Sample Research Agenda
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Sample Research Agenda
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Research Meeting Agenda
Findings Derived from Regression Analysis Regression analysis yields surprising results about how community and service characteristics affect ADA paratransit demand. Each of these findings suggests possible further research as discussed below. In addition, some refinements and extensions of the regression analysis are discussed. Age and Disability The regression analysis did not find a significant effect of the percentage of people in older age groups or with different types of disabilities. In the case of disability, the lack of any detectable effect most likely indicates that the Census measures do not correlate well with ADA paratransit eligibility. In the case of age, the result is a bit surprising, as older people typically account for a high percentage of paratransit riders. However, younger people with disabilities are often very frequent riders and generate a disproportionate share of ridership. For example, in a planning project for the Whatcom Transit Authority in Bellingham, Washington, NelsonNygaard determined that 60% of riders were 65 and older, but people under 65 made 58% of the trips. If it is true, as found in the regression, that ridership is proportional to the total population rather than the population in older age groups, then paratransit ridership may grow much less dramatically than expected. A relationship based on the total population is also much easier to use to predict ridership than one based on the older population, since total population projections are more readily available than projections by age category. The lack of relationship between age and ADA paratransit transportation can also be taken as an indication that many older people require demand-responsive services other than ADA paratransit. In recent work for the National Cooperative Highway Research Program, Bailey and others analyzed national transit databases and census data and found a strong positive relationship between general demand response transit ridership growth and growth in the 75 to 84 and 85 and older. age groups. Their analysis did not distinguish ADA transportation from other demand-responsive riders, such as general public service call-a-ride.41 Additional regression analysis with a larger sample may help to substantiate these results. Disaggregated analysis can serve the same function. 54 CHAPTER 7 Research Agenda 41 ICF Consulting, NCHRP Web Only Document 86: Estimating the Impact of the Aging Population on Transit Ridership, National Cooperative Highway Research Program, TRB, January 2006.
The poverty rate and income The model shows a very strong relationship between higher poverty rates and reduced demand for ADA paratransit. This suggests research to explore the following: â¢ How and why does the poverty rate in a community suppress the demand for ADA paratransit? â¢ Is the effect really as strong as suggested by the regression results? â¢ Is demand reduced mainly because of the limited income of individual travelers or because of community characteristics associated with widespread poverty? In general, the demand for goods increases with income. In the case of public transport, it can be thought that the increase in income will be accompanied by more availability of other modes and thus reduce the demand. Some research has in fact found a negative elasticity of the transit passenger with respect to income.42 Two hypotheses for the observed impact of the poverty rate (corresponding to lower income) on the demand area of paratransit as follows: they use paratransit more than their income decreases, and the observed effect mainly reflects differences in communities. â¢ Lower income may correspond to higher paratransit ridership to some extent, but at very low income levels associated with poverty status (which is disproportionately common among people with disabilities), demand for total travel is so suppressed that over the effect of mode availability. Both effects may be at work. Research to test these findings and expand on how they work would be valuable. This research will have to look at the choices of individual consumers. (An additional consideration is discussed in the later section on population growth.) Availability of Transit Service The regression results indicate that communities with more transit service have more demand for paratransit. This contrasts with the expectation that more transit service would lead to lower demand for paratransit, as transit would be a more viable alternative than in cities with less transit service. Clearly, adding transit service does not increase paratransit use. A possible explanation for the observed effect was proposed in the model development chapter, namely that the effect is the result of less dependence on private cars from the city with more transit service The observed effect can be explained as transit riders who can no longer drive transit are more likely to use paratransit than drivers who can no longer drive. In other words, if a significant fraction of people are used to traveling by public transportation, they create a large demand for paratransit when they can no longer use conventional service. However, if almost everyone is used to driving for all their trips, and they drive until they can’t anymore, then they create much less demand for paratransit, as they are unlikely to consider transit or paratransit as a realistic alternative. when they can no longer drive. The effect will be amplified if, on average, people lose the ability to drive later than they lose the ability to ride transit. These speculations indicate fundamental research into the travel needs and preferences of people with disabilities and the elderly. Questions include the following: â¢ How do people make choices between driving, walking, transit and using para-transit in response to disability or in response to age restrictions? Research Agenda 55 42 McLeod Jr., M. S.; Flannelly, K.J.; Flannelly, L.; Behnke, R. W., “Multivariate Time-Series Model of Transit Ridership Based on Historical, Aggregated Data: The Past, Present, and Future of Honolulu,” Transportation Research Record 1297, Transportation Research Board, National Research Council, Washington, DC, 1991, pp. 76–84.
â¢ How are these choices influenced by income, family situation and the availability of each mode, especially transit and paratransit services? As in the case of research on the poverty rate and income, this research will have to look at the choices of individual consumers. Cross-Sectional Effects and Changes in a Paratransit System The model was estimated by comparing ridership in different systems. The model is most useful as a tool for comparing paratransit systems. The effects in a paratransit system (âlongitudinal effectsâ) can be different or take a long time to occur. For example, the analysis yielded a cross-sectional price elasticity of â0.77 for paratransit demand. This result suggests that paratransit trip performance is much more sensitive to fares than overall transit use. In paratransit systems without capacity constraints, this can be expected given the overall low income of people with disabilities and the relatively high fares that characterize many paratransit systems. In the first interim report for this project, evidence from the literature was presented that the estimated elasticity of fare for individual paratransit systems is between â0.2 to â0.8. The literature review also found evidence of elasticity above 1.0 when tariff levels are high. Another possibility is that the estimated cross-price elasticity of 0.77 is consistent with long-term effects, but not necessarily short-term effects. Research on the response to transport rates has shown that long-run elasticities are much larger than short-run elasticities. The Transport Policy Institute of Victoria’s online TDM Encyclopedia cites the following results for transit fare elasticity from research by the British Transport Research Laboratory: 43 Bus: Short-term â0.4 Medium â0.56 Long-term 1.0 Metro rail: Short-term â0.3 Long-term â0.6 The results of the regression analysis are quite consistent with these long-run elasticities. Similar considerations apply to other factors in the model, particularly the punctuality window. Further research into the differences between cross-sectional and longitudinal effects and between long-term and short-term effects will help practitioners apply the model’s findings. Disaggregated analysis can provide some evidence on these questions. A relatively simple analysis applies cross-sectional analysis to fixed-route transit transportation to see what difference there is in the impact of fares measured in this way or as a short-term response to fare changes in a system. Additional issues about rates It is possible that differences in the cost of living or income between service areas affect responses to rates. People with lower incomes will see a $2.00 fee as a stronger disincentive to travel than people with higher incomes. This
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