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Author: Mubassira Khan Publisher: ISBN: Category : Languages : en Pages : 360
Book Description
Commercial vehicle movement within an urban area is an integral part of a region's economic growth and has significant impact on the quality of life. Commercial traffic grows with economic activity and population growth. However, in regional models commercial traffic is not described as well as person travel. Modeling commercial vehicles is complex because of the involvement of multiple decision agents including shippers, carriers, and receivers and their interactions. The proprietary nature of truck data often limits development of behavioral econometric models that have superior predictive and policy analysis abilities. The efficient movement of goods is a very important component to urban civilization and economic development and therefore, understanding truck movement behavior is an important area of interest for transportation policy planning. The objective of this dissertation is to contribute to apply advanced choice modeling methods to analyze commercial vehicle travel behavior within an urban area. This research collects disaggregate level truck generation data from the business establishments located in a sample urban region and uses the collected data to evaluate factors that affect truck trip generation patterns using linear regression and ordered logit model structures. The results of the study show that employment size, business industrial class, truck ownership, land-use class, and land-value affect trip generation behavior. This research also analyzed three different multiple discrete-continuous (MDC) choice situations encountered by commercial vehicles on a daily basis. These choices are 1) the choice of tour chain(s) and the number of trips in each tour chain, 2) the time (s) of day choice to perform daily activities and the corresponding vehicle-miles traveled; and 3) the choice of destination location(s) among alternative destination zones and the number of stops at each destination zone. The study find that commercial vehicle characteristics, shipment characteristics, transportation network attributes, base location and intermediate stop location features affect the first two choice situations while the level of service and zonal attributes affect the destination choice behavior of commercial vehicle daily travel.
Author: Mubassira Khan Publisher: ISBN: Category : Languages : en Pages : 360
Book Description
Commercial vehicle movement within an urban area is an integral part of a region's economic growth and has significant impact on the quality of life. Commercial traffic grows with economic activity and population growth. However, in regional models commercial traffic is not described as well as person travel. Modeling commercial vehicles is complex because of the involvement of multiple decision agents including shippers, carriers, and receivers and their interactions. The proprietary nature of truck data often limits development of behavioral econometric models that have superior predictive and policy analysis abilities. The efficient movement of goods is a very important component to urban civilization and economic development and therefore, understanding truck movement behavior is an important area of interest for transportation policy planning. The objective of this dissertation is to contribute to apply advanced choice modeling methods to analyze commercial vehicle travel behavior within an urban area. This research collects disaggregate level truck generation data from the business establishments located in a sample urban region and uses the collected data to evaluate factors that affect truck trip generation patterns using linear regression and ordered logit model structures. The results of the study show that employment size, business industrial class, truck ownership, land-use class, and land-value affect trip generation behavior. This research also analyzed three different multiple discrete-continuous (MDC) choice situations encountered by commercial vehicles on a daily basis. These choices are 1) the choice of tour chain(s) and the number of trips in each tour chain, 2) the time (s) of day choice to perform daily activities and the corresponding vehicle-miles traveled; and 3) the choice of destination location(s) among alternative destination zones and the number of stops at each destination zone. The study find that commercial vehicle characteristics, shipment characteristics, transportation network attributes, base location and intermediate stop location features affect the first two choice situations while the level of service and zonal attributes affect the destination choice behavior of commercial vehicle daily travel.
Author: Moshe Ben-Akiva Publisher: MIT Press ISBN: 0262536404 Category : Architecture Languages : en Pages : 414
Book Description
Discrete Choice Analysis presents these results in such a way that they are fully accessible to the range of students and professionals who are involved in modelling demand and consumer behavior in general or specifically in transportation - whether from the point of view of the design of transit systems, urban and transport economics, public policy, operations research, or systems management and planning. The methods of discrete choice analysis and their applications in the modelling of transportation systems constitute a comparatively new field that has largely evolved over the past 15 years. Since its inception, however, the field has developed rapidly, and this is the first text and reference work to cover the material systematically, bringing together the scattered and often inaccessible results for graduate students and professionals. Discrete Choice Analysis presents these results in such a way that they are fully accessible to the range of students and professionals who are involved in modelling demand and consumer behavior in general or specifically in transportation - whether from the point of view of the design of transit systems, urban and transport economics, public policy, operations research, or systems management and planning. The introductory chapter presents the background of discrete choice analysis and context of transportation demand forecasting. Subsequent chapters cover, among other topics, the theories of individual choice behavior, binary and multinomial choice models, aggregate forecasting techniques, estimation methods, tests used in the process of model development, sampling theory, the nested-logit model, and systems of models. Discrete Choice Analysis is ninth in the MIT Press Series in Transportation Studies, edited by Marvin Manheim.
Author: Wenzhong Shi Publisher: Springer Nature ISBN: 9811589836 Category : Social Science Languages : en Pages : 941
Book Description
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.
Author: David A. Hensher Publisher: Taylor & Francis ISBN: 1000362973 Category : Transportation Languages : en Pages : 872
Book Description
Originally published in 1979, this study deals on a fully comprehensive level with both passenger and freight travel. The 40 chapters deal with an extensive range of related topics, including equilibrium modelling, theoretical and conceptual developments in demand modelling, goods movement and forecasting and policy. It outlines approaches to understanding travel behaviour, which move beyond the individual choice theory towards a broader consideration of activities.
Author: Bruce D. Spear Publisher: ISBN: Category : Choice of transportation Languages : en Pages : 176
Book Description
The report documents the application of individual choice (disaggregate) travel demand models in urban transportation planning. Three general areas of application are covered: (1) The traditional travel demand forecasting process; (2) short range, transportation systems management evaluation; and (3) patronage and revenue forecasting for new transportation systems. For each application, the suitability of the model is discussed, recent applications are summarized, and two detailed case studies are presented to demonstrate how the models were used. A short primer on individual choice models is included to provide the planner with enough information to understand how the models work and their differences from more conventional planning models.
Author: Cheng Zhuo Publisher: ISBN: 9781369201215 Category : Languages : en Pages :
Book Description
With respect to VMT: · The initial simplified model of ln(VMT+1), which includes only income and driving mobility limitation as explanatory variables, and was estimated with the NHTS dataset, has an R-squared of 0.1486. · The explanatory power of the initial model improves drastically after incorporating household-related variables into the model specification: when variables related to number of workers, number of drivers, and number of children enter the model, the R-squared value for the this "intermediate" model increases to 0.3204. · Furthermore, accounting for residential neighborhood land use also improves the explanatory power of the log-linear model of VMT. When we include density of the residential census tract as an additional explanatory variable into the "intermediate" model from above, the R-squared value increases to 0.3382. Therefore, the inclusion of a single land use variable improves the R-squared of the model by 5.6%. · Finally, the most complete model, which includes the density variable, and which is segmented based on neighborhood type and city size/presence of rail to allow model coefficients to vary in the six different clusters, has an R-Squared of 0.3429. This thesis makes a useful contribution in improving our understanding of why certain households choose to own fewer vehicles than usual. First, the study highlights the incremental contributions of specific groups of variables toward explaining observed behaviors. Several of these variables, e.g. personal attitudes, are often not controlled for in vehicle ownership and travel behavior studies. Accordingly, several conclusions of interest to planners and policy-makers can be drawn from the analysis of the results from this study: we find that the inclusion of attitudinal variables increases by a modest, but not trivial, amount the model's ability to predict observed choice. In particular, all else equal, pro-environment and pro-transit attitudes are found to contribute to explaining the choice of households who do not own a car, while a pro-driving attitude is found to have a positive effect on the choice of owning more vehicles than expected. Thus, the study highlights the importance of including individual attitudes in future surveys that collect information on household vehicle ownership and travel behavior. Doing so would improve the ability to correctly predict individuals' choices (including heterogeneity in choice processes across individuals), which can better support the evaluation of planning policies. Further, this study confirms the importance of residential location and of the characteristics of land use in affecting vehicle ownership and VMT. Specifically, the estimation results from the models that control for residential neighborhood characteristics highlight not only how VO and VMT vary for households across different regions, from a small town to a large city served by rail transit, but also how the influence of specific variables on VO and VMT differs by land-use type. For instance, the presence of more children in the household contributes to greater VMT, and the beta coefficient for the children variable is roughly the same across all lower-density neighborhoods (LDNs), whether in a small town or large metro area. However, for higher-density neighborhoods (HDNs), households living in smaller towns have greater increases in VMT compared to those living in large cities with rail, when the number of children under 16 increases. The presence of richer public transportation and the greater practicality of active transportation in densely-populated portions of large metropolitan areas may explain this phenomenon. The study provides insights into ways to increase the share of households who have ZVO or less than expected VMT. For example, the results from the study support the principle that policies designed to improve public transit and expand high-density neighborhoods can successfully contribute to reducing vehicle ownership and VMT, although the specific results in terms of reduction of car use also depend on the characteristics of the household. And although bicycling infrastructure was not a basis for the land use segmentation we employed in this study, given that higher-density neighborhoods can shorten trip lengths and thereby make active transportation more competitive with the automobile for short trips, the study suggests that creating a more effective bicycling infrastructure, especially in higher-density neighborhoods, would also be effective in reducing VO and VMT.
Author: Kenneth Train Publisher: Cambridge University Press ISBN: 0521766559 Category : Business & Economics Languages : en Pages : 399
Book Description
This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
Author: Mubassira Khan
Author: Mubassira Khan
Author: Moshe Ben-Akiva
Author: Pat Burnett
Author: Peter R. Stopher
Author: Wenzhong Shi
Author: David A. Hensher
Author: Bruce D. Spear
Author: Peter R. Stopher
Author: Cheng Zhuo
Author: Kenneth Train