Download or read book Modeling and Forecasting the Impact of Major Technological and Infrastructural Changes on Travel Demand written by Feras El Zarwi and published by . This book was released on 2017 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Implementing Innovation in Planning Practice written by Gregory Louis Newmark and published by . This book was released on 2011 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: Travel demand modeling is a core technology of transportation planning and has been so for half a century. This technology refers to the structured use of mathematical formulae and spatial data to forecast the likely travel impacts of possible transportation, land use, and demographic scenarios. Although this planning practice is pervasive, critics have long argued that is has been resistant to innovation. As the policy scenarios explored through modeling become increasingly complex, particularly in the face of climate change, the question arises of whether regional planning agencies will be able to change their practices through implementing innovation. This research addresses this question by examining the history of travel demand modeling as practiced at regional planning agencies, interviewing travel demand modeling experts, conducting detailed case studies of model practice evolution at two metropolitan planning organizations, the San Francisco Bay Area's Metropolitan Transportation Commission (MTC) and the capital region's Sacramento Area Council of Governments (SACOG), and analyzing the early impacts of California's groundbreaking climate change legislation on the modeling practiced in the Golden State. The findings suggest that far from being a static practice, travel demand modeling at regional agencies has advanced, particularly with public interest in exploring the impacts of major policy interventions. The nature of travel demand models does not naturally foster changes in practice; however, government action can structure the innovation process by establishing clear expectations of agency modeling capabilities to meet legislative mandates, providing resources for investments in new approaches, and creating forums for interagency interaction and information dissemination.

Download or read book Urban Informatics written by Wenzhong Shi and published by Springer Nature. This book was released on 2021-04-06 with total page 941 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity.

Download or read book Handbook of Choice Modelling written by Stephane Hess and published by Edward Elgar Publishing. This book was released on 2024-06-05 with total page 797 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thoroughly revised second edition Handbook provides an authoritative and in-depth overview of choice modelling, covering essential topics range from data collection through model specification and estimation to analysis and use of results. It aptly emphasises the broad relevance of choice modelling when applied to a multitude of fields, including but not limited to transport, marketing, health and environmental economics.

Download or read book Metropolitan Travel Forecasting written by National Research Council (U.S.). Committee for Determination of the State of the Practice in Metropolitan Area Travel Forecasting and published by Transportation Research Board. This book was released on 2007-10-18 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: TRB Special Report 288, Metropolitan Travel Forecasting: Current Practice and Future Direction, examines metropolitan travel forecasting models that provide public officials with information to inform decisions on major transportation system investments and policies. The report explores what improvements may be needed to the models and how federal, state, and local agencies can achieve them. According to the committee that produced the report, travel forecasting models in current use are not adequate for many of today's necessary planning and regulatory uses.

Download or read book Innovations in Travel Demand Modeling Papers written by and published by Transportation Research Board. This book was released on 2008 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 31 individual authored papers from the breakout sessions are contained in Volume 2"--Pub. desc.

Download or read book Forecasting Travel in Urban America written by Konstantinos Chatzis and published by MIT Press. This book was released on 2023-07-11 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: A history of urban travel demand modeling (UTDM) and its enormous influence on American life from the 1920s to the present. For better and worse, the automobile has been an integral part of the American way of life for decades. Its ascendance would have been far less spectacular, however, had engineers and planners not devised urban travel demand modeling (UTDM). This book tells the story of this irreplaceable engineering tool that has helped cities accommodate continuous rise in traffic from the 1950s on. Beginning with UTDM’s origins as a method to help plan new infrastructure, Konstantinos Chatzis follows its trajectory through new generations of models that helped make optimal use of existing capacity and examines related policy instruments, including the recent use of intelligent transportation systems. Chatzis investigates these models as evolving entities involving humans and nonhumans that were shaped through a specific production process. In surveying the various generations of UTDM, he delves into various means of production (from tabulating machines to software packages) and travel survey methods (from personal interviews to GPS tracking devices and smartphones) used to obtain critical information. He also looks at the individuals who have collectively built a distinct UTDM social world by displaying specialized knowledge, developing specific skills, and performing various tasks and functions, and by communicating, interacting, and even competing with one another. Original and refreshingly accessible, Forecasting Travel in Urban America offers the first detailed history behind the thinkers and processes that impact the lives of millions of city dwellers every day.

Download or read book Innovations in Travel Demand Modeling Session summaries written by and published by Transportation Research Board. This book was released on 2008 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 31 individual authored papers from the breakout sessions are contained in Volume 2"--Pub. desc.

Download or read book Forecasting the Impact of Sociodemographic Changes on Travel Demand written by Konstadinos G. Goulias and published by . This book was released on 1992 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: Microanalytic Integrated Accounting System (MIDAS) forecasting model.

Download or read book Advanced Practices in Travel Forecasting written by Rick Donnelly and published by Transportation Research Board. This book was released on 2010 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: TRB's National Cooperative Highway Research Program (NCHRP) Synthesis 406: Advanced Practices in Travel Forecasting explores the use of travel modeling and forecasting tools that could represent a significant advance over the current state of practice. The report examines five types of models: activity-based demand, dynamic network, land use, freight, and statewide.

Download or read book A Disaggregate Travel Demand Model written by Martin Gomm Richards and published by . This book was released on 1975 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Forecasting Urban Travel written by David E. Boyce and published by Edward Elgar Publishing. This book was released on 2015-02-27 with total page 661 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting Urban Travel presents in a non-mathematical way the evolution of methods, models and theories underpinning travel forecasts and policy analysis, from the early urban transportation studies of the 1950s to current applications throughout the

Download or read book Travel Demand Forecasting Parameters and Techniques written by and published by Transportation Research Board. This book was released on 2012 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: TRB’s National Cooperative Highway Research Program (NCHRP) Report 716: Travel Demand Forecasting: Parameters and Techniques provides guidelines on travel demand forecasting procedures and their application for helping to solve common transportation problems.

Download or read book Directions to Improve Urban Travel Demand Forecasting written by and published by . This book was released on 1978 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Advanced Urban Travel Demand Forecasting written by and published by . This book was released on 1999 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This course attempts to communicate to travel modeling professionals some of the [travel demand forecasting] procedures developed by their colleagues around the U.S. and abroad, most of which have been implemented as part of an existing travel demand modeling system."--p.1-5

Download or read book Activity based Travel Demand Models written by Joe Castiglione (Writer on transportation) and published by . This book was released on 2015 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: TRB's second Strategic Highway Research Program (SHRP 2) Report S2-C46-RR-1: Activity-Based Travel Demand Models: A Primer explores ways to inform policymakers' decisions about developing and using activity-based travel demand models to better understand how people plan and schedule their daily travel. The document is composed of two parts. The first part provides an overview of activity-based model development and application. The second part discusses issues in linking activity-based models to dynamic network assignment models.

Download or read book New Mobility Options in Travel Demand Forecasting and Modeling written by Naveen Eluru and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Emerging transportation technologies and shared mobility services, or new mobility options (NMOs), are affecting travel behavior and demand. NMOs may include shared micromobility, transportation networking companies (TNCs), and connected and autonomous vehicles (CAVs). As NMOs grow in availability and use, transportation planners and decision-makers need to be able to understand how to harness positive and mitigate negative impacts. NCHRP Research Report 1113: New Mobility Options in Travel Demand Forecasting and Modeling: A Guide, from TRB's National Cooperative Highway Research Program, provides travel demand modeling practitioners with ways to consider NMOs in travel demand forecasting models (TDFMs) - one of the primary tools available to understand potential impacts and future uncertainties."--Publisher's website