This paper reports the results of tests of the hypotheses that attitudinal variables are important in mode choice decisions and that they can significantly increase the explanatory power of network-based mode choice models. Conflicts between the results of previous work by Lovelock and Johnson are resolved by this study. Attitudinal items used by Johnson and by Lovelock in separate studies in the San Francisco Bay area were included in a survey of Chapel Hill households. Tests of the incremental explanatory power of the attitudinal variables in mode choice models confirm that the items used by Johnson do not contribute to the explanatory power of models using network time and cost data. Similar tests showed that Lovelock's attitudinal items do significantly increase the predictive ability of the models. The conflicting results of these previous studies are therefore due to the content of the items. Attitudinal data, including both attitude items and measures of perceptions of system attributes, do enhance the predictive power of models involving network data.
The regulation of taxicab services is receiving an increasing amount of attention by city governments. At issue are the questions of whether local regulations should limit the supply of taxicabs and whether the regulations should control taxi fares.Recently, deregulation has become a popular suggestion; however, little empirical or theoretical evidence has existed to indicate the effects of taxi deregulations. This paper discusses these effects within a framework of eight regulatory scenarios involving different price, entry, and industry concentration factors. The analysis provides support for a public brokerage function.-are showing the same signs of financial distress that overtook their mass transit counterparts two decades ago. Finally, taxi regulatory issues have grown in significance because planners have recognized that capital expenditures for new facilities are not the only way to provide high quality trans-
This paper develops two mathematical models of housing turnover in a neighborhood. The first of these draws upon the theory of non-homogeneous Markov processes and includes the effects of present neighborhood composition upon future turnover probabilities. The second model considers the turnover process as a Markov renewal process and therefore allows the inclusion of length of occupancy as a determinant of transition probabilities. Example calculations for both models are included, and procedures for using the models are outlined.
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