Download or read book Travel Mode Choice Modeling Using Artificial Neural Networks ANN written by Amudapuram Mohan Rao and published by LAP Lambert Academic Publishing. This book was released on 2012-07 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mode choice modeling is the most important element of transportation planning as it affects the general efficiency of travel and the allocation of resources. It is the third step in the conventional four-step transportation forecasting model The analysis of mode choice using conventional techniques is tedious process, the latest techniques like neural networks application is getting popular in recent days. Neural network is a system composed of many simple processing elements operating in parallel whose function is determined by network structure, connection strength, and the processing performed at computing elements or nodes. This book explained systematically how one can formulate a problem and selection of variables and identifying the influence of variables through a case studies selected in India, by reading this study one can easily attack the problem without any difficulty. This book is useful for academic people and practicing engineers for solve the mode choice analysis.

Download or read book Mode Choice Analysis and Prediction of Trip Chaining Behavior written by Chun-Wei Lin and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper examines the relationship between mode choice of trip chaining and several variables, such as the complexity of trip chaining and the trip maker's characteristics. A multinomial logit model is developed in this paper. Model parameters are estimated using the 2001 National Household Travel Survey (NHTS) data collected from Dane county, Wisconsin, USA. The choice set of the modes are drive alone (DA), shared ride (SR), bus, bike, and walk. With the understanding of travel behavior of trip chaining, it could be easier for agencies to make decision on establishing transportation policy to avoid travel delay. Planners can also have a better understanding about how to improve transportation policy. For example, public transit is seldom used in trip chaining. The reason is probably that the mobility of public transit is not suitable for the travelers. Travelers are more likely to choose drive alone because of its convenience. However, delays will increase if most of the travelers choose drive alone as their mode. If the planners can increase mobility of public transit, improve the connection between each place for public transit, or encourage more travelers to use public transit, they could solve this problem. With deep understanding of the trip chaining behavior, we could also make better predictions on how different types of modes such as automated vehicles or UBER would have impacts when involved in the transportation system. Based on the findings from the 2001 NHTS data, the difference between trip chaining patterns are further examined to see if there is robust growth in trip chaining over time. The 2009 and 2017 NHTS data collected from Wisconsin, USA are used. Besides, the difference between trip chaining patterns over locations are examined as well. Seven cities chosen as the seven smartest cities from the U.S. Department of Transportation are examined in this study. They are Austin, Columbus, Denver, Kansas City, Pittsburgh, Portland, and San Francisco respectively. After examining which factors have statistically significant impacts on mode choice of trip chaining, 10-fold cross validation method is applied to find how well the multinomial logit models are on predicting the trip chaining travel behavior. Besides, the accuracy rate of the neural network model is also computed for the purpose of comparison with the multinomial logit model. Keywords: Tours, Trip chaining, Travel behavior, Mode choice, Multinomial logit model, Machine learning, Neural network model, Ensemble bagging model, 10-fold cross validation

Download or read book Artificial Neural Network Modelling written by Subana Shanmuganathan and published by Springer. This book was released on 2016-02-03 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling.

Download or read book Neural Networks in Transport Applications written by Veli Himanen and published by Routledge. This book was released on 2019-07-09 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: First published in 1998, this volume enters the debate on human behaviour in the form of neural networks in a spatial context. As most transportation research techniques had been developed in the 1960s and 1970s, these authors sought to bring that research into the modern era. Featuring 17 articles from 37 contributors, it begins with an overview and proceeds to examine aspects of travel behaviour, traffic flow and traffic management.

Download or read book Mode Choice Models for Long Distance Travel in United States of America written by Isaradatta Rasmidatta and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The influences of technology and economic development on human activity are increasingly taking place on larger spatial scales. As a consequence, complementary interactions between urban regions are getting stronger, which causes urban regions to change. In order to stimulate the integration between regions and states level, policy makers need increased knowledge of the factors that influence long distance travel. From an environmental perspective, long distance trips can be very important because most of the trips are made in personal vehicles or airplanes that affect emissions and fuel consumption. Mode choice alternatives for long distance travel include: personal vehicle, air, and ground. Trip purposes (business, personal business, and pleasure) are considered in modeling. Based on the research results, a household located in an urban area plays an important role in the mode choice decision. A traveler's occupation may affect the mode choice decision between personal vehicle and public carrier; a traveler in the sales, service, or other occupational categories tends to travel by a personal vehicle rather than a public carrier. A traveler who travels over long weekends, has a household income below $20,000, lives in an urban area, has many household members on the trip, or spends not many nights on the trip prefers to make a long distance trip by personal vehicle. Considering age, as the age of traveler increases, the traveler tends to travel by the air mode; this is the same as route distance increases. In this study, variables that are exclusive to specific trip purposes between business, personal business, and pleasure include the number of vehicles in a household, traveler occupation, and household income. The prediction results show that Neural Network models (piecewise linear network floating search) outperform the percent correct for two mode choice (personal vehicle and air mode) cases and nested logit models outperform the percent correct for three mode choice (personal vehicle, air, and ground) cases. The results indicate that Neural Networks are a possible model for estimating long distance travel mode choices; however, for data mining, logistic regression provides better explanations of the variables, especially, for independence of irrelevant alternatives.

Download or read book Multivariate Statistical Machine Learning Methods for Genomic Prediction written by Osval Antonio Montesinos López and published by Springer Nature. This book was released on 2022-02-14 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.

Download or read book Equality of Opportunity in Travel Behavior Prediction with Deep Neural Networks and Discrete Choice Models written by Yunhan Zheng and published by . This book was released on 2021 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although researchers increasingly adopt machine learning to model travel behavior, they predominantly focus on prediction accuracy, while largely ignore the ethical challenges and the adverse social impacts embedded in the machine learning algorithms. This study introduces the important missing dimension - computational fairness - to travel behavioral analysis. It highlights the accuracy-fairness tradeoff instead of the single dimensional focus on prediction accuracy in the contexts of deep neural network (DNN) and discrete choice models (DCM). The author firstly operationalizes computational fairness by equality of opportunity, then differentiates between the bias inherent in data and the bias introduced by modeling. The models inheriting the inherent biases can risk perpetuating the existing inequality in the data structure, and the biases in modeling can further exacerbate it. The author then demonstrates the prediction disparities in travel behavioral modeling using the National Household Travel Survey 2017. Empirically, DNN and DCM reveal consistent prediction disparities across multiple social groups, although DNN can outperform DCM in prediction disparities because of DNN's smaller misspecification error. To mitigate prediction disparities, this study introduces an absolute correlation regularization method, which is evaluated with the synthetic and the real-world data. The results demonstrate the prevalence of prediction disparity in travel behavior modeling, which can exacerbate social inequity if the prediction results without fairness adjustment are used for transportation policy making. As such, the author advocates for careful considerations of the fairness problem in travel behavior modeling, and the use of bias mitigation algorithms for fair transport decisions.

Download or read book Applied Choice Analysis written by David A. Hensher and published by Cambridge University Press. This book was released on 2005-06-02 with total page 746 pages. Available in PDF, EPUB and Kindle. Book excerpt: Almost without exception, everything human beings undertake involves a choice. In recent years there has been a growing interest in the development and application of quantitative statistical methods to study choices made by individuals with the purpose of gaining a better understanding both of how choices are made and of forecasting future choice responses. In this primer the authors provide an unintimidating introduction to the main techniques of choice analysis and include detail on themes such as data collection and preparation, model estimation and interpretation and the design of choice experiments. A companion website to the book provides practice data sets and software to estimate the main discrete choice models such as multinomial logit, nested logit and mixed logit. This primer will be an invaluable resource to students as well as of immense value to consultants and professionals, researchers and anyone else interested in choice analysis and modelling.

Download or read book Recent Trends in Mechatronics Towards Industry 4 0 written by Ahmad Fakhri Ab. Nasir and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents part of the iM3F 2020 proceedings from the Mechatronics track. It highlights key challenges and recent trends in mechatronics engineering and technology that are non-trivial in the age of Industry 4.0. It discusses traditional as well as modern solutions that are employed in the multitude spectra of mechatronics-based applications. The readers are expected to gain an insightful view on the current trends, issues, mitigating factors as well as solutions from this book.

Download or read book Explainable AI Interpreting Explaining and Visualizing Deep Learning written by Wojciech Samek and published by Springer Nature. This book was released on 2019-09-10 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.

Download or read book Deterministic Artificial Intelligence written by Timothy Sands and published by BoD – Books on Demand. This book was released on 2020-05-27 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kirchhoff’s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton’s laws, while rotational motion mechanics comply with Euler’s moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research culminating here with a text on the ability to make rigid bodies in rotation become self-aware, and even learn. This book is meant for basic scientifically inclined readers commencing with a first chapter on the basics of stochastic artificial intelligence to bridge readers to very advanced topics of deterministic artificial intelligence, espoused in the book with applications to both electromechanics (e.g. the forced van der Pol equation) and also motion mechanics (i.e. Euler’s moment equations). The reader will learn how to bestow self-awareness and express optimal learning methods for the self-aware object (e.g. robot) that require no tuning and no interaction with humans for autonomous operation. The topics learned from reading this text will prepare students and faculty to investigate interesting problems of mechanics. It is the fondest hope of the editor and authors that readers enjoy the book.

Download or read book Handbook of Choice Modelling written by Stephane Hess and published by Edward Elgar Publishing. This book was released on 2014-08-29 with total page 721 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Choice Modelling, composed of contributions from senior figures in the field, summarizes the essential analytical techniques and discusses the key current research issues. The book opens with Nobel Laureate Daniel McFadden calling for d

Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

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 A Comparison of the Predictive Potential of Artificial Neural Networks and Nested Logit Models for Commuter Mode Choice written by David A. Hensher and published by . This book was released on 1998 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Choice and Constraints Oriented Modeling written by Pat Burnett and published by . This book was released on 1978 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: Development of Household Interaction Game, pilot-tested on elderly group in Oklahoma City.

Download or read book Dynamic Travel Choice Models written by Huey-Kuo Chen and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.