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Guidelines for Providing Access to Public Transportation Stations (2012)

Chapter: Chapter 5 - Travel Demand Considerations

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Suggested Citation:"Chapter 5 - Travel Demand Considerations." National Academies of Sciences, Engineering, and Medicine. 2012. Guidelines for Providing Access to Public Transportation Stations. Washington, DC: The National Academies Press. doi: 10.17226/14614.
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Suggested Citation:"Chapter 5 - Travel Demand Considerations." National Academies of Sciences, Engineering, and Medicine. 2012. Guidelines for Providing Access to Public Transportation Stations. Washington, DC: The National Academies Press. doi: 10.17226/14614.
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Suggested Citation:"Chapter 5 - Travel Demand Considerations." National Academies of Sciences, Engineering, and Medicine. 2012. Guidelines for Providing Access to Public Transportation Stations. Washington, DC: The National Academies Press. doi: 10.17226/14614.
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Suggested Citation:"Chapter 5 - Travel Demand Considerations." National Academies of Sciences, Engineering, and Medicine. 2012. Guidelines for Providing Access to Public Transportation Stations. Washington, DC: The National Academies Press. doi: 10.17226/14614.
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Page 47
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Suggested Citation:"Chapter 5 - Travel Demand Considerations." National Academies of Sciences, Engineering, and Medicine. 2012. Guidelines for Providing Access to Public Transportation Stations. Washington, DC: The National Academies Press. doi: 10.17226/14614.
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Page 48
Page 49
Suggested Citation:"Chapter 5 - Travel Demand Considerations." National Academies of Sciences, Engineering, and Medicine. 2012. Guidelines for Providing Access to Public Transportation Stations. Washington, DC: The National Academies Press. doi: 10.17226/14614.
×
Page 49
Page 50
Suggested Citation:"Chapter 5 - Travel Demand Considerations." National Academies of Sciences, Engineering, and Medicine. 2012. Guidelines for Providing Access to Public Transportation Stations. Washington, DC: The National Academies Press. doi: 10.17226/14614.
×
Page 50
Page 51
Suggested Citation:"Chapter 5 - Travel Demand Considerations." National Academies of Sciences, Engineering, and Medicine. 2012. Guidelines for Providing Access to Public Transportation Stations. Washington, DC: The National Academies Press. doi: 10.17226/14614.
×
Page 51
Page 52
Suggested Citation:"Chapter 5 - Travel Demand Considerations." National Academies of Sciences, Engineering, and Medicine. 2012. Guidelines for Providing Access to Public Transportation Stations. Washington, DC: The National Academies Press. doi: 10.17226/14614.
×
Page 52
Page 53
Suggested Citation:"Chapter 5 - Travel Demand Considerations." National Academies of Sciences, Engineering, and Medicine. 2012. Guidelines for Providing Access to Public Transportation Stations. Washington, DC: The National Academies Press. doi: 10.17226/14614.
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Page 53

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44 Reliable estimates of travel demand both for a station as a whole and for the station’s individual passenger access modes are important, but elusive challenges. The goal is to produce reasonable and reliable estimates that can be used to evaluate access planning options and to provide input to facility design. Ridership demand can be estimated for three basic conditions, representing decreasing levels of existing knowledge: (1) existing stations, (2) new stations along an existing (or extended) rapid transit line, and (3) stations along an entirely new proposed line. This chapter reviews current ridership estimating practice. It presents guidelines for estimating station ridership and access modes, including a ridership estimation model that was specifically developed for this research using data from many cities and over 450 individual stations. The station access planning tool is available on the CD accompanying this report, and online at www.trb.org/Main/Blurbs/166516.aspx. Appendix C provides further details about this tool, including instructions for its use. Appendix B and the literature review developed for this research study (TCRP Web-Only Document 44: Literature Review for Providing Access to Public Transportation Stations) provide additional detail on existing evaluation tools and demand modeling techniques. Review of Practice Travel demand models are a familiar tool for estimating transit ridership and have been used for decades to predict transit ridership for rapid transit services, especially large capital projects. Appendix B provides a detailed summary of the current state of travel demand modeling with respect to transit access. Nearly all MPOs have demand models available, and most of these models provide at least some level of ability to estimate transit use. Thus, most transit agencies have access to at least some travel demand model without the need to develop in-house expertise in building and calibrating demand models However, many existing demand models lack the sensitivity needed to adequately assess the impacts of specific transit station access alternatives. TRB Special Report 288: Metropolitan Travel Forecasting: Current Practice and Future Direction evaluated the ability of current travel demand models to meet a broad set of needs, including modeling transit demand. This report noted several issues with current travel demand models that impede their ability to accurately assess transit access modes. As detailed in Appendix B and TCRP Web-Only Document 44: Literature Review for Providing Access to Public Transportation Stations, this research effort conducted a detailed review of published C H A P T E R 5 Travel Demand Considerations

Travel Demand Considerations 45 literature on transit access demand. This review suggests several specific factors that appear highly correlated with access decisions and will likely be important in any transit access model: • Parking cost and supply; • Quantity and quality of feeder transit service; • Type and diversity of land uses; • Residential and employment density; • Quality and continuity of pedestrian facilities; • Station area demographics; • Safety; • Auto ownership; and • Travel time. Factors that are positively correlated with auto access include parking supply and auto ownership, while factors positively correlated with walking access include density and land use mix. No one model incorporates all of the factors listed above and some are used as proxies for other factors. For example, higher densities and a mix of uses tend to be correlated with higher quality pedestrian infrastructure. Several studies also emphasize the importance of concentrating residential development within a ½-mile radius of a rapid transit station. • Residents who live within a 5-minute walk of transit stations are 2.7 times more likely to commute by rail (12). • Most pedestrians are willing to walk up to ½ mile to access stations. For each additional 0.3 mile of walking distance, the probability of walking drops 50 percent. Density, local retail, and absence of major arterials near the station are the most important factors influencing walk trips to BART, together with individual characteristics such as gender and availability of a car (13). • WMATA (Washington, D.C.) finds that the likelihood of riding rail transit declines as the distances to both residential and non-residential developments near stations increase. A zone study indicated the access percentages shown in Exhibit 5-1. Exhibit 5-2 shows how the percent of riders using transit in Toronto, Edmonton, Washington, D.C., and San Francisco declines as distance from the rail station increases. • With regard to bicycle access, the international literature review shows that it is possible for bicycles to comprise up to 40 percent of transit access trips. However, realizing such a high percentage is largely dependent on factors outside transit agency control, as system-wide quality of bicycle facilities, topography, weather, and bicycle culture all play large roles in people’s willingness to bike. Even so, research indicates that provision of bicycle facilities at transit stations, in particular high-quality bike parking, does have a significant impact on bicycle access. Distance Percent Using Rail Office (%) Residential (%) At Station 35 54 ¼-mile 23 43 ½-mile 10 31 Source: Adapted from TCRP Report 95 (14, 15) Exhibit 5-1. Development distance related Metro rail ridership (2002).

46 Guidelines for Providing Access to Public Transportation Stations • The research shows that there are many factors other than distance that affect the decision on whether to walk, including urban design, pedestrian facilities, crime, and individual charac- teristics. By considering these factors, agencies have the potential to increase walking mode share to stations. Station Access Model A station access model was developed based on land use and ridership information assem- bled from public transport systems across the United States, including heavy rail and com- muter systems serving strong downtown areas (New Jersey Transit, New York Metro-North, Washington, D.C., Boston, and San Francisco) as well as light rail systems in smaller cities (Denver and Portland). In total, data for over 450 stations were used in development of the station access tool. The planning model can be accessed on the accompanying CD or downloaded from www.trb.org/Main/Blurbs/166516.aspx. Exhibit 5-3 gives the linear regression ridership models (equations) for estimating station ridership for each of the auto, bicycle, walking, and transit access modes. These equations can help quantify the likely ridership at new stations along an existing line or future growth at existing stations. The table also gives the relevant input variables used for each mode, linear regression coefficients, and statistical measures-of-fit. Note that the presence of heavy rail public transport acts essentially as a surrogate for CBD employment. In general, Exhibit 5-3 shows relatively high R-squared coefficients (greater than 0.7) for each of the access modes with the exception of feeder bus service. This is likely the result of a lack of data Source: TCRP Report 95 (12, 14) Exhibit 5-2. Work trip rail mode share by distance from residential sites to station.

Travel Demand Considerations 47 collected on available feeder transit due to limited resources and lack of a centralized data source. Only the total number of routes serving a given station was collected, meaning that information on the overall quality of the service at a given station (e.g., reliability, frequency, service coverage) was unavailable for incorporation into the modeling. The results of the modeling effort are consistent with the literature review findings: population density, employment density, and available parking are the most important factors determining station access decisions. Additional notes on each of the modal models include: • Automobile—As expected, the number of available parking spaces was the primary determinant of auto access. The percentage of zero-car households was also found to be significant, with auto access to transit decreasing in areas with lower auto ownership. • Bicycle—Bicycle access to transit increased in areas with higher population density and also those with lower auto ownership. Bike access was also higher in areas where more people travel by bike in general (measured by bicycle commute mode share) indicating the overall Exhibit 5-3. Station ridership estimation model. Coefficient t Significance Auto Ridership Model (R2 = 0.821) Constant 133.597 2.290 .023 Heavy rail dummy variablea 782.449 13.319 .000 Car parking spaces 1.282 35.452 .000 Percent zero-car households -347.494 -1.900 .058 Bicycle Ridership Model (R2 = 0.771) Constant -102.015 -6.594 .000 Jobs within ½ mile -0.001 -.864 .389 Population within ½ mile 0.008 4.446 .000 Bicycle parking spaces 1.032 6.980 .000 Bicycle commute mode share 3,241.579 5.730 .000 Percent zero-car households within ½ mile 249.852 3.164 .002 Walk Ridership Model (R2 = 0.717) Constant -456.090 -3.665 .000 Heavy rail dummy variablea 1,444.994 8.069 .000 Jobs within ½ mile 0.015 1.598 .111 Workers within ½ mile 0.481 5.370 .000 Workers who walked to work within ½ mile 2.390 8.639 .000 Feeder Transit Ridership Model (R2 = 0.373) Constant -261.387 -1.733 .084 Heavy rail dummy variablea 520.732 4.868 .000 Connecting transit lines 62.799 9.687 .000 Workers within ½-mile .019 1.554 .121 Parking utilization at station 211.484 1.661 .098 a Heavy rail = 1; other = 0

48 Guidelines for Providing Access to Public Transportation Stations bicycle-friendliness of an area contributes to bicycle access. The number of bicycle parking spaces available was positively correlated with bicycle access but this may not reflect a causal relationship, as agencies may be more likely to concentrate bicycle parking in areas with the highest underlying demand. • Pedestrian—Both employment density and population density are positively correlated with increased pedestrian access trips. Note that worker density (i.e., the number of employed residents) was found to be more strongly associated with pedestrian than simple population density. In addition, walk access to transit was higher in places where pedestrian travel is more common (measured by walking commuters), indicating the overall pedestrian-friendliness of an area contributes to pedestrian access. • Feeder Transit—The number of available transit connections is strongly associated with more feeder transit access trips, as expected. Feeder transit access is also higher in areas of higher population density (which are likely to support higher frequency feeder service) and at stations with higher parking utilization (indicating that passengers may be more likely to switch to feeder transit if parking is difficult to obtain). Effects of Improved Station Access There are many situations where it is desirable to improve access to an existing station. In these cases, quantified estimates of usage, benefits, and costs should be developed. Relevant information relating to existing station usage includes station boardings by time of day, modes of travel used by boarding and alighting passengers, and off-street parking accumulations by time of day. Information on bus routes, frequencies, and passenger loads should also be assembled. Transit agencies often periodically collect this information, but when the information is not already available, field studies should be conducted. Past trends in station boardings and access modes should be analyzed. These can provide a basis for estimating likely future trends. Obtaining population, worker, demographic and car ownership trends in a ½-mile (or sometimes 1-mile) radius of the station will prove useful. Park-and-Ride Many park-and-ride facilities operate near, at, or beyond their capacities. This excess demand can result in spillover parking impacts to surrounding neighborhoods and also inhibits ridership. Where a station’s park-and-ride facilities operate at or near capacity (i.e., over 90 percent occupancy), providing more spaces will likely increase ridership. This has been the experience of both BART and Metro-North. Exhibit 5-4 summarizes Metro-North’s experience in Connecticut. The net daily boarding increase at the origin station per parking space added was 0.11 in New Haven, 0.60 in South Norwalk, and 0.92 in Bridgeport. The exhibit suggests that up to one new rider can be gained per parking space added. Of course, demand for parking is always finite, suggesting that agencies should conduct a more thorough demand analysis in situations where parking is being expanded significantly (e.g., an increase of more than 25 percent). Some communities along Metro-North commuter lines manage parking. Often there are waiting lists for reserved parking spaces. In similar situations, some or all of these parkers should be added to the observed parking utilization for the purposes of demand estimation. Pricing parking spaces provides an important means of recovering some of the initial devel- opment costs and/or ongoing operating costs of the parking. However, charging for parking may also reduce demand for parking and thus ridership. BART’s experience has been that pricing

Travel Demand Considerations 49 parking has not reduced parking usage or rapid transit ridership; however, CBD parking costs are relatively high in BART’s service area. In general, park-and-ride demand is less likely to be replaced when the CBD all-day parking charge is less than the round trip rapid transit fare plus the daily parking charges at the station. The effects of changing park-and-ride supply and pricing at BART stations are shown in Exhibit 5-5. The various demand elasticities (shrinkage factors) shown in the exhibit provide a basis for estimating the likely effects of changing parking supply and bus service. Note that when parking is not fully utilized, pricing shows an elasticity of –0.33 (i.e., demand is reduced). Where spaces are fully occupied, removing spaces would either increase parking spillover in areas adjacent to the station or would reduce auto access trips and rapid transit ridership. New Haven South Norwalk Bridgeport Time Period Studied 1985-1999 1996-1999 1985-1999 Parking Spaces Added +628 +325 +500 Additional Rail Ridership Gross Ridership Increase +467 +250 +736 Ordinary Growth (estimated 1.5% / year) +400 +55 +277 Ridership Increase Attributed to Mode Shifts Induced by Parking (“New Riders”) +67 +195 +459 Additional Rail Ridership per Parking Space Added Gross Ridership Increase / Space Added 0.74 0.77 1.47 “New Riders” / Space Added 0.11 0.60 0.92 Note: External factors affecting Bridgeport included lowered train fares, free parking at state lot, and station area improvements. Source: TCRP Report 95 (14, 16) Exhibit 5-4. Changes in parking supply and demand at three Connecticut stations. A. ELASTICITIES 1. Parking space is 90% utilized Parking pricing no effect 2. Parking space is less than 90% utilized Parking pricing -0.33 3. Feeder bus service hours +0.60 B. PERCENTAGE SHIFTS 1. Auto to Bus when Feeder Bus Service is increased 2% 2. Shift from auto to other when parking is removed (parking 90% or more utilized) 34% 3. Bus to auto when parking is added (parking 90% or more utilized) 34% Source: BART Exhibit 5-5. BART elasticities and defaults (shrinkage factors).

50 Guidelines for Providing Access to Public Transportation Stations Transit Service Changes Changes in feeder bus revenue miles, travel times, frequencies, and fares will also influence ridership. Elasticity factors are commonly used to quantify these changes. Ridership elasticity is defined as the change in ridership corresponding to a 1 percent change in bus fares, revenue miles, travel times, or service frequency. Elasticity Methods Three types of methods can be used to compute elasticity: (1) shrinkage factors, (2) midpoint linear arc elasticity and (3) log arc elasticity: 1. Shrinkage Factor. The shrinkage factor has been used as a “rule of thumb” for many years to estimate the ridership effects of fare changes. It is the simplest method to apply and gives a reasonable approximation for small fare changes. The percentage increase in ridership is equal to the percentage change in an attribute (e.g., fare) times the appropriate elasticity factor. The equations are as follows: R R ER X X X 2 1 1 2 1 1 = + −( ) where: E = elasticity R1 = base ridership R2 = estimated future ridership X1 = quantity of base attribute (such as travel time or frequency) X2 = quantity of future attribute 2. Midpoint (Linear) Arc Elasticity. This method is commonly used in estimating ridership changes and is the method used in Chapter 5. It is defined as follows: R E X R E X R E X E X R F2 1 1 2 1 2 1 1 1 1 1 1 = −( ) − +( ) −( ) − +( ) = where: E = elasticity R1 = base ridership R2 = estimated future ridership X1 = quantity of base attribute (such as travel time or frequency) X2 = quantity of future attribute F = multiplier 3. Log Arc Elasticity. Log arc elasticities are another method of calculating elasticities, but has seen relatively few transit applications. As such, the formulas are not reproduced here. A comparison of three elasticity computation methods is shown in Exhibit 5-6. For small changes (± 10 percent), the three methods give similar results. However, for large changes, results obtained from the shrinkage factor diverge considerably from the other two methods. Therefore, users of these methods should always be aware of the method originally used to develop the elasticity factor and should use the corresponding calculation method when applying the factor. Applications The application of elasticity factors is straightforward. Typical midpoint elasticities (Method 2) are shown in Exhibit 5-7. Where a transit agency has produced specific elasticity values, these should be used instead.

Travel Demand Considerations 51 Assume that travel times to the rapid transit station decrease from 12 to 10 minutes as a result of a service improvement to feeder transit service. The following changes in ridership are anticipated based on an elasticity of -0.35 and a base ridership of 1,000. By the shrinkage factor method: R2 1 000 0 35 1 000 10 12 12 1 058 5 8= + −( )( ) −( ) = =, . , . . % By the midpoint arc elasticity method: R2 0 35 1 12 1 000 0 35 1 10 1 000 = − −( )( )( )− − +( )( )(. , . , ) − −( )( )− − +( )( ) = = +0 35 1 10 0 35 1 12 1 066 6 6. . , . % Estimating Ridership for New and Infill Stations Estimating ridership for new stations as well as infill stations is normally done for future plan- ning or horizon years. The process requires knowledge of existing travel patterns and reasonable estimates of future population, employment, and land development. Estimates can be made either by using this report’s station ridership model and access planning tool (or an agency-specific model) or using a traditional four-step model using trip generation, trip distribution, modal allocation, and trip assignment to transit and highway networks. When a four-step model is used, the model should be calibrated for both the transit and automobile Fare Change (%) -50 -30 -10 +10 +30 +50 +100 Log Arc Elasticity -0.300 -0.300 -0.300 -0.300 -0.300 -0.300 -0.300 Midpoint Arc Elasticity -0.311 -0.303 -0.300 -0.300 -0.302 -0.311 -0.311 Shrinkage Factor -0.46 -0.38 -0.32 -0.28 -0.25 -0.23 -0.19 Source: Calculated Exhibit 5-6. Elasticity values for different methods of computation. Item Travel Time Bus Miles BusFrequencies Application New routes replace or complement existing routes Service expansion Greater frequency of existing routes Range -0.3 to -0.5 0.6 to 1.0 0.3 to 0.5 Typical -0.4 0.7 to 0.8 0.4 Source: TCRP Report 99 (18) Exhibit 5-7. Typical midpoint arc elasticities.

52 Guidelines for Providing Access to Public Transportation Stations modes, and the model’s network design should include highway and transit links and a means to model multi-modal trips, such as park-and-ride. The modal allocation of travel is a major concern in rapid transit demand estimation. Most regional planning agencies use a logit model to estimate mode splits. Logit models assume that the share of trips by a specific mode is a function of the mode’s utility (i.e., attractiveness to passengers, based on various user and system characteristics such as vehicle ownership, travel time, and price) divided by the sum of the utilities of all possible modes for the trip. Ratio Method for Infill Stations In addition to the methods described, a simple ratio method may also prove to be a valuable tool in estimating demand for infill stations. This method works by assuming that the proposed station will have similar relation of ridership to surrounding land uses. To apply this method, information should be assembled on the population, demographic, and development characteristics for an area within ½ to 1 mile of the proposed station and the two adjacent stations. Exhibit 5-8 summarizes the information that should be assembled. Basic information should be compared for the proposed station and the two existing adjacent stations. These key comparisons include total population, resident workers, and employment in the station areas. The catchment area characteristics of the proposed station should be compared with those of the two adjacent stations. The ratios of ridership to the key demographic factors (i.e., population, workers, and employment) can be determined for the two existing stations and then applied at the proposed station to estimate number of boardings. Exhibit 5-9 provides an illustrative example. The new station ridership can be expressed as either a range or as average. The analysis may also be extended to also estimate mode split and parking demand. Station Characteristics Station Area Demographics Status Population Rapid transit mode Workers Station type Jobs Predominant Land Use Median household income Topography Percent zero-car households Vehicles per worker Access Provisions Daily parking spaces (at the station) Reserved parking spaces Daily and monthly parking rates Parking occupancy at 9 am Bicycle parking spaces Round trip transit fares Connecting transit lines Transfer charge (if any) Exhibit 5-8. Desired station profile information within ½-mile radius of existing and proposed stations.

Travel Demand Considerations 53 Exhibit 5-9 shows that the proposed station could have daily boardings of between approximately 3,000 and 3, 400. This ratio or interpolation method requires that the land uses at the proposed stations are similar to those at adjacent stations. When this is not the case, the characteristics for the planned station should be compared with those for stations elsewhere in the system with similar uses. Note that this method assumes all of the ridership at the infill station consists of new riders. In practice, this is unlikely to be true, and analysis of infill stations should consider the potential that an infill station will simply re-distribute existing ridership rather than generate new riders. Existing Station A Proposed Station Ratio Boardings 4,000 boardings X Population 10,000 population 8,000 0.80 Employment 6,000 workers 5,000 0.83 x 0.80 * 4,000 = 3,200 x 0.83 * 4,000 = 3,320 Existing Station B Boardings 6,000 boardings X Population 16,000 population 8,000 0.50 Employment 9,000 workers 5,000 0.56 x 0.50 * 6,000 = 3,000 x 0.56 * 6,000 = 3,360 Exhibit 5-9. Illustrative computations for estimating boardings at a proposed station.

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TRB’s Transit Cooperative Research Program (TCRP) Report 153: Guidelines for Providing Access to Public Transportation Stations is intended to aid in the planning, developing, and improving of access to high capacity commuter rail, heavy rail, light rail, bus rapid transit, and ferry stations. The report includes guidelines for arranging and integrating various station design elements.

The print version of TCRP Report 153 is accompanied by a CD-ROM that includes a station access planning spreadsheet tool that allows trade-off analyses among the various access modes--automobile, transit, bicycle, pedestrian, and transit-oriented development--for different station types. The appendices to TCRP Report 153 are also available on the CD-ROM.

The items contained in the CD-ROM are also available for download below.

In 2009 TRB released TCRP Web-Only Document 44: Literature Review for Providing Access to Public Transportation Stations, which describes the results of the literature review associated with the project that developed TCRP Report 153.

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