March 31, diversity. density. 4 D Model Development. submitted to: design. submitted by: destination

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1 March 31, 2010 diversity density 4 D Model Development submitted to: design submitted by: destination

2 4 D Model Development Team SANDAG: Mike Calandra Rick Curry Rob Rundle Parsons Brinckerhoff: Bill Davidson Joel Freedman Erin Wardell Jinghua Xu

3 Introduction San Diego Association of Governments (SANDAG), along with a team of consultants from Parsons Brinckerhoff (PB), evaluated the existing SANDAG travel demand model for its sensitivity to land use-related transportation emission reduction strategies. The land use characteristics that relate to transportation and behavior are known as the 4Ds, which stand for Diversity, Density, Design, and Destination Accessibility. The 4Ds measures are used to quantify urban characteristics such as compact mixed use areas associated with Smart Growth planning. Smart Growth encourages non-motorized and transit mode usage that results in shorter trips and decreased automobile dependency, which help reduce the overall vehicle miles traveled in the region. In order to test Smart Growth policies, the regional travel demand model should be sensitive to 4D land use characteristics and be capable to conduct forecasting in accordance with SANDAG's advanced land use plan: High Density Mixing of uses (Diversity) Transit and pedestrian-oriented Design Access to Destinations via multi-modal accessibility Traditional regional travel demand models are primarily used to forecast long-distance auto travel on freeways and major roads and system-level transit use, and they are generally not sensitive enough to model short-distance travel, local roads, and non-motorized travel modes due to the aggregate zone structure of land use zones. The Series 11 SANDAG regional travel demand model utilizes three different zonal systems for a balance between model accuracy and model run times. There are 2,000 Transportation Distribution Zones (TDZs) which are used in the trip distribution and feedback steps. There are 4,605 Traffic Analysis Zones (TAZs) which are used during the trip generation and trip assignment steps. The SANDAG regional travel demand model, uses a much finer level of spatial detail for transit access and the mode choice step. There are 33,353 Master Geographic Reference Areas (MGRAs). For a more accurate representation of non-motorized travel impedance, network access at this level takes into account actual distance as well as slope and other physical barriers such as freeways or canyons. The MGRA level also allows the mode choice model to show more sensitivity to the 4D measures of density. This paper describes the data analysis and methodology used to evaluate the effects of the 4Ds on trip length and mode choice. Based on the results of the analysis, measures of density were incorporated into the trip distribution and mode choice steps of the model. Unlike other 4D post-processor analytical tools, the sensitivities to 4D effects are now built into the SANDAG trip-based model, and SANDAG staff can rely on this tool to provide comprehensive analysis of transport and land-use policies and observe effects across multiple dimensions of travel behavior including trip distribution, mode choice, and ultimately trip assignment. The enhanced model is sensitive to changes in 4D characteristics and is based on sound travel forecasting principles. It is suitable for testing Smart Growth and emissions reductions policies. The enhanced model will support the development of the Climate Action Strategy, as well as a future Urban Area Transit Strategy and the 2050 Regional Transportation Plan. Page 1

4 Data Development SANDAG conducted a travel behavior survey of households in San Diego County in The survey form was a one day long trip diary which collected detailed information about trip origins, destinations, purpose, and mode. The survey contains 31,147 records, which are expanded to represent 9,621,531 trips. Transit usage data is also provided by the onboard transit passenger survey. For that survey, every fixed route in the region at the time of study was surveyed, 164 in all. The survey has 19,929 records which are expanded to represent 228,078 transit trips. Information about trip origin and destination, passenger demographics, and trip behavior was collected. The eight trip purposes are defined with their abbreviations in Table 1. Table 1: Trip Purposes Purpose Home Based Work Home Based College/University Home Based School Home Based Shop Home Based Other Non Home Work Non Home Other Serve Passenger Abbreviation HBW HBC HBE HBS HBO NHW NHO SP SANDAG provided the following 4D variables at the MGRA level: Employment Density, Dwelling Unit Density, Population Density, and Intersection Density. These variables were selected for analysis because the data are available as an input to the travel demand model and all measures are quantifiable. There are many other variables that represent urban form, accessibility, and density that could be evaluated, if they were available. Some of these are: bike paths, sidewalks, different floorspace types, parking space density, and proximity of frequent service transit lines/stops. Calculations for density values were done at the Master Geographic Reference Area (MGRA) geography level. MGRAs are aggregations of parcels, and subsequently are aggregated into Traffic Analysis Zones (TAZs) and Traffic Distribution Zones (TDZs). Rather than use a simple distance buffer for calculating density values, a floating MGRA methodology was used that has the same characteristics as the current transit walk accessibility calculations in the transportation model. The logic behind the methodology is the same as a simple buffer but includes elevation and walk barrier constraints. Density calculations for intersections, dwelling units, and employment were broken into two distinct categories based on the method applied. Page 2

5 Intersection Density Base intersections were developed from a 2008 SanGIS road layer which includes all roadway facilities. The SanGIS roads line feature class was queried to remove all roads not traversable by pedestrians (i.e. freeways). The feature class was then intersected with a copy of itself using the 'Intersect Lines (Make Points)' tool from the Hawth's Tools extension for ArcGIS. The resulting feature class consisted of points located at the intersection of two or more roads, and contained a 'count' field indicating the number of lines intersected at the point. While intersections can be calculated somewhat precisely for a current year network, future years are difficult since the underlying local street pattern in not known. The transportation model instead uses a network of existing and planned facilities at the collector level and higher. A conservative approach to determining future year intersections is to assume that the ratio of the model highway network controlled intersections to all intersections by MGRA will remain constant over time. This may be too conservative for some greenfield and brownfield developments and code changes will allow for an override. Intersection density data is calculated at the MGRA geography level. For each MGRA the distance is calculated between the MGRA centroid and each intersection. The distance calculations account for elevation by factoring the vertical distance by 3 to reflect the onerous nature of hills for non-motorized accessibility. Intersections that fall with 0.5 miles of the MGRA centroid and do not cross a walk barrier are added to the total. Walk barriers are line features that define where geographic or physical barriers would prevent non-motorized travel such as canyons or impassible freeway sections. Socio-Demographic Densities Because socio-demographic information, dwelling units, and employment are stored at the MGRA geography level, the calculation methodology is slightly varied from the intersection point method. MGRA centroid to MGRA centroid distances are calculated instead of MGRA centroid to a point. This distance calculation includes the vertical distance factor and walk barriers. Accessible MGRAs add both the demographic information and the area for calculation of the density term. The socio-demographic variables tested for statistical significance are included in Table 2. These variables were selected because the data was already available to SANDAG and because they represent different types of places. For example, a high retail employment density signifies an area with a lot of shopping nearby, as opposed to an area with little retail employment nearby. A zone with a high dwelling unit density is located in a high density residential area as opposed to a low density population residential area. Table 2: Socio-demographic Variables Name Employment Density Dwelling Unit Density Retail Employment Density Population Density Definition Number of Employees per acre Number of Occupied Dwelling Units per acre Number of Retail Employees per acre Number of Residents per acre Page 3

6 Methodology Limitations The first limitation is in assuming that the MGRA centroid is a realistic representation of the entire mgra for computing walkable distances. Ideally this could be done at the parcel level with the average density determined at the MGRA as an automated pre-processing step rather than during each model run. Any change to land use or employment would require re-running the automated procedure. Since the calculations are done for each model run, computation speed combined with the overall model run time forced this limitation. The second limitation is creating distances as the crow flies instead of using a network based procedure. Future scenarios do not have detailed local network information and would require additional logic and assumptions to use a network based procedure. Ideally, a non-motorized network would be created for analyzing more accurate walk distances. Classification of 4D Variables The household and transit on-board survey data was analyzed to determine whether there are significant differences in trip length and mode choice for trips produced by and attracted to various types of 4D classification schemes. The results of the analysis showed a strong inverse relationship between trip length and density. The variables selected as the most significant (based on the relationship to trip distance) were employment density, dwelling unit density, and intersection density. The weighted average trip distance (weighted by the expansion factor) was calculated by 10 density bins for dwelling units and intersections, and 7 bins for employment density. The results are contained in Table 3. Page 4

7 Table 3: Average Survey Trip Distance by Density and Purpose Density Category HBW HBU HBC HBS HBO NHW NHO HBP Employment Density 0 to to to to to to over Dwelling Unit Density 0 to to to to to to to to to over Intersection Density 0 to to to to to to to to to over A similar analysis was conducted for mode choice. The survey data was summarized by the number of expanded observations in each density bin by mode. This data is summarized in Table 4 for drive alone, transit, and non-motorized. The shared-ride mode did not show much variation across the density bins and is not included in this table. Page 5

8 Table 4: Survey Mode Choice by Density and Purpose Mode Employment Density HBW HBU HBC HBS HBO NHW NHO HBP DA 0 to 1 89% 71% 8% 66% 47% 81% 40% 65% 1 to 2 88% 76% 3% 57% 42% 94% 41% 65% 2 to 4 83% 59% 4% 53% 43% 79% 41% 68% 4 to 5 72% 56% 2% 53% 39% 86% 49% 63% 5 to 8 72% 69% 0% 42% 37% 76% 46% 66% 8 to 19 69% 47% 5% 40% 32% 75% 56% 61% over 19 55% 54% 29% 39% 34% 71% 56% 76% NM 0 to 1 1% 6% 16% 1% 8% 3% 4% 0% 1 to 2 1% 3% 26% 3% 11% 1% 6% 1% 2 to 4 3% 4% 25% 8% 12% 6% 10% 0% 4 to 5 12% 13% 42% 10% 21% 2% 8% 0% 5 to 8 9% 0% 26% 17% 21% 11% 10% 0% 8 to 19 8% 14% 40% 28% 27% 8% 7% 0% over 19 31% 33% 0% 32% 34% 16% 9% 0% TR 0 to 1 2% 9% 10% 1% 1% 2% 2% 0% 1 to 2 3% 7% 11% 3% 3% 0% 2% 0% 2 to 4 6% 29% 17% 12% 5% 2% 2% 0% 4 to 5 10% 19% 15% 4% 6% 3% 3% 0% 5 to 8 11% 31% 25% 5% 4% 1% 3% 0% 8 to 19 12% 31% 20% 4% 6% 1% 2% 0% over 19 12% 12% 29% 15% 12% 3% 6% 0% Mode Dwelling Unit Density HBW HBU HBC HBS HBO NHW NHO HBP 0 to % 78% 9% 75% 46% 80% 52% 63% DA to % 64% 11% 53% 48% 85% 48% 63% to % 75% 6% 65% 46% 82% 45% 63% to % 68% 3% 56% 45% 79% 43% 67% to % 77% 7% 69% 44% 76% 50% 66% to % 73% 3% 63% 47% 80% 45% 70% to % 62% 2% 44% 39% 74% 46% 64% to % 53% 5% 53% 40% 73% 43% 70% to % 49% 0% 38% 30% 71% 42% 63% over % 58% 0% 30% 32% 62% 38% 68% NM 0 to % 0% 8% 1% 8% 8% 1% 0% to % 10% 16% 0% 7% 4% 6% 0% to % 0% 17% 2% 7% 7% 5% 0% to % 8% 20% 6% 13% 12% 6% 0% to % 0% 26% 0% 14% 7% 6% 0% to % 3% 28% 7% 12% 6% 7% 0% to % 5% 23% 10% 12% 12% 7% 2% to % 11% 18% 10% 12% 12% 11% 0% to % 7% 45% 22% 29% 11% 21% 0% Page 6

9 over % 12% 56% 24% 25% 22% 17% 0% TR 0 to % 0% 21% 0% 1% 1% 2% 0% to % 8% 14% 2% 1% 1% 1% 0% to % 20% 10% 0% 0% 1% 2% 0% to % 10% 9% 1% 1% 1% 2% 0% to % 18% 8% 3% 2% 2% 3% 0% to % 19% 14% 2% 3% 1% 2% 0% to % 19% 10% 11% 5% 1% 2% 0% to % 22% 23% 7% 5% 4% 3% 0% to % 42% 25% 18% 9% 5% 3% 0% over % 29% 16% 9% 12% 3% 8% 0% Mode Intersection Density HBW HBU HBC HBS HBO NHW NHO HBP DA 0 to % 58% 10% 73% 48% 80% 54% 70% to % 70% 9% 59% 41% 80% 50% 55% to % 67% 6% 55% 48% 82% 46% 66% to % 74% 7% 66% 46% 87% 44% 60% to % 47% 2% 56% 41% 77% 45% 71% to % 82% 4% 60% 45% 81% 41% 72% to % 66% 5% 59% 37% 82% 45% 56% to % 64% 3% 50% 44% 68% 48% 67% to % 64% 1% 40% 34% 63% 43% 80% over % 54% 0% 37% 37% 66% 38% 67% NM 0 to % 0% 4% 3% 6% 9% 2% 0% to % 8% 20% 4% 10% 5% 5% 0% to % 8% 19% 3% 9% 5% 5% 0% to % 3% 20% 4% 8% 5% 7% 0% to % 3% 27% 9% 18% 7% 8% 0% to % 0% 21% 3% 9% 7% 4% 0% to % 3% 29% 6% 11% 6% 7% 2% to % 12% 23% 11% 20% 12% 10% 0% to % 20% 22% 17% 17% 22% 15% 0% over % 9% 46% 21% 25% 20% 20% 0% TR 0 to % 12% 19% 0% 2% 2% 2% 0% to % 12% 15% 1% 1% 2% 1% 0% to % 16% 10% 0% 2% 2% 2% 0% to % 17% 8% 1% 2% 1% 2% 0% to % 38% 16% 5% 4% 1% 2% 0% to % 13% 5% 5% 3% 0% 2% 0% to % 17% 11% 4% 4% 1% 2% 0% to % 10% 14% 8% 3% 6% 3% 0% to % 15% 34% 12% 9% 4% 4% 0% over % 29% 25% 13% 6% 1% 6% 0% Page 7

10 Evaluating the data with up to ten density bins showed a clear relationship between mode choice and trip distance as the density changed. However, with so many bins there were many cases with few observations and some unexpected variation of distance and mode choice across bins. A series of maps and tables were prepared showing different numbers of bins, starting with 10, until a final set of three bins (low, medium, high) were selected as the best representation of the area, based on the knowledge and judgment of the project team. For employment density, the team determined that the highest density category used in this analysis did not have a high enough threshold to distinguish the unique CBD areas. The highest density threshold in Table 5 is therefore higher than what was initially evaluated in Table 4. Initially, the intersection density variable was calculated the same as the socio-economic variables. The model team decided that a simple count of the intersections within the buffered distance of the MGRA would be more effective due to the nature of intersections as an urban form variable. The methodology for the determining the bin ranges is explained in the Data Development section of this paper. Table 5 shows the data ranges that fall into each bin. Table 5: Density Ranges Employment (floating number of employees per acre) Dwelling Unit (floating number of all dwelling units per acre) Low Intersection (floating number of intersections per ½ mile buffer around MGRA) Medium High Figure 1 shows the employment density. There are only a few high density areas and they are in the major CBDs. The medium density locations are more geographically dispersed, and represent secondary employment centers and corridors outside of the center cities. Figure 2 shows dwelling unit density. San Diego's high density residential areas are located very close to the San Diego CBD and other cities in San Diego County, including Chula Vista, Escondido, and Oceanside. Medium density residential areas are found along the I-5 and I-8 freeway corridors. Figure 3 shows intersection density. Due to the limitations of the road network coverage and the fact that some very high density zones may not have contained intersections due to the geography of their boundaries, approximately 750 MGRAs were hand adjusted by SANDAG staff based on their local knowledge to better reflect their intersection density classification. High intersection density means that the MGRA has a greater potential for non-motorized and walktransit access due to a better connected street system. High intersection density closely follows employment and household density in San Diego County. Page 8

11 Figure 1: 2006 Employment Density Page 9

12 Figure 2: 2006 Dwelling Unit Density Page 10

13 Figure 3: 2008 Intersection Density Page 11

14 Statistical Analysis Trip Distribution As noted above, the SANDAG trip distribution model operates at a TDZ level. The model utilizes a gravity formulation with segmentation by trip purpose, and reflects accessibility through average composite utilities (mode choice logsums). In order to compare the household and on-board survey data against the current SANDAG model, household, employment, and intersection density variables were calculated at the TDZ level as a weighted average of the MGRA densities within each TDZ. In addition, a mix variable was calculated that combines the household, employment and intersection variables, at the TDZ level. This was necessary because of the difficulty in representing multiple measures of density within the gamma function that the SANDAG gravity model uses to calculate friction factors. The mix variable was collapsed into four bins and used to classify each production TDZ, so that trip length distributions and average trip lengths by TDZ classification and purpose could be prepared and compared to survey data, and so that the models could be re-calibrated based on the mix index of the production TDZ. The combined 4D variable is referred to as the mix variable, and is based on previous work by Portland Metro 1. The formula is given Equation 1: Equation 1: Mix Variable Where: Households = households within ½ mile of TDZ centroid Employment = employment within ½ mile of TDZA centroid Intersections = intersections within ½ mile of TDZ centroid Factor 1 = Factor 2 = The factors convert the households and employment to units equivalent to intersections. The equation results in a measurement of density, but its value is highest where all three factors are highest. Table 6 shows the density bins for the Mix Index. Figure 4 shows the location of the density categories. The highest density areas are around the CBDs of San Diego and the other major cities within the county. 1 Metro Travel Forecasting Trip Model Methodology Report. Metro Planning Department, Travel Forecasting Division, Page 12

15 Table 6: Mix Index Density Bins Mix Index Low 0-20 Medium Low Medium High High Page 13

16 Figure 4: Mix density (2006 and 2008 data) Page 14

17 An analysis of the estimated (modeled) versus observed (survey) trip length was conducted. The average trip length by purpose as well as the average trip length within each density category were compared. The comparisons (see Table 7) show a strong relationship between trip length and the density classifications for most of the purposes. Page 15

18 Table 7: Average Trip Length Employment Low Medium High Dwelling Unit Low Medium High Intersection Low Medium High Mix Variable Low Medium Low Medium High High Trip Purposes HBW HBC HBE HBS HBO NHW NHO HBP Survey Model Survey Model Survey Model Survey Model Survey Model Survey Model Survey Model Survey Model Page 16

19 As expected, in most cases the survey data shows the longest distances in the low density category and the shortest distances in the high density category. Some of the categories (specifically the HBP purpose) had a very low number of observations which explains the difficulty of showing a clear trend. The school and college purpose are clearly not as affected by density as some of the other purposes, which makes sense intuitively since school location is selected for a variety of reasons beyond accessibility. The comparison of model to survey showed that the model data needed to be further calibrated to match the trip distances in the survey data. Trip length frequency distributions were also prepared and coincidence indices calculated. The coincidence index measures the area in common to two distributions. The formula for the coincidence index is given in Equation 2: Equation 2: Coincidence Index min, max, where 1, count +T = value of estimated distribution at time T count + = total count of estimated distribution count -T = value of observed distribution at time T count - = total count of observed distribution The coincidence index lies between zero and one, where zero indicates two disjoint distributions and one indicates identical distributions. This analysis was conducted by purpose, as well as by mix index for the home based purposes and employment density bin for the non-home based purposes. The initial trip length frequency and coincidence index calculations showed that some purposes were not matching very well, particularly at shorter distances, although the fit was very good for all purposes aggregated together. A coincidence index of 80% or higher indicates a good fit between the estimated and observed values. The results are in Table 8. Page 17

20 Table 8: Coincidence Index Results Purpose Coincidence Index All Purposes 88% Home Based Work 84% Home Based College 67% Home Based School 82% Home Based Shop 61% Home Based Other 90% Non Home Based Work 80% Non Home Based Other 81% Home Based Serve Passenger 61% Based on these results, it was determined that the trip distribution model should be further calibrated with inclusion of the mix density variable. Trip Distribution Calibration The calibration process was automated by adapting the existing SANDAG GISDK calibration scripts. The scripts were adapted to adjust the friction factor gamma function calculations to include a factor specific to each purpose and to the density classification of the production zone. The factors adjusted the beta parameter either up or down, making friction factors more or less sensitive to accessibility, which adjusted the trip length. The beta factors increase with respect to density, which has an inverse relationship to trip length. Therefore, as the density increases, the trip lengths are shorter. The factors used are in Table 9 and Table 10. The mix variable density classification (low, medium low, medium high, high) was used for the Home Based trip purposes (HBW, HBC, HBE, HBS, HBO, SP). For the Non-home based trip purposes (NHW and NHO), the employment density classification (low, medium, high) was used, since neither end of a NHB trip is at home and therefore residential density should not be relevant. Trip purpose definitions are located in Table 1. Table 9: Mix Variable Density Factors Purpose Low Density Medium Low Density Medium High Density High Density HBW HBC HBE HBS HBO SP Page 18

21 Table 10: Employment Variable Density Factor Purpose Low Density Medium Density High Density NHW NHO The tables below show the results of trip distribution calibration. Table 7 in the previous section shows the average trip distances before model calibration, compared to the survey results. Table 11 shows the results after calibration within the mix density bins for the home based purposes. The average distances are very good overall. The college purpose is difficult to match due to limitations in the survey data. College students are usually under-represented in surveys and a small percentage of the population. n. Table 12 shows the coincidence indexes for the mix density bins. The column on the left for each purpose is the original coincidence index, and on the right is the new one. As the tables show, with the improved average trip distances, some of the coincidence index values decreased. In some cases, it was not possible to match both the average trip distance and improve the coincidence index by adjusting only gamma function beta parameters. It was determined that matching trip distance was more important, so calibration continued until the trip distance matched as well as possible without making the coincidence index unreasonable. Page 19

22 Table 11: Calibrated Average Trip Distance by Mix Variable Mix Variable HBW HBC HBE HBS HBO HBP Survey Model Survey Model Survey Model Survey Model Survey Model Survey Model Low Medium Low Medium High High Average Table 12: Coincidence Index by Mix Variable HBW HBC HBE HBS HBO HBP Origin al Calibra ted Origin al Calibra ted Origin al Calibra ted Origin al Calibra ted Origin al Calibra ted Origina l Calibr ated Mix Variable Low 57% 57% 15% 15% 58% 56% 49% 48% 70% 69% 58% 58% Medium Low 87% 87% 64% 64% 79% 75% 81% 78% 85% 85% 84% 84% Medium High 72% 72% 59% 59% 84% 76% 80% 83% 75% 77% 78% 78% High 67% 67% 47% 45% 50% 62% 62% 80% 75% 76% 48% 47% Average 87% 87% 68% 67% 82% 76% 85% 82% 86% 87% 89% 88% Page 20

23 The non home based purposes were treated the same as the home based purposes. The results are in Table 13 and Table 14. The average distances were calibrated to match very well for nonhome based work trips. The non-home based other trip purpose does not match as well, but since these trips are connected to home-based trips in a tour, they are influenced by factors other than employment density. These trips also do not have as many observations in the survey or model as other purposes. Table 13: Calibrated Average Trip Distance by Employment Density WO OO Survey Model Survey Model Employment Density Low Medium High Average Table 14: Coincidence Index by Employment Density WO OO Original Calibra ted Original Calibra ted Employment Density Low 61% 80% 86% 89% Medium 79% 83% 84% 83% High 56% 61% 54% 50% Average 87% 86% 89% 94% Mode Choice Model Estimation Mode choice analysis involved two separate tasks. First, mode choice models were re-estimated with 4D variables to determine whether the estimation would yield reasonable and significant effects for those variables. Second, the existing mode choice model was analyzed and compared to survey data to determine if recent calibration yielded reasonable comparisons to survey data when segmented on 4D variable bins. The next section describes the estimation work first, then the calibration task. It should be noted that the results of the estimation work were not ultimately used in the final model, as discussed below. PB staff previously estimated a mode choice model for SANDAG in The mode choice estimation for the 4D project built off of the estimation files prepared for that work for the home based work (HBW), home based other (HBO) and non home based (NHB) purposes. The 4D variables were interacted with the alternative specific constants for various modes to determine whether there is a significant relationship between mode choice and urban form. The 4D variables were tested a variety of ways, first as continuous variables and then in the density bins (high, medium, low) evaluated for trip distribution. The analysis focused primarily on transit and non-motorized modes. For HBW and HBO, the dwelling unit and intersection density variables were applied at the production end (the home end), and employment density was applied at the attraction end. For NHB, dwelling unit density was not tested. Page 21

24 Home Based Work For the HBW purpose, there was a limited effect of urban form on the nonmotorized trips. Only the high density intersections at the production end had a significant effect. The effect has a positive sign, which makes sense because locations with high densities of intersections are in the downtown area and there are plenty of attractors within walking or biking distance. For the transit alternatives, there was significance for all three of the density variables tested. The medium and high density categories were significant, and had a small positive effect. In order to measure the effect of interactions between all the 4D variables on mode choice, 2-way and 3-way interaction terms were added to the utility equations and the models were re-estimated. In order to ensure that the model converged, the single interactions were held constant from a previous run. All interaction terms were significant and negatively signed, indicating that simply adding effects for each 4D variable independently can result in an over-estimation of elasticity with respect to density. Table 15 shows the results of the estimation. Table 15: HBW Mode Choice Estimation Results Variable Coeff T-Stat In-Vehicle Time Cost Low Medium-High Very High NA/Imputed Generic ASCs Shared-Ride Shared NonMotorized Bike Transit Express Bus Commuter Bus LRT Commuter Rail Transit Drive Transit KNR Drive-Express Drive-Commuter Bus Drive-LRT Drive-Commuter Rail ASCs by Auto Own Shared-Ride 0 auto Shared-Ride 3+ 1 auto Non-Motorized 0 auto Non-Motorized 1 auto Transit 0 auto Transit 1 auto KNR 0 auto KNR 1 auto Page 22

25 Variable Coeff T-Stat Out-Vehicle Time Transit Access Time Walk Time Wait Time First Wait Transfer Wait Bike Mode Time rat_pr_sup Knrexp Knrcom Knrlrt Knrhea tdrva D Variables High Density Intersection - Non-motorized Med Density Employment Transit 1.11 High Density Employment Transit 0.86 Med Density Dwelling Unit Density Transit 0.58 High Density Dwelling Unit Density Transit 0.87 Med Density Intersection Transit 0.48 High Density Intersection Transit D Two Way Interactions Med Density Dwelling Unit, Med Density Employment Transit Med Density Dwelling Unit, High Density Employment Transit High Density Dwelling Unit, Med Density Employment Transit High Density Dwelling Unit, High Density Employment Transit D Three Way Interactions Med Density Dwelling Unit, Med Den Intersection, Med Density Employment - Transit Med Density Dwelling Unit, Med Den Intersection, High Density Employment - Transit Med Density Dwelling Unit, High Den Intersection, Med Density Employment - Transit Med Density Dwelling Unit, High Den Intersection, High Density Employment - Transit High Density Dwelling Unit, Med Den Intersection, Med Density Employment - Transit High Density Dwelling Unit, Med Den Intersection, High Density Employment - Transit High Density Dwelling Unit, High Den Intersection, Med Density Employment - Transit High Density Dwelling Unit, High Den Intersection, High Density Employment - Transit Table 16 shows the interaction effects of the 4D variables in combination with each other for walk-transit modes. The interaction effect is measured in the equivalent minutes of in-vehicle time. Table 16 contains the unique results of each potential combination of dwelling unit, employment, and intersection density. In general, as the density categories become higher, the equivalent minutes of in-vehicle time becomes more negative, which indicates an increase in utility for walk-transit. This is best seen as the intersection density increases from low to high. For example, there is no time saving for the low density in all categories, but there is a time savings of 13 minutes when intersection density increases to medium, and a savings of 18 Page 23

26 minutes when intersection density increases to high. However, the interaction effects are not necessarily monotonically increasing with respect to density. For example, within the high dwelling unit density category, the medium employment density category has a higher equivalent bonus to utility (49 minutes) than the high employment density category (11 minutes). Such relationships caused the project team some concern in using the results of the estimation in the final mode choice model. Table 16: Equivalent Minutes of Travel Time for Walk-Transit by 4D Variable Combination, HBW Purpose Low Density DU Employment Intersection Low Den Emp Med Den Emp High Den Emp Low Den Int Med Den Int High Den Int Med Density DU Employment Intersection Low Den Emp Med Den Emp High Den Emp Low Den Int Med Den Int High Den Int High Density DU Employment Intersection Low Den Emp Med Den Emp High Den Emp Low Den Int Med Den Int High Den Int Home Based Other The Home Based Other results were similar to HBW. However, there was a negative effect for the high density employment on non-motorized, and a positive effect for the high density intersections on non-motorized. The medium and high density categories had positive and significant effects for transit. The originally estimated parameters were held constant and 4D interaction parameters were allowed to float in order to study the interaction effects. The results of the estimation are in Table 17 and the summary of the interaction effects are in Table 18. Page 24

27 Table 17: HBO Mode Choice Estimation Results Variable Coeff T-Stat In-Vehicle Time Cost Transit Access Time Walk Time First Wait Transfer Wait D Variables High Density Employment - Non-motorized High Density Intersection - Non-motorized Med Density Employment Transit 0.57 High Density Employment Transit 0.65 Med Density Dwelling Unit Density Transit 0.61 High Density Dwelling Unit Density Transit 1.13 Med Density Intersection Transit 0.55 High Density Intersection Transit D Three Way Interactions Med Density Dwelling Unit, Med Density Intersection, Med Den Employment - Transit Med Density Dwelling Unit, Med Density Intersection, High Den Employment - Transit Med Density Dwelling Unit, High Density Intersection, Med Den Employment - Transit Med Density Dwelling Unit, High Density Intersection, High Den Employment - Transit High Density Dwelling Unit, Med Density Intersection, Med Den Employment - Transit High Density Dwelling Unit, Med Density Intersection, High Den Employment - Transit High Density Dwelling Unit, High Density Intersection, Med Den Employment - Transit High Density Dwelling Unit, High Density Intersection, High Den Employment - Transit Table 18: HBO Mode Choice Estimation Results Interaction Effects Low Density DU Employment Intersection Low Den Emp Med Den Emp High Den Emp Low Den Int Med Den Int High Den Int Med Density DU Employment Intersection Low Den Emp Med Den Emp High Den Emp Low Den Int Med Den Int High Den Int High Density DU Employment Intersection Low Den Emp Med Den Emp High Den Emp Low Den Int Med Den Int High Den Int Page 25

28 Non Home Based The non home based purpose did not show significance for the dwelling unit density, which is reasonable considering that these trips do not have anything to do with the home location. Therefore, only the employment and intersection density variables were included and tested for interaction effects. As with the other two purposes, originally estimated parameters were held constant and 4D interaction parameters were allowed to float, but this time as a two way interaction instead of a three way. The interaction effects for NHB are very high, particularly for the high density to high density group, probably due to the fact that there are few observations for this purpose. The results of the estimation are in Table 19 and the summary of interaction effects are in Table 20. Table 19: NHB Mode Choice Estimation Results Variable Coeff T-Stat In-Vehicle Time Cost Walk Time Bike Mode Time D Variables High Density Intersection - Non-motorized Med Density Emp Transit 0.34 High Density Emp Transit 0.79 Med Density Dwelling Unit Density Transit High Density Dwelling Unit Density Transit Med Density Intersection Transit 0.40 High Density Intersection Transit D Two Way Interactions Med Density Intersection, Med Density Employment Transit Med Density Intersection, High Density Employment Transit High Density Intersection, Med Density Employment Transit High Density Intersection, High Density Employment Transit Table 20: NHB Mode Choice Estimation Results Interaction Effects Employment Intersections Low Den Emp Med Den Emp High Den Emp Low Den Int Med Den Int High Den Int Due to some of the problems with the estimation results, such as interaction effects that were not consistently increasing across density categories, and very high parameter values for certain density categories, the project team concluded that a more data-focused calibration method would be preferred for incorporating 4D effects in mode choice. That work is described below. Mode Choice Calibration First, mode choice model outputs from the existing mode choice model (recently calibrated for the Mid-Coast Transit Study) were summarized by 4D variable and compared to observed data Page 26

29 assembled from the 2006 household survey (auto and non-motorized trips) and the 2004 transit on-board survey (transit trips). The initial comparison showed large differences in the results. This was because the mode choice model was initially calibrated with constants for Central Business District and Golden Triangle area. Including those constants in addition to the 4D constants would potentially overestimate the effect of these high density areas on the travel patterns in the model. The CBD and Golden Triangle constants were therefore removed before adding in the 4D variables. The results showed a need for further model calibration to match the mode share observed by density bin. The model constants were estimated by calculating the natural log of the observed share over the estimated share, by purpose and density bin. Within each density bin, the constant was constrained to 30 equivalent minutes of In-Vehicle Time (IVT). For any combination of bins, it was constrained to 45 minutes of IVT. The constraints ensured reasonable elasticities with respect to density. Constants were only calculated so that the contribution of the constant could result in increasing utility across the range of density categories for any given trip purpose. Several rounds of mode choice calibration were conducted. Results from the calibration are contained in the tables below. Table 21: Mode Choice Calibration, Household Density Transit Auto Non-Motorized Purpose Household Density Survey Model Survey Model Survey Model HBW HBU HBC HBO NHB SP 1 3.7% 3.6% 95.3% 95.5% 1.0% 0.9% 2 8.2% 8.1% 89.2% 89.4% 2.6% 2.5% % 17.9% 69.3% 74.2% 9.0% 7.9% 1 4.9% 5.6% 93.7% 93.2% 1.4% 1.2% % 14.0% 84.3% 83.8% 1.5% 2.2% % 21.3% 75.5% 74.8% 3.2% 3.9% 1 1.2% 1.0% 78.1% 77.4% 20.7% 21.6% 2 1.9% 2.3% 63.1% 63.5% 35.0% 34.2% 3 2.2% 2.6% 38.3% 51.2% 59.5% 46.2% 1 0.8% 0.8% 96.9% 97.1% 2.3% 2.1% 2 2.8% 2.4% 90.7% 92.0% 6.4% 5.6% 3 7.6% 4.4% 83.5% 87.2% 8.9% 8.4% 1 0.2% 0.1% 99.1% 99.4% 0.7% 0.5% 2 0.4% 0.2% 97.7% 99.1% 2.0% 0.7% 3 0.9% 1.2% 96.5% 91.1% 2.6% 7.7% 1 0.0% 0.0% 99.9% 100.0% 0.1% 0.0% 2 0.2% 0.1% 99.8% 99.9% 0.0% 0.0% 3 0.0% 0.0% 100.0% 100.0% 0.0% 0.0% Table 22: Mode Choice Calibration, Employment Density Transit Auto Non-Motorized Purpose Employment Density Survey Model Survey Model Survey Model HBW 1 4.8% 3.8% 92.9% 94.4% 2.3% 1.8% 2 5.0% 5.3% 94.1% 93.6% 0.9% 1.1% Page 27

30 HBU HBC HBO NHB SP % 17.0% 83.1% 78.5% 2.8% 4.5% 1 5.0% 4.3% 93.5% 94.5% 1.6% 1.1% 2 9.9% 13.9% 89.2% 83.6% 0.9% 2.5% % 35.2% 50.5% 60.9% 4.9% 3.9% 1 1.0% 1.3% 73.1% 72.6% 25.9% 26.1% % 3.2% 57.8% 72.2% 31.6% 24.6% 3 1.4% 1.4% 98.6% 65.9% 0.0% 32.7% 1 0.1% 1.0% 96.1% 95.6% 3.8% 3.4% 2 3.3% 1.8% 94.5% 95.7% 2.2% 2.4% % 6.8% 79.0% 85.2% 3.1% 8.0% 1 0.0% 0.1% 99.0% 99.5% 1.0% 0.4% 2 0.5% 0.3% 98.4% 99.0% 1.1% 0.6% 3 2.3% 1.9% 95.7% 88.2% 2.1% 9.9% 1 0.0% 0.0% 99.9% 100.0% 0.1% 0.0% % 0.0% 99.6% 100.0% 0.0% 0.0% 3 0.0% 0.0% 100.0% 100.0% 0.0% 0.0% Table 23: Mode Choice Calibration, Intersection Density Transit Auto Non-Motorized Purpose Intersection Density Survey Model Survey Model Survey Model HBW HBU HBC HBO NHB SP 1 3.3% 3.2% 95.8% 95.7% 1.0% 1.0% 2 7.7% 7.0% 90.8% 91.5% 1.5% 1.5% % 14.4% 77.7% 79.8% 6.8% 5.8% 1 5.7% 5.7% 93.0% 92.9% 1.3% 1.4% 2 8.1% 9.8% 90.8% 89.0% 1.1% 1.2% % 19.9% 72.1% 76.8% 3.9% 3.3% 1 0.8% 0.8% 77.0% 76.2% 22.2% 23.0% 2 2.5% 2.2% 75.3% 74.3% 22.2% 23.5% 3 2.5% 2.7% 48.9% 52.5% 48.6% 44.9% 1 0.7% 0.8% 96.8% 96.9% 2.5% 2.3% 2 1.9% 1.8% 94.2% 94.4% 3.9% 3.8% 3 5.4% 3.9% 86.1% 88.2% 8.5% 7.8% 1 0.1% 0.1% 99.1% 99.4% 0.7% 0.5% 2 0.3% 0.2% 98.5% 99.1% 1.2% 0.7% 3 0.7% 0.8% 96.5% 94.6% 2.7% 4.6% 1 0.0% 0.0% 99.9% 100.0% 0.1% 0.0% 2 0.2% 0.0% 99.8% 100.0% 0.0% 0.0% 3 0.0% 0.0% 100.0% 100.0% 0.0% 0.0% The results of model calibration show that the model is close to matching the survey in almost all cases. There are a few exceptions, which are mostly the result of capping the maximum benefit at a reasonable 30 minutes of in-vehicle time. For example, the transit mode share for the HBO Employment Density in the highest density category is less than half of the survey result. It Page 28

31 would probably match better if the constants were not capped, but the project team felt that a maximum benefit of 30 minutes for any density bin (and 45 minutes for any combination of density bins) was necessary to ensure that 4D elasticities are reasonable. Within the household and intersection density bins, the transit and non-motorized shares are very close overall. All trip purposes match closely within household density, with the exception of HBO, which still underestimates transit trips in the high density bin. However, that purpose already has a very high alternative specific constant. For intersection density, HBW, HBC and HBO are all performing very well. By employment bins, the HBU transit trips are also better. HBO and NHB are improved, but are not as good as they could be if the benefits from the constant were not capped. Model Application In order to test the effects of model enhancements on Smart Growth policies, a number of model applications were performed. The land use scenarios tested included a 2008 base scenario, a 2030 Existing Policies (EP) scenario, and a 2030 Smart Growth (SG) scenario. All land use scenarios were run using the Existing Policies (EP) highway and transit network scenario from the 2030 Regional Transportation Plan (RTP): Pathways for the Future (approved in 2007). The four scenarios were run through the version of the model used for the 2007 RTP (Series 11, version 2), the current working version of the model (Series 11, version 3), and a 4Dimplemented version of the model to compare model results. Scenario Development Compared to the Realistic Expectations scenario, the Smart Growth scenario is a fairly conservative application of Smart Growth Policies. To create the SG scenario, SANDAG staff added population and employment growth to the identified Smart Growth locations. It was not a full visionary type of land use forecast. There was no change to intersection density for this application. Due to the conservative nature of the scenario, large differences were not expected. As part of the scenario testing, 4D variables were analyzed and plotted for each scenario and compared to 2008 base-year data. Table 24 contains a summary of the number of MGRAs in each density bin and summary statistics for the minimum, maximum and average density of the variable in all MGRAs. Dwelling unit density shows an increased number of MGRAs in both the medium and high density categories. The maximum density also increases in the Smart Growth scenario. Employment density shows a large increase in the medium density category, but actually shows a small decrease in the high density category between the 2030 and 2030 Smart Growth scenarios. The maximum density stays the same. Page 29

32 Table 24: Number of MGRAs by Density Bins Intersection Density EP 2030 SG EP SG Low 21,755 21,134 21, % -2.9% medium 6,682 7,083 7, % 6.0% high 4,928 5,148 5, % 4.5% EP 2030 SG EP SG Min max % 24.5 average % 2.9% Dwelling Unit Density EP 2030 SG EP SG Low 24,878 23,338 22, % -10.2% medium 6,232 7,014 7, % 26.3% high 2,255 3,013 3, % 39.3% EP 2030 SG Min max % 96.8% average % 25.0% Employment Density EP 2030 SG EP SG Low 30,662 30,233 30, % -2.0% medium 2,194 2,548 2, % 25.5% high % EP 2030 SG Min max % 37.8% average % 25.0% The maps in Figure 5 and Figure 6 show the difference in density between dwelling units and employment in 2030 and the 2030 Smart Growth scenario at the MGRA level. The maps show that while the Smart Growth scenario is not dramatic, there are definitely some differences particularly in the most urban areas. Intersection density was not included since it did not have any changes in the Smart Growth scenario. Page 30

33 Figure 5: Dwelling Unit Density by MGRA, 2030 and 2030 Smart Growth Page 31

34 Figure 6: Employment Density by MGRA, 2030 and 2030 Smart Growth Page 32

35 The summaries of the number of TDZs were prepared for the mix index density and employment density. These are the density measures used for trip distribution, which is implemented at the TDZ level. Table 25 shows those results. In both the EP and SG scenario, there is an increase in the number of TDZs in the medium high and high density bins for the mix variable, as expected. There is a greater increase in the SG than in the EP. The TDZ statistics also show an increase in the maximum and the average for the Smart Growth scenario. Employment density increases for the highest category, but has the same maximum and average. Table 25: Number of TDZs by Density Bins Mix Density EP 2030 SG EP SG Low % -18.6% medium low 1,066 1, % -9.8% medium high % 14.0% high % 46.0% EP 2030 SG EP SG min max 60,188 94,811 98, % 64.4% average 2,590 3,474 3, % 42.7% Employment Density EP 2030 SG EP SG Low 1,619 1,551 1, % -5.3% medium % 24.1% high % 15.7% EP 2030 SG EP SG min max % 43.0% average % 14.3% The maps in Figure 7 and Figure 8 show the difference in density between the Mix Index and the Employment density at the TDZ level. The maps show that while the Smart Growth scenario is not dramatic, there are definitely some differences, particularly in the most urban areas. Page 33

36 Figure 7: Mix Index Density by TDZ, 2030 and 2030 Smart Growth Page 34