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21 analysis would examine productivity (in terms of passenger origin/destination data for current riders. Obtain boardings per revenue hour) by time of day and day of the employee addresses from major employers along the week for a similar route serving a similar area and would then proposed route. apply the productivity values to the planned revenue hours of Implementation of a new mode such as BRT: evaluate service by time of day and day of the week. For a headway existing travel times and potential savings. Look at sta- change from 60 to 30 min, the expected productivity would tion sites and potential for reroutes for transfers from be adjusted downward depending on type of route and reason other areas. Extract origin/destination data for current for the change. VIA categorizes its routes as major radial, riders. Evaluate all trip generators within one-quarter minor radial, crosstown, feeder/circulator, and express/limited mile of stations. Evaluate automobile drive times and stop, with different productivity standards for each category. traffic volumes. All ridership forecasts are for 6 months after the service Prediction of next year's ridership as part of the budget change, recognizing that it takes time for ridership to develop. process: base the forecast on the service plan included Any new service is implemented on a 180-day trial basis and in the budget and past ridership trends. can be discontinued or altered if it does not meet the produc- A 10-year ridership forecast as part of a long-range tivity standards for its service category. Short-range forecasts plan: base the forecast on past ridership trends, service are made for a typical weekday/Saturday/Sunday, whereas revisions included in the plan, and expected growth and long-range forecasts are at the more aggregate level of annual development. systemwide ridership. VIA would not necessarily make any changes to its cur- VIA's goal for ridership forecasting is that all forecasts be rent methodology, but would like to obtain more data linked within 10% of actual ridership at the route or system level, to GIS. Use of GIS has made it much easier to develop visual and this goal has been met. The professional experience of representations of ridership activity. Lessons learned include the forecaster plays a significant role in understanding how the need to understand the limitations of the data used in rid- trip productions and attractions, schools (especially middle ership forecasting. schools), and timed transfers affect ridership. Field work is essential in developing and applying this experience. Signs This case study provides an example of a traditional of good transit potential can go beyond the obvious, such as approach that relies heavily on professional judgment and an high residential density and presence of major trip genera- understanding gained through experience of the factors con- tors, to factors such as the presence of oil stains and trans- tributing to transit ridership. The value of ridership forecast- mission fluid leaks on residential streets. ing is perceived to have declined as a dedicated sales tax and other funding sources have lessened reliance on farebox Technology has made the forecasting process faster and revenue. more reliable, but has not changed the methodology itself. A ridership forecast for a simple route realignment can usually be generated within one hour. REGIONAL TRANSPORTATION DISTRICT (DENVER, COLORADO) Ridership forecasts would be developed under the scenar- ios included in the survey as follows: RTD prepares ridership forecasts for most service changes except for minor adjustments to schedules or route segments. Half-mile rerouting of an existing route to serve a new There is no explicit threshold triggering the need for a rider- shopping center: base the forecast on similar routes and ship forecast; however, a change of more than 10% in service trips generated by similar retail developments with the hours suggests ridership implications, and any service reduc- understanding that it may take time to develop new tion indicates a potential loss of riders. Ridership forecasting retail customers. is part of the general duties of staff members in the planning, Extension of an existing route for one mile to serve a operations planning, and budget departments, depending on new residential development: base the forecast on sim- the type of forecast being generated. Forecasts are distributed ilar routes and trips generated per dwelling unit based internally, to board members, and to stakeholders. on median value of homes. Residential areas must have at least 200 occupied homes before service begins. The most common change is in route frequency. RTD Change in headway from 12 to 10 min during peak uses a service elasticity of 0.5 to forecast the ridership hours: forecast would depend on load factors and over- impact of frequency changes, based on the average value all usage in the transit corridor. A shift from five to six from TCRP Report 95 (11). RTD calibrates this average elas- trips per hour would not increase ridership unless the ticity upward or downward based on its previous experience, route is at its maximum load factor and there are more depending on existing route frequency, similar routes, and potential riders in the corridor. setting. For example, RTD has found a greater elasticity for Implementation of a new crosstown route: evaluate headway improvements to infrequent service, with dimin- existing travel times and potential savings and extract ishing returns seen on headway improvements to frequent

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22 service. Also, headway improvements in response to over- management. GIS can better represent the complexities not crowding show a higher elasticity than improvements for only of the transit network structure but also of the planning other reasons. The inelastic nature of service elasticities has analysis. RTD has combined GIS with aerial photographs to not been well understood in many communities served by develop presentations that clearly articulate its proposals and RTD, and staff has been aided in educating policymakers by rationales, and reports very positive reception by the public. its documentation of prior experience and by summaries of As an analytical and communications tool, GIS has helped to experience in other areas. build support for RTD initiatives. RTD is now incorporating origin/destination data from household travel surveys con- RTD also relies on service standards. Although these do ducted in the counties within its service area every 5 years on not forecast ridership, standards do set a minimum threshold a rolling basis into GIS. of performance for existing routes and proposed new service. RTD also evaluates route sustainability by examining popu- RTD assesses its ridership forecasting methods as gener- lation and employment per acre. ally adequate for short-term service planning and is now much better documented with the release of TCRP Report 95. RTD considers various inputs to its ridership forecasts Experience in applying these methods will result in addi- depending on the type of change being analyzed. System rid- tional refinements. Desired improvements include the avail- ership, ridership on similar routes, and demographic factors ability and accuracy of input data at the appropriate scale, are considered for changes in span of service; timed transfers inclusion of more predictive variables, and incorporation of are an important component of RTD service, therefore consis- TCRP Report 95 into service standards and guidelines. tency in span of service is important. A route deviation requires examination of route segment ridership. Ridership on Ridership forecasts would be developed under the scenar- similar routes, origin/destination information, and demo- ios included in the survey as follows: graphic and land use characteristics are important for new routes and route extensions, and most of these factors also bear Half-mile rerouting of an existing route to serve a new on forecasts for a new mode or corridor. Economic trends are shopping center: first assess current ridership; estimate factored into annual ridership forecasts for budget purposes. new ridership based on similar routes and shopping centers. Along with service elasticities and service standards, Extension of an existing route for one mile to serve a RTD uses rules of thumb and similar route analysis in fore- new residential development: estimate new ridership casting ridership impacts of most service changes. A signif- based on similar routes and developments. icant change, such as a new mode or new corridor, calls for Change in headway from 12 to 10 min during peak the regional four-step travel model, used for rail and long- hours: use an elasticity of 0.5 to estimate the ridership range planning. Trend analysis is used for annual budget impact of the frequency improvement. This elasticity forecasting. Professional judgment is applied to all ridership may be adjusted up and down as suggested in TCRP forecasts to ensure reasonableness of the results. Report 95 based on similar routes and settings and (more broadly) on existing frequency of service. RTD uses ridership, origin/destination, land use, and cen- Implementation of a new crosstown route: assess cur- sus demographic data in developing its forecasts. New tech- rent ridership on related routes. Examine origin/desti- nologies (including the introduction of APCs along with a nation data. Consider the performance of similar routes. focused effort to establish confidence in the APC data; new Analyze transfer data and evaluate the setting of the software that integrates ridecheck, supervisor point check, proposed route. and APC data and converts it to a usable format for service Implementation of a new mode such as BRT: run the planning purposes; GIS; and new, more reliable fareboxes) four-step travel model. have improved the quality of ridership data. Having gone Prediction of next year's ridership as part of the budget through a standard debugging period, RTD is now confident process: base the forecast on a trend analysis. in the APC data and is developing new applications. For A 10-year ridership forecast as part of a long-range example, stop-level boardings, alightings, and loads are plan: run the four-step travel model. exported into GIS and are mapped along with population and employment density. In terms of one improvement to its forecasting method- ology, RTD sees value in the adoption of written guidelines RTD views an optimum amount of data as a balance for how to do service planning, including ridership fore- among data availability, methods to analyze the data appro- casting, in a rational way. These guidelines are not viewed priately, and the ability to present results in a meaningful way as limiting planners to an inflexible approach, but rather as to decision makers. GIS has been very important in terms of ensuring that key elements are addressed. Although these presenting results. Service planners at RTD are moving guidelines would be valuable internally, their primary value toward the use of GIS in place of Microsoft PowerPoint in could be in helping potential partners such as city planning making presentations to the general public and to senior agencies in service area jurisdictions to understand transit