Dynamic Route Choise Modelling Of The Effects Of Travel Information Using Rp Data
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Dynamic Route Choise Modelling Of The Effects Of Travel Information Using Rp Data
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Author : Giselle de Moraes Ramos
language : en
Publisher:
Release Date : 2015
Dynamic Route Choise Modelling Of The Effects Of Travel Information Using Rp Data written by Giselle de Moraes Ramos and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Route choice categories.
Dynamic Travel Choice Models
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Author : Huey-Kuo Chen
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Dynamic Travel Choice Models written by Huey-Kuo Chen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Business & Economics categories.
Contains up-to-date and accessible material, plus all the necessary mathematical background. By verifying the asymmetric property of the dynamic link travel time function, while identifying the inflow, exit flow and number of vehicles on a physical link as three different states over time, the author adopts a variational inequality approach using one time-space link variable. This is then used to formulate problems with deterministic, stochastic and fuzzy traffic information. The book is thus of particular interest to those readers involved in aspects of model formulation, solution algorithm, equivalence analysis and numerical examples.
Decision Field Theoretical Analysis And Modelling Of Dynamic Route Choice Deliberation Process
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Author : Hoda M. Talaat
language : en
Publisher:
Release Date : 2008
Decision Field Theoretical Analysis And Modelling Of Dynamic Route Choice Deliberation Process written by Hoda M. Talaat and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Automobile drivers categories.
Intelligent Transportation Systems applications require a thorough understanding of drivers' route choice behaviour in a complex network under real-time information. This research attempts to describe and model route choice behaviour at the disaggregate individual level and from a psychological decision-making process perspective. We base our proposed behavioural route choice theory and model of the drivers' mental deliberation process on the scientifically-sound Decision Field Theory (DFT). DFT is a process-oriented modelling ground of individuals' decision making that simulates the evolution of preferences during deliberation. Laboratory experiments are conducted that expose human subjects to realistic network and traffic conditions while monitoring and recording their route choices under varying experimental conditions. Recorded data are used for analyzing drivers' route choices and for the development and calibration of a DFT-based route choice theory and framework. A simple "mixed reality" simulator is developed to serve as an experimentation platform. The mixed reality platform enables a driver to use a Backpedal steering device to navigate through a microscopic simulation model of the waterfront portion of downtown Toronto. Analysis results reveal the significance of the impacts of some situational factors (e.g. information content, information reliability, and inertia effects), and some personal factors (e.g. gender differences), on drivers' route choice attitudes. Estimation of the DFT route choice model parameters is performed based on the experimental observations. Genetic algorithms are used as the optimization tool to calibrate model parameters and minimize the discrepancy between model output and observed behaviour. The developed DFT model is used to study the impact of time pressure constraints on drivers' compliance behaviour. Variations in impact trends are estimated with varying information characteristics (form and reliability). Finally, an alternative structural-oriented parameter estimation methodology is adopted for comparative purposes. In the structural-oriented methodology, the deliberation time dimension is completely ignored during the estimation of the model parameters. Analysis results reveal the superiority of the process-oriented DFT route choice model in improving the credibility of route choice predictions. Furthermore, the developed DFT model contributes to enhancing the understanding of the impact and the influence mechanisms of personal/situational factors on drivers' route choice attitudes.
Traffic Information And Learning In Day To Day Route Choice
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Author : Enide A. I. Bogers
language : en
Publisher:
Release Date : 2009
Traffic Information And Learning In Day To Day Route Choice written by Enide A. I. Bogers and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Automobile drivers 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.
Advances In Dynamic Network Modeling In Complex Transportation Systems
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Author : Satish V. Ukkusuri
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-21
Advances In Dynamic Network Modeling In Complex Transportation Systems written by Satish V. Ukkusuri and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-21 with Business & Economics categories.
This edited book focuses on recent developments in Dynamic Network Modeling, including aspects of route guidance and traffic control as they relate to transportation systems and other complex infrastructure networks. Dynamic Network Modeling is generally understood to be the mathematical modeling of time-varying vehicular flows on networks in a fashion that is consistent with established traffic flow theory and travel demand theory. Dynamic Network Modeling as a field has grown over the last thirty years, with contributions from various scholars all over the field. The basic problem which many scholars in this area have focused on is related to the analysis and prediction of traffic flows satisfying notions of equilibrium when flows are changing over time. In addition, recent research has also focused on integrating dynamic equilibrium with traffic control and other mechanism designs such as congestion pricing and network design. Recently, advances in sensor deployment, availability of GPS-enabled vehicular data and social media data have rapidly contributed to better understanding and estimating the traffic network states and have contributed to new research problems which advance previous models in dynamic modeling. A recent National Science Foundation workshop on “Dynamic Route Guidance and Traffic Control” was organized in June 2010 at Rutgers University by Prof. Kaan Ozbay, Prof. Satish Ukkusuri , Prof. Hani Nassif, and Professor Pushkin Kachroo. This workshop brought together experts in this area from universities, industry and federal/state agencies to present recent findings in this area. Various topics were presented at the workshop including dynamic traffic assignment, traffic flow modeling, network control, complex systems, mobile sensor deployment, intelligent traffic systems and data collection issues. This book is motivated by the research presented at this workshop and the discussions that followed.
Development Of Regional Airports
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Author : M. N. Postorino
language : en
Publisher: WIT Press
Release Date : 2010
Development Of Regional Airports written by M. N. Postorino and has been published by WIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Technology & Engineering categories.
This book gives an overview of the main aspects of the potential development of regional airports particularly the economic aspects, the role of low-cost companies, demand modelling, the airport, airline and access mode choices, and the relationships between capacity constraints on hubs and the growth of regional airports.
Transportation Research Record
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Author :
language : en
Publisher:
Release Date : 2002
Transportation Research Record written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Air travel categories.
"For more than 50 years, the Transportation Research Record has been internationally recognized as one of the preeminent peer-reviewed journals for transportation research papers from authors in the United States and from around the world. One of the most cited transportation journals, the TRR offers unparalleled depth and breadth in the coverage of transportation topics from both academic and practitioner perspectives. All modes of passenger and freight transportation are addressed in papers covering a wide array of disciplines, including policy, planning, administration, economics and financing, operations, construction, design, maintenance, safety, and more."--Publisher's website
Handbook Of Choice Modelling
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Author : Stephane Hess
language : en
Publisher: Edward Elgar Publishing
Release Date : 2024-06-05
Handbook Of Choice Modelling written by Stephane Hess and has been published by Edward Elgar Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-05 with Business & Economics categories.
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.
Dynamic Decision And Adjustment Processes In Commuter Behavior Under Real Time Information
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Author : Karthik K. Srinivasan
language : en
Publisher:
Release Date : 2002
Dynamic Decision And Adjustment Processes In Commuter Behavior Under Real Time Information written by Karthik K. Srinivasan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Commuters categories.
Advanced Traveler Information Systems (ATIS), by providing real-time traffic information, can assist trip-makers in selecting efficient travel choices, and aid the attainment of desirable system goals including reduced costs and increased efficiencies. The success of ATIS in achieving such goals critically depends on user behavior in response to information. This research focuses on investigating dynamic aspects in commuter behavior under real-time information. A dynamic interactive travel-behavior simulator, that enables a consistent representation of the nonlinear time-dependent interactions between network performance, trip-makers choices, and information, is used to observe trip-maker behavior. Using the simulator, interactive experiments are conducted where a range of experimental factors including network loading, day-to-day traffic evolution and ATIS information strategies are varied and the consequent trip-maker behavior is observed. Constituent models are proposed to analyze the choice dimensions of route, departure time, and compliance. The dynamic kernal logit (DKL) formulation is presented for analyzing these data and its theoretical and computational suitability established. The results confirm the significance of compliance and inertia as key mechanisms influencing route choice. Departure time adjustments appear to be based on a sequential heuristic search. Calibrated models also provide evidence of learning, adjustment, perception, judgment, and updating processes in trip-maker behavior. Empirical results indicate that real-time information and time-dependent network conditions are strong determinants of trip-maker behavior in a commuting context. The nature and quality of ATIS information (accuracy and reliability), the magnitude of network loading and its day-to-day evolution, and users' past traffic experience are important influences on how commuters select routes and departure times. At the unobserved level, general dynamic and stochastic patterns, including, heterogeneity, state-dependence, habit-persistence, and correlations are present in trip-makers' decisions. These substantive results have important implications for network state prediction, travel demand forecasting, design and evaluation of ATIS services and deployment of Intelligent Transportation System (ITS) programs. User behavior models developed here can be integrated with dynamic network traffic assignment models to obtain more accurate system performance modeling capabilities with considerable applications in tactical and strategic system planning and traffic operations.
