| dc.description.abstract | In this research, the multiple linear regression analysis was applied to forecast the road traffic impact. The selections of the independent variables and the dependent variable were refered to the literatures and the relative regulations or experiences. And the practicablilities of data collections were considered, too. 14 independent variables were brought up initially according to the three components: the “site scale”, the “site pattern” and the “soc-economics and traffic situation surrounding the site”. And the only dependent variable was determined to be the difference of velocity in the major adjacent road between the sites being operated and not yet. We got 226 samples, and 2 clusters are suggested standing on the result of the “cluster analysis”. The samples of the 1st cluster were similar to the sites in the residential district while the ones of the 2nd cluster were considered as those in the business district. After inspecting the linear relationships between all the variables by “correlation analysis”, “stepwise regression analysis” was used to build up the road traffic impact forecast model of these 2 clusters. The key factors affecting the traffic impact in the 1st cluster were “the floor area”, “the volume”, “the automobile parking spaces supplied“of the developing site, “the population density”, “the average earnings” in the neighborhood, and “the number of lanes”, “the traffic flow rate”, and “the velocity” in the major adjacent road. The 8 variables could take effect to 0.5323 in this forecasting system. In the meantime, 4 key factors were brought up in the 2nd cluster: “the floor area”, “the volume” of a site, “the rate of the floar area using for business or office purpose”, and “the velocity” in the major adjacent road. The adjuted cofficient of determination could be reached to 0.6068 by using this regression model.
Moreover, pondering over the lack of robustness in those analytical variables selection and the data collection procedures, we looked for the fuzzy linear regression (FLR) models for further analyses. 2 common-used linear programming (LP) based models were solved in this research: (1) minimizing the sum of the forecasting parameters’ fuzzy intervals, (2) minimizing the sum of the estimations’ fuzzy intervals. Only the parameter of “the average earnings” in the neighborhood was fuzzy in model (1), all others including the intercept were crisp. And each independent variable in model (2) was crisp, while the intercept was fuzzy. That means the FLR models are toward crisp. Furthermore, different value of H didn’t cause wide variation whether in which one of the 2 FLR models. Once the FLR models were adopted in future days, the lowest value of H = 0.0 would be wonderful in analyses.
Though the road traffic impact forecast model was demonostrated by the past Taipei city samples, it can be applied to the site developing decision analysis by cooperating with other aspects of impact evaluation models or with the adjacent traffic situation. | en_US |