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27 of relying on farebox data is that it is available every day from Prediction of next year's ridership as part of the budget every bus. Getting to the point where farebox data are usable process: analyze past and current ridership trends; use not only at the route level but also at the stop level has been the annual ridership goal as defined by the Authority. the primary objective for many years. OCTA is also explor- The operations planning department tracks ridership ing the development of origin/destination trip tables through compared with the goal, and will send up red flags to the use of the APC data. senior management and other departments when the trends do not match the goal. OCTA is satisfied with the accuracy of its input data, A 10-year ridership forecast as part of a long-range with the caveats that improvements in the reliability of plan: use a combination of the mode choice model and APC data through field verification and calibration would trend line analysis. be useful, and that demographic data are not always avail- able at the desired geographic scale. More accurate APC The one improvement to forecasting methodology would ridership data would go a long way toward ensuring that an be to develop a more automated approach. This would involve optimal amount of data is available. Constructing an origin/ the use of new technologies and tools, and would result in fore- destination trip table with APC data would be a significant casts based on a choice of methodology from a wide variety of improvement over use of on-board surveys, which are difficult proven and accurate methods that best fits the goal. and expensive. OCTA reports two main lessons learned from its experi- OCTA would like to see several improvements in its rid- ence: ership forecasting methods, including the availability and accuracy of input data at the appropriate scale, less time- Use experience and results from the past to justify rid- intensive methodologies, inclusion of more predictive vari- ership forecasts. ables, simplification of procedures, enhanced accuracy, and Carefully review mode choice model results with flexibility to address a wide variety of situations. The ideal those obtained by peers and in other corridors and to next step would be to develop an automated methodology for elasticity-based forecasts. short-range ridership forecasting. Also, the long-range model predicts ridership for 2030; however, demographic variables OCTA sees ridership forecasting methodologies adding are available in 5-year increments. A spreadsheet model that value in three areas: could predict interim year ridership (say at 5-year intervals) would complement and not compete with the long-range Budget: good forecasts provide more accurate informa- model, which requires too much work to generate interim tion regarding ridership and revenue for budgeting pur- year forecasts. poses. Service planning: forecasts help to prioritize potential Ridership forecasts would be developed under the scenar- service improvements by quantifying the benefits ios included in the survey as follows: (increased ridership) of each improvement. Long-range planning: forecasts also quantify benefits Half-mile rerouting of an existing route to serve a new attributable to transit in the long-range model, includ- shopping center: (1) determine interest level based on ing increased ridership, decreased vehicle-miles trav- public comments, (2) look at similar shopping centers, eled, and net reduction in travel delay. (3) analyze whether this change would affect existing customers, and (4) consider improved transfer opportu- This case study indicates that GIS programs, formal mod- nities and connections. eling efforts, use of elasticities, and professional judgment Extension of an existing route for one mile to serve a can together provide a menu of ridership forecasting method- new residential development: estimate the additional ologies for use as appropriate. The various departments that revenue vehicle-hours required and multiply by the require ridership forecasts are comfortable with the method- minimum productivity standard to project the ridership ologies and confident in the results. Additional work is ongo- needed to meet the minimum productivity standard. ing to enhance accuracy and simplify the use of these Change in headway from 12 to 10 min during peak hours: methodologies; however, OCTA has achieved a high level multiply the peak-hour change in service hours by the cur- of confidence in its ridership forecasts in a wide variety of rent productivity of the route by the appropriate elasticity situations. from Table 40 (within the range of +0.5 to +0.7). Implementation of a new crosstown route: analyze rid- ership, productivity, and transfer points of similar TRIMET (PORTLAND, OREGON) routes with a crosstown alignment. Use the long-range model to analyze further. TriMet prepares ridership forecasts for virtually every ser- Implementation of a new mode such as BRT: use the vice change. There is no specific threshold triggering the mode choice model. need for a ridership forecast. Forecasts are used internally

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28 and distributed to interest groups and stakeholders in TABLE 41 response to service requests, as needed. One employee han- SERVICE ELASTICITIES USED BY TRIMET IN ITS' RIDERSHIP FORECASTING MODEL dles the ridership forecasting for bus changes. Light rail is evaluated using the four-step travel model. Change in Headway Elasticity Factor New service or new time period +1.00 60 min to 15 min +0.58 The Operations Planning and Planning Departments pre- 60 min to 30 min +0.80 pare ridership forecasts related to short-term service changes. 30 min to 15 min +0.73 Ridership and demographic data, including population, 20 to 15 min; 15 to 10 or 12 min +0.20 employment, and retail employment are the primary inputs. 12 to 10 min; 10 to 7.5 min; 7.5 to 5 min +0.10 APCs provide current ridership, supplemented as needed with ridecheck data. TriMet uses census data and origin/destination data gathered through on-board surveys. Employment data are TriMet calibrated each of the three models (for regional, available from the 2000 Metro Employment Database. local, and employer shuttle routes) using data from 12 regional routes, 12 local routes, and 5 employer shuttles. Cal- TriMet first looks for similar routes and uses professional ibration minimized the differences between predicted and judgment to forecast ridership for most service changes. If actual ridership for the group of routes as a whole. Elasticity there are no similar routes, it implements a two-step process factors of 1.0 were used for very frequent service outside the using regression and service elasticities to predict ridership. scope of changes shown in Table 41 and for local service and The first step involves regression equations developed in- employer shuttles, based on the calibration efforts and house for three different types of service and calibrated using because this high number of trips is usually associated with TriMet routes: an increase in the span of service. For regional routes: Ridership1 0.06704 * popula- TriMet is satisfied with the reliability of ridership data tion 0.0018 * non-retail employment 0.02 * retail collected through its APC/AVL system. Numerous samples employment. are obtained for each trip and the data have proven to be For local routes: Ridership1 0.00984 * population both detailed and accurate down to the trip and stop levels. 0.004 * non-retail employment 0.008 * retail employ- Census data and origin/destination data are not quite so reli- ment. able. Census data becomes dated relatively quickly. Origin/ For employer shuttles: Ridership1 0.01 * non-retail destination data do not provide a large enough sample to employment 0.0135 * retail employment. work with below the route level. All population and employment values are calculated Technology has had a significant effect on ridership fore- within one-quarter mile of the route using GIS. Specifically, casting. The APC/AVL systems have greatly improved the all census blocks with a centroid within one-quarter mile of accuracy and reliability of ridership data. GIS has allowed the bus route are included in the route buffer. TriMet to associate census data more accurately with routes and ridership. Results of the regression model are for a "typical" route; the second step of the model adjusts the regression-based Ready availability of more detailed data, such as vehicle forecast using service elasticities that vary based on the pro- ownership and income, at the stop level would be welcome. posed level of service. The equation is in logarithmic form. The ridership models were designed to use population and For regional routes, the equation is: employment because data for both is readily available at the census block level. TriMet is satisfied with its current fore- Ridership Exp (((LN (# daily trips) LN (62)) * Elasticity) casting methods. LN (Ridership1)) Ridership forecasts could be developed under the scenar- For local routes and employer shuttles, the equation is: ios included in the survey as follows: Ridership Exp (((LN (# daily trips) LN (36)) * Elasticity) Half-mile rerouting of an existing route to serve a new LN (Ridership1)) shopping center: (1) identify existing service with com- parable headways to a shopping center that is similar in A daily total of 62 trips for regional routes and 36 trips for terms of land use, population, retail employment, and other routes represent service at 30 min headways for a typ- nonretail employment, and assume similar ridership; ical service span. Service elasticities (Table 41) were taken (2) if no similar service is identified, then enter popula- from the Traveler Response to Transportation System tion and employment data into the ridership model; and Changes study (10) and calibrated using TriMet data. These (3) consider added travel time for existing customers as service elasticities are also used to forecast ridership based a result of the deviation and, if deemed significant, on all service changes involving changes in headway only. apply a travel time elasticity from Pratt (10).

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29 Extension of an existing route for one mile to serve a ridership response to potential service improvements in new residential development: (1) look at comparable low-density areas. existing service and (2) if no similar service is identi- Using population, retail employment, and nonretail fied, apply the ridership model. employment as the independent variables in a ridership Change in headway from 12 to 10 min during peak hours: model results in accurate estimates. Other variables apply a headway elasticity of 0.1 (see Table 41). such as vehicle ownership and income do not provide Implementation of a new crosstown route: (1) look at enough improvement in accuracy to warrant the time comparable existing service and (2) if no similar service and difficulty in acquiring and compiling the data at the is identified, apply the ridership model. appropriate scale. Implementation of a new mode such as BRT: the MPO If sufficient data are available, derive elasticities from would use its travel model to forecast ridership. local experience, not industry-wide averages. Prediction of next year's ridership as part of the budget process: generally, TriMet does not forecast next year's The value of ridership forecasting for TriMet is that it ridership. provides a sound basis for making decisions. In most cases, A 10-year ridership forecast as part of a long-range ridership is the bottom line in the evaluation of existing and plan: no experience with 10-year forecasts. proposed service. Ridership forecasts aid TriMet in making an informed choice among competing alternatives. TriMet lists the following lessons learned from its experience: This case study provides an example of a ridership fore- casting model in use at a transit agency. It is noteworthy that Forecast models from external sources that the agency TriMet's first choice of methodology for incremental service has experimented with in the past are complicated, changes is similar-route analysis, but the model is useful in require substantial staff time, are data intensive, and addressing unique situations. TriMet also relies heavily on provide results that are often inferior to a simple analy- service headway elasticities to assess the impact of changes sis of similar routes. One regression model, for example, in frequency. TriMet believes that its model and approach relied heavily on service levels as an indepen- could be used at other transit agencies, once calibrated with dent variable. The projections suggested unreasonable that agency's ridership data.