Internal Capture in Mixed-Use Developments (MXDs) and Vehicle Trip and Parking Reductions in Transit-Oriented Developments (TODs)

Transcription

1 Internal Capture in Mixed-Use Developments (MXDs) and Vehicle Trip and Parking Reductions in Transit-Oriented Developments (TODs) Reid Ewing City and Metropolitan Planning University of Utah

2 The Ds of the Built Environment Density Diversity Design Destination Accessibility Distance to Transit Development Scale Demand Management

3 Internal Capture in MXDs Ewing, R., Greenwald, M., Zhang, M., Walters, J., Feldman, M., Cervero, R., & Thomas, J. (2010). Traffic generated by mixed-use developments Six-region study using consistent built environmental measures. Journal of Urban Planning and Development, 137(3), Tian, G., Ewing, R., White, A., Hamidi, S., Walters, J., Goates, J. P., & Joyce, A. (2015). Traffic Generated by Mixed-Use Developments: Thirteen- Region Study Using Consistent Measures of Built Environment. Transportation Research Record: Journal of the Transportation Research Board, (2500),

4 Introduction Mixed-use development (MXD) Put offices, shops, restaurants, residences, and other codependent activities in close proximity to each other; Shorting trips and allow what might otherwise be external car trips to become internal walk, bike, or transit trips; Reduce the vehicle miles; RiverPlace, Portland: residential, commercial, dining, medical, salon, entertainment

5 Conventional traffic impact analysis ITE method for MXDs 1) Determine the amounts of different land-use types (residential, retail, and office) contained within the development; 2) preliminary estimation = Land-use types * ITE s perunit trip generation rates (no interactions); 3) Reduced estimation = preliminary estimation - a certain percentage to account for internal-capture of trips within MXDs. The reductions are based on lookup tables; 4) For each pair of land uses, productions and attractions of internal trips are reconciled

6 Weaknesses of the current method 1) The two lookup tables are based on data for a limited number of multiuse sites in Florida ; 2) The land-use types and adjustments embodied in the lookup tables are limited to the three uses: residential, retail, and offices; 3) The scale of development is disregarded; 4) The land-use context of development projects is ignored; 5) The possibility of mode shifts for well-integrated, transit-served sites is not explicitly considered;

7 Accurately estimating the proportion of trips captured internally by MXDs is vitally important to accurately assess MXD projects traffic impacts. Top down: assemble enough data on MXDs to estimate statistical models of traffic generation in terms of standard built environmental variables; Taking this approach, Ewing et al. (2010) modeled internal capture rates and external walk and transit mode shares with data for more than 35,000 trips to/from/within 239 MXDs in six metropolitan regions of the U.S.

8 This paper builds on the earlier work of Ewing et al. (2010) with more and newer data. Regions MXDs Trips Earlier study ,877 Current study ,074 We now have enough regions in our database so that regional variables may prove statistically significant in a multi-level analysis. This means that, for the first time, traffic analysts can tailor their analyses to the unique characteristics of their home regions. We now have enough bike trips in our database to model the probability that an external trip will be by bicycle.

9 Conceptual framework

10 Sample selection Household travel survey Regional household survey with XY coordinates; Parcel level land-use data;

11 MXDs selection A mixed-use development or district consists of two or more land uses between which trips can be made using local streets, without having to use major streets. The uses may include residential, retail, office, and/or entertainment. There may be walk trips between the uses. Expert-based process Gateway district, Salt Lake City: dining, entertainment, retail, residential, office

12 Survey year MXDs Mean acreage per MXD Total trip ends Mean trip ends per MXD Atlanta , Austin , Boston , Denver , Eugene , Houston , Kansas City , Minneapolis-St. Paul , Portland , Sacramento , Salt Lake City , San Antonio ,62 87 Seattle , Total ,

13 Variables Outcome variables Definition INTERNAL Dummy variable indicating that a trip remains internal to the MXD (1 = internal, 0 = external). WALK Dummy variable indicating that the travel mode on an external trip is walking (1 = walk, 0 = other). BIKE Dummy variable indicating that the travel mode on an external trip is biking (1 = bike, 0 = other). TRANSIT Dummy variable indicating that the travel mode on an external trip is public bus or rail (1 = transit, 0 = other). Explanatory variables HHSIZE VEHCAP BUSSTOP Level 1 traveler/household level Number of members of the household. Number of motorized vehicles per person in the household. Dummy variable indicating that the household lives within 1/4 mile of a bus stop (1 = yes, 0 = no)

14 Explanatory variables Level 2 MXD explanatory variables AREA Gross land area of the MXD in square miles. POP EMP ACTIVITY ACTDEN DEVLAND JOBPOP LANDMIX INTDEN EMPMILE EMP30T EMP10A, EMP20A, EMP30A STOPDEN RAILSTOP Resident population within the MXD; prorated sum of the population for the census block groups that intersect the MXD. Prorating was done by calculating density of population per residential acre (tax lots designated single-family or multifamily) for the entire census block group, then multiplying the density by the amount of residential acreage within the block group contributing to the MXD, and finally, summing over all block groups intersecting the MXD area. For Houston, data at the traffic analysis zone (TAZ) level were prorated. Employment within the MXD; weighted sum of the employment within the MXD for all Standard Industrial Classification (SIC) industries. For Portland, employment estimates were based on the average number of employees in each size category, summed across employer size categories. For other regions, data at the TAZ level were prorated. Resident population plus employment within the MXD. Activity density per square mile within the MXD. Sum of population and employment within the MXD, divided by gross land area. Proportion of developed land within the MXD. Index that measures balance between employment and resident population within MXD. Index ranges from 0, where only jobs or residents are present in an MXD, not both, to 1 where the ratio of jobs to residents is optimal from the standpoint of trip generation. Values are intermediate when MXDs have both jobs and residents, but one predominates. a Another diversity index that captures the variety of land uses within the MXD. This is an entropy calculation based on net acreage in land-use categories likely to exchange trips b. The entropy index varies in value from 0, where all developed land is in one of these categories, to 1, where developed land is evenly divided among these categories. Number of intersections per square mile of gross land area within the MXD. Total employment outside the MXD within one mile of the boundary. Weighted average for all TAZs intersecting the MXD. Weighting was done by proportion of each TAZ within the MXD boundary relative to an entire TAZ area (i.e., clipping the block group with the MXD polygon). Percentage of total regional employment accessible within 30-min travel time of the MXD using transit. Percentage of total regional employment accessible within 10, 20, and 30-min travel time of the MXD using an automobile at midday. Number of transit stops within the MXD per square mile of land area. Uses 25 ft buffer to catch bus stops on periphery. Rail station located within the MXD (1 = yes, 0 = no). Commuter, metro, and light rail systems are all considered.

15 Explanatory variables Level 3 regional explanatory variables REGPOP Population within the region. INDEX Measure of regional compactness developed by Ewing and Hamidi (Ewing and Hamidi, 2014). Index derived by extracting the common variance from multiple measures through principal component analysis.

16 Average internal capture rates and walk, bike and transit mode shares for external trips to/from MXDs Internal capture (percentage of all trips) Mode share percentage for external trips Walk Bike Transit Region share share share Atlanta 16.4% 4.1% 1.1% 2.6% 7.8% Austin 16.8% 1.4% 1.8% 0.2% 3.4% Boston 21.0% 36.7% 2.2% 28.3% 67.2% Denver 26.5% 7.2% 1.5% 6.9% 15.5% Eugene 24.8% 9.6% 5.0% 13.2% 27.8% Houston 14.8% 1.2% 0.4% 1.5% 3.1% Kansas City 11.1% 2.6% 0.9% 2.9% 6.4% Minneapolis-St. Paul 18.7% 10.7% 2.4% 12.4% 25.4% Portland 25.6% 13.6% 4.5% 12.4% 30.5% Sacramento 16.4% 2.9% 0.4% 0.4% 3.8% Salt Lake City 11.6% 7.4% 1.4% 4.2% 13.0% San Antonio 4.6% 2.4% 0.8% 6.0% 9.2% Seattle 20.2% 7.8% 2.0% 10.0% 19.8% Overall 19.7% 11.9% 2.1% 11.3% 24.3% Sum of walk, bike and transit

17 Models Trip purpose: home-based work, homebased other, non-home-based; Dichotomous outcomes: internal versus external, walk versus other, bike versus other, and transit versus other; Hierarchical modelling: trips/households are nested within MXDs; MXDs are nested within regions; Random intercept models: only the intercepts were allowed to randomly vary across higher level units. Level 3 Regions Level 2 MXDs Level 1 Trips/Households

18 Results Table 1. Log odds of internal capture (log-log form) Home-based work Home-based other Non-home based Coef. t-ratio p-value Coef. t-ratio p-value Coef. t-ratio p-value Constant REGPOP INDEX < AREA < EMP JOBPOP < < INTDEN HHSIZE < < < VEHCAP < < < Pseudo-R

19 Table 2. Log odds of walking on external trips (log-log form) Home-based work Home-based other Non-home-based Coefficient t-ratio p-value Coefficient t-ratio p-value Coefficient t-ratio p-value Constant AREA ACTDEN < < JOBPOP LANDMIX INTDEN EMPMILE < EMP10A HHSIZE < < VEHCAP < < BUSSTOP < Pseudo-R

20 Table 3. Log odds of biking on external trips (log-log form) Home-based work Home-based other Non-home-based Coefficient t-ratio p-value Coefficient t-ratio p-value Coefficient t-ratio p-value Constant REGPOP < INDEX AREA ACTDEN JOBPOP INTDEN EMPMILE < < STOPDEN < EMP30T HHSIZE VEHCAP < BUSSTOP < Pseudo-R

21 Table 4. Log odds of transit on external trips (log-log form) Home-based work Home-based other Non-home-based Coefficient t-ratio p-value Coefficient t-ratio p-value Coefficient t-ratio p-value Constant ACTDEN < < EMPMILE < JOBPOP STOPDEN < HHSIZE < VEHCAP < < BUSSTOP Pseudo-R 2 NA NA 0.43

22 Conclusion Failure to account for internal capture and external walk, bike, and transit trips ends up penalizing MXDs, ITE s trip generation overestimates of the traffic impacts of MXD projects; This research sought to advance the state of knowledge on the relationships that govern travel to, from, and within mixed-use development projects and to enumerate tangible and verifiable traffic reductions relative to the rates in the ITE Trip Generation report. This study represents the first large-sample national study of the traffic generation by mixed-use developments, making use of household travel survey data from 13 metropolitan regions.

23 Mode Shares and Vehicle Trip and Parking Generation at TODs Tian, G., Ewing, R., Weinberger, R., Shively, K., Stinger, P., & Hamidi, S. (2016). Trip and parking generation at transit-oriented developments: a case study of Redmond TOD, Seattle region. Transportation, Ewing, R., Tian, G., Lyons, T., Weinberger, R., Shively, K., & Stinger, P. (2016) Trip and parking generation at transit-oriented developments. National Institute for Transportation and Communities (NITC) report.

24 Introduction How best to allocate land around transit stations? large park-andride lots VS. active uses such as multifamily housing, office, and retail Redmond TOD, Seattle

25 In practice Officials usually assuming that TODs generate the same number of vehicle trips as conventional development and that transit stations require the same number of park-and-ride spaces as non-tod stations.

26 In literature The average trip generation rate in areas with TOD is well below the trip generation rate from the ITE report (Arrington & Cervero 2008; Cervero & Arrington 2008; Cervero et al. 2004). Residents living within TODs are reported to have higher rates of transit trips than who are living outside TOD (SFBAMTC 2006; Cervero et al. 2002; Faghri & Venigalla 2013; Zamir et al. 2014), especially for commuting trips (Arrington & Cervero 2008; Cervero 1994; Lund et al. 2004; Lund et al. 2006).

27 Research Question Much of the travel demand is captured internally and much of the transit demand is generated by TODs themselves. Internal trips Transit trips Vehicle trips There are a few studies of vehicle trip generation (Arrington & Cervero, 2008; Cervero & Arrington, 2008; Zamir et al. 2014) at multifamily developments near transit. There is only one study of vehicle trip generation at TODs (defined as mixed-use developments Handy et al. 2013). The question of how much vehicle trip reduction occurs with TOD is largely unexplored in the literature.

28 TOD Definition TODs are widely defined as compact, mixed-use developments with high-quality walking environments near transit facilities (ITE 2004, pp. 5-7; Jacobson & Forsyth 2008; Renne 2009). For our purposes, TODs are developed by a single developer under a master development plan, and can also include a clustering of development projects near transit facilities that are developed by one or more developers pursuant to a master development plan. Dense Mixed use Pedestrianfriendly Adjacent to transit Built after transit Fully developed or nearly so Self-contained parking

29 TOD Selection Mixed use developments (MXDs) near transit Regional transit agencies and MPOs Google Satellite Imagery Site visit

30 Redmond TOD, Seattle Rhode Island Row, Washington D.C. Wilshire/Vermont, Los Angeles Fruitvale Village, San Francisco Englewood TOD, Denver

31 Table 1. Net and Gross Residential Densities, and Floor Area Ratios for Commercial Uses, for the Five TODs Studied

32 Data Collection A full count of all persons entering and exiting the building A brief intercept survey of a sample of individuals entering and exiting the building o How did you get here? (e.g., by what mode of travel?), and 7:30 a.m. and 9:00 p.m. on a workday in spring or fall 2015 o What is the purpose of your trip?

33 Mode Choice and Trip Generation Table 2. Average Mode Shares for TODs Studied Table 3. Average Vehicle Trip Reductions Relative to ITE Rates

34 Figure 2. Vehicle Trip Generation vs. Auto Mode Share

35 Parking Supply and Demand Table 5. Residential Parking Supplies as a Percentage of ITE, and Residential Peak Parking Demand as a Percentage of Actual Supplies

36 Table 6. Commercial/Mixed Use Parking Supplies as a Percentage of ITE, and Commercial/Mixed Use Peak Parking Demand as a Percentage of Actual Supplies Table 7. Aggregate Parking Supplies as a Percentage of ITE Supplies, and Aggregate Peak Parking Demand as a Percentage of Actual Supplies

37 Conclusion Walk mode shares fall within a fairly narrow band, from 16.6 % at Rhode Island Row to 28.3 % at Fruitvale. They mostly reflect the environment in which the TOD is located, and secondarily the number of commercial trip attractions contained within the TOD; The smallest rail mode share is 13.6 % at Englewood. The largest shares are 27.2 % at Rhode Island Row and 26.1 % at Fruitvale; The number of vehicle trips at TODs range from onethird below to two-thirds below ITE rates;

38 The supply of parking stalls for residential use at TODs ranges from 0.81 to 1.60 stalls per dwelling unit; the peak demand for parking ranges from 0.44 to 1.29 spaces per occupied dwelling ; the occupancy of residential parking spaces ranges from 54.3% to 80.6 %; Actual parking supplies for commercial and mixed-use garages and lots in our TODs range from 22.6% to 61.2% of ITE supplies at Englewood; At the overall peak hour, parked cars would fill only 19.0% to 45.8% of parking spaces if built to ITE standards and only 58.3% to 84.0% of parking spaces will be filled.

39 Thank you!