Effects of spatial dependency on transportation demand models / Efeitos da dependência espacial em modelos de previsão de demanda por transporte

AUTOR(ES)

Simone Becker Lopes

DATA DE PUBLICAÇÃO

2005

RESUMO

The degree of spatial data dependence, which is among the issues of spatial analysis that should be considered in transportation planning, is the focus of this study. Ignoring this particularity of data can: produce wrong estimates; jeopardize the results of analyses; and, as a consequence, lead to unsuccessful planning. Therefore, the basic assumption of this work was that the inclusion of spatial dependence indicators can produce more accurate and reliable estimates than those obtained with traditional model structures. In order to test this hypothesis, the main objective of this study was to compare demand predictions produced by traditional models with those produced by alternative models that include indicators of spatial dependence. The study was limited to home-based production trip models, which are part of the trip generation phase of the traditional four-step modeling approach. All work was conducted in a GIS (Geographic Information System) environment, making use of spatial statistics and analysis tools, as well as transportation planning tools available in a GIS-T (i.e., a dedicated GIS for Transportation). Spatial analyses tools were used to generate the spatial dependence indicators and to evaluate the results of the application. A case study was carried out in the city of Porto Alegre, which is the capital of the brazilian state of Rio Grande do Sul, for evaluating the impacts of the addition of global and local indicators of spatial dependence in the models. Two O-D surveys carried out in the years 1974 and 1986 provided the data needed for calibration and validation. The first one was taken as the base year and the second one as the goal year. The results of the application showed that the performance of the models can be improved in both calibration and validation phases with the insertion of spatial variables. However, the urban growth observed in a very dynamic context, such as in the city studied, may dramatically change the relationships between variables, including their spatial patterns. That aspect was responsible for the fact that the model with the best performance in the calibration phase was not the one producing the most accurate forecasts. It raised the hypothesis, to be explored in future research, that the analysis of those dynamic processes and their consideration into transportation demand models are also needed to improve even further the performance of the models

ASSUNTO(S)

spatial statistics estatística espacial spatial autocorrelation autocorrelação espacial sig análise de demanda por transportes analysis of transportation demand gis

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