Journal of Urban Planning and Development

When conducting a feasibility analysis for multifamily housing development plans, changes in key cost elements in the financial analysis, such as construction costs or land values, can be analyzed using simple financial analysis. To conduct a quantitative analysis of key planning elements influencing the feasibility analysis, such as the floor-area ratio, a design must be created on the basis of related laws followed by the financial analysis. Few methods have been presented on the analysis of the more general effects of such planning elements rather than on the conditions of specific land values and construction costs. If these problems can be overcome, the development cost of multifamily housing projects under random conditions may be quantitatively explained with a few input variables, such as key cost elements and key planning elements before the design stage. Therefore, this study developed a standardized financial analysis model using simulation analysis and the varying coefficient model. The development cost was analyzed on the basis of the planning characteristics of multifamily housing development projects under loan redemption. The standardized analysis model and the regression equation that resulted from this study are expected to offer a quantitative analytical method to determine how feasibility studies for multifamily housing development projects are affected by (1) planning characteristics such as the floor-area ratio, the dwelling-area factor, and the construction duration; and (2) key cost elements such as the construction cost, the land value, and the sales rate.

Recent advances in integrated land use and transport modeling have included a shift from aggregate-level modeling to disaggregate, household-level modeling. One potential advantage of this shift is that interdependencies of changes that influence household decisions can be more systematically modeled. However, existing models have not embraced this opportunity fully. Especially in the context of long-term mobility decisions (relocation/car ownership), decisions made on the basis of various dimensions are modeled as independent and cross sectional, whereas in reality they are strongly interlinked. To address these shortcomings, this paper proposes a conceptual framework that offers a more general approach to modeling the dynamics and interdependences across different time horizons of a household’s lifecycle and mobility decisions. The framework incorporates the concept of stress, defined as a discrepancy between a household’s present situation and its aspiration level, which in turn depends, among other things, on the household’s social network. Bayesian belief networks are used to represent the complex direct and indirect dependencies between life-cycle events and long- and short-term mobility decisions.

The urban canopy is a very valuable element in cities, and its environmental benefits are significant. It is very important for cities to assess adequately their canopy. An important part of its value is related to the tree number density according to the population density. The most commonly used methods for tree appraisal use subjective means to obtain the influence of location. A new methodology is developed in this work to obtain an objective location index. The proposed index gives a value to the tree according to its location in the city. A multiple regression model (function of the canopy density per inhabitant and the urban area percentage) is used to calculate the location index. The index has been calculated for the neighborhoods of the city of Santiago del Estero, Argentina, but can be applied to any city with objective variables, available from any local administration.

This work describes the modeling of Year 2030 land-use patterns of the Austin, Texas region by using UrbanSim, an open-source model for microscopic simulation of land development, location choices, and land values at fine spatial resolution. An accompanying travel demand model was run every five years, resulting in accessibility indexes for use in UrbanSim location choice models and $\mathrm{C}{\mathrm{O}}_2$ emissions estimates. To appreciate UrbanSim’s performance and the potential land-use, travel, and energy impacts of such policies, a business-as-usual trend scenario was compared with urban-growth-boundary and added transportation-cost-sensitivity scenarios, an expanded-highway-capacity scenario, and an added-state-highway 130 scenario. As expected, various land-use conditions, travel patterns, and energy consumption results demonstrated some sensitivity to scenario contexts. Although UrbanSim’s specification limitations are multiple and its data requirements are very serious, the model does run reasonably fast and may make good sense over the longer term for interested regions with sophisticated planning staff on board to pursue.

Current methods of traffic impact analysis, which rely on rates and adjustments from the Institute of Transportation Engineers, are believed to understate the traffic benefits of mixed-use developments (MXDs), leading to higher exactions and development fees than necessary and discouraging otherwise desirable developments. The purpose of this study is to create new methodology for more accurately predicting the traffic impacts of MXDs. Standard protocols were used to identify and generate data sets for MXDs in six large and diverse metropolitan regions. Data from household travel surveys and geographic information system (GIS) databases were pooled for these MXDs, and travel and built environmental variables were consistently defined across regions. Hierarchical modeling was used to estimate models for internal capture of trips within MXDs, walking and transit use on external trips, and trip length for external automobile trips. MXDs with diverse activities on-site are shown to capture a large share of trips internally, reducing their traffic impacts relative to conventional suburban developments. Smaller MXDs in walkable areas with good transit access generate significant shares of walk and transit trips, thus also mitigating traffic impacts. Centrally located MXDs, small and large, generate shorter vehicle trips, which reduces their impacts relative to outlying developments.

This paper investigates a number of alternative methods for addressing the empty-cell problem of traditional cross-classification analysis. Data used in the study were collected in the Toronto region in 1986, 1996, 2001, and 2006. Alternative models, developed on each year’s data, were assessed for how well they predicted travel at the disaggregate household level and at the aggregate traffic analysis zone level in the respective years. In addition, the alternative models estimated on the 1986 data set were assessed for their ability to replicate travel in 1996 and 2006. The results show that a method proposed by Mandel and a model developed in this research, which estimates the household trip rate for an empty cell through a linear combination of the predictions yielded by row and column models, overall give the best forecast performance of travel. They perform better than multiple classification analysis, which is the current industry standard for addressing this shortcoming of traditional cross-classification analysis. The combined categories model also performed very well, particularly in predicting travel at the aggregate level of planning interest.

This study estimates a stratified multinomial logit mode choice model of the trip preferences of academicians employed at the Ayazaga campus of Istanbul Technical University. The mode choice model contains three alternatives that are dominantly preferred among academicians: private vehicle, public transport, and service vehicle modes. The data are obtained from a face-to-face questionnaire distributed to a randomly selected number of academicians working on campus. The estimated model is used for a simulation study that aims to understand the differences in modal preferences based on academic ranks and for a scenario analysis to observe the attitudes of the private vehicle users under policy changes. According to the assessment of the analyses, among academician groups of different ranks, different tendencies mostly related to income and age are recognized for actual mode choices, whereas similar approaches prioritizing convenience are noticed under suggested policy changes.

The mobility management tool proposed in this paper estimates the percentage of road space that should be reallocated from private vehicles to public transport according to internalization of the congestion externality generated by private vehicles. This reallocation measure could be implemented on a strategic mobility corridor in the city. The initial increase in trip time that a private vehicle experiences because of the reduction of its road space favors an improvement in mobility. This improved scenario is characterized by a more efficient reassignment of the traffic of private vehicles in the network, a modal change toward public transport, and better trip times in public transport. This tool could achieve a better social welfare compared to an uncontrolled user equilibrium condition. It could have, under specific circumstances, similar results to those by road pricing regarding the discouragement of the travel demand of automobiles and a greater acceptance on behalf of citizens and local administrations.

This paper evaluates the transport and environmental variables affecting individual usage of playgrounds, with a focus on cultural differences. The paper was developed on the basis of a case study that was carried out in Haifa, the third largest city in Israel. The results indicate significant differences between Jewish and Arab populations regarding frequency of playground visits and the extent of playground satisfaction. Differences were also discovered concerning the impact of transport and environmental variables. All of these parameters were found to be more significant among the Jewish population, which seems to be more aware of these variables. An analysis of the transport variables shows that the Jewish population considers factors, such as playground accessibility, distance to other activities, trip chaining, and parking availability, as significantly more essential, compared with the Arab population. The perceived air pollution variable was found to negatively affect frequency of playground visits (only among the Jewish community), as well as the self-satisfaction variable among both communities. The noise variable was found to adversely affect both the frequency of visits and self-satisfaction in both populations.

Whereas brownfield development is of widespread interest, there is scant literature on the environmental impacts of brownfield developments relative to conventional developments. We assembled a set of two residential brownfield and two conventional greenfield developments for a sample of U.S. cities including Baltimore, Chicago, Milwaukee, Minneapolis, Pittsburgh, and St. Louis. Using the travel time and modes of transportation information from the 2000 U.S. Decennial Census, we analyzed the long-term commuting impacts from the two types of developments. Relative to greenfield development neighborhoods, we find that the brownfield development neighborhoods are closer to center cities, have higher public transportation use for commuting, comparable average travel times to work, and lower energy and greenhouse gas emissions for commuting. Future work will extend these results to consider other differential impacts of the two types of developments.

To predict the duration of time that consumers spend in shopping environments, researchers often use hazard models to predict the probabilities of ending the shopping trip as a function of individual sociodemographics and static environmental factors. Models are established in such a way as if the duration has been determined prior to the shopping trip since the environmental factors are constant. The influence of environmental variations attributable to an individual’s shopping experience during the shopping process on the decision making has not been studied. This paper proposes a model that incorporates individuals’ dynamic shopping experiences into the decision of ending a shopping trip. A nested logit model is developed to explain pedestrians’ decisions of going home during a shopping trip, combined with the decision of choosing a walking direction. The model is estimated on two data sets of pedestrian shopping behavior in two Chinese cities. The results show that incorporating dynamic experiences can significantly improve the statistical accuracy of predicting the go-home decisions. However, the practical improvement is limited. Temporal factors turn out to be good predictors of the go-home probability; relatively robust spatial factors for the direction choice are identified by comparing the two cases.

This work summarizes a very extensive study conducted in the city of Valencia, Spain, on the role of green areas in the comfort of the city. Previously, the environmental parameters that make up the urban environment were studied. With these values, a very large series of comfort indexes was analyzed and validated, and the indexes that exhibited the best behavior were determined. The role of green areas in public city spaces was also studied: the retention of solar radiation and pollution retention capability. This culminated in the formulation of the comfort indexes according to the amount of green areas, which, through statistical correlation, allowed the determination of the surface area of green areas required for the city to be considered theoretically comfortable. This work completes the analysis of these formulations for a wider spectrum of measurements in the city with a greater scope and greater variety of spaces and situations. The three comfort indexes that performed best, i.e., those with greatest statistical certainty, have been reformulated for Valencia. The work concludes with research from the previous experimentation applied to the green plan of Valencia. The final solution takes into account the proposals put forward by the Valencian Authority on the plan for protection of the Huerta, the natural market garden setting on which the city of Valencia stands, following the philosophy of the European Territorial Strategy and the European Landscape Convention.

This paper studies the trend of urbanization in China, examines the evolution of the urban scale structure in China through the development of a quadratic regression model for the urban rank-size rule and significantly improves the fitness of large-sized cities to the model, formulates a power function to simulate the relationship between urban size and urban population density, and studies the evolution trend of the function in China. This paper also includes the design of a scenario of compact urban growth according to the evolution trend of urban scale structure and urban size–population density function based on farmland preservation. In contrast to express-highway-based urban sprawl, the author advocates a railway-based compact urban growth mode in China and provides related measures to implement it, such as large city in-advance development, concentrated urban growth, urban subway system construction, high-population-density community building, filling in urban growth, and intercity high-speed railway system construction. It provides a way of urbanization with higher land-use efficiency and lower energy consumption for developing countries in the new era.

Although a large number of studies have examined the impact of housing and neighborhood characteristics on housing prices, they have not systematically considered the impact of physical conditions of the immediate neighborhood on housing prices, specifically conditions associated with physical disorder. Most likely, this failure has occurred because such data do not exist in published form. The main purpose of this study is to focus on the impact of physical disorder at the microneighborhood level on property resale values by using a hedonic-price-model approach. The results highlight the costs that neighbors may inflict on one another by not maintaining their houses and neighborhood. For the first time, the results provide specific estimates of the cost associated with individual physical-disorder attributes. The results are also indicative of the importance externalities play in neighborhood change and the need for policy intervention in preventing neighborhood decline.

A methodology is proposed to assess the effectiveness of the National Ecological Reserve (REN) law, which aims to protect ecologically valuable and/or vulnerable areas, in the municipalities of Sintra and Cascais, Portugal. Landsat satellite data classification and land change analysis show that urban area almost doubled between 1989 and 2001, when REN was already in force covering 36.5% of the study area. The approach involved multiple ordinary least squares (OLS) linear regression models to evaluate the evolution of five landscape metrics that translate the impact of urban sprawl in REN main functions: soil, water, and nature protection, and protection of people and belongings settled in hazard-vulnerable areas. Results show that REN had an impact on landscape change attributable to urbanization. However, these changes were unfavorable to its core ecological functions. Multiple linear OLS regression modeling proved to be an effective and simple method to compare and understand the evolution of landscape structure between areas protected by REN and their surroundings.