effect of the new technology to understand and quantify how the size of the network, governed by the new technology, influences the adoption behavior. We build on the formulation of discrete mixture models and specifically dynamic latent class choice models, which were integrated with a network effect model. We employed a confirmatory approach to estimate our latent class choice model based on findings from the technology diffusion literature that focus on defining distinct types of adopters such as innovator/early adopters and imitators. Latent class choice models allow for heterogeneity in the utility of adoption for the various market segments i.e. innovators/early adopters, imitators and non-adopters. We make use of revealed preference (RP) time series data from a one-way carsharing system in a major city in the United States to estimate model parameters. The data entails a complete set of member enrollment for the carsharing service for a time period of 2.5 years after being launched. Consistent with the technology diffusion literature, our model identifies three latent classes whose utility of adoption have a well-defined set of preferences that are statistically significant and behaviorally consistent. The technology adoption model predicts the probability that a certain individual will adopt the service at a certain time period, and is explained by social influences, network effect, socio-demographics and level-of-service attributes. Finally, the model was calibrated and then used to forecast adoption of the carsharing system for potential investment strategy scenarios. A couple of takeaways from the adoption forecasts were: (1) highest expected increase in the monthly number of adopters arises by establishing a relationship with a major technology firm and placing a new station/pod for the carsharing system outside that technology firm; and (2) no significant difference in the expected number of monthly adopters for the downtown region will exist between having a station or on-street parking. The second component in the proposed framework entails modeling and forecasting the evolution of preferences, lifestyles and transport modality styles over time. Literature suggests that preferences, as denoted by taste parameters and consideration sets in the context of utility-maximizing behavior, may evolve over time in response to changes in demographic and situational variables, psychological, sociological and biological constructs, and available alternatives and their attributes. However, existing representations typically overlook the influence of past experiences on present preferences. This study develops, applies and tests a hidden Markov model with a discrete choice kernel to model and forecast the evolution of individual preferences and behaviors over long-range forecasting horizons. The hidden states denote different preferences, i.e. modes considered in the choice set and sensitivity to level-of-service attributes. The evolutionary path of those hidden states (preference states) is hypothesized to be a first-order Markov process such that an individual's preferences during a particular time period are dependent on their preferences during the previous time period. The framework is applied to study the evolution of travel mode preferences, or modality styles, over time, in response to a major change in the public transportation system. We use longitudinal travel diary from Santiago, Chile. The dataset consists of four one-week pseudo travel diaries collected before and after the introduction of Transantiago, which was a complete redesign of the public transportation system in the city. Our model identifies four modality styles in the population, labeled as follows: drivers, bus users, bus-metro users, and auto-metro users. The modality styles differ in terms of the travel modes that they consider and their sensitivity to level-of-service attributes (travel time, travel cost, etc.). At the population level, there are significant shifts in the distribution of individuals across modality styles before and after the change in the system, but the distribution is relatively stable in the periods after the change. In general, the proportion of drivers, auto-metro users, and bus-metro users has increased, and the proportion of bus users has decreased. At the individual level, habit formation is found to impact transition probabilities across all modality styles; individuals are more likely to stay in the same modality style over successive time periods than transition to a different modality style. Finally, a comparison between the proposed dynamic framework and comparable static frameworks reveals differences in aggregate forecasts for different policy scenarios, demonstrating the value of the proposed framework for both individual and population-level policy analysis. The aforementioned methodological frameworks comprise complex model formulation. This however comes at a cost in terms.
Biennial Report
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Author : University of California, Davis. Institute of Transportation Studies
language : en
Publisher:
Release Date : 1998
Biennial Report written by University of California, Davis. Institute of Transportation Studies and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Transportation categories.
Travel Demand Forecasting Parameters And Techniques
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Author : National Research Council (U.S.). Transportation Research Board
language : en
Publisher:
Release Date : 2012
Travel Demand Forecasting Parameters And Techniques written by National Research Council (U.S.). Transportation Research Board and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Choice of transportation categories.
"TRB's Transportation Research Record: Journal of the Transportation Research Board, No. 2303 consists of 13 papers that examine regional modeling of nonmotorized travel; surveys of departure time choice; an integrated land use-transport model system; residential relocation choice; green and active access to rail transit stations; predictions of travel behavior before and after transportation system changes; and estimating mobile-source greenhouse gas emissions. This issue of the TRR also explores socioeconomic model systems for activity-based modeling; trip rates and accessibility; the outcome of transportation projects under uncertainty; transportation alternatives costs and benefits; quantitative approaches for project prioritization; and estimating rest area use"--Publication information.
Travel Demand Forecasting 2012
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Author :
language : en
Publisher:
Release Date : 2012
Travel Demand Forecasting 2012 written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Choice of transportation categories.
"TRB's Transportation Research Record: Journal of the Transportation Research Board, No. 2303 consists of 13 papers that examine regional modeling of nonmotorized travel; surveys of departure time choice; an integrated land use-transport model system; residential relocation choice; green and active access to rail transit stations; predictions of travel behavior before and after transportation system changes; and estimating mobile-source greenhouse gas emissions. This issue of the TRR also explores socioeconomic model systems for activity-based modeling; trip rates and accessibility; the outcome of transportation projects under uncertainty; transportation alternatives costs and benefits; quantitative approaches for project prioritization; and estimating rest area use"--Publication information.
Integration Of Travel Demand Models With Operational Analysis Tools
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Author : Jiaqi Ma
language : en
Publisher:
Release Date : 2013
Integration Of Travel Demand Models With Operational Analysis Tools written by Jiaqi Ma and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Traffic engineering categories.
Continuing growth in urban travel demand inevitably leads to a need for more physical capacity within the transportation system. However, limited financial resources, high construction costs, environmental considerations, long timelines, and an increasingly complex regulatory process have essentially rendered capacity-adding projects to be actions of last resort. Before such projects are undertaken, decision makers, planners, and engineers evaluate alternative operational improvement strategies that can eliminate, mitigate, or forestall the need for a more traditional highway construction project. Effectively evaluating the wide range of operational improvement strategies that are available is not a trivial matter, and this is particularly true when the performance of such strategies is compared to the construction of new lanes. The purpose of this study was to recommend methods to obtain input data for operational analysis tools that operate as post-processors to travel demand models. Among all operational planning tools compatible with the four-step planning process, the Florida ITS Evaluation (FITSEval) tool was selected to be integrated with the primary planning software used by the Virginia Department of Transportation, i.e., Cube. To achieve the objective of this study, methods for estimating peak period flows from travel forecasting model outputs were investigated and Virginia data were examined for areas where planning forecasts and 24-hour travel patterns were available. Relationships between peak period flows and 24-hour data were studied. Procedures for obtaining the time-of-day factors for link and trip tables are provided using continuous count stations and National Household Travel Survey Data for Virginia. The modeling process was demonstrated by two case studies for the Hampton Roads area where the latest travel demand model was recently completed and many potential capacity enhancing operational strategies were available. Two case studies, Incident Management systems and High Occupancy Toll (HOT) lanes deployment, were evaluated, and the results of the base case and operational strategy deployment scenarios were compared to make recommendations on the feasibility of the evaluated projects. This report is designed to serve as a reference for users of FITSEval or similar operational analysis tools for evaluating operational capacity enhancements.
Synthesis Of Traveler Choice Research
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Author : Hani S. Mahmassani
language : en
Publisher:
Release Date : 2013
Synthesis Of Traveler Choice Research written by Hani S. Mahmassani and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Traffic estimation categories.
Over the last 50 years, advances in the fields of travel behavior research and travel demand forecasting have been immense, driven by the increasing costs of infrastructure and spatial limitations in areas of high population density together with externalities in these areas. The field has changed from supply-oriented planning to incorporating and managing demand. As such, methods from a variety of disciplines have been borrowed and extended to explain human behavior and interaction. Many experts have called for better data collection and methods of analysis across a number of time horizons, that is, integrated supply and demand models that capture travel behavior over time and space. A new paradigm may be called for to address the present challenges of model integration; user preferences, heterogeneity, and endogeneity; habitual behavior; and human socializing. This report provides a synthesis of the state of knowledge in travel behavior research and identifies gaps in existing data, methods, and practices that must be filled to meet the analysis needs of an emerging class of supply- and demand-side interventions that seek to leverage the opportunities of real-time information.
An Integrated Multi Model Approach For Predicting The Impact Of Household Travel On Urban Air Quality And Simulating Population Exposure
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Author : Marianne Hatzopoulou
language : en
Publisher:
Release Date : 2008
An Integrated Multi Model Approach For Predicting The Impact Of Household Travel On Urban Air Quality And Simulating Population Exposure written by Marianne Hatzopoulou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Air categories.
The population and economic growth experienced by Canadian metropolitan areas in the past twenty years, has been associated with increased levels of car ownership and vehicle kilometres travelled leading to a deterioration of air quality and public health and an increase in greenhouse gas emissions. The need to modify urban growth patterns has motivated planning agencies in Canada to develop a broad range of policies aiming at achieving a more sustainable transportation sector. The challenge however, remains in the ability to test the effectiveness of proposed policy measures. This situation has led to a renewed interest in integrated land-use and transport models to support transport policy appraisal. This research is motivated by the need to improve transport policy appraisal through the use of integrated land-use and transport models linked with a range of sub-models that can reflect transport externalities. This research starts with an exploration of the transport policy environment in Canada through a questionnaire-based survey conducted with planners and policy-makers. The survey results highlight the need for tools reflecting the sustainability impacts of proposed policies. While the second part of this research explores sustainability indicators and recommends a set of social, economic, and environmental measures, linked with integrated land-use and transport models; effort is dedicated to estimate the environmental indicators as part of this thesis. As such, the third part of this research involves the development of an emission-dispersion-exposure modelling framework. The framework includes a suite of sub-models including an activity-based travel demand model (TASHA), an emission factor model (Mobile6.2C), a meteorological model (CALMET), and a dispersion model (CALPUFF). The framework is used to estimate link-based emissions of light-duty vehicles in the Greater Toronto Area under a base scenario for 2001. Dispersion of emissions is then conducted and linked with population in order to estimate exposure to air pollution.