Journal of Urban Planning and Development

The study investigates water quality pollution impacts on urbanization by analyzing temporal and spatial characteristics of different water quality parameters, and simulating economic loss of water quality pollution in Xi’an, China from 1996 to 2003. Results show that organic pollutants were the greatest contributors of surface water quality pollution from 1996 to 2003. High values existed in petroleum concentration, chemical oxygen demand index (KMnO4), biochemical oxygen demand index, and phenol concentration, followed by nitrogen concentration (TN and NH3-N). From spatial analysis in different buffers from central urban area (inner buffers: $1–5\mathrm{km}$ ; central buffers: $5–10\mathrm{km}$ ; outer buffers: $>10\mathrm{km}$ ), socioeconomic activities such as business activities, car transportation, industry factories, agriculture practices, and households were likely to lead to different behaviors of water quality parameters in nature. Results also reveal that both surface and ground water quality improved gradually after enforcement of control measures within the $7\text{years}$ from 1996 to 2003. It shows the total economic loss, including cost of water use and supply, agriculture economic loss, ecosystem conservation costs, and economic loss of human health, reached $\$1.12\times {10}^9$ from 1996 to 2003, which increased $\$1.79\times {10}^7$ from $\$1.26\times {10}^8$ in 1996 to $\$1.46\times {10}^8$ in 2003. However, economic loss of water quality pollution increased while water quality pollution alleviated in the past years. This can be explained by more intensive social activities in broader regions, more populations were moved from rural area into urban area, and more costs were input in water quality pollution treatment.

The location of rail transit lines and stations is a challenging problem because the number of possible alternatives is astronomical, the number of requirements and constraints is very large, and the evaluation of each alternative is based on numerous measures of effectiveness. Furthermore, large amounts of geographic information of many types are needed to compute some of the effectiveness measures. For instance, detailed spatial data on household locations and characteristics are needed to estimate accessibility to transit stations and the resulting access times and effects on demand. Detailed information on existing street networks, topography, and geology is needed to estimate construction costs and detailed information on land use and land values are needed to estimate right-of-way costs. In this paper we adapt the genetic algorithm and geographic information system approach to rail line optimization. Two types of costs are formulated and evaluated in the model: (1) operator cost which consists of track-related construction cost, station construction cost, right-of-way cost, earthwork cost, and rail operating cost; and (2) user cost which consists of access cost, riding time cost, and wait cost. The model is applied to a section of Anne Arundel County, Md., to reoptimize between a pair of stations an existing light rail transit line connecting Glen Burnie to the Baltimore Washington International Airport. The prototype model presented here produces very good alignments based on its criteria and can be extended to use far more comprehensive and complex evaluation functions. Additional factors and cost components that should be considered in more advanced models are also discussed.

The concern over sustainable development gives rise to the importance of the environmental factors in evaluating an environment-sensitive project. In this regard, the widely adopted exercise which involves the environmental impact assessment (EIA) and the traditional cost–benefit analysis (CBA) in a sequential way may be flawed with a failure to calculate the resultant economic and environmental impacts in the same setting and at the same time. It is mainly because the EIA is qualitative in nature and hence the environmental impacts are hard and difficult to translate into monetary terms. In contrast, by monetizing the environmental impacts, the study shows that it is feasible to integrate the CBA with the environmental factors on an equal basis. The empirical base of the paper draws on the case of the Pinglin Interchange in Taiwan. In particular, the empirical results show that an integrated CBA may produce an entirely different conclusion from the one resulting from the common traditional EIA and CBA exercises. This is because the EIA focuses mainly on the quantity of pollution, but the latter depends on the socioeconomic context.

Across the nation, new residential development requires the construction of new infrastructure. However, financing the construction and maintenance of this infrastructure has become a contentious issue because of the various demands placed on local government budgets. The objective of this study is to ascertain how costs for certain infrastructures vary with the density of single-family residential development. Specifically, it examines how capital and life-cycle costs for roads, and sewer and water lines vary with density. Only on-site costs are examined, that is, the costs for infrastructure located within subdivisions. We find that life-cycle costs for roads and sewer and water lines increase as density decreases. Annual user fees for sewer and water cover the annual life-cycle costs for these infrastructures at lot sizes less than $0.4\text{hectare}$ . We did not observe a relationship between annual life-cycle costs, expressed as a percent of revenues (the sum of property taxes and user fees for sewer and water), and density.

Transportation models have not been adequate in addressing severe long-term urban transportation problems that transportation disadvantaged groups overwhelmingly encounter, and the negative impacts of transportation on the disadvantaged have not been effectively considered in the modeling studies. Therefore this paper aims to develop a transportation modeling approach in order to understand the travel patterns of the transportation disadvantaged, and help in developing policies to solve the problems of the disadvantaged. Effectiveness of this approach is tested in a pilot study in Aydin, Turkey. After determining disadvantaged groups by a series of spatial and statistical analyses, the approach is integrated with a travel demand model. The model is run for both disadvantaged and nondisadvantaged populations to examine the differences between their travel behaviors. The findings of the pilot study reveal that almost two thirds of the population is disadvantaged, and this modeling approach could be particularly useful in disadvantage-sensitive planning studies to deploy relevant land use and transportation policies for disadvantaged groups.

The purpose of this study is to analyze the impact on land-use and population change in a rural area close to the construction of a new expressway through location modeling with spatial interaction analysis. Chungbu Expressway, constructed in 1987, going through the southern area of Kyunggi Province from Seoul, Republic of Korea, was selected as a case expressway, and two cities and 18 county subdivisions in its surrounding area, as a case study area. To analyze the change in characteristics before and after the construction, land-use maps of 1986 and 1996 were collected, including census data for the years in cities’ and counties’ yearbooks. Remote sensing technology was applied to classify the land-use maps with six types of land use. A geographic information system was also used for spatial analysis, such as land-use and accessibility changes. A 5-km buffer zone from the tollgate of the expressway showed about two times the increase of urbanized, built-up areas than a 5-km buffer zone from the expressway. Accessibility from Seoul and cities was improved in most areas, which is accessing Seoul through the existing Youngdong Expressway. Ten rural areas showed increased population with an average accessibility of $52\min$ to Seoul and $19\min$ to cities, while eight areas showed decreased population with an average of $73\min$ to Seoul and $35\min$ to cities. This shows the threshold time, which is the time distance from Seoul and middle cities, for population increase or decrease (66 and $33\min$ , respectively) by optimizing the spatial interaction model developed empirically in this study. Urbanized areas were increased in most areas, even in population decrease areas. This indicates that there are thinning rural areas and increasing urbanized areas while population decreases at the same time.

Several models have been created over the years that are used in simulation packages to predict pedestrian speed and flow on sidewalks. However, the predictive power of these models has been weak. In this paper the results of a modeling exercise in the United Kingdom is reported. A number of video surveys of pedestrian movement on sidewalks, which are representative of different levels of service, have been made. These videos have been analyzed to yield data on pedestrian flows and speeds and a variety of other variables. From this data regression models have been constructed and the predictive performance of these models assessed and found to perform reasonably well. The value of the models to designers is then discussed.