The relationship between travel behavior and the local built environment remains far from entirely resolved, despite several research efforts in the area. The significance and explanatory power of a variety of urban form measures on nonwork activity travel mode choice have been investigated. The travel data used for analysis are from the 1995 Portland Metropolitan Activity Survey conducted by Portland Metro. The database on the local built environment was developed by Song in 2002 and includes a more extensive set of variables than previous studies that have examined the relationship between travel behavior and the local built environment by using the Portland data. The results of the multinomial logit mode choice model indicate that mixed uses promote walking behavior for nonwork activities.
At the time of publication Chandra R. Bhat and Marisol Castro were at the University of Texas at Austin, and Rajesh Paleti was at Parsons Brinckerhoff.
The Texas Department of Transportation (TxDOT), in conjunction with the metropolitan planning organizations (MPOs) under its purview, oversees the travel demand model development and implementation for most of the urban areas in Texas. In these urban areas, a package of computer programs labeled as the “Texas Travel Demand Package” or the “Texas Package” is used as the decision making tool to forecast travel demand and support regional planning, project evaluation, and policy analysis efforts. The Texas Package currently adopts the widely used four step trip-based urban travel demand modeling process, which was developed in the 1960s when the focus of transportation planning was to meet long-term mobility needs through the provision of additional transportation infrastructure supply. The trip-based model was intended to provide basic, aggregate-level, long-term travel demand forecasts for long-range regional transportation plans and evaluation of major infrastructure investments. Over the past three decades, however, the supply-oriented focus of transportation planning has expanded to include the objective of evaluating a range of travel demand management strategies and policy measures to address rapidly growing transportation problems, including traffic congestion and air quality concerns. The travel demand management emphasis, combined with federal regulations, has placed additional information demands on the capabilities of travel demand models. As a result, new approaches have been developed to model and forecast travel demand. The new approaches include the tour-based modeling approach, which employs tours instead of trips as the unit of analysis. The tour-based approach enhances the behavioral realism in modeling travel demand and the abilities of travel forecasting models in assessing transportation policies and evaluating alternative transportation investments. Hence, TxDOT is considering the implementation of tour-based modeling procedures. As a first step of a potential advanced model implementation, this proposed project evaluates the feasibility of, and documents the potential benefits from, a tour-based modeling process. It documents the steps to transition toward a tour-based framework, including an evaluation of data needs, software requirements, and software enhancements, ease of implementation and application, and staffing and related resource needs.
This article formulates an empirical discrete land use model within a spatially explicit economic structural framework for land use change decisions. The underlying framework goes beyond mechanistically fitting models for the spatial process of land use change to more closely link landowner decision behavior to land use patterns. At the same time, the article explicitly considers spatial spillover effects in the decisions of landowners of proximately located parcels. These spillover or peer influences may be due to strategic or collaborative partnerships between landowners, and can be associated with variables observable to the analyst (such as accessibility to city centers and market places) and variables unobservable to the analyst (such as perhaps soil quality and neighborhood attitudes/politics). In addition to spatial spillover effects, heterogeneity is also likely to exist in the decision‐making process of different landowners because of differential responsiveness to various signals relevant to decision making. This leads to correlation in land uses across time that is stationary for the same spatial unit. The article accommodates these technical considerations by formulating a random coefficients spatial lag discrete choice model using a fine resolution for the spatial unit of analysis. Time‐varying random effects are also considered to capture the effects of time‐varying unobserved factors (for instance, unobserved landowner attitudes regarding specific land uses may shift over time). The model is estimated using B hat's maximum approximate composite marginal likelihood inference approach. The analysis is undertaken using the C ity of A ustin parcel‐level land use database for multiple years (1995, 2000, 2003, and 2006). The estimation results indicate that proximity to highways and other roadways, distance from floodplains, parcel location in the context of existing development, and distance from schools are important determinants of land use. As importantly, the results provide very strong evidence of temporal dependency and spatial dynamics in land use decisions. There is also a suggestion that major highways may not only physically partition regions, but may also act as social barriers for didactic interactions among individuals. Este artículo presenta un modelo empírico discreto de uso de tierra dentro de un marco económico estructural espacialmente explícito para la toma de decisiones de cambio de uso de suelo. El marco utilizado por los autores va más allá del ajuste mecánico de modelos al proceso espacial de cambio de uso de suelo pues vincula más estrechamente el comportamiento y decisión del terrateniente a los patrones de uso observados. Al mismo tiempo, el estudio considera explícitamente los efectos espaciales de difusión en las decisiones de los propietarios de las parcelas cercanas. Esta difusión ( spillover ) o influencia de los pares puede deberse a alianzas estratégicas o de colaboración entre los terratenientes. También pueden estar asociadodos a variables observables (como la accesibilidad a los centros de las ciudades y plazas de mercado), así como a las variables no observables (por ejemplo, la calidad del suelo y las actitudes o tendencias políticas de los residentes). Además de los efectos de spillover , también es probable que exista heterogeneidad en el proceso de toma de decisiones de los diversos terratenientes, debido a su diferente capacidad de respuesta a las distintas señales que influencian la toma de decisiones. Esto conduce a que exista correlación en los usos del suelo a través del tiempo que es estacionaria para la misma unidad espacial. En el estudio todas las consideraciones técnicas mencionadas son tomadas en cuenta mediante la formulación de un modelo de elección discreta con rezago espacial con coeficientes aleatorios ( random coefficients spatial lag discrete choice model ) usando unidades espaciales de alta resolución. Para capturar los efectos de los factores no observados que varían temporalmente los autores utilizan efectos aleatorios ( random effects ) (por ejemplo, las actitudes de los terratenientes con respecto a usos específicos de tierras pueden cambiar con el tiempo). El modelo propuesto es estimado utilizando el enfoque inferencial de similitudes marginales de aproximaciones máximas compuestas de Bhat (2011) ( maximum approximate composite marginal likelihood ‐ MACML). El análisis se lleva a cabo usando una base de datos de a nivel de parcela base de uso de la tierra de la ciudad de Austin, Texas para varios años (1995, 2000, 2003 y 2006). Los resultados de la estimación indican que la proximidad a las autopistas y otras carreteras, la distancia de las llanuras de inundación, la ubicación de parcela en el contexto del desarrollo urbano existente, y la distancia a las escuelas, son factores importantes para el uso de suelo. Adicionalmente, los resultados proporcionan evidencia muy clara de la dependencia temporal y la dinámica espacial en las decisiones de uso de suelo. El estudio también sugiere que las carreteras principales dividen las regiones no sólo físicamente, sino que también pueden actuar como barreras sociales para las interacciones entre los individuos. 本文提出了土地利用变化决策下空间经济结构框架的一种经验离散土地利用模型。土地利用变化的空间过程与土地所有者决策行为更为紧密连接的土地利用模式，使得潜在的框架超出已有拟合模型。同时，本文明确认为毗邻地块区位在地主决策中具有空间溢出效应。这种溢出或对等效应可能源于与地主有战略上或合作伙伴的关系，并且这种效应分为分析中可测的变量关联（如城市中心和市场区的可达性）和不可测的变量关联（如可能为土地质量和邻居态度/政策）两种。除空间溢出效应，异质性也可能存在于不同地主决策制定的过程中，因为多种信号的不同响应与决策制定相关。这种异质性导致了土地利用在相同空间单元时间演化过程中的相关性是平稳的。本文在理解这些含义的基础上对此采纳的技术考虑是，通过利用随机高分辨率的空间滞后离散选择模型来分析空间单元。随时间变化的随机效应也被考虑用来捕获不可测的时变因子效应（如不可测的关于特殊土地利用的地主态度可能随时间改变）。本文的模型通过Bhat’s（2011）提出的近似最大联合边缘似然推断方法（MACML）进行估计。分析数据来源于奥斯汀市土地利用关于地块尺度多年的数据库（1995，2000，2003，2006）。估计结果表明，与高速公路和其它道路的近邻性，与洪泛平原的距离，现状开发条件下的地块区位，以及跟学校的距离对于土地利用模式的确定是非常重要的决定因素。同样重要的是，该结果为空间动力学与时间依赖性的土地利用决策提供了非常强的证据。本结果也显示主要高速公路不仅是区域分割的物质表征，而且在个体之间的交流中扮演着社会障碍的角色。
Residential relocation or mobility is a critical component of land use dynamics. Models of land use dynamics need to consider residential relocation or mobility behavior of households to be able to predict population demographics land use patterns that are critical to activity and travel demand forecasting. Unfortunately, little is known about residential relocation behavior at the disaggregate level, in terms of both the reasons for relocation and the duration of stay at a given residential location. This paper aims to fill this gap in knowledge by formulating and estimating a joint model of the reason for residential relocation and the duration of stay at a location. The model is estimated on a data set derived from a survey conducted in Zurich, Switzerland, that captures information about residential moves over a 20-year period spanning 1985 to 2004. The paper provides elasticity estimates demonstrating how the model can be applied to evaluate impacts of changes in exogenous factors on residential mobility events.
Given the forecast growth in the Texas population and freight movements, it is clear that substantial demands will be placed on the already heavily used transportation infrastructure of the state. Railroads are thus viewed as a key element of a greater intermodal solution to supply increased travel demand. It is widely hypothesized that rail service (particularly commuter rail on existing tracks) can be less costly than highway expansions when used to supply personal travel. However, it is foreseen that the Texas Department of Transportation (TxDOT) will face many challenges, and in some cases opposition, when the agency proposes to accommodate both passenger and freight trains on the same track or the same right-of-way. In 2004 the TxDOT contracted with the Center for Transportation Research at the University of Texas at Austin to outline and explain the environments in which public agencies and private railroads operate and to highlight the negotiation issues and concerns regarding passenger rail sharing freight infrastructure from both parties' perspectives. The research culminated in this report.
A companion paper proposed a new Generalized Heterogeneous Data Model (GHDM) to jointly model mixed types of dependent variables, including multiple nominal outcomes, multiple ordinal variables, and multiple count variables, as well as multiple continuous variables, and estimate the model using the maximum approximate composite marginal likelihood (MACML) method. This paper undertakes a simulation experiment within the virtual context of integrating residential location choice and travel behavior to evaluate the ability of the MACML approach to recover parameters. The simulation results show that the MACML approach effectively recovers underlying parameters, and also that ignoring the multi-dimensional nature of the relationship among mixed types of dependent variables can lead not only to inconsistent parameter estimation, but also have important implications for policy analysis.
