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Application of disaggregate choice models to urban transportation sketch planning
BibliographicSection technology appear to have played a major part in keeping project costs down over the years. Efficiency seems to have played a major role in the observed technology change, although the magnitude and rate of the decrease in resource requirements attributable to efficiency has lessened over time. Substitution brought about by factor price changes, on the other hand, seems to have had effectively no part in the technology change, although it seems likely that expectations of labor’s cost rising relative to that of capital, among other conditions, may have tended to induce technology change in the direction of saving labor as was observed. Increased mechanization and the introduction of new types of equipment appear to constitute the primary means of accomplishment of such technology change before the fifties, while since then it has been largely just improving the equipment and the effectiveness with which it is used. As for the future, although the same basic motivations may be expected to continue, perhaps in a somewhat dampened state, gains in productivity and efficiency achieved by a simple continuation of past means of accomplishing change may be expected to be somewhat less than those previously, if past trends can be taken as indicative of those of the future. As for the developing countries, it appears that the development of technical packages since the early part of this century has been focused on the capital-intensive end of the production isoquant; the labor and animal-intensive packages of the past seem to have been essentially forgotten, although they still appear to be efficient and, under some conditions, economic and their use potentiahy worth considering.
Application of Disaggregate Choice Models to Urban Transportation S&etch Planning, Thawat Watanatada, c/o World Bank Headquarters, Transportation Department, 1818 H Street, N.W.. Washington, DC 20433. (Dissertation in the Department of Civil Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, 1977.) This study is concerned with the application of disaggregate choice models to urban transportation sketch planning situations in which large geographic units are used. Existing sketch planning procedures, which operate on a high level of geographic aggregation and network abstraction, are capable of quickly examining a large number of transportation policy alternatives. However, the travel demand models employed in these procedures are typicaIly policy-insensitive, geographically non-transferable, and confined to the level of geographic aggregation on which they were calibrated. These weaknesses can be overcome by explicit aggregation of disaggregate choice models. The problem of spatial aggregation, the primary focus of this study, is concerned with the aggregation of spatial alternatives and spatially distributed individuals to produce required aggregate travel demand forecasts.
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Sketch planning needs require an efficient solution method for the spatial aggregation problem. This study proposes one such solution method. The basic methodology employs mathematical functions, expressed in terms of coordinates in the urban space, to describe the spatial choice process and to represent the geographic distribution of behavioral units, spatial alternatives, level-of-transportation-service characteristics and locational attributes. This allows the spatial aggregation problem to be solved efficiently by means of integration over space. As an application example of the basic methodology, an areawide travel demand prediction model was developed. The model predicts on an areawide basis the number of trips made, mode shares, person-miles and vehicle-miles of travel, the average vehicle occupancy rates, and the average auto ownership per family. Designed to operate on readily available aggregate input data, the model was developed primarily for performing preliminary national level transportation policy analysis. The model can also be applied to regional and statewide transportation planning. With the urban area treated as a single analysis unit, the distributions of population and employment were modelled in negative exponential form in terms of aggregate land use distribution parameters. The level-oftransportation-service distributions were represented in terms of the trip end coordinates in the form of multiple regression equations. Monte Carlo simulation techniques were employed throughout the aggregation process, which basically consisted of: the generation of a sample of representative households distributed over the urban area; the generation of a sample of destinations for each household; and the computation of travel demand forecasts for each household based on the sampled destinations. Analytical/empirical relationships were developed which enabled the Monte Carlo forecasting errors to be readily controlled. Experimental computer runs took 0.40-0.5OCPU minutes and cost $6-7 per run on an IBM 370/168; the forecast standard errors were reduced to about one percent of the mean forecasts. The elasticities of travel demand estimated by the model were generally comparable to the elasticities obtained from before-and-after data and from other studies. For a range of transportation pricing and operating policies tested, the model’s forecasts exhibited a degree of policy-sensitivity not present in existing sketch planning methodolpgies based on aggregate travel demand models. The empirical results led to the basic conclusion that it is feasible and desirable to employ disaggregate choice models in macro-level sketch planning situations. The methodology proposed would enable input data to be represented in highly aggregate form while still maintaining a full degree of policy-sensitivity of the travel demand forecasts. Recommended future research diiections include the development of aggregation techniques for multiple zones of varying sizes and the development of concomitant transportation supply and traffic assignment models.
Application of Disaggregate Choice Models to Urban Transportation S&etch Planning, Thawat Watanatada, c/o World Bank Headquarters, Transportation Department, 1818 H Street, N.W.. Washington, DC 20433. (Dissertation in the Department of Civil Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, 1977.) This study is concerned with the application of disaggregate choice models to urban transportation sketch planning situations in which large geographic units are used. Existing sketch planning procedures, which operate on a high level of geographic aggregation and network abstraction, are capable of quickly examining a large number of transportation policy alternatives. However, the travel demand models employed in these procedures are typicaIly policy-insensitive, geographically non-transferable, and confined to the level of geographic aggregation on which they were calibrated. These weaknesses can be overcome by explicit aggregation of disaggregate choice models. The problem of spatial aggregation, the primary focus of this study, is concerned with the aggregation of spatial alternatives and spatially distributed individuals to produce required aggregate travel demand forecasts.
71
Sketch planning needs require an efficient solution method for the spatial aggregation problem. This study proposes one such solution method. The basic methodology employs mathematical functions, expressed in terms of coordinates in the urban space, to describe the spatial choice process and to represent the geographic distribution of behavioral units, spatial alternatives, level-of-transportation-service characteristics and locational attributes. This allows the spatial aggregation problem to be solved efficiently by means of integration over space. As an application example of the basic methodology, an areawide travel demand prediction model was developed. The model predicts on an areawide basis the number of trips made, mode shares, person-miles and vehicle-miles of travel, the average vehicle occupancy rates, and the average auto ownership per family. Designed to operate on readily available aggregate input data, the model was developed primarily for performing preliminary national level transportation policy analysis. The model can also be applied to regional and statewide transportation planning. With the urban area treated as a single analysis unit, the distributions of population and employment were modelled in negative exponential form in terms of aggregate land use distribution parameters. The level-oftransportation-service distributions were represented in terms of the trip end coordinates in the form of multiple regression equations. Monte Carlo simulation techniques were employed throughout the aggregation process, which basically consisted of: the generation of a sample of representative households distributed over the urban area; the generation of a sample of destinations for each household; and the computation of travel demand forecasts for each household based on the sampled destinations. Analytical/empirical relationships were developed which enabled the Monte Carlo forecasting errors to be readily controlled. Experimental computer runs took 0.40-0.5OCPU minutes and cost $6-7 per run on an IBM 370/168; the forecast standard errors were reduced to about one percent of the mean forecasts. The elasticities of travel demand estimated by the model were generally comparable to the elasticities obtained from before-and-after data and from other studies. For a range of transportation pricing and operating policies tested, the model’s forecasts exhibited a degree of policy-sensitivity not present in existing sketch planning methodolpgies based on aggregate travel demand models. The empirical results led to the basic conclusion that it is feasible and desirable to employ disaggregate choice models in macro-level sketch planning situations. The methodology proposed would enable input data to be represented in highly aggregate form while still maintaining a full degree of policy-sensitivity of the travel demand forecasts. Recommended future research diiections include the development of aggregation techniques for multiple zones of varying sizes and the development of concomitant transportation supply and traffic assignment models.