This paper evaluates the effects of including transfers between service zones on overall service performance in a paratransit system. Transfers were included to improve the operational efficiency of a system when maintenance of a desirable zoning structure was obligatory. This proposed innovative service design was compared with more traditional cases of no transfer zoning and no zoning. A set of instances was generated from demand data obtained from the Metropolitan Transit Authority of Harris County, Texas, and evaluated through simulation analyses. The results demonstrated that under a zoning structure, this transfer design (in comparison with a nontransfer design) provided noticeable improvements in efficiency measures and better passenger trips per vehicle revenue hour while maintaining a minimum customer service standard; however, the overall performance of the no-zoning strategy used by the Houston, Texas, Metropolitan Transit Authority of Harris County performed the best, on average.
A reliable method for predicting paratransit ridership is important, especially for the efficiency of the services offered. The commonly used aggregate regression model is most accurate for forecasting the total demand for regional areas such as whole counties or cities; however, it is likely to be geographically inaccurate. This paper proposes a geographical weight regression (GWR) model for predicting the demand for the types of para-transit services required by the Americans with Disabilities Act. The GWR model reflects better the characteristic of each area having its own coefficient for predictors rather than the same value throughout. The results show that trip demand increased proportionately to (a) the population size, (b) the ratio of senior citizens, (c) the ratio of people below the poverty line, and (d) the ratio of African-American riders. These results suggest that the predictive performance of the GWR model is better than that of the ordinary least squares (OLS) regression model. The GWR model is of greater value than the OLS model to researchers and practitioners, because the predictor variables are readily available from census data; this availability of data allows researchers to use the model after calibration.
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