Journal of Urban Planning and Development

In this paper, fire station redistricting of the Centre Region in Pennsylvania is considered to improve the efficiency of service and reflect the changing characteristics of the region. The problem is formulated as a multiobjective mathematical optimization problem. Due to difficulties in building a comprehensive analytical model and associated computational complexity, simulation, aided by a geographic information systems application software package, is employed to solve the problem. The effectiveness of this analysis indicates that simulation can be used to solve problems of this nature and satisfactory districting plans can be generated efficiently.

Built investment in buildings, facilities, and infrastructure underpin the performance of cities and regions, so the ways built investments influence the economic success of regions are important. This paper reports analyses of the dynamics of built investments in four regions. We assembled F.W. Dodge proprietary data on built investments from 1990 through 2000 in two midwestern “old economy” regions (Indianapolis, Ind. and Columbus, Ohio), and two new economy stars (Austin, Tex. and Raleigh–Durham, N.C.). The purpose of this analysis is to examine to what extent public and private built investment patterns are linked within each region. The comparative analysis finds that these regions each entertained anywhere from 100,000 to 200,000 construction projects apiece over the 11-year period, ranging from a total of $23 to $49 billion. On a per square mile basis, the regions’ built investments were about even. Shares of built investment originated from the public sector differ widely by category, with higher public shares of transportation, telecommunications, and laboratories generally occurring in the new economy regions. Correlations between public and private built investment vary among the regions. Simple models using local public investments to estimate private investment patterns show strong linkages between education, street, water, and other public infrastructure and private building, but these coefficients also differ across the regions. Austin and Raleigh–Durham show higher levels of covariation between some public infrastructure variables and private built investment.

Hong Kong, the busiest container port in the world, has been using a regression analysis approach to forecast port cargo throughput for its port planning and development over the decades. In this paper, the neural network models are proposed and developed for forecasting 37 types of freight movements and hence Hong Kong port cargo throughput from 2002 to 2011. The historical data (1983–2000) of freight movements and explanatory factors are the input data used for model development. The models developed are used to forecast 1 year of freight movements for validation with actual data in 2001 and comparison with those forecasted by regression analysis. Using the same models, freight movements are then forecasted for the next 10 years based on projected explanatory factors and combined to form the predicted port cargo throughputs. The Monte Carlo simulation is used to assess the reliability of the forecasts due to projection error of explanatory factors and compare the results forecasted by regression analysis for three different growth rate scenarios. Results show that forecasts made by the proposed neural network models are more conservative, more reliable, and more comparable to reality.

Urbanization is one of the most evident global changes. Research in the field of urban growth modelling has generated models that explore for drivers and components of the urban growth dynamics. Cellular automata (CA) modeling is one of the recent advances, and a number of CA-based models of urban growth have produced satisfactory simulations of spatial urban expansion over time. Most application and test of CA-based models of urban growth which provide likely and reliable simulations has been developed in urban regions of developed nations; urban regions in the United States, in particular. This is because most of the models were developed in universities and research centers of developed nations, and these regions have the required data, which is extensive. Most of the population growth in the world, however, occurs in the developing world. While some European countries show signs of stabilization of their population, in less developed countries, such as India, population still grows exponentially. And this growth is normally uncoordinated, which results in serious environmental and social problems in urban areas. Therefore, the use of existing dynamic–spatial models of urban growth in regions of developing nations could be a means to assist planners and decision makers of these regions to understand and simulate the process of urban growth and test the results of different development strategies. The pattern of growth of urban regions of developing nations, however, seems to be different of the pattern of developed countries. The former use to be more dense and centralized, normally expanding outwards from consolidated urban areas; while the second is normally more fragmented and sparse. The present paper aims to investigate to how extent existing CA-based urban growth models tested in developed nations can also be applied to a developing country urban area. The urban growth model was applied to Porto Alegre City, Brazil. An expected contiguous expansion from existing urban areas has been obtained as following the historical trends of growth of the region. Moreover, the model was sensitive and able to portray different pattern of growth in the study area by changing the value of its parameters.

This paper focuses on analyzing traffic facilities for an intermediate time frame. There are two methodologies examined in this work, the first uses extrapolated, historical traffic count data and the second uses an urban transportation model. Using several intersections within Huntsville, Ala., as case study intersection locations, this paper applies both methodologies to forecast near-future traffic and compares the forecasted results with the actual traffic counts to determine which methodology better replicated actual volumes. The results of this work indicate that a properly validated and applied urban transportation planning model has the ability to provide more accurate traffic forecasts to support the traffic engineering analysis decision than the commonly used extrapolated traffic trends.

Feasibility analyses of large development projects have been mainly concerned with Indexes such as IRR & NPV based on the cash flow statement and $B/C$ ratio, ignoring the return structure of projects or the cost structure of projects. Knowing cost and return structure is important when investors consider feasible projects. This paper uses the principles of index to address an important question: given feasible projects, how should an investor decide whether the project matches his/her investment purpose? As such, the goal of this technical note is to propose the diminishment index (DI) consisting of the cost to equity ratio, the liability on return (NPV on this paper) ratio, and the equity to liability ratio in order to estimate the return structure of projects and the cost structure of projects. The results indicated that the DI had best described which project among feasible projects satisfying the requirements of NPV>0 and IRR>required rate of return should be first rejected. The DI noted the order of diminishing a probability selected among feasible projects and the financial structure of projects. Eventually, a player can select a more appropriate project, where considering concurrently profitability of the project and the investment purpose of the player by the DI. Further, players can simulate variability in cost, equity, liability, and NPV of the DI.