Click for next page ( 12


The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 11
11 TABLE 14 TABLE 16 RIDERSHIP FORECASTS: LINKED VERSUS STRUCTURE OF RIDERSHIP FORECASTING FUNCTION UNLINKED TRIPS Agencies Agencies No. Agencies Responding No. Agencies Responding Structure Responding (%) Type of Trip Responding (%) Part of general duties 13 38 Unlinked 24 71 Depends on scale/extent of forecast 13 38 Both linked and unlinked 10 29 Dedicated person or group 8 24 Total responding 34 100 Total responding 34 100 The use of qualitative methods such as similar routes Time and Effort Required analysis or professional judgment is widespread among tran- sit agencies for route, schedule, and fare changes. Service A range of estimates were given for the time and effort elasticities are the major quantitative method in use. Several required to prepare ridership forecasts. Table 17 shows that transit agencies are satisfied with the use of qualitative tech- simple or short-range forecasts can generally be completed niques, noting their accuracy and simplicity of use. in 3 days or less, whereas complex or long-range forecasts can take much longer. The wide time range in long-range ORGANIZATIONAL ISSUES forecasts reflects the method used: trend line analysis takes much less time [one day or less was reported by seven Transit agencies have different structures. This section respondents (Table 17)] than a four-step model run. explores where the ridership forecasting function is located within an agency and whether it is a dedicated function or How Forecasts Are Used part of a planner's overall responsibilities. This section also considers the time and effort required to prepare a ridership Ridership forecasts are nearly always distributed and used forecast and how forecasts are distributed. internally. Most responding agencies also share the fore- casts with their boards. Table 18 shows that it is less com- Responsibility for Ridership Forecasts mon to distribute these forecasts to other groups. Four respondents cited local stakeholders among "others" Fourteen transit agencies reported more than one lead depart- who receive ridership forecasts, whereas three mentioned ment in preparation of ridership forecasts. The transit the FTA. agency's planning department is the most common location for the ridership forecasting function, as shown in Table 15. Among the "other" departments are Strategic Planning and RIDERSHIP FORECASTING UNDER A VARIETY Policy, Transit Research Section (under the Marketing OF SCENARIOS Department), and Business Development. Twelve of the 20 Survey results in the previous sections shed light on how agencies that reported a single lead department for ridership transit agencies go about the process of forecasting rider- forecasting named Transit Planning. ship. However, the very different types of circumstances under which a ridership forecast is needed or desired can be Table 16 shows a fairly even split in terms of whether rid- lost in an aggregation of overall responses. To better under- ership forecasting is the responsibility of a dedicated person stand how ridership forecasts are generated and used, the or group. The results suggest that this responsibility is some- survey included seven scenarios and asked transit agencies what more likely to be part of general duties for all but major changes. TABLE 17 TIME REQUIRED FOR RIDERSHIP FORECASTS TABLE 15 DEPARTMENT OR AGENCY RESPONSIBLE Agencies FOR RIDERSHIP FORECASTS No. Agencies Responding Time Responding (%) Agencies Simple or Short-Range Forecasts No. Agencies Responding Department or Agency Responding (%) Less than one day 8 32 Transit planning department 22 65 One to three days 12 48 Transit operations planning department 9 26 Two weeks or longer 5 20 Transit budget/finance department 8 24 Total responding 25 100 MPO 6 18 Complex or Long-Range Forecasts Transit operations department 3 9 One day or less 7 47 Other 4 12 One to three months 3 20 Total responding 34 100 Longer than three months 5 33 Total responding 15 100 MPO = metropolitan planning organization.

OCR for page 11
12 TABLE 18 Examples of specific responses include: DISTRIBUTION AND USE OF RIDERSHIP FORECASTS We would use the GIS to provide an integrated comprehensive Agencies market analysis using Census demographics, APC ridership by No. Agencies Responding stop, and other land use data as available to compare this service Distribution and Use Responding (%) with our current same type of service and project from there Internally 33 97 using professional judgment. To board members 23 68 To the MPO 10 29 Size of shopping center, demographics of current route ridership, To elected officials 9 26 level of current route ridership, proximity of transfers to/from To others 10 29 other routes that have ridership that would be attracted by the shopping center, [and] number of existing riders adversely Total responding 34 100 affected by the deviation. MPO = metropolitan planning organization. Impact on existing customers--travel time, access, egress, fare, etc. to describe how they would forecast ridership under each Trip generation/distribution based on size, type of shopping center. scenario. Responses included data to be used and method- ologies. This section summarizes data and techniques men- Prior experience. tioned by at least 10% of respondents under each scenario. A complete list of responses is included in Appendix B. Quick spreadsheet analysis. Each section also provides verbatim responses from selected agencies as examples of approaches to ridership forecasting. The case study chapter (chapter five) includes Scenario B: Extension of Existing Route for One Mile to Serve a New Residential Development all responses to these scenarios from the six case study agencies As with Scenario A, the most common approaches under this scenario were to evaluate similar routes and previous service Percentages in the scenario tables are based on answers changes of this nature and evaluate similar conditions in from all 36 responding agencies. One agency indicated that terms of residential developments elsewhere in the service it would not forecast ridership under any of the scenarios, and area. The socioeconomic and demographic profile of the area others indicated that they would not forecast ridership for is useful to know, as is the population and population den- certain scenarios. "Would not analyze" characterizes these sity. Several agencies noted route productivity as a consider- responses in the table for each scenario. ation; most would expect the same level of productivity for the extension, but would consider the productivity of similar Scenario A: Half-Mile Rerouting of Existing Route route segments or of similar previous changes. Trip genera- to Serve a New Shopping Center tion rates and professional judgment were also cited as tools in developing a ridership forecast. Perhaps the most interest- The most common approaches under this scenario were to ing response was to assume that the extension would meet evaluate similar conditions in terms of shopping centers else- minimum performance standards in terms of boardings per where in the service area and to evaluate similar routes and revenue hour or other factors, with the implication that if per- previous service changes of this nature. Current route rider- formance fell short, the extension would be discontinued. ship is important, as is consideration of the impact of this Table 20 presents the responses. detour on existing through ridership. Agencies also reported the use of trip generation rates and professional judgment. TABLE 20 Table 19 summarizes responses. RIDERSHIP FORECASTING FOR SCENARIO B: ROUTE EXTENSION TO SERVE NEW RESIDENCES Agencies TABLE 19 No. Agencies Responding RIDERSHIP FORECASTING FOR SCENARIO A: REROUTING Response Responding (%) TO SERVE A NEW SHOPPING CENTER Similar routes/service change 12 33 Agencies Similar conditions/area 11 31 No. Agencies Responding Socioeconomic/demographic data 7 19 Response Responding (%) Route productivity 6 17 Similar conditions/area 13 36 Trip generation rate 5 14 Similar routes/service change 11 31 Assume minimum performance 5 14 Current route ridership 9 25 standard Consideration of through ridership 8 22 Would not analyze 5 14 Trip generation rate 6 17 Population/population density/no. 5 14 Professional judgment 5 14 households Would not analyze 5 14 Professional judgment 5 14

OCR for page 11
13 Examples of specific responses include: TABLE 21 RIDERSHIP FORECASTING FOR SCENARIO C: HEADWAY CHANGE Would not prepare a specific forecast, but would ensure that the new development has a sufficient number and density of residents Agencies to ensure that it could support service meeting our transit service No. Agencies Responding guidelines. Depending on the type of service, our guidelines call Response Responding (%) for (1) a minimum density of 20 to 30 residents per hectare (8 to Elasticities 12 33 12 residents per acre) or 20 to 25 jobs per hectare (8 to 10 jobs per Route productivity 10 28 acre) over a minimum developed area of 10 hectares (four acres); Would not analyze 8 22 (2) a road and pedestrian access system that permits safe access Professional judgment 4 11 and efficient operation of transit service; (3) a minimum of 175 to 250 total person trips (by all modes) per additional bus service Similar routes/changes 4 11 hour. Other factors such as the socioeconomic characteristics of the community and existence of travel demand management pro- grams may also be considered in applying these guidelines. Estimate the cost of more frequent service. I've used two different methodologies. Most commonly, I per- From ride counts, obtain existing boardings on the route during form an analysis that compares community and service charac- the AM peak. teristics with similar parts of the route network. Typically, I'll consider headways, span of service, residential and employment densities, and family incomes. If there is a major generator along From our transit assignment model, obtain the average the extension, I'll consider it separately. For example, if we were `weighted' travel time for customers who use this route (note considering an extension to a community college, I'd look at the that weighted travel time is the time for a customer's trip from number of students and apply a mode split. That mode split the beginning of the trip at the origin to their final destination would vary depending on where students are coming from and with each time component weighted; e.g., wait time weighted by whether we can coordinate bus schedules with class times and a factor of 1.5, walk time weighted by 2.0, etc.). whether we will offer any fare incentive to customers. Use elasticity model to estimate the number of new customers The second approach I've used is a small sketch planning model attracted due to the percentage decrease in their overall that I originally developed in the 1990s. It considers residential weighted travel time; e.g., TWTT [total weighted travel time] = and employment densities within TAZs along the route, family 60 minutes, reduction in travel time will be 1 minute savings income, headways, and average travel time from each TAZ along multiplied by wait weight of 1.5 = 1.5 minutes; therefore, per- the route to several different types of traffic generators (malls, centage savings in travel time = 1.5/60 = 2.5%. Our AM peak hospitals, community colleges, etc.). The relative importance of weighted travel time elasticity is 1.5; therefore, number each variable is then calibrated to achieve maximum consistency of new customers = number of existing customers * 1.5 * between projected and actual boardings on established routes. 2.5% (note 2.5% is negative because it represents a travel Doing this, I found that the model is about as reliable as if I make time savings). an informed guess. Accordingly, I seldom use it. Compute number of customers gained per dollar spent. Scenario C: Change in Headway from If greater than agency threshold of 0.23, then recommend for six month trial; [otherwise] do not recommend. 12 to 10 Minutes During Peak Hours A typical approach to forecasting ridership in response to a Note: more frequent service can also be recommended without any ridership forecast if current loads on the buses exceed change in headway is to use elasticities. Several respondents agency standards. mentioned route productivity as a factor. One application of productivity is to use historical or comparable productivity changes elsewhere as the basis for the ridership forecast. Scenario D: Implementation of New Crosstown Another is to forecast ridership changes only if the route's Route to Enhance Service Area Coverage and current load is above the maximum load factor. This scenario Provide More Direct Connections had the highest number of responses indicating that an agency would not perform a ridership forecast for this type An examination of other crosstown routes is the most com- of change (see Table 21). mon response. Evaluating transfer data and how connecting routes are used is also important. Respondents also men- Examples of specific responses include: tioned the need to understand the demographics in the area to be served. Productivity was cited as the best metric to Work with MPO and use service elasticities from regional trans- use in comparison with other routes and areas. Other portation model to forecast ridership increases. approaches included using a four-step travel model (be- cause this would be a new route), considering similar con- We would generally not conduct such a forecast due to the ditions or areas, evaluating trip generators and land use inelasticity of our ridership. Over 70 percent are transit depen- within one-quarter mile of the proposed route, and assum- dent and are not generally swayed if frequencies change by such a small amount in either direction. However, we would gener- ing that the new route would meet minimum performance ally assume that any additional service hours would generate the standards for a cross-town route. Table 22 summarizes same number of passengers per hour. responses.

OCR for page 11
14 TABLE 22 Examples of specific responses include: RIDERSHIP FORECASTING FOR SCENARIO D: CROSSTOWN ROUTE Code BRT service, modify models to add new mode, and use Agencies updated four-step model. No. Agencies Responding Response Responding (%) Similar routes/changes 15 42 If BRT was being examined, we would likely use elasticities to examine how current ridership would be impacted based on Transfer data/connecting routes 8 22 incremental improvements over regular bus service. If a new rail Socioeconomic/demographic data 6 17 line was being examined, the rigor of the analysis would be Productivity 5 14 based on how the forecast is being used. For conceptual design Would not analyze 5 14 purposes, ridership would likely be developed using rule of thumb methods. For projects beyond conceptual design, we Four-step travel model 4 11 would likely use the MPO's four-step model. Similar conditions/area 4 11 Evaluate trip generators/land use 4 11 For a simple feasibility analysis (not one requiring environ- within 0.25 mile mental clearance) we would calculate additional service hours Assume minimum performance 4 11 on the corridor, including any time savings resulting from the standard BRT improvements. We would then review the number of pas- sengers per service hour on the existing service, and assume that the additional service hours would at least meet the exist- Examples of specific responses include: ing threshold. Then, we would review the ridership trend analysis for any of the other BRT corridors we have imple- mented in order to make assumptions of similar performance. Would model using regional transportation model, but would We have experienced a six percent increase in ridership on our interpret the results based on comparison with existing compa- first BRT corridor. That increase would be compared to other rable routes given the inaccuracy of the regional model at the proposed corridors to determine whether similar increases route level. could be anticipated. For BRT projects that would require envi- ronmental clearance [Environmental Impact Statement], we Population along proposed route, demographics of population, would perform a series of model runs using the countywide traffic generators along route, convenient transfers to other travel demand model. routes. Review of all generators, attractions, service frequency, span, Scenario F: Prediction of Next Year's Ridership fares, competitive/complementary services in area, demograph- as Part of Budget Process ics, employment. Most agencies forecast next year's ridership using trend line analysis, with some consideration for expected service and Scenario E: Implementation of New Mode fare changes and professional judgment. A few agencies do such as Bus Route Transit not forecast ridership one year ahead. Table 24 summarizes responses. This scenario presents the most drastic change to the exist- ing transit system and calls for the most formal analytical Examples of specific responses include: techniques to forecast ridership. Nearly half of all respon- dents indicated that they would rely on the four-step travel Service evaluation uses an historical trend methodology compar- model. Several who mentioned that they would not analyze ing the ridership trends between consecutive months over time this type of change noted that there are no plans for a new and disaggregating by weekday, Saturday, and Sunday. Service mode of transit service and thus there would be no need to elasticities are used when evaluating service changes prior to implementation of the methodology. For FY 99 through FY 06, analyze ridership impacts. Many agencies would hire a con- percentage differences between forecast and actual annual system sultant to develop a ridership forecast. Examination of travel ridership have ranged from 0.01 percent to 0.85 percent. time changes and application of appropriate elasticities were also mentioned (see Table 23). We use an econometric model for this. TABLE 23 TABLE 24 RIDERSHIP FORECASTING FOR SCENARIO E: RIDERSHIP FORECASTING FOR SCENARIO F: NEW MODE SUCH AS BUS ROUTE TRANSIT RIDERSHIP FORECAST FOR NEXT YEAR Agencies Agencies No. Agencies Responding No. Agencies Responding Response Responding (%) Response Responding (%) Four-step travel model 17 47 Trend line 21 58 Would not analyze 7 19 Service level changes 13 36 Hire a consultant 6 17 Fare changes 5 14 Analyze travel times 4 11 Professional judgment 4 11 Elasticities 4 11 Would not analyze 4 11