Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
29 TOD has attracted interest as a tool for promoting smart growth, leveraging economic development, and catering to shifting market demands and lifestyle preferences. Part of the appeal TODs hold is they behave differently from conven- tional development patterns. People living and working in TODs walk more, use transit more, and own fewer cars than the rest of their region. TOD households are twice as likely to not own a car, and own roughly half as many cars as the average household. At an individual station TOD can in- crease ridership by 20% to 40% and up to 5% overall at the regional level. Residents living near transit are 5 to 6 times more likely to commute by transit than other residents in their region. Self-selection is a major contributor to the ben- efits of TOD, meaning that people choosing to live in a TOD are predisposed to use transit (Cervero, et al., 2004). Given their performance characteristics, TODs present an opportunity to accommodate increased density without many negative impacts associated with the automobile. While research clearly points to how TODs perform differently, the body of information on TOD travel characteristics has yet to have an impact on industry guidance for projects near major transit stations. This research seeks to bridge one of the widest knowledge gaps on the effects of TOD on travel demand: automobile trip generation rates for residential TODs. Empirical evidence on vehicle trip generation can inform the setting of parking re- quirements for projects near major transit stations. Despite the existing body of research and supportive local develop- ment, codes developers and financial institutions still tend to prefer conventional parking ratios in TODs. As a conse- quence most TODs are oblivious to the fact that a rail stop is nearby and as a result, their potential benefits (e.g., reduced auto travel) are muted. Structured parking in particular has a significant impact on development costs and is prohibitively expensive in most markets. Lower TOD parking ratios and reduced parking could reduce construction costs, leading to somewhat denser TODs in some markets. Similarly, many proposed TOD projects have been halted abruptly or redesigned at lower densities due to fears that dense development will flood surrounding streets with auto traffic. Part of the problem lays in the inadequacy of current trip generation estimates, which are thought to overstate the potential auto impacts of TOD. ITE trip generation and parking generation rates are the standards from which local traffic and parking impacts are typically derived, and impact fees are set. Some analysts are of the opinion that there is a serious suburban bias in the current ITE rates. Typically, empirical data used to set generation rates are drawn from suburban areas with free and plentiful parking and low-density single land uses. Moreover, ITEâs auto trip reduction factors, to reflect internal trip capture, are based on only a few mixed- use projects in Florida; there has been little or no observation of actual TODs. The end result is that auto trip generation is likely to be overstated for TODs. This can mean that TOD de- velopers end up paying higher impact fees, proffers, and exactions than they should since such charges are usually tied to ITE rates. Smart growth requires smart calculations, thus impact fees need to account for the likely trip reduction effects of TOD. Study Projects This study aims to fill knowledge gaps by compiling and analyzing original empirical data on vehicle trip generation rates for a representative sample of multi-family housing projects near rail transit stations. This was done by counting the passage of motorized vehicles using pneumatic tubes stretched across the driveways of 17 transit-oriented housing projects of varying sizes in four urbanized areas of the country: Philadelphia/N.E. New Jersey; Portland, Oregon; metropoli- tan Washington, D.C.; and the East Bay of the San Francisco Bay Area (Figure 2.1). Rail services in these areas are of a high quality and span across four major urban rail technologies: commuter rail (Philadelphia SEPTA and NJ Transit); heavy S E C T I O N 2 Does TOD Housing Reduce Automobile Trips?
30 rail (San Francisco BART and Washington Metrorail); light rail (Portland MAX); and streetcar (Portland). Case study sites were chosen in conjunction with the H-27A panel. The most current ITE Trip Generation Manual (7th Edition) includes trip generation data for nearly 1,000 land uses and combinations. The primary focus of this research is on resi- dential housing (ITE, 2003). The aim is to seed the ITE manual with original and reliable trip generation data for one impor- tant TOD land useâresidential housingâwith the expectation that other TOD land uses and combinations (e.g., offices) will be added later. There is hope the research prompts local offi- cials to challenge how they evaluate the likely traffic impacts of housing near major rail transit stations as well as the parking policies for these projects. The research, moreover, complements several other studies presently underway that aim to further refine trip generation rates to account for the trip-reducing impacts of mixed-use development (typically through internal capture). The trip-reduction effects of transit-oriented housing are thought to come from three major sources: 1) residential self- selection: for lifestyle reasons people consciously seek out housing near major transit stops for the very reason they want to regularly take transit to work and other destinations; stud- ies in California suggest as much as 40% of the mode choice decision to commute via transit can be attributed to the self- selection phenomenon (Cervero, 2007); 2) the presence of in-neighborhood retail sited between residences and stations that promote rail-pedestrian trip-chaining; an analysis of the American Housing Survey suggests the presence of retail near rail stations can boost transitâs commute mode share by as much as 4% (Cervero, 1996); and 3) car-shedding (i.e., the tendency to reduce car-ownership when residing in efficient, transit-served locations) (Holtzclaw, et al., 2002). For studying traffic impacts of multi-family housing near rail stations, we selected mainly multi-family apartments (rental) and in one instance, a condominium project (owner- occupied). Table 2.1 provides background information on the selected TOD-housing projects and Figures 2.2 through 2.5 show their locations within metropolitan areas and photo perspectives of the sampled housing projects. Housing projects ranged in size from 90 units (Gresham Central Apartments) to 854 units (Park Regency). Most proj- ects were garden-style in design, around three to four stories in height. The sampled Washington Metrorail projects, however, tended to be much higher as revealed by the photo images, with the exception of Avalon near the Bethesda Metro- rail station. The average number of parking spaces per proj- ect was around 400, yielding an average rate of 1.16 spaces per dwelling unit. The only nonapartment project surveyed was Wayside Plaza in Walnut Creek, near the Pleasant Hill BART stations, a condominium project. Six of the surveyed housing projects had ground-floor retail and/or commercial uses, however all were primarily residential in nature (i.e., more than 90% of gross floor area was for residential activities). Another selection criterion was the project not be immedi- ately accessible to a freeway interchange. All of the sampled projects were more than 500 feet from a freeway entrance; five were situated within a quarter mile of a freeway on-ramp. The Figure 2.1. Case study metropolitan areas.
31 average walking distance from the project entrance to the nearest rail station entrance was 1,060 feet. Study Methods Local traffic engineering firms were contacted about the availability of pre-existing data, however no examples of re- cent trip generation analyses for TOD housing projects were found that had relevant information to include in this study. After agreement was reached with the TCRP H-27A panel to survey projects in the four rail-served metropolitan areas, candidate sites were visited to make sure they met the selec- tion criteria and also had limited access points and driveways where pneumatic tube count data could be reliably collected. (As shown in Table 2.1, all had five or fewer driveways and in most instances just a few ways to drive in and out of a project.) Once sites that met the selection criteria were chosen, prop- erty owners and property managers were contacted, informed about the purpose of the study, and asked permission to allow on-site observation and the installation of pneumatic-tube recorders at curb cuts and driveways. After receiving permission from property owners to in- stall pneumatic tube counters on their properties, empirical field-work commenced. Local traffic engineering firms that specialize in vehicle trip data-collection were contracted to set up the tube counters and compile the data. Pneumatic tube counters recorded daily vehicle traffic volumes by hour of day and day of week in accordance with standard ITE methods. [Due to the primarily residential nature of the projects, in- ternal trip making was not expected to be as significant as it would be in larger TODs with a broad array of mixed uses. Measuring internal trip making would require supplemental surveys of residents (e.g., travel diaries) and/or local mer- chants, and the team has currently not budgeted to estimate these trips.] The consecutive two-day periods chosen to com- pile tube-count data were considered to correspond with peak conditions: middle of the week and prior to summer vacation season: Tuesday, May 29 and Wednesday, May 30, 2007 for the seven projects on the east coast (Washington, D.C. metro- politan area and Philadelphia/N.E. New Jersey); and Wednes- day, May 30 and Thursday, May 31 for the 10 projects on the west coast (Portland, Oregon and East Bay). To further segment collected data, the team used a national database from the CTOD to compile basic demographic data for the neighborhoods of each of the rail stations serving the selected TODs, including information on residential densities, car ownership, and median income. Also, pedestrian surveys were conducted to record measures regarding the quality of Housing Other Characteristics Housing Type # Stories # Units # On-Site Parking Spaces # Driveways Nearest Rail Station Shortest Walking Distance from Project to Nearest Station (feet) Philadelphia/NJ Gaslight Commons (S. Orange NJ) A 4 200 500 3 NJ Transit: South Orange 990 Station Square Apartments (Lansdale PA) A 1-3 346 222 3 Pennbrook SEPTA 625 Portland Center Commons (Portland) A 4 288 150 2 60th Avenue MAX 450 Collins Circle Apartments (Portland) A 6 124 93 1 Goose Hallow MAX 525 Gresham Central Apartments (Gresham) A 3 90 135 2 Gresham Central MAX 620 Merrick Apartments (Portland) A 6 185 218 1 Convention Center MAX 700 Quatama Crossing Apartments (Beaverton) A 3 711 3 Quatama MAX 2000 San Francisco Mission Wells (Fremont) A 2-4 391 508 4 Fremont BART 3810 Montelena Apartment Homes (Hayward) A 3 188 208 3 South Hayward BART 950 Park Regency (Walnut Creek) A 3 854 1352 5 Pleasant Hill BART 1565 Verandas (Union City) A 5 282 282 2 Union City BART 830 Wayside Plaza (Walnut Creek) C 3-4 156 166 1 Pleasant Hill BART 1555 Washington DC Avalon (Bethesda) A 4 497 746 2 Grosvenor Metro 1020 Gallery (Arlington) A 20 231 258 2 Virginia Square Metro 50 Lenox Park Apts. (Silver Spring) A 16 406 406 3 Silver Spring Metro 420 Meridian (Alexandria) A 10-16 457 560 2 Braddock Metro 920 Quincy Plaza (Arlington) A 15-21 499 499 2 Virginia Square Metro 1020 Note: A = Apartments (rental); C = Condominiums (owner-occupied) Table 2.1. Background on case study TOD housing projects.
32 walking, the availability of amenities (e.g., street trees and fur- niture), lack of provisions (e.g., no pedestrian cross-walks), and the shortest distance between the main entrance of each case-study project and the fare gates of the nearest rail station. Data Compilation Collected data were compiled, coded, cleaned, and entered into a data base. First, simple descriptive statistics were prepared on vehicle trip generation rates, defined in such standard terms as: average weekday vehicle trips per dwelling unit and one-hour AM and PM vehicle trips per dwelling unit. [ITE define average weekday trip rate as the weekday (Monday through Friday) average vehicle trip generation rate during a 24-hour period. Average rate for the peak hour is the trip generation rate during the highest volume of traffic entering and exiting the site during the AM or PM hours.] Vehicle count data obtained in the field were converted to 24-hour as well as AM and PM peak-hour rates per dwelling unit for each project. (Since 24-hour counts were obtained for two Station Square Gaslight Commons STATION SQUARE APARTMENT GASLIGHT COMMONS Figure 2.2. Locations of study sites in Philadelphia and Northeast New Jersey: Station Square Apartments and Gaslight Commons.
33 Avalon at Grosvenor Station Lenox Park Apartments Gallery at Virginia Square Quincy Plaza Meridian at Braddock Park Figure 2.3. Locations of study sites in metropolitan Washington, D.C.: Avalon; Gallery at Virginia Square; Meridian; Quincy Plaza; Lenox Park.
34 Quatama Crossing Apartments Collins Circle Apartments The Merrick Apartments Center Commons Gresham Central Apartments Figure 2.4. Locations of study sites in metropolitan Portland, Oregon: Center Commons; Collins Circle; Gresham Central; The Merrick; Quatama Crossing.
35 Montelena Apartments Mission Wells Apartments Wayside Commons Park Regency Verandas Apartments Figure 2.5. Locations of study sites in San Francisco-Oakland Metropolitan Area: Mission Wells, Montelena, Park Regency, Verandas, Wayside Commons.
consecutive weekdays, one-day estimates were computed by dividing the two 24-hour counts by two.) For all 17 TOD- housing projects combined, a weighted average trip generation rate was estimated. (The ITE manual defines weighted aver- age as the sum of trip ends for all projects divided by the sum of the independent variable, which in this case is number of dwelling units.) The computed rates for TOD-housing proj- ects were compared to those found in the latest edition of the ITE manual for the equivalent land use (i.e., apartments and condominiums) (ITE, 2003). Comparisons are drawn using the ITE manualâs weighted averages as well as estimates de- rived from best-fitting regression equations. The degree to which there are systematic differences in estimated and actual trip generation and parking generation rates of TODs are highlighted. The types of TOD projects for which there appear to be the largest discrepancies are identified. Additionally, results were cross-classified among sampled projects in terms of distance to CBD, distance to the nearest station, parking provisions, and other factors including the quality of walking environment (e.g., with or without adjoin- ing sidewalks). Multivariate regression equations that predict the trip generation rates of TOD housing as a function of these and other variables also are estimated. Lastly, the implications of research findings for various pub- lic policies and practices are discussed. To the degree that TOD- housing projects exhibit below-normal trip generation rates, a strong case can be made for using sliding-scale impact fees to evaluate new TOD proposals. This might, for instance, result in lowering the estimated trip generation rates within a quarter mile of a station and with continuous sidewalk access and in a mixed-use neighborhood by a fixed percent, such as 20%. Comparison of Vehicle Trip Generation Rates TOD-housing clearly reduces auto trips in the four urban- ized areas that were studied. Below, results for both 24-hour periods as well as peak periods are summarized. Average Weekday Trip Comparisons Table 2.2 shows that in all cases, 24-hour weekday vehicle trip rates were considerably below the ITE weighted average rate for similar uses. [The comparable ITE land use category for 16 of the 17 projects is Apartments (ITE Code 220). The average trip rate for apartments is 6.72 vehicle trips per dwelling unit on a weekday based on the experiences of 86 apartment projects across the United States (averaging 212 dwelling units in size). The best-fitting regression equation for apartments is: T = 6.01(X) + 150.35 (R2 = 0.88) where T = Vehicle Trip Ends and X = Number of Dwelling Units. For the Wayside Commons projects, the corresponding ITE land-use category is Residential Condominium (ITE Code 230). The average trip rate for condominiums is 5.68 vehicle trips per dwelling unit on a weekday based on the experiences of 54 owner-occupied condominium and town- house projects across the United States (averaging 183 dwelling units in size). The best-fitting regression equation for condo- miniums is: Ln(T) = 0.85(X) + 2.55 (R2 = 0.83) where T = Vehicle Trip Ends, X = Number of Dwelling Units, and Ln = natural logarithm. Taking the (unweighted) average across the 17 case-study projects, TOD-housing projects generated around 47% less vehicle traffic than that predicted by the ITE manual (3.55 trips per dwelling unit for TOD-housing versus 6.67 trips per dwelling unit by ITE estimates). This held true using both the weighted average ITE rate as well as the ITE rates predicted using the best fitting regression equations. Results were quite similar in both cases. The biggest trip reduction effects were found in the Washington, D.C. metropolitan area. Among the five mid-to- high rise apartment projects near Metrorail stations outside the District of Columbia, vehicle trip generation rates were more than 60% below that predicted by the ITE manual. There, 24-hour vehicle trip rates ranged from a high of 4.72 trip ends per dwelling unit at the more suburban Avalon project near the Grosvenor Metrorail Station (and outside the beltway) to a low of around one vehicle weekday for every two dwelling units at the Meridian near Alexandriaâs Braddock Station. The comparatively low vehicle trip generation rates for TOD- housing near Washington Metrorail stations matches up with recent findings on high transit modal splits for a 2005 survey of 18 residential sites (WMATA, 2006). For projects within a quarter mile of a Metrorail station (which matched the locations of all five TOD housing projects studied in the Wash- ington metropolitan area), on average 49% of residents used Metrorail for their commute or school trips. One of the proj- ects surveyed, the Avalon apartments at Grosvenor Station, also was surveyed in the 2005 study. The Avalon, which had the highest trip generation rate among the five projects surveyed in the Washington area, had an impressively high work-and- school trip transit modal split in the 2005 survey: 54%. It is important to realize that high transit ridership levels and significant trip reduction in metropolitan Washington is tied to the regionâs successful effort to create a network of 36
37 TODs, as revealed by the Rosslyn-Ballston corridor (and discussed in detail in TCRP Report 102: Transit Oriented Development in the United States: Experiences, Challenges, and Prospects). Synergies clearly derive from having transit- oriented housing tied to transit-oriented employment and transit-oriented shopping. After the Washington, D.C. area, TOD-housing in the Portland area tended to have the lowest weekday trip gen- eration rates, on average, around 40% below that predicted by the ITE manual. The range of experiences, however, var- ied a lot, from a low of 0.88 weekday vehicle trips per dwelling unit for Collins Circle in downtown Portland to a high of 6.34 for more suburban Quantama Crossing (only slightly below the average rate from the ITE manual and a bit above the regression-generated estimate from the ITE manual). Also among the surveyed Portland-area apartments, notable for its low trip generation rate, is The Merrick Apartments near the MAX light rail Convention Center station in the Lloyd District, across the river from downtown Portland: 2.01 weekday trips. Travel behavior of the residents of The Merrick apartments also was studied in 2005 (Dill, 2005). Based on a 43% response rate from 150 surveyed households at The Merrick apartments, trip generation estimates can be imputed from that survey. The 2005 survey asked: âIn the past week (Saturday January 29 through Friday February 4), Average ITE Rate (24 Hours) Regression ITE Rate (24 Hours) TOD Veh. Trip Rate (24 hr.) ITE Rate (24 hr.) TOD rate as % of ITE Rate (24 hr.) % point difference from ITE Rate ITE Rate (24 hr.) TOD rate as % of ITE Rate (24 hr.) % point difference from ITE Rate Philadelphia/NE NJ Gaslight Commons 5.08 6.72 75.52% -24.48% 6.76 75.05% -24.95% Station Square 4.76 6.72 70.81% -29.19% 6.44 73.84% -26.16% Mean 4.92 -- 73.17% -26.83% 6.60 74.45% -25.55% Std. Dev. 0.22 -- 3.33% 3.33% 0.22 0.86% 0.86% Portland, Oregon Center Commons 4.79 6.72 71.30% -28.70% 6.53 73.36% -26.64% Collins Circle 0.88 6.72 13.08% -86.92% 7.22 12.17% -87.83% Gresham Central 5.91 6.72 87.95% -12.05% 7.68 76.95% -23.05% The Merrick Apts. 2.01 6.72 29.84% -70.16% 6.82 29.39% -70.61% Quatama Crossing 6.34 6.72 94.38% -5.62% 6.22 101.95% 1.95% Mean 3.99 -- 59.31% -40.69% 6.52 58.76% -41.24% Std. Dev. 2.42 -- 36.05% 36.05% 0.62 36.88% 36.88% San Francisco Bay Area Mission Wells 3.21 6.72 47.80% -52.20% 6.39 50.23% -49.77% Montelena Homes 2.46 6.72 36.57% -63.43% 6.81 36.09% -63.91% Park Regency 5.01 6.72 74.61% -25.39% 6.19 81.04% -18.96% Verandas 3.10 6.72 46.17% -53.83% 6.54 47.42% -52.58% Wayside Commons 3.26 5.86 55.68% -44.32% 6.00 54.34% -45.66% Mean 3.41 -- 52.17% -47.83% 6.39 53.83% -46.17% Std. Dev. 0.95 -- 14.27% 14.27% 0.31 16.66% 16.66% Washington, D.C. Area Avalon 4.72 6.72 70.21% -29.79% 6.31 74.75% -25.25% Gallery 3.04 6.72 45.25% -54.75% 6.66 45.66% -54.34% Lennox 2.38 6.72 35.41% -64.59% 6.38 37.29% -62.71% Meridian 0.55 6.72 8.24% -91.76% 6.34 8.73% -91.27% Quincey 1.91 6.72 28.49% -71.51% 6.31 30.34% -69.66% Mean 2.52 -- 37.52% -62.48% 6.40 39.35% -60.65% Std. Dev. 1.53 -- 22.76% 22.76% 0.15 24.06% 24.06% Unweighted Average 3.55 6.67 53.29% -46.71% 6.59 53.92% -46.08% Note: Fitted Curve Equation for Apartments: T = 6.01(X) + 150.35, where T = average vehicle trip ends and X = number of dwelling units. Fitted Curve Equation for Condominiums (Wayside Commons): Ln(T) = 0.85 Ln(X) + 2.55 Table 2.2. Comparison of TOD housing and ITE vehicle trip generation rates: 24 hour estimates.
how many times did you go to the following place from your home in a vehicle, walking, bicycling, riding the bus, or riding MAX light rail? Each time you left your home during the week is a trip.â From household responses, an average of 1.42 daily vehicle trips per dwelling from The Merrick apartments was made. Doubling this rate (assuming those who drove away each day also returned) yields an estimated daily rate of 2.84 ve- hicle trips per dwelling unit. This is a bit higher than that found in the tube count survey, but still substantially lower than the ITE rate. (Differences are likely due to several factors. These re- sults are based on objective physical counts whereas the 2005 survey results were based on a sample of self-reported re- sponses. Also, the 2005 study included weekend days whereas this study was based on middle-of-the-week experiences.) The 2005 survey also estimated that 18% of all trips made by resi- dents of The Merrick apartments are by transit (both rail and bus). For work and school trips, transitâs estimated modal split was 23%. A follow-up 2005 survey of The Merrick apartment residents further indicated that transit is the primary commute mode for 27.9% of residents (Dill, 2006). Another study further sheds light on the results for one of Portlandâs surveyed apartments: Center Commons in east Portland. This studyâs survey found a weekday rate of 4.79 trips per dwelling unit for Center Commons, more than one- quarter below ITEâs estimated rates for apartments. For a thesis prepared for the Master of Urban and Regional Plan- ning degree at Portland State University, a mailback survey of 246 residents of Center Commons was conducted in 2002, producing a response rate of 39%. That survey found that 45.8% of responding residents of Center Commons takes MAX light rail or bus to work. As with metropolitan Washington D.C., Portlandâs success at reducing automobile trips around transit-oriented hous- ing cannot be divorced from the regional context. High rid- ership and reduced car travel at the surveyed housing projects stems from the successful integration of urban development and rail investments along the Gresham-downtown-westside axis. In Portland, as in Washington, TODs are not isolated islands but rather nodes along corridors of compact, mixed- use, walking friendly development. The San Francisco Bay Area also averaged vehicle trip generation rates substantially below estimates by the ITE manual. Among the East Bay TOD-housing projects studied, Montelena Homes (formerly Archstone Barrington Hills) had the lowest weekday rate: 2.46 trip ends per dwelling unit, 63% below ITEâs rate. A 2003 survey of residents of this proj- ect found very high transit usage among Montelena Homes residents: 55% stated they commute by transit (both rail and bus) (Lund, et al, 2004). The 2003 survey found the following commute-trip transit modal splits (compared to this researchâs recorded weekday trip rates): Wayside Commons: 56% (3.26 daily trips per dwelling unit); Verandas: 54% (3.1 daily trips per dwelling unit); Park Regency: 37% (5.01 daily trips per dwelling unit); and Mission Wells: 13% (3.21 daily trips per dwelling unit). Lastly, the two apartment projects near suburban com- muter rail stations outside Philadelphia and the Newark met- ropolitan area of northeast New Jersey averaged weekday vehicle trip generation rates roughly one-quarter less than the number predicted by the ITE manual. This is an appre- ciable difference given the relatively low-density settings of these projects and that commuter rail offers limited midday and late-night services. AM Peak Comparisons Table 2.3 compares recorded trip generation rates with those from the ITE manual for the AM Peak. In tabulating the results, the one-hour period in the AM peak with the highest tube count was treated as the AM peak. In most instances, this fell between the 7 AM and 9 AM period. In general, patterns were quite similar to those found for the 24-hour period. As before, the greatest differential between AM trip generation and ITE estimates were for TOD-housing closest to CBDs - notably, Collins Circle and The Merrick Apartments in the case of Portland, and the Meridian Apart- ments near the Braddock Metrorail station in Alexandria, Virgina. PM Peak Comparisons Table 2.4 shows the results for the PM peak. (The one-hour period in the PM peak with the highest tube count was treated as the PM peak. This generally occurred in the 4 PM to 7 PM period.) PM trip generation rates are generally higher than the morning peak since commuter traffic often intermixes with trips for shopping, socializing, recreation, and other activities. In general, PM trip generation rates for TOD- housing were closer to ITE predictions than the AM peak. Notable exceptions were the lowest trip generators. For example, the PM rates for Collins Circle and Meridian were 84.3% and 91.7% below ITE predictions, respectively. For the AM period, the differentials were 78.7% and 90.0%, re- spectively (from Table 2.3). Weighted Average Comparisons The summary results presented so far are based on un- weighted averages, that is, each project is treated as a data point in computing averages regardless of project size. The ITE manual, however, presents weighted averages of trip genera- tion by summing all trip ends among cases and dividing by the sum of dwelling units. Thus for apple to apple comparisons, weighted average vehicle trip rates were computed for all 38
39 17 projects combined for weekday, AM peak, and PM peak. (As done in the ITE manual, the weighted average was com- puted by summing all trip ends among the 17 projects and dividing by the sum of dwelling units.) Figure 2.6 summa- rizes the results. Over a typical weekday period, the 17 sur- veyed TOD-housing projects averaged 44% fewer vehicle trips than estimated by the ITE manual (3.754 versus 6.715). The weighted average differentials were even larger during peak periods: 49% lower rates during the AM peak and 48% lower rates during the PM peak. To the degree that impact fees are based on peak travel conditions, one can infer that traffic impacts studies might end up overstating the poten- tial congestion-inducing effects of TOD-housing in large rail-served metropolitan areas, such as Washington, D.C., by as much as 50%. Scatterplots The ITE Trip Generation manual reports summary findings in a scatterplot form, with summary best-fitting regression equations. Figures 2.7 through 2.9 show the best-fitting plots for the average weekday, AM peak, and PM peak periods, re- spectively. Linear plots fit the data points reasonably well, explaining over two-thirds of the variation in vehicle trip ends. The Merrick Apartments in Portland stands as an outlier, producing far fewer vehicle trip ends relative to its project size Average Rate Regression Rate Veh. Trip Rate (AM peak hr.) ITE Rate (AM peak hr.) TOD rate as % of ITE Rate (AM pk hr.) % Below ITE Rate ITE Rate (AM peak hr.) TOD rate as % of ITE Rate (AM pk hr.) % Below ITE Rate Philadelphia/NE NJ Gaslight Commons 0.40 0.55 72.73% -27.27% 0.55 72.59% -27.41% Station Square 0.36 0.55 66.21% -33.79% 0.54 67.17% -32.83% Mean 0.38 -- 69.47% -30.53% -- 69.88% -30.12% Std. Dev. 0.03 -- 4.61% 4.61% -- 3.83% 3.83% Portland, Oregon Center Commons 0.25 0.55 45.45% -54.55% 0.54 45.90% -54.10% Collins Circle 0.12 0.55 21.26% -78.74% 0.56 20.74% -79.26% Gresham Central 0.59 0.55 107.07% 7.07% 0.58 102.10% 2.10% The Merrick Apts. 0.13 0.55 23.10% -76.90% 0.55 22.98% -77.02% Quatama Crossing 0.30 0.55 54.98% -45.02% 0.54 56.42% -43.58% Mean 0.28 -- 50.37% -49.63% -- 39.70% -60.30% Std. Dev. 0.19 -- 34.83% 34.83% -- 23.65% 23.65% San Francisco Bay Area Mission Wells 0.48 0.55 86.72% -13.28% 0.54 88.20% -11.80% Montelena Homes 0.17 0.55 31.43% -68.57% 0.55 31.30% -68.70% Park Regency 0.34 0.55 61.85% -38.15% 0.53 63.59% -36.41% Verandas 0.19 0.55 35.14% -64.86% 0.54 35.47% -64.53% Wayside Commons 0.21 0.44 47.35% -52.65% 0.62 33.50% -66.50% Mean 0.28 -- 52.50% -47.50% -- 50.41% -49.59% Std. Dev. 0.13 -- 22.53% 22.53% -- 24.88% 24.88% Washington Avalon 0.44 0.55 80.30% -19.70% 0.54 82.02% -17.98% Gallery 0.25 0.55 44.86% -55.14% 0.55 45.01% -54.99% Lennox 0.18 0.55 32.47% -67.53% 0.54 33.05% -66.95% Meridian 0.05 0.55 9.95% -90.05% 0.54 10.15% -89.85% Quincey 0.18 0.55 32.91% -67.09% 0.54 33.62% -66.38% Mean 0.22 -- 40.10% -59.90% -- 21.88% -78.12% Std. Dev. 0.14 -- 25.78% 25.78% -- 16.60% 16.60% Unweighted 0.28 0.54 51.30% -48.70% 0.55 50.64% -49.36% Average Note: Fitted Curve Equation for Apartments: T = 0.53(X) + 4.21 where T = average vehicle trip ends and X = number of dwelling units. Fitted Curve Equation for Condominium (Wayside Commons): Ln(T) = 0.82 Ln(X) + 0.17 Table 2.3. Comparison of TOD housing and ITE vehicle trip generation rates: AM peak estimates.
40 than the other TOD-housing projects. Omitting this single case improved the regression fits considerably, with respective R-square values of 0.829, 0.800, and 0.847 for the weekday, AM peak, and PM peak. Using the average weekday best-fitting regression equation in Figure 2.8, the estimated number of daily vehicle trips gen- erated by a 400-unit apartment project is 1,508.3 [â523.7 + (5.26 â 400) = 1,508.3]. For the same apartment land-use category (ITE code of 220), the latest ITE Trip Generation Manual would predict 2,554.35 daily vehicle trips for the same 400-unit apartment [150.35 + (6.01 â 400) = 2,554.35]. Based on the empirical experiences of the sampled projects, the ITE regression equation for apartments overstates traffic impacts of transit-oriented housing by 39%. How Do Rates Vary? To better understand the nature of vehicle trip generation for TOD housing projects, additional analyses that explored associations between trip generation and various explanatory variables were carried out. For ratio-scale variables, scatter- plots and bivariate regression equations were estimated. Such analyses treat every observation the same, thus the cases are un- weighted. For those analyses with reasonably good statistical Average Rate Regression Rate Veh. Trip Rate (PM peak hr.) ITE Rate (PM peak hr.) TOD rate as % of ITE Rate (PM pk hr.) % Below ITE Rate ITE Rate (PM peak hr.) TOD rate as % of ITE Rate (PM pk hr.) % Below ITE Rate Philadelphia/NE NJ Gaslight Commons 0.460 0.67 68.66% -31.34% 0.688 66.90% -33.10% Station Square 0.558 0.67 83.25% -16.75% 0.651 85.73% -14.27% Mean 0.51 -- 75.96% -24.04% 0.67 76.32% -23.68% Std. Dev. 0.07 -- 10.32% 10.32% 0.03 13.32% 13.32% Portland, Oregon Center Commons 0.380 0.67 56.75% -43.25% 0.661 57.53% -42.47% Collins Circle 0.105 0.67 15.65% -84.35% 0.741 14.14% -85.86% Gresham Central 0.461 0.67 68.82% -31.18% 0.795 58.03% -41.97% The Merrick Apts. 0.170 0.67 25.41% -74.59% 0.695 24.51% -75.49% Quatama Crossing 0.487 0.67 72.63% -27.37% 0.625 77.91% -22.09% Mean 0.32 -- 47.85% -52.15% 0.70 46.42% -53.58% Std. Dev. 0.17 -- 25.85% 25.85% 0.07 26.32% 26.32% San Francisco Bay Area Mission Wells 0.487 0.67 72.72% -27.28% 0.645 75.56% -24.44% Montelena Homes 0.202 0.67 30.17% -69.83% 0.693 29.16% -70.84% Park Regency 0.435 0.67 64.93% -35.07% 0.621 70.10% -29.90% Verandas 0.367 0.67 54.78% -45.22% 0.662 55.43% -44.57% Wayside Commons 0.337 0.52 64.72% -35.28% 0.586 57.47% -42.53% Mean 0.37 -- 57.46% -42.54% 0.64 57.55% -42.45% Std. Dev. 0.11 -- 16.53% 16.53% 0.04 17.98% 17.98% Washington Avalon 0.370 0.67 55.26% -44.74% 0.635 58.28% -41.72% Gallery 0.234 0.67 34.89% -65.11% 0.676 34.59% -65.41% Lennox 0.220 0.67 32.90% -67.10% 0.643 34.28% -65.72% Meridian 0.056 0.67 8.33% -91.67% 0.638 8.74% -91.26% Quincey 0.201 0.67 30.06% -69.94% 0.635 31.71% -68.29% Mean 0.22 -- 32.29% -67.71% 0.65 33.52% -66.48% Std. Dev. 0.11 -- 16.69% 16.69% 0.02 17.55% 17.55% 0.391 0.661 62.10% -37.90% 0.664 49.42% -50.58% Unweighted Average Note: Fitted Curve Equation for Apartments: T = 0.60(X) + 17.52 where T = average vehicle trip ends and X = number of dwelling units Fitted Curve Equation for Condominium (Wayside Commons): T = 0.34(X) + 38.17 Table 2.4. Comparison of TOD housing and ITE vehicle trip generation rates: PM peak estimates.
41 fits, cases were broken into subgroups and weighted average values are presented for each category. As suggested by Tables 2.2 through 2.4, the greatest variations in TOD trip generation rates are by metropolitan area/rail systems. Metropolitan Washington, with some of the nationâs worst traffic conditions, most extensive modern-day railway networks, and densest (and arguably best planned) TOD hous- ing projects, had the lowest trip generation rates. This was fol- lowed by Metro Portland, whose comparatively low rates are all the more remarkable given that it is smaller than the other urbanized regions and has a less extensive light rail system that operates in mixed-traffic conditions. Average trip generation rates were slightly higher for Bay Area TODs than in Portland and, as noted earlier, were the highest for the Philadelphia and Northeast New Jersey cases, due in part to the nature of commuter rail services (focused mainly on peak periods). TOD trip generation rates are examined as a function of: 1) distance of project to CBD; 2) distance of project to station; Figure 2.6. Comparison of weighted average vehicle trip rates: TOD housing and ITE estimates. 5000 4000 3000 2000 1000 0 0 200 400 600 X = Number of Dwelling Units T = W ee kd ay T rip E nd s 800 1000 T=-523.7+5.262X R2=0.729 Figure 2.7. TOD housing weekday vehicle trip ends by number of dwelling units. X = Number of Dwelling Units T= A .M . P ea k Tr ip E nd s 0 0 100 200 300 200 400 600 800 1000 T=-16.774+0.327X R2=0.693 Figure 2.8. TOD housing AM peak vehicle trip ends by number of dwelling units. 400 300 200 100 0 0 200 400 600 X = Number of Dwelling Units T = P. M . P ea k Tr ip E nd s 800 1000 T=-31.757+0.436X R2=0.734 Figure 2.9. TOD housing PM peak vehicle trip ends by number of dwelling units.
42 3) residential densities around station; and 4) parking provi- sions. While relationships were explored for other variables as well, only these factors proved to be reasonably strong pre- dictors. The analysis ends with best-fitting multiple regression equations for predicting trip generation rates of TOD housing. Distance to CBD For the weekday period, a fairly weak relationship was found between TOD housing trip generation rates and distance to the CBD. This is suggested by Figure 2.10; rates were actually lower for projects more than 12 miles from the CBD than more intermediate-distance projects in the 6 to 12 mile range. (The >12 mile group is dominated by Bay Area cases; all five projects are more than 20 miles form downtown San Francisco.) During peak periods, however, relationships were stronger; rates increased with distance of a project from the CBD. Table 2.5 summarizes the bivariate results for predicting trip generation rates as well as TOD rates as a proportion of ITE rates. In all cases, vehicle trip generation rates tend to rise as one goes farther away from the urban core. The weakest fit was for the 24-hour period whereas the strongest was for the PM peak. The best fit was the prediction of the TOD trip generation rate as a proportion of the ITE rate during the PM peak. That model explained more than 38% of the variation in vehicle trip rates. The scatterplot shown in Figure 2.11 reveals a fairly good fit for this variable (based on the reasonably steep slope). Residential Densities The finding that trip generation rates tend to be lower for TOD housing near urban centers suggests residential density is an important predictor. This is supported by the results shown in Table 2.6. The predictor variable in all of these equations is residential density, specifically the number of dwelling units per gross acre within a half mile radius of the rail station closest to the TOD housing project, estimated from the 2000 census. Residential densities were obtained from the national TOD database maintained by the CTOD. In all cases shown in Table 2.6, TOD trip generation de- clines as surrounding residential densities increase. We sus- pect that residential density is serving as a broader surrogate of urbanicity, that is denser residential settings tend to have nearby retail and other mixed-use activities, better pedestrian connectivity, and often a more socially engaging environ- ment. Residential densities most strongly influenced PM trip Figure 2.10. Vehicle trip generation rates by distance to CBD: comparisons of weighted averages for weekday, AM peak, and PM peak. Period of Analysis Dependent Variable X = Distance of Project to Bivariate Equation CBD (miles) R-Square Vehicle Trip Ends per Dwelling Unit 2.796 + .056X 0.097 Weekday (24 hours) TOD Rate as a Proportion of ITE Rate 0.414 + .009X 0.109 Vehicle Trip Ends per Dwelling Unit 0.198 + .006X 0.156 AM Peak Hour TOD Rate as a Proportion of ITE Rate 0.358 + .012X 0.176 Vehicle Trip Ends per Dwelling Unit 0.209 + .009X 0.350 PM Peak Hour TOD Rate as a Proportion of ITE Rate 0.309 + .015X 0.388 Table 2.5. Summary regression equations for predicting TOD housing trip generation rates as functions of distance to CBD.
43 generation rates among the sample of 17 TOD housing proj- ects. Figure 2.12 shows the scatterplot of these two variables. TOD Parking Supplies Parking provisions have a strong influence on travel be- havior, particularly in suburban settings where most sample projects are located (Shoup, 2005; Willson, 1995). Bivariate equations for predicting TOD housing trip generation rates as a function of parking per dwelling unit are presented in Table 2.7. Relationships are weaker than that found for âDis- tance to CBDâ and âResidential Densities.â Vehicle trip gen- eration rates tend to be higher for TOD projects with more plentiful parking. The strongest fit was between AM peak trip generation and parking supply. Figure 2.13 presents the scat- terplot of this relationship. The results in Table 2.7 and Figure 2.13 are unweighted by project size. Figure 2.14 compares average rates for three levels of parking supplies, weighted by project size. No clear pattern emerges from these weighted-average results, consistent with Period of Analysis Dependent Variable per Gross Acre within Â½ Mile of X = Dwelling Units Bivariate Equation Station R-Square Vehicle Trip Ends per Dwelling Unit 5.369 - .211X 0.430 Weekday (24 hours) TOD Rate as a Proportion of ITE Rate 0.801 - .096X 0.424 Vehicle Trip Ends per Dwelling Unit 0.400 - .014X 0.276 AM Peak Hour TOD Rate as a Proportion of ITE Rate 0.731 - .026X 0.274 Vehicle Trip Ends per Dwelling Unit 0.493 - .019X 0.449 PM Peak Hour TOD Rate as a Proportion of ITE Rate 0.741 + .028X 0.423 Table 2.6. Summary regression equations for predicting TOD housing trip generation rates as functions of residential densities (within 1/2 mile of stations). Period of Analysis Dependent Variable X = Parking Spaces Bivariate Equation per Dwelling Units R-Square Vehicle Trip Ends per Dwelling Unit 1.683 + 1.504X 0.158 Weekday (24 hours) TOD Rate as a Proportion of ITE Rate 0.258+ .221X 0.153 Vehicle Trip Ends per Dwelling Unit 0.098 + .145X 0.206 AM Peak Hour TOD Rate as a Proportion of ITE Rate 0.189 + .260X 0.202 Vehicle Trip Ends per Dwelling Unit 0.207 + .098X 0.088 PM Peak Hour TOD Rate as a Proportion of ITE Rate 0.325 + .140X 0.078 Table 2.7. Summary regression equations for predicting TOD housing trip generation rates as functions of parking per dwelling unit. X = DU/acre within 1/2 mile of station T= P M p ea k tri p ge ne ra tio n ra te 0 0.0 .1 .2 .3 .4 .6 .5 10 20 30 Figure 2.12. Scatterplot of PM trip generation rate with residential densities. X = Distance to CBD (miles) T= P M p ea k ra te a s pr op o f I TE ra te 0 0.0 .2 .4 .6 .8 1.0 10 20 30 Figure 2.11. Scatterplot of PM trip generation rate to ITE rate with distance to CBD.
44 the fairly weak fits shown in Table 2.7. In general, trip gener- ation rates were lower for TOD projects with intermediate levels of parking (1.0 to 1.15 spaces per dwelling unit). This was mainly an artifact of three of these projects being in met- ropolitan Washington, D.C. Walking Distance to Station The relationship between TOD housing trip generation and walking distance from the project to the nearest station was generally weaker than the other variables reviewed so far. Table 2.8 shows a positive slope for the explanatory variable, distance to station. This indicates that the closer a TOD hous- ing project is to a rail station, the vehicle trip generation rates tend to be lower. The relationships were thrown off, in part, by Mission Wells, a Bay Area project situated beyond a half-mile of the nearest station. Figure 2.15 shows the weak scatterplot fit for the weekday (24 hour) estimate, with the Mission Wells observation (nearly 4000 feet from the station) standing out as an outlier. Dropping this single case provides an appreciably better fit, as revealed in Figure 2.16. As Table 2.8 indicates, the strongest linear pattern between TOD trip rate (as a proportion of the ITE rate) and distance to station was for the PM peak hour. Figure 2.17 shows this scatterplot. Retaining the Mission Wells observation, a slightly better fit was obtained using a quadratic equation of the form: T = 0.195 + 0.21X â 0.0000032X2 R2 = .195 X = Parking Spaces per DU Y= A M P ea k R at e 0.0 .5 0.0 .1 .2 .3 .4 .6 .5 1.0 2.01.5 2.5 3.0 Y=0.098X+0.145X R2=0.206 Figure 2.13. Scatterplot of AM trip generation rate with parking spaces per dwelling unit. Figure 2.14. Vehicle trip generation rates by parking spaces per dwelling unit: comparisons of weighted averages for weekday, AM peak, and PM peak. Period of Analysis Dependent Variable Distance to Nearest Rail Station X = Walking Bivariate Equation (in 1000s of feet) R-Square Vehicle Trip Ends per Dwelling Unit 3.149 + .325X 0.027 Weekday (24 hours) TOD Rate as a Proportion of ITE Rate 0.047 + .052X 0.030 Vehicle Trip Ends per Dwelling Unit 0.209 + .060X 0.126 AM Peak Hour TOD Rate as a Proportion of ITE Rate 0.382 + .00011X 0.137 Vehicle Trip Ends per Dwelling Unit 0.249 + .071X 0.168 PM Peak Hour TOD Rate as a Proportion of ITE Rate 0.374 + .00011X 0.182 Table 2.8. Summary regression equations for predicting TOD housing trip generation rates as functions of walking distance to nearest station.
45 where T is TOD-housing PM trip rate as a proportion of ITE rate and X is the walking distance of project to the nearest sta- tion (in 1,000s of feet). Multiple Regression Predictions of TOD Housing Trip Generation Rates The previous section found modest to moderate rela- tionships between TOD housing trip generation rates and four variables: distance to CBD, residential density, parking per dwelling unit, and distance to station. In general, the bivariate relationships between TOD trip generation and other explanatory variables (such as compiled in the pedes- trian survey and through the CTOD database) were very weak and statistically insignificant. This section presents a multiple regression equation that combines explanatory variables to produce the best-fitting predictive models. These results provide insight into how other factors combine with proximity of multi-family housing to rail stations to influence vehicle trip generation rates. Weekday TOD Trip Generation Model The simple bivariate models shown in Table 2.6 pro- vided the best fit for predicting weekday TOD trip genera- tion rates (as well as rates as a proportion of the ITE rate). That is, once controlling for residential density around the station, none of the other variablesâwalking quality, parking supply, socio-demographic characteristics of the surround- ing neighborhoodâprovided significant marginal explana- tory power. Again, density is thought to function as a proxy for many of these factors. The finding that walking quality has little bearing on vehicle trip generation rate also is con- sistent with research findings from California (Lund, et al, 2004). That work suggested the presence of an indifference zone; as long as most residents were within five or so min- utes of a station, walking quality matters relatively little. The presence of an integrated sidewalk network, street trees, and various pedestrian amenities likely have more influence on longer-distance walking behavior than encountered by most TOD residents. X = Distance to Station (Feet) Y= V eh ic le T rip R at e (24 ho urs ) 0 0 1 2 3 4 7 6 5 1000 2000 3000 4000 Y=3.15+0.00032X R2=0.027 Figure 2.15. TOD housing vehicle trip rates by shortest walking distance to station; N = 17 (all cases). X = Distance to Station (Feet) Y= V eh ic le T rip R at e (24 ho urs ) 0 0 1 2 3 4 7 6 5 1000500 1500 2000 2500 3000 Y=2.37+0.0012X R2=0.127 Figure 2.16. TOD housing vehicle trip rates by shortest walking distance to station, without Mission Wells Case; N = 16. X = Distance to Station from Project (feet) T= P M p ea k ra te a s pr op o f I TE ra te 0 0.0 .2 .4 .6 .8 1.0 1000 2000 3000 4000 Figure 2.17. TOD-housing vehicle trip rate (as a proportion of ITE rate) by walking distance to station; quadratic curve; N = 17.
46 Model 1: TOD Trip Generation Model for the AM Peak In predicting trip rates for the morning peak hour, the below output reveals that trip generation falls with residential densi- ties and increases with project parking supplies (Table 2.9). The combination of higher densities and lower parking sup- plies holds promise for driving down morning vehicle trips for transit-based housing. The parking variable, however, is not statistically significant at the 0.10 probability level. Model 2: TOD Trip Generation Model for AM Peak (as a Proportion of ITE Rate) Comparable results were found for predicting AM peak rates as a proportion of the ITE rate (Table 2.10). Model 3: TOD Trip Generation Model for the PM Peak A better fitting model was obtained for predicting TOD trip generation in the afternoon peak (Table 2.11). The results, which explained 60% of the variation in PM trip rates, reveal that vehicle travel in the afternoon rises with distance to the CBD and falls with both residential density and household size. Model 4: TOD Trip Generation Model for PM Peak (as a Proportion of ITE Rate) The best-fitting multiple regression equation was pro- duced for predicting PM peak trip rates as a proportion of ITE rates (Table 2.12). This model explained 63% of the vari- ation. Like the previous model, this one showed that TOD projects closest to the CBD, in higher density residential set- tings, and in neighborhoods with smaller household sizes averaged the lowest PM trip rates. Using the best-fitting multiple regression model for the PM peak, Figure 2.18 reveals how PM trip rates for the TOD projects differ as a proportion of the rates predicted by the ITE manual. Assuming an average household size of two per- sons, the predicted values as a function of distance to CBD (horizontal axis) and residential densities (within half mile of the nearest rail station, represented by the five bars) are shown in the Figure. For example, the model predicts that for a transit-oriented apartment 20 miles from the CBD in a neighborhood with 10 units per residential acre, the PM trip rate will be 55% of (or 45% below) the ITE rate. If the same apartment in the same density setting were 5 miles from the CBD, the PM trip rate would be just 38% of the ITE rate. For AM Peak Rate Coeff. Std. Err. t Statistic Prob. Residential Density: Dwelling Units per Gross Acre within Â½ mile of station -0.012 0.006 -1.961 .075 Parking Supply: Parking Spaces per Dwelling Unit 0.106 0.070 1.507 .154 Constant 0.250 0.116 2.152 .039 Summary Statistics: F statistics (prob.) = 3.800 (.048) R Square = .352 Number of Cases = 17 AM Peak Rate Coeff. Std. Err. t Statistic Prob. Residential Density: Dwelling Units per Gross Acre within Â½ mile of station -0.021 0.011 -1.948 .072 Parking Supply: Parking Spaces per Dwelling Unit 0.189 0.128 1.484 .160 Constant 0.462 0.210 2.196 .045 Summary Statistics: F statistics (prob.) = 4.154 (.038) R Square = .372 Number of Cases = 17 Table 2.9. Best-fitting multiple regression equation for predicting AM peak trip generation rates for TOD housing projects. Table 2.10. Best-fitting multiple regression equation for predicting AM peak trip generation rates as a proportion of ITE rate for TOD housing projects. AM Peak Rate Coeff. Std. Err. t Statistic Prob. Residential Density: Dwelling Units per Gross Acre within Â½ mile of station -0.018 0.006 -2.846 .014 Household Size: Persons per Dwelling Unit within Â½ mile of station -0.103 0.074 -1.390 .188 Constant 0.608 0.182 3.346 .005 Distance to CBD (in miles) 0.007 0.003 2.145 .051 Summary Statistics: F statistics (prob.) = 6.497 (.006) R Square = .600 Number of Cases = 17 Table 2.11. Best-fitting multiple regression equation for predicting AM peak trip generation rates for TOD housing projects.
47 two transit-oriented apartments 10 miles from the CBD, if the surrounding residential densities are 10 units per acre, the PM trip rate will be 45% of the ITE manualâs rate. If the surrounding densities are 20 units per acre, the PM trip rate will be just 20% of the ITE rate (or 80% lower). Applying the Research: Four TOD Housing Case Studies This section looks at some of the physical implications of varying residential parking by analyzing four TOD case stud- ies designed with two different parking ratios. Using four dif- ferent representative TOD residential development products, the analysis provides a glimpse at how changing parking within a TOD can have an impact, such as improving physical form, increasing the density of potential development, lowering the capital cost for parking, enhancing the financial viability of TODs, and increasing transit ridership. Building TOD Case Studies As an input to this part of the research, TOD master plan- ners from PB PlaceMaking were asked to prepare alternative site plans for an eight-acre residential TOD. Parking ratios were varied between the alternatives: one reflected conven- tional ratios in many existing TODs and one tested tighter ratios more consistent with the results of this research. The site plans were prepared for four different representative TOD residential development products (garden apartments, town- homes, a Texas Donut and mid-rise housing) for a total of eight different site plans. (A Texas Donut refers to a parking struc- ture surrounded by usable residential space. In an article in Places, Brian OâLooney and Neal Payton describe Texas Donuts as unadorned parking decks bordered on two sides by a 10-15 foot zone for open ventilation, and wrapped on all four sides by 35-40 foot deep four-story wood-frame liner residen- tial buildings (http://repositories.cdlib.org/cgi/viewcontent. cgi?article=1998&context=ced/places). The development types tested were selected because they are indicative of the resi- dential development products found in a number of U.S. TODs. The potential development types reflect the range of built products included in the field research for this study. The site plans ranged in density from 24 to 120 units per acre. Since there are no clear national standards for parking TODs, a quick survey of parking ratios in adopted station area plans was conducted. The review revealed a considerable range of latitude in how TODs are parked. For the case stud- ies, parking ratios were selected from two TOD zoning ordi- nances for station areas on the Washington Metrorail: one in Maryland and one in Virginia. The TOD 1 ratio of 1.1 park- ing spaces per unit (one space per unit and one visitor space for every 10 units) is consistent with how Arlington County, Virginia parks high density TOD in the Rosslyn-Ballston Corridor on the Orange Line (U.S. EPA, 2006). The TOD 2 ratio is 2.2 parking spaces per unit (two spaces per unit and one visitor space for every five units) and is consistent with how Prince Georges County, Maryland parks high density TOD for the West Hyattsville TOD on the Green Line (Prince Georges County, 2006). For an apples to apples comparison, the underlying as- sumptions were held constant for each potential development product, even though in a real word example they would be expected to vary somewhat to respond to unique site condi- tions. In each case study the unit size was assumed to be 910 square feet net or 1200 square feet gross. This provides for a mix of unit sizes (1, 2, and 3 bedroom units) within the project. AM Peak Rate Coeff. Std. Err. t Statistic Prob. Residential Density: Dwelling Units per Gross Acre within Â½ mile of station -0.026 0.009 -2.893 .013 Household Size: Persons per Dwelling Unit within Â½ mile of station -0.190 0.107 -1.772 .100 Constant 0.964 0.264 3.657 .003 Distance to CBD (in miles) 0.013 0.005 2.631 .021 Summary Statistics: F statistics (prob.) = 7.491 (.004) R Square = .634 Number of Cases = 17 Table 2.12. Best-fitting multiple regression equation for predicting PM peak trip generation rates as a proportion of ITE rate for TOD housing projects. Distance to CBD (miles) PM R at e as p ro p. o f I TE R at e 0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 5 10 15 20 25 1 unit/acre 5 units/acre 10 units/acre 15 units/acre 20 units/acre Figure 2.18. Influences of residential densities and distance to CBD on transit-oriented housing PM trip rate as a proportion of the ITE rate.
48 Parking is assumed to consume 300 square feet per space allowing for aisles and landscaping. While parking ratios vary considerably across the United States, these ratios provide a means to help isolate the impacts of parking ratios on urban form. The parking ratios tested in the site plans were 2.2 spaces per unit and 1.1 spaces per unit. Learning from the Case Studies Representative site plans (Figures 2.19â2.22) help illustrate some potential implications for TOD housing of how adjust- ing parking ratios reflect the actual transportation perfor- mance of TODs in form, density, and performance. Varying parking ratios and holding other factors constant suggest a number of important differences in what could be constructed on the eight-acre theoretical TOD. Table 2.13 provides a summary of some of the quantifiable differences in density, cost, and ridership from varying parking ratios for the potential residential TOD products analyzed in the case studies. Those differences include: â¢ A 20% to 33% increase in the number of potential units in a TOD. As might be expected, a lower parking ratio results in less land being used for parking which can be used for development. In the four case studies, potential additional residential units from lower parking ratios ranged from an increase between 20% to 33%. The case studies show how the two ratios result in sig- nificantly different density on a site. The most pronounced percentage increase in potential units was seen with the lower density garden apartment and townhome examples because all the parking is surface spaces. Reducing parking from 2.2 to 1.1 spaces per unit resulted in the ability to in- crease the potential number of units on the site by 33% for both garden apartments and townhomes. The greatest absolute increase in the number of units was achieved by Total Area: 8 acres 2.2 Parking spaces per unit 1.1 Parking spaces per unit Total Area: 8 acres Total Units: 256 Total Units: 196 Additional units: 60 Density: 32 Dwelling units per acre Density: 24 Dwelling units per acre Increase in density: 33% Parking Spaces: 432 Parking Spaces: 288 Parking capital cost: $2.02m Parking capital cost: $2.1m Parking cost savings: $98,000 Annual incremental ridership: +19,500 Annual incremental fare revenue: $19,750 Figure 2.19. Comparison of representative TOD housing: garden apartments.
49 lowering the parking ratios for the higher density products, the Texas Donut and the mid-rise building. â¢ Lower total construction costs for parking even with more residential units. Parking in any form is expensive to build. Reducing the amount of parking required in a TOD by rightsizing parking as suggested by the results of this re- search can be important to the economic viability of a TOD. To help understand the cost implications of parking, a review of 2007 parking costs was completed (G. Stewart, e-mail message, December 2007). The review shows just how expensive parking can be. Surface parking spaces can cost from $5,000 per space for low-end asphalt to $10,000 with details like cobbles and brick pavers. Parking tucked under a townhome can cost about $14,000 a space. In dense conventional multi-family development such as the Texas Donut or mid-rise buildings open undecorated parking decks cost anywhere from $17 - $20,000 per space. If the parking deck is to be incorporated into the urban fabric of a community the cost of a special feature like a re- tail wrap or an enhanced faÃ§ade typically pushes the cost of a space to around $28,000 to $32,000. As the site plan studies help demonstrate, tighter park- ing ratios can be a key driver in the capital cost of TODs. The cost savings were most pronounced with the higher density development prototypes (mid-rise and Texas Donut) where structured parking is employed. In these ex- amples the savings in reducing parking ranged from 25% to 36%. For the lower density examples the parking savings was in the order of between 5% and 11% depending on the development product. The real significance of the parking capital cost numbers indicated in the case studies is to understand the numbers are not simply an apples-to-apples comparison of reduc- ing the parking by half. As the case study shows, a reduction in parking results in an increase in the number of potential units on the site (which need to be parked) by 20% to 33% Total Area: 8 acres Total Area: 8 acres Total Units: 384 Total Units: 288 Additional units: 96 Density: 48 Dwelling units per acre Density: 36 Dwelling units per acre Increase in density: 33% Parking Spaces: 648 Parking Spaces: 448 Parking capital cost: $5.82m Parking capital cost: $6.56m Parking cost savings: $736,000 Annual incremental ridership: +31,200 Annual incremental fare revenue: $31,600 2.2 Parking spaces per unit 1.1 Parking spaces per unit Figure 2.20. Comparison of representative TOD housing: townhomes.
50 (see Table 2.14). With the mid-rise case study, for exam- ple, an additional 162 units could be built and still result in a developer saving approximately $12 million in the cost of parking. In this instance reducing the parking ratio by 50% resulted in a capital cost savings of 25% for parking while also increasing the number of residential units by 20%. â¢ Higher transit ridership. Increasing the potential number of residential units in a TOD also can be expected to increase transit ridership. The actual increase in ridership can be expected to vary considerably depending on local conditions. Drawing on the body of existing research summarized in the literature review, it is possible to make some crude prelimi- nary assessments of the ridership implications of increasing the potential density in a TOD. [Transit ridership was esti- mated consistently for each of the case studies: drawing on the field research, 3.55 trips were assumed for each TOD household. Transit ridership: 3.55 trips per TOD household allocated as follows: 1.5 work trips per TOD HH â TOD units â .40 TOD work mode share + 4 nonwork trips per TOD HH â TOD units â .10 TOD nonwork mode share (Lund et al., 2004) = daily ridership Ã 325 annualization factor = the annual incremental increase in ridership attrib- utable to changes in parking ratios. Because the mode share factors are specifically for TODs, no additional adjustments for changes in density or automobile ownership were made.] As one might expect the incremental ridership benefit in- creases proportionally to the number of additional units. The additional annual transit ridership which might be attributable from the potential units made possible by low- ering parking ratios is summarized in Table 2.15. â¢ Parking and financial feasibility of TODs. Apart from the im- pacts on the physical form of a TOD the shear amount and cost of parking can be a driver in the financial viability of a proposed TOD and in turn the financial return to a developer. As discussed earlier, lowering parking ratios can affect the financial viability of a TOD in a number of ways. In Total Area: 8 acres Total Area: 8 acres Total Units: 963 Total Units: 801 Additional units: 162 Density: 120 Dwelling units per acre Density: 100 Dwelling units per acre Increase in density: 20% Parking Spaces: 1800 Parking Spaces: 1152 Parking capital cost: $21.31m Parking capital cost: $33.3m Parking cost savings: $12m Annual incremental ridership: +52,650 Annual incremental fare revenue: $53,330 2.2 Parking spaces per unit 1.1 Parking spaces per unit Figure 2.21. Comparison of representative TOD housing: mid-rise 6-story.
51 particular, lower capital costs for parking and a greater yield of units on a site could be expected to result in more TOD projects being financially viable since a developer would be able to potentially increase the number of units on a site while at the same time reduce the capital cost for parking. With land cost constituting a growing percentage of housing prices, potentially increasing the number of units on a particular site can play an increasingly important role in the financial viability of a TOD. A 2006 Federal Reserve study shows the growing impact of land on housing prices. Averaging across the 46 largest U.S. cities, the value of residential land accounted for about 50% of the total market value of housing, up from 32% in 1984 (Davis and Palumbo, 2006). â¢ Parking and urban form. Creating an active pedestrian en- vironment is a core principle and an essential characteris- tic of well planned TODs. For TOD designers that means creating as many active street edges (lining streets with people oriented uses) as possible. TOD site plans help to demonstrate the impact different parking ratios can have on creating an active pedestrian environment. The result is most noticeable with the moderate density garden apart- ment example where surface parking is employed. With the 2.2 parking ratio, approximately 50% of the street edge is dominated by parking. With the 1.1 parking ratio, the amount of the street edge taken by parking decreases by half to 25% of the total site street edge. Implications of Applying New Standards for TOD Housing The research findings and literature review provide solid evidence to support the belief that people living in TODs drive less often than their neighbors in conventional develop- ments. Based on this evidence, public officials and govern- ment regulators may chose to develop new, more realistic standards for parking, assessing impact fees, and mitigation for TODs. The research suggests important implications are Total Area: 8 acres Total Area: 8 acres Total Units: 963 Total Units: 738 Additional units: 225 Density: 120 Dwelling units per acre Density: 92 Dwelling units per acre Increase in density: 20% Parking Spaces: 1152 Parking Spaces: 864 Parking capital cost: $15.98m Parking capital cost: $21.31m Parking cost savings: $5.3m Annual incremental ridership: +82,875 Annual incremental fare revenue: $83,950 2.2 Parking spaces per unit 1.1 Parking spaces per unit Figure 2.22. Comparison of representative TOD housing: Texas Donut.
52 likely to flow from permitting and developing TODs based on an accurate assessment of their parking needs and trip generation. Some of the likely consequences of permitting and build- ing TOD consistent with the findings of this research include: â¢ More compact development. As the site plan case studies help to demonstrate, more compact environmentally sus- tainable development can result from less land being con- sumed for parking. Case studies showed an increase of 20% to 33% in density for residential TOD could be achieved. This tracks well with U.S. EPA estimates that each on-site parking space at infill locations can reduce the number of new housing units or other uses by 25% or more (EPA, 2006). It must be noted that the ability to increase density Units Density Parking Total Additional Per acre % increase Spaces Cost Difference Annual Incremental Ridership Garden Apartments TOD 1 ratio 256 +60 units 32 +33% 288 $2.02m TOD 2 ratio 196 24 432 $2.1m $98,000 savings 19,500 transit trips $19,750 fares Townhomes TOD 1 ratio 384 +96 units 48 + 33% 448 $5.82m TOD 2 ratio 288 36 648 $6.56m $736,000 savings 31,200 transit trips $31,600 fares Mid Rise 6-Story TOD 1 ratio 963 +162 units 120 +20% 1152 $21.31M TOD 2 ratio 801 100 1800 $33.3m $12 million savings 52,650 transit trips $53,330 fares Texas Donut TOD 1 ratio 963 +225 units 120 +30% 1152 $21.31m TOD 2 ratio 738 92 864 $15.98m $5.3 million savings 82,875 transit trips $83,950 fares Assumptions: Parking ratios: TOD 1 - 1.1 spaces per unit; TOD 2 - 2.2 spaces per unit Cost per space: surface parking $7,000; tuck under parking $14,000; structured parking $18,500 Transit ridership: 3.55 trips per TOD household allocated as follows: 1.5 work trips per TOD HH * TOD units * .40 TOD work mode share + 4 non-work trips per TOD HH * TOD units * .10 TOD non- work mode share. (Lund et al) = daily ridership x 325 annualization factor = annual incremental increase in ridership. Fare revenue: assumes average fare of $1.013 TriMet March 2008 Month Performance Report, year-to-date Average Fare, April 2008. HH=household Table 2.13. Summary of analysis for potential TOD housing site plan case studies: impact of lower TOD parking ratios. should not necessarily translate to the higher density in all cases. Parking and trip generation are only two variables of many in the very complex issue of increasing density. â¢ Easier development approvals. One major challenge devel- opers face with TOD is the increased time and expense getting development approvals for infill development be- cause of inevitable neighborhood concerns about traffic. Interviews with TOD developers (Parsons Brinckerhoff, 2002) reveal an interesting cycle that plays itself out over and over in response to community concerns about traffic impacts of new development. One way to explain the sequence is in a five act TOD morality play: 1. Act One: vision. Planners, citizens and smart growth ad- vocates secure adoption of a compact transit village plan
53 allowing compact dense residential development around a rail station. 2. Act Two: optimism. Time passes and a progressive de- veloper presents the local community with a proposal for a dense TOD allowed under the transit village plan. 3. Act Three: opposition. Community membersâ con- cerns about change inevitably focus on perceived traf- fic impacts and overflow parking from the dense TOD development. 4. Act Four: compromise. The developer offers to cut the density below transit supportive levels in the adopted plan and increase the parking in order to get a devel- opment approval and recover his fixed costs. 5. Act Five: the lesson. Many of the hoped for community benefits of TOD at the rail station and the financial return to the developer are not realized because the development is built below the allowed density with increased parking, and the developer may be less apt to pursue TOD. Getting new information on the performance of TODs out into the field may help to break this cycle of compromis- ing away the benefits of TOD. Local officials and neighbor- hoods may be more apt to support increases in residential densities near transit if they are shown proof that up to half of the trips result from TODs than in conventional de- velopment. Using a 700-unit California condominium proj- ect as a reference point, the expected daily traffic rates would be reduced by as much as half with a likely number of 2,350 trips with the TOD traffic generation rates rather than 4700 daily trips using the ITE rates (S. Zuspan, personal e-mail, November 5, 2007). â¢ Lower fees for TODs. Applying new standards for trip gen- eration could result in wholesale changes in how we ad- dress the cost, impact, and feasibility of residential devel- opment near transit. The implications of new standards are varied and would need to be scaled to the quality of transit service present. Developers likely would pay lower fees and exactions by as much as 50% to reflect the actual performance of residential TODs. Those savings could be passed on to homeowners and tenants as lower housing costs. For in- stance, that same 700-unit condominium development could see its traffic impact fee reduced by halfâfrom $4,500 per unit to $2,250 per unitâif it were based on the likely traffic generation of a TOD rather than the ITE rates. In this case, the developer would save $1.6 million, presum- ably making the units more affordable. â¢ Downsizing new road construction. Traffic-based impact fees are used to help fund intersection and roadway im- provements such as street widening. The same mathe- matical equations that result in over-charging impact fees for TODs also can result in over-building road facilities to serve TODs. With lower levels of traffic generated from TODs, it can be argued that it makes no sense to construct roadway improvements to serve TOD related traffic that is not likely to materialize. Right-sizing new road and intersection improvements to reflect the actual transportation performance can result in more compact development patterns and a higher qual- ity pedestrian environment since less land may be used for road improvements. â¢ Enhanced housing affordability. Housing affordability is one area where research may have significant implications. Housing affordability is driven by a myriad of factors, with Units Gained Spaces Saved Capital Cost Savings Garden Apartments 60 144 $98,000 5% Townhomes 96 200 $736,000 11% Mid Rise 6-Story 162 648 $12,000,000 36% Texas Donut 225 288 $5,300,000 25% Table 2.14. Impact of lower TOD parking ratios. Additional Units Annual Incremental Ridership Annual Incremental Fare Revenues Garden Apartments +60 units 19,500 transit trips $19,750 Townhomes +96 units 31,200 transit trips $31,600 Mid Rise 6-Story +162 units 52,650 transit trips $53,330 Texas Donut +225 units 82,875 transit trips $83,950 Table 2.15. Impact of lower TOD parking ratios.
land costs constituting 50% of the total market value of housing. TOD site plan case studies suggest reducing park- ing ratios to reflect that the transportation performance of TODs also can have the additional benefit of increasing the number of housing units on the same piece of land by between 20% and 33%, which can translate into lower housing costs (Davis and Palumbo, 2006). The TOD housing affordability connection has received attention from some housing advocates because automobile ownership is one of a householdâs largest expenses, second only to the cost of housing. [According to the Bureau of Transportation Statistics, in 1998 the average household spent 33% of its income on housing and 19% on transporta- tion (Only 6% of transportation spending went toward travel by air, taxi, and public transportation). Food related expen- ditures come in third, at 14%. Bureau of Transportation Sta- tistics. Pocket Guide to Transportation, U. S. Department of Transportation, BTS00-08, 2000.] The poorest families spend the greatest share of their income on transportation (Surface Transportation Policy Partner- ship, 2001). Instead of paying a quarter or a third of their income for housing, low-income families sometimes pay half or even more for a place to live. Reducing transportation expenditures by living in TODs can free-up disposable in- come to be used for other uses such as housing. Conclusion and Recommendations This research helps confirm what had been intuitively ob- vious: TOD housing produced considerably less traffic than is generated by conventional development. Yet most TODs are parked on the assumption that there is little difference between TOD and conventional development with respect to the traffic they generate. One likely result of this fallacious assumption is that fewer TOD projects get built. TOD de- velopments that do get built are certainly less affordable and less sustainable than they might be because they are subject to incorrect assumptions about generated traffic impact. Therefore many hoped for benefits (such as less time stuck in traffic and lower housing costs) from nearly $75 billion in public dollars invested in rail transit (J. Neff, personal e-mail, October 26, 2007) over the past 11 years are not being realized. One end result is that auto trip generation is likely to be overstated for TODs. This can mean TOD developers end up paying higher impact fees, proffers, and exactions than they should since such charges usually are tied to ITE rates. An- other implication of the research is that parking ratios for residential TODs also are likely to be overstated for TODs by the same order of magnitude since they also are based on ITE data. More research on parking generation will be needed to confirm whether TOD residents own cars at the same rate as conventional development, but use them less. Some cumulative impacts of over-parking TODs are illus- trated in the site plan case studies. The TOD site plan case studies help to demonstrate that under the right conditions lowering residential parking ratios by 50% for TODs in station areas with quality transit service can result in: â¢ An increase in the density of a residential TOD by 20% to 33% depending on the residential building type; â¢ Savings on residential parking costs from 5% to 36% after accounting for increases in the number of units to be parked from increased residential density; and â¢ Potentially greater developer profits and/or increased hous- ing affordability from higher densities, lower capital costs for parking, and reduced traffic impact fees. Rightsizing parking ratios and traffic generation to the ac- tual performance of TOD is likely to result in some important implications on the physical form and performance of TOD developments: â¢ Local officials and neighborhoods may be more apt to sup- port increases in residential densities near transit if they are shown proof that fewer trips result from TODs than in conventional development. â¢ TOD developers likely would pay lower traffic related im- pact fees and exactions. Those savings can be passed on to consumers as lower housing costs. â¢ With lower levels of traffic generated from TODs, it can be argued that it simply makes no sense to construct roadway improvements for TOD related traffic that is not likely to materialize. â¢ Right-sizing new road and intersection improvements to reflect the actual transportation performance can result in more compact development patterns and a higher quality pedestrian environment since less land may be used for road improvements. Clear policy directions come from this research. The ap- preciably lower trip-generation rates of transit-oriented housing projects call for adjustments in the measurement of traffic impacts. For peak periods (that often govern the design of roads and highways), this research shows transit- oriented apartments average around one half the norm of vehicle trips per dwelling unit. The rates varied, however, from 70%-90% lower for projects near downtown to 15% to 25% lower for complexes in low-density suburbs. Re- gardless, smart growth needs smart calculus; those who build projects that reduce trips should be rewarded in the form of reduced traffic impact fees and exactions. The ex- pectation is developers would pass on some of the cost 54
savings to tenants, thus making housing near rail stations more affordable. To date, few jurisdictions have introduced sliding scale fee structures to reflect the lowering of trip generation for TODs. Santa Clara County Californiaâs Congestion Management Agency has produced guidelines for a 9% trip reduction for housing within 2,000 feet of a light-rail or commuter-rail station. While this is a positive step, according to our research findings, this adjustment is a bit tepid. Similarly, the URBEMIS software program sponsored by the California Air Resources Board, used to estimate the air quality impacts of new devel- opment, calls for up to a 15% lowering of trip rates for hous- ing in settings with intensive transit servicesâagain, likely on the low side based on these findings. More in line with the findings presented here are the vehicle trip reductions granted to the White Flint Metro Center project, a mega-scale, mixed- use joint development project being built at Washington, D.C. Metrorailâs North Bethesda Station. With some 1.2 mil- lion square feet of office space, 250,000 square feet of commercial-retail, and 375 residential units scheduled at build out, the project was granted a 40% reduction in esti- mated trip rates for the housing component based on prox- imity to transit. The trip reduction benefits of TOD call for other develop- ment incentives, like lower parking ratios, flexible parking codes, market-responsive zoning, streamlining the project review and permitting process, and investments in support- ive public infrastructure. Trip reduction also suggests TODs are strong markets for car-sharing. Recent research in the San Francisco Bay Area reveals that those who participate in carsharing lower their car ownership levels around 10%, with higher vehicle-shedding rates among those living near rail stations (Cervero, Golub, and Nee, 2007). The combination of reducing off-street parking and increasing carsharing options would yield other benefits, including reducing the amount of impervious surface (and thus water run-off and heat island effects) and the creation of more walkable scales of development. Such practices are not heavy-handed plan- ning interventions but rather market-oriented responses, namely efforts to set design standards and provide mobility options in keeping with the market preferences of those who opt to live near rail transit stations. Recommendations With this research data to support the belief that people living in TODs drive less often than their neighbors in con- ventional developments, public officials and government regulators have the evidence needed to develop new, more realistic standards for assessing impact fees and mitigation for TODs. Developing residential TODs based on an accurate assessment of their traffic impacts should result in easier development approvals, better planned and more compact communities, increased transit ridership, and more afford- able housing. Tightening residential TOD parking ratios to reflect the actual transportation performance of TODs will be a very important step toward realizing the expected commu- nity benefits of TOD and enhancing their financial feasibil- ity. In many TODs, the community and developer benefits have been understated because they have been over-parked. Additional research also is suggested to further address some of the questions addressed in the literature review. To help realize the benefits of TOD the team recommends the following: 1. Work with ITE and ULI to develop new trip generation and parking guidance for TOD. In the opinion of the authors, the highest priority should be placed on working with ITE and the ULI to develop and implement new guidance on trip generation and parking for TOD housing. The research suggests developers are being charged impact fees for non-existent trips and re- quired to build expensive parking spaces that are not needed. Parking ratios developed using ITE trip generation rates over-park TODs by as much as 50%. In developing new guidance on parking, it will be important to account for a variance in trip generation factors such as the quality of transit service and the distance of a station from the CBD. The project team contacted ITE to share the panelâs in- terest in working with ITE to develop new guidelines. In response, Lisa M. Fontana Tierney, P.E., Traffic Engineering Senior Director ITE, commented, âOnce the results of the study are finalized and submitted to ITE, we will review the information and consider it for inclusion in a future ITE resource. Based on my understanding of the work, it seems that it would be appropriate to consider the results of your study as part of a future edition of the ITE Trip Generation Handbook. We expect to begin the update process for this Handbook in early 2009.â 2. Broadly disseminate the findings of this research. Benefits of TOD are muted since most TODs parking and traffic impacts assessments are oblivious to the fact that a rail stop is nearby. Broadly distributing results of the research can help lead to right-sizing TOD-housing regu- lations for parking and transportation impact fees and higher intensity of development appropriate for TODs. With information in hand to confirm TOD housing produces fewer trips than conventional development, it should be somewhat easier to get local approval to build additional TODs without unnecessarily negotiating away the intensity of development envisioned in adopted TOD plans. As an interim step, the findings of the research have been presented at the 2007 Rail~Volution Conference in 55
56 Miami, Florida, the 2008 Congress for the New Urbanism Conference in Austin, Texas, and a transportation semi- nar at Portland State University. Findings also are slated to be presented at the 2008 ITE Annual Conference in Ana- heim, California, and have been accepted for publication by the Journal of Public Transportation. The findings also will be shared with other researchers doing similar research, including the mixed-use trip generation research being done at the Texas Transporta- tion Institute and NCHRP Project 08-66, âTrip Genera- tion Rates for Transportation Impact Analysis of Infill Development.â 3. Seek funding for additional research on TOD land uses. The research presented here covers only one land use type found in TODs. Additional research will be necessary to broaden the knowledge of the trip generation, the park- ing characteristics of TOD land uses, and the impact of TOD on ensuring ridership in TODs. The research needs identified by the team and the panel flow from the field research, the literature review and the state-of-the practice of what we know and donât know about ensuring ridership from TOD: a. Research into the parking demand and trip generation characteristics of office, retail, and mixed-use in TODs. This research also should consider the parking demands of the land uses and the degree to which different land uses have different annual peak parking demands, and how the annual peak parking demands differ from the average daily demand. Parking utilization information is needed for all TOD land uses. b. Research into self selection and change in travel pat- terns after residents move into a TOD. A mode share survey could be mailed to residents of selected TODs and analyzed at a cost of approximately $3,500 per TOD. The before and after study of Center Commons referenced in the literature review was done in this manner. c. Research on the impact of design features (e.g., mixed land-use, traffic calming, bus bulbs, short blocks, street furniture), travel patterns, transit ridership, or the deci- sion to locate in a TOD. Intuitively we know âdesign mattersâ but there is very little data to show the impact of design on transit use, location decisions to live in a TOD or what design features have the greatest impact. d. Research into what motivates employers to locate in TODs. There is a growing body of information on res- idential TODs and locational decisions. At the same time, there is very little understanding how to impact retail and commercial locational decisions to be part of a TOD. As a starting point, phone interviews of com- mercial leasing agents and tenants in TODs could be taken to distinguish the role TOD/transit may play in locational decisions.