Prepared for: San Diego Association Of Governments 401 B Street, Suite 800 San Diego, California 92101

Transcription

1 Activity-Based Travel Model Validation for 2012 Using Series 13 Data: Coordinated Travel Regional Activity Based Modeling Platform (CT-RAMP) for San Diego County Prepared for: San Diego Association Of Governments 401 B Street, Suite 800 San Diego, California Prepared by: PB Americas, Inc nd Avenue, Suite 700 North San Francisco, CA November 2013

2 Table of Contents Table of Contents... i List of Tables... ii List of Figures... iii 1. Introduction General Findings... 5 Mode Choice... 5 Cross Border Calibration at Tecate... 7 Assignment: Model Year 2012 Validation... 7 Highway Validation... 7 Highway Validation Summary After Adjustments:... 9 Transit Validation Transit Boarding Summaries Conclusions i -

3 List of Tables Table 1. Tour Mode Choice HOV and Toll Constants... 5 Table 2. Trip Mode Choice HOV and Toll Constants... 5 Table 3. Tour Mode Choice Comparisons Before and After HOV/Toll Constants Revisions... 5 Table 4. Trip Mode Choice Comparisons Before and After HOV/Toll Constants Revisions... 6 Table 5: Tecate Border Crossing Tours and Adjustment Factor... 7 Table 6. Comparison of Daily Traffic by MSA Before Adjustments... 7 Table 7. Daily Traffic at Key Count Locations Before Adjustments... 8 Table 8. Comparison of Daily Traffic by MSA After Adjustments Table 9. Daily Traffic at Key Count Locations After Adjustments Table 10. Percent RMSE by MSA After Adjustments Table 11. Daily Screenline Comparisons After Adjustments Table 12. Daily Screenline Comparisons by Link ID After Adjustments Table 13. Daily Traffic on SR Table 14: Final Transit Boardings by Mode Table 15: Comparison of Transit Boardings by Mode Table 16: Estimated Transit Boardings - Access Mode and Line Haul Mode by Aggregate Mode Table 17: Transit Boardings by Mode Table 18: Comparison of Transit Trip Mode by Model Component ii -

4 List of Figures Figure 1. SANDAG Key Count Locations Figure 2. Scatterplot of Daily Observed Counts by Daily Estimated Volumes and by Count Source. 25 Figure 3: SANDAG Screenline Map Figure 4: AM Period Drive Alone Toll Trip Travel Time Savings to La Mesa (blue star) (Drive Alone Non-toll Time Drive Alone Toll Time) Figure 5: AM Period Drive Alone Toll Cost to La Mesa (blue star) Figure 6: AM Period Drive Alone Toll Trip Travel Time Savings to SR-125 at Otay Lakes (blue star) (Drive Alone Non-toll Time Drive Alone Toll Time) Figure 7: AM Period Drive Alone Toll Cost to SR-125 at Otay Lakes (blue star) Figure 8: AM Period Drive Alone Toll Trip Travel Time Savings to SR-125 at Otay Mesa (blue star) (Drive Alone Non-toll Time Drive Alone Toll Time) Figure 9: AM Period Drive Alone Toll Cost to SR-125 at Otay Mesa (blue star) iii -

5 1. Introduction This document describes the San Diego Association of Governments (SANDAG) Activity-Based Model (ABM) validation for The two available sources of data for validation were: traffic counts from CalTrans, PeMS Traffic Research Associates (TRA), and local jurisdictions. Counts were also available for SR125 (toll road) transit boardings by time of day and route from SANDAG s Passenger Counting Program. There were several components of the model that were updated or changed from the 2010 validation. They are listed below: New 2012 inputs (series 13 land-use data, and 2012 highway and transit networks) Constants for HOV in tour and trip mode choice were set to 0. Constants for the toll mode were set to 0 for work and university tour purposes, while the toll constant was set to -20 minutes of equivalent in-vehicle time for tour mode choice, and - 10 minutes of equivalent in-vehicle time for trip mode choice for all other tour purposes. Auto operating costs were inconsistent for the Accessibilities and BestTransitPath uecs. Both UECS were reset to be consistent with the auto operating costs in the tour and trip mode choice UECs. The commercial vehicle calibration factor was decreased from 1.75 to airport enplanements were increased from 11,441,719 in 2010 to 11,673, airport transfers were increased from 463,624 in 2010 to 473,027 in Total border crossings were lowered from 123,276 in 2010 to 96,639, and the Tecate border crossing target was set to 4,263. The external-external and external-internal control totals were scaled up with a growth factor of The 2012 validation described in this report focuses on the bulleted list of items above. The document SANDAG Activity Based Model Calibration and Validation describes the other model components not changed or updated in this report and Activity Based Travel Model Validation for 2010 Using Series 13 Data describes the 2010 validation. For more detailed information on the design and estimation of the models, refer to the document SANDAG Activity Based Model Specifications and SANDAG Activity Based Model Estimation, respectively

6 2. General Findings Mode Choice HOV and toll constants in the tour and trip mode choice were revised. Constants for HOV in tour and trip mode choice were set to 0. Constants for the toll mode were set to 0 for work and university tour purposes, while toll constant was set to 20 minutes in equivalent minutes of invehicle time for tour mode choice, and 10 minutes in equivalent minutes of in-vehicle time for trip mode choice for all other tour purposes (see Table 1 and Table 2 below). These changes were performed to be consistent with the toll mode choice diversion calibration performed several years ago for the trip-based model. The results in Table 3 show that the revisions decreased the number of HOV tours, and increased the number of toll tours. Table 4 shows that the revisions added slightly more HOV and toll trips but the overall distribution by mode remained the same. Table 1. Tour Mode Choice HOV and Toll Constants in Equivalent Minutes of In-vehicle Time Constant Work University School Maintenance Discretionary At-Work Old Constants HOV TOLL New Constants HOV TOLL Table 2. Trip Mode Choice HOV and Toll Constants in Equivalent Minutes of In-vehicle Time Constant Work University School Maintenance Discretionary At-Work Old Constants HOV TOLL New Constants HOV TOLL Table 3. Tour Mode Choice Comparisons Before and After HOV/Toll Constants Revisions Before Revisions After Revisions Difference Tour Mode # tours Percent # tours Percent # tours Percent Drive Alone Free 1,381, % 1,361, % -20, % Drive Alone Pay 90, % 100, % 10, % Shared 2 Free 767, % 753, % -13, % Shared 2 HOV 56, % 50, % -5, % Shared 2 Pay 3, % 3, % % Shared 3+ Free 770, % 757, % -13, % Shared 3+ HOV 39, % 35, % -3, % Shared 3+ Pay 1, % 1, % % Walk 347, % 344, % -3, % Bike 35, % 34, % % Walk to Local 40, % 41, % % - 5 -

7 Walk to Express % % 8 0.0% Walk to Light Rail 25, % 25, % 8 0.0% Walk to Commuter Rail % % 4 0.0% Park-Ride to Local 1, % 1, % % Park-Ride to Express % % 7 0.0% Park-Ride to Light Rail 10, % 10, % % Park-Ride to Commuter Rail 1, % 1, % % Kiss-Ride to Local 2, % 2, % % Kiss-Ride to Express % % % Kiss-Ride to Light Rail 5, % 5, % % Kiss-Ride to Commuter Rail % % % School Bus 76, % 77, % % Total 3,659, % 3,610, % -48, % Table 4. Trip Mode Choice Comparisons Before and After HOV/Toll Constants Revisions Before Revisions After Revisions Difference Trip Mode # trips Percent # trips Percent # trips Percent Drive Alone Free 4,963, % 4,829, % -134, % Drive Alone Pay 47, % 130, % 82, % Shared 2 Free 1,808, % 1,736, % -71, % Shared 2 HOV 22, % 56, % 33, % Shared 2 Pay 1, % 3, % 2, % Shared 3+ Free 1,246, % 1,205, % -40, % Shared 3+ HOV 10, % 27, % 17, % Shared 3+ Pay % 1, % % Walk 864, % 856, % -7, % Bike 78, % 77, % -1, % Walk to Local 90, % 91, % % Walk to Express % % % Walk to Light Rail 51, % 51, % % Walk to Commuter Rail % % % Park-Ride to Local 3, % 3, % % Park-Ride to Express % % % Park-Ride to Light Rail 15, % 15, % % Park-Ride to Commuter Rail 2, % 2, % % Kiss-Ride to Local 3, % 3, % % Kiss-Ride to Express % % 2 0.0% Kiss-Ride to Light Rail 7, % 7, % % Kiss-Ride to Commuter Rail % % 3 0.0% School Bus 153, % 154, % % Total 9,377, % 9,258, % -118, % - 6 -

8 Cross Border Calibration at Tecate The Tecate point of entry target was 4,263 but the model was over-estimating crossings at this entry, so the Tecate constant needed to be recalibrated. Table 5 below shows the target tours and final calibrated tours and adjustment factor for the Tecate crossing. Table 5: Tecate Border Crossing Tours and Adjustment Factor Tecate Tours Adjustment Factor Observed Crossings 4,263 n/a Initial 2012 Crossings 7, Final Calibrated Crossings 4, Assignment: Model Year 2012 Validation This section presents the highway and transit assignment results for the year 2012, with comparisons to observed data from the Caltrans highway traffic counts, PEM databases, TRA count databases, and arterial count databases 1 and reported transit operator system boardings in Highway Validation The 2010 highway assignment showed that VMT matched observed counts well (2.9% different) 2. However, the initial 2012 highway assignment showed that the VMT was over by 5.8% (see Table 6). The MSA with the highest percent difference is East County. However this MSA only accounted for 0.2% of the total observed volumes for links with counts 3. A closer look at the volumes at several key count locations showed that most over-estimated volumes were over by 25 to 45% (see Table 7). So the commercial vehicle calibration factor was reduced further from 1.75 to 1.4. Table 6. Comparison of Daily Traffic by MSA Before Adjustments Observed Estimated MSA Count Volume Difference % Difference Center City 1,617,111 1,730, ,803 7% Central 12,086,035 12,479, ,352 3% North City 20,829,984 22,918,886 2,088,902 10% South Suburban 3,839,731 3,763,128 (76,603) -2% East Suburban 6,477,887 6,578, ,231 2% North County West 8,258,592 8,478, ,350 3% North County East 8,563,272 9,194, ,455 7% 1 The Caltrans State Highway Traffic database is the 2012 Caltrans Traffic Census for interstate freeways and state highways. The PEMS database is CalTrans Performance Measurement System of statewide traffic volume data collection for all major metropolitan areas in California. The TRA counts were gathered by Traffic Research and Analysis, Inc. for various screenline locations that SANDAG did not already have counts for. 2 See Activity Based Travel Model Validation for 2010 Using Series 13 Data report, November SANDAG mentioned that they factored East County volumes down in the trip based model since those roads showed too much estimated volume on it. The team decided not to factor the tour-based model since this area accounts for a small percentage of the overall traffic in the SANDAG area

9 East County 137, ,803 87,941 64% Total 61,810,474 65,369,906 3,559, % Table 7. Daily Traffic at Key Count Locations Before Adjustments Key Count Locations Facility Avg. Weekday Daily Traffic Estimated Percent Observed Estimated less Obs. Difference # 1 - I-15S at RAINBOW VALLEY BOULEVARD 135, ,395-2,609-2% # 2 - I-5N at CAMP PENDLETON 63,139 60,508-2,631-4% # 2 - I-5S at CAMP PENDLETON 63,579 60,950-2,629-4% # 3 - I-15N at VALLEY PARKWAY 97,866 99,375 1,509 2% # 3 - I-15S at VALLEY PARKWAY 96,115 99,312 3,197 3% # 4 - I-15N at CARMEL MOUNTAIN ROAD 118, , % # 4 - I-15S at CARMEL MOUNTAIN ROAD 117, ,073 9,288 8% # 5 - I-5N at CARMEL MOUNTAIN ROAD 108, ,116 2,852 3% # 5 - I-5S at CARMEL MOUNTAIN ROAD 100,162 99,011-1,151-1% # 6 - I-5N at SORRENTO VALLEY ROAD 76,679 85,010 8,331 11% # 6 - I-5S at SORRENTO VALLEY ROAD 72,632 82,640 10,008 14% # 8 - I-15N at JCT. RTE , ,745 9,194 6% # 8 - I-15S at JCT. RTE , ,170 17,979 12% # 9 - I-52E at MAST BOULEVARD 48,132 61,220 13,088 27% # 9 - I-52W at MAST BOULEVARD 49,433 64,777 15,344 31% # 10 - I-52E at CONVOY STREET 55,802 62,134 6,332 11% # 10 - I-52W at CONVOY STREET 53,777 67,484 13,707 25% # 11 - I-5N at JCT. RTE. 8/CAMINO DEL RIO 104, ,796-2,147-2% # 11 - I-5S at JCT. RTE. 8/CAMINO DEL RIO 102,264 98,724-3,540-3% # 12 - I-805N at SAN YSIDRO BLVD 26,838 35,408 8,570 32% # 12 - I-805S at SAN YSIDRO BLVD 30,277 37,604 7,327 24% # 13 - I-5N at SOUTH JCT. RTE ,461 24,327 7,866 48% # 13 - I-5S at SOUTH JCT. RTE ,442 19, % - 8 -

10 Highway Validation Summary After Adjustments: After running the model with the commercial vehicle factor adjustment and the scaled externalexternal and external-internal control totals, the 2012 highway assignment showed that the VMT was very close, only slightly overestimated by 0.9% (see Table 8). The daily observed versus estimated at key count locations matched better after decreasing the commercial vehicle factor to 1.4. See Figure 1 for map of key count locations. The percent RMSE for most MSAs except Center City and East County was under 30% which is excellent. These two had the fewest counts available. Center City s percent RMSE was 39% which is still good, and East County s was 57.8% but as was mentioned above this MSA usually attracts too many trips on its facilities but also only makes up a small percentage of overall traffic in the SANDAG area. Figure 2 shows the scatterplot of daily observed versus estimated volumes by count source. The points are lying nicely along the 45 degree line which shows that the model on average is matching the counts well. Table 11 shows the daily screenline comparisons. The screenlines that are shaded grey did not have any counts for them. The yellow shaded screenlines (screenlines 3, 5, 8, 9, and 18) showed a large over-estimate or large under-estimated volume (i.e. ±30%). See Figure 3 for screenline map. Table 12 shows the daily screenline comparisons by link ID. This table compares the observed count to the estimated volume only if an observed count was available for that linkid. It shows the estimated volume in the last column for all link IDs. Notice that all of the yellow shaded screenlines noted above, except screenline #9, have links with no observed counts, and the estimated volumes on these links are large. So it does not make sense to compare the observed and estimated for these screenlines. The yellow shaded rows in Table 12 highlight the links with estimated volumes greater than 20,000 that did not have an observed count. These links show a high estimated volume of traffic and one cannot compare observed versus estimated across the whole screenline if there is no observed count for such links. Only screenline # 9 and # 24 have observed counts for all links within the screenline, or the estimated volume for links with no observed count is very small. Finally, Table 13 displays the comparison of daily observed versus estimated volumes on the tolled SR-125 facility. The estimated volumes on SR-125 are extremely low, and more so at the southern end of the tolled facility. This was initially found in the trip-based model where changes were made to address the low volumes, but the trip based models used different volume delay functions from the current activity based model. Figure 4, Figure 6, Figure 8 are maps of the drive alone toll travel time savings for the AM period from all MGRAs to various points along the tolled SR-125 facility. The time savings to La Mesa seem reasonable, as MGRAs near the I-15 corridor would see a travel time savings, and those near SR125 would see time savings. The map of time savings to SR-125 and Otay Lakes (middle of SR-125) seem reasonable as most MGRAs near that point would not use SR125 and would use the surrounding local streets or west to east facilities. The map of time savings to the southern end of the tolled SR-125 facility indicates that most MGRAs near the southern end would use local streets to travel to Otay Mesa to avoid the toll. Figure 5, Figure 7, and Figure 9 displays the toll costs to - 9 -

11 three chosen destinations around SR 125. The toll skims and costs look reasonable so further analysis needs to be done to determine why the volumes are low on SR Table 8. Comparison of Daily Traffic by MSA After Adjustments MSA Observed Estimated Count Volume Difference % Difference Center City 1,617,111 1,613,109-4,002 0% Central 12,086,035 11,845, ,695-2% North City 20,829,984 21,873,766 1,043,782 5% South Suburban 3,839,731 3,504, ,254-9% East Suburban 6,477,887 6,235, ,161-4% North County West 8,258,592 8,174,715-83,877-1% North County East 8,563,272 8,910, ,066 4% East County 137, ,578 57,716 42% Total 61,810,474 62,353, , % Table 9. Daily Traffic at Key Count Locations After Adjustments Key Count Locations Facility Avg. Weekday Daily Traffic Estimated Percent Observed Estimated less Obs. Difference # 1 - I-15S at RAINBOW VALLEY BOULEVARD 135, , % # 2 - I-5N at CAMP PENDLETON 63,139 60,290-2,849-5% # 2 - I-5S at CAMP PENDLETON 63,579 60,785-2,794-4% # 3 - I-15N at VALLEY PARKWAY 97,866 98, % # 3 - I-15S at VALLEY PARKWAY 96,115 96, % # 4 - I-15N at CARMEL MOUNTAIN ROAD 118, , % # 4 - I-15S at CARMEL MOUNTAIN ROAD 117, ,157 5,372 5% # 5 - I-5N at CARMEL MOUNTAIN ROAD 108, ,391-3,873-4% # 5 - I-5S at CARMEL MOUNTAIN ROAD 100,162 93,374-6,788-7% # 6 - I-5N at SORRENTO VALLEY ROAD 76,679 83,042 6,363 8% # 6 - I-5S at SORRENTO VALLEY ROAD 72,632 80,295 7,663 11% # 8 - I-15N at JCT. RTE , ,503 3,952 2% # 8 - I-15S at JCT. RTE , ,410 14,219 9% # 9 - I-52E at MAST BOULEVARD 48,132 59,405 11,273 23% # 9 - I-52W at MAST BOULEVARD 49,433 63,208 13,775 28% # 10 - I-52E at CONVOY STREET 55,802 59,526 3,724 7% # 10 - I-52W at CONVOY STREET 53,777 64,651 10,874 20% # 11 - I-5N at JCT. RTE. 8/CAMINO DEL RIO 104,943 98,828-6,115-6% # 11 - I-5S at JCT. RTE. 8/CAMINO DEL RIO 102,264 95,121-7,143-7% # 12 - I-805N at SAN YSIDRO BLVD 26,838 30,386 3,548 13% # 12 - I-805S at SAN YSIDRO BLVD 30,277 32,750 2,473 8% # 13 - I-5N at SOUTH JCT. RTE ,461 19,904 3,443 21% # 13 - I-5S at SOUTH JCT. RTE ,442 16, % 4 A more detailed analysis of SR125 is currently being done under the ABM Maintenance contract

12 Table 10. Percent RMSE by MSA After Adjustments MSA % RMSE Count Center City 39.3% 114 Central 24.7% 424 North City 27.6% 619 South Suburban 30.6% 149 East Suburban 25.3% 223 North County West 31.5% 210 North County East 27.8% 282 East County 57.8% 39 Total 28.6% 2060 Table 11. Daily Screenline Comparisons After Adjustments Screenline Avg. Weekday Daily Traffic Estimated Percent Observed Estimated less Obs. Difference 1 137, ,256 (1,524) -1.1% % 3 182, ,568 (68,158) -37.3% % 5 31,300 50,236 18, % 6 252, ,802 83, % % 8 12,200 12, % 9 6,585 11,067 4, % , ,898 50, % 11 33,000 31,874 (1,126) -3.4% , ,108 56, % , ,970 (28,164) -8.4% , ,275 (9,925) -8.2% , ,940 (36,423) -14.8% % , ,509 (20,558) -12.9% 18 13,900 19,038 5, % , ,653 (32,617) -7.6% , ,658 (1,432) -1.2% , ,148 5, % , ,343 3, % , ,474 (935) -0.4% 24 10,035 6,804 (3,231) -32.2%

13 Table 12. Daily Screenline Comparisons by Link ID After Adjustments Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,504 5 Estimated volumes in this table are only shown where there is an observed count

14 Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume , , , , , , , , , , , , , , , , , , , , , , , , , , , ,

15 Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,

16 Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,

17 Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,

18 Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume , , , , , , , , , , ,253, , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,

19 Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,

20 Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,

21 Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,

22 Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,

23 Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,

24 Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume , Grand Total ,780,924 Table 13. Daily Traffic on SR-125 SR125 Intersection Average Weekday Observed Daily Traffic Estimated Estimated less Observed Percent Difference North to South SR54 - San Miguel 11,453 4,690 (6,763) -59% San Miguel to East H Street 10,327 2,919 (7,408) -72% East H Street to Otay Lakes 8,723 2,499 (6,224) -71% Otay Lakes to Olympic Pkwy 6, (5,990) -97% Olympic Pkwy to Birch 4, (4,647) -97% Birch to Otay Mesa 3, (3,733) -98% South to North Otay Mesa to Birch 3, (3,854) -98% Birch to Olympic Pkwy 4, (4,651) -97% Olympic Pkwy to Otay Lakes 7, (6,902) -97% Otay Lakes to East H Street 10,451 3,729 (6,722) -64% East H Street to San Miguel 12,342 5,892 (6,450) -52%

25 Figure 1. SANDAG Key Count Locations

26 MODEL ESTIMATED Figure 2. Scatterplot of Daily Observed Counts by Daily Estimated Volumes and by Count Source Degree Line ARTERIALS PEMS COUNT CALTRANS TRA COUNTS OBSERVED COUNTS

27 Figure 3: SANDAG Screenline Map

28 Figure 4: AM Period Drive Alone Toll Trip Travel Time Savings to La Mesa (blue star) (Drive Alone Non-toll Time Drive Alone Toll Time)

29 Figure 5: AM Period Drive Alone Toll Cost to La Mesa (blue star)

30 Figure 6: AM Period Drive Alone Toll Trip Travel Time Savings to SR-125 at Otay Lakes (blue star) (Drive Alone Non-toll Time Drive Alone Toll Time)

31 Figure 7: AM Period Drive Alone Toll Cost to SR-125 at Otay Lakes (blue star)

32 Figure 8: AM Period Drive Alone Toll Trip Travel Time Savings to SR-125 at Otay Mesa (blue star) (Drive Alone Non-toll Time Drive Alone Toll Time)

33 Figure 9: AM Period Drive Alone Toll Cost to SR-125 at Otay Mesa (blue star)

34 Transit Validation Transit Boarding Summaries Overall, total estimated boardings match well (-2%). The estimated boardings matched well for the all modes except express bus. Table 14: Final Transit Boardings by Mode Aggregate Mode Observed Boardings Modeled Boardings Difference % Difference Local 216, ,901 7,466 3% Express 1,430 1,034 (396) -28% Light Rail 123, ,517 (15,212) -12% Commuter Rail 5,482 5,472 (10) 0% Total 347, ,924 (8152) 0% Table 15 shows the comparison of observed versus estimated boardings by peak and off-peak periods. Local bus was slightly over-estimated in the peak (14%), while the off-peak was right on (- 5%). Express boardings were under-estimated by -24% in the peak contributing mostly to the daily underestimate. Light rail boardings were slightly under-estimated (-11, -14% respectively for peak and off-peak) while the commuter rail boardings matched well (3, -7% respectively for peak and off-peak). Table 15: Comparison of Transit Boardings by Mode Aggregate Mode Observed Boardings Modeled Boardings Difference % Difference Peak Off Peak Peak Off Peak Peak Off Peak Peak Off Peak Local 96, , , ,149 13,144 (5,678) 14% -5% Express 1, ,034 - (333) (63) -24% -100% Light Rail 59,030 64,700 52,806 55,711 (6,224) (8,989) -11% -14% Commuter Rail 3,773 1,709 3,884 1, (121) 3% -7% Total 160, , , ,448 6,698 (14,850) 4% -8% Table 16 shows the transit boardings by access mode and line haul mode by aggregate mode. Notice that for premium express boardings, there were boardings (56%) coming from the light rail access mode. The express bus riders transferring to and/or from LRT should be examined further in the next task order to determine if these are likely paths. Table 16: Estimated Transit Boardings - Access Mode and Line Haul Mode by Aggregate Mode Access Mode and Aggregate Mode Total

35 Trip Table Line Haul Mode 10-Local 4-Coaster 5-Sprinter /Trolley 8-Premium Express 1-Walk Access 204,764 1,968 83, ,934 1-Local 131, ,459 2-Express Light Rail 70,472-82, ,966 4-Commuter Rail 2,521 1,968 1, ,918 2-Park and Ride 10,580 2,866 16, ,196 1-Local 3, ,949 2-Express Light Rail 5,303-16, ,479 4-Commuter Rail 1,196 2, ,511 3-Kiss and Ride 8, , ,794 1-Local 3, ,861 2-Express Light Rail 4,283-8, ,703 4-Commuter Rail ,170 Total 223,901 5, ,517 1, ,924 Transit boardings by route name and mode are in Table 17. The local bus mode matched well as seen above, but some of the routes were over or underestimated by over 30%. The commuter rail (Coaster) matched almost perfectly (-0.2%). The light rail route, Sprinter (Route 399) matched well at 3%, while the trolleys, (Route 510 and 530) matched satisfactorily at -13% and 12% respectively. The Route 510 and 530 boardings are lower since the total cross border tours was reduced for 2012 and thus transit tours/trips on light rail also decreased (see Table 18). And trolley route 520 was under-estimated by -31%. The estimated boardings on premium express routes were underestimated for most of the routes. Table 17: Transit Boardings by Mode Route Name observed estimated difference % Difference Local 216, ,520 4,085 2% 1 5,842 3,240 (2,602) -45% 2 5,074 2,632 (2,442) -48% 4 3,086 1,319 (1,767) -57% 5 2,646 1,556 (1,090) -41% 6 2,158 2, % 7 12,344 7,144 (5,200) -42%

36 Route Name observed estimated difference % Difference 8 1,822 1,180 (642) -35% 9 1,675 1,202 (473) -28% 10 5,401 3,484 (1,918) -36% 11 8,941 5,942 (2,999) -34% 13 7,596 6,010 (1,586) -21% % 15 5,584 5,035 (549) -10% % 20 4,468 4, % , % 27 1,470 1,407 (63) -4% 28 1,549 1, % 30 7,316 8, % % 35 1,843 1, % 41 4,676 5,998 1,322 28% 44 4,991 6,019 1,028 21% 50 1,146 1, % % % (125) -30% 105 1,449 1,159 (291) -20% 115 1,263 4,006 2, % 120 3,947 4, % 150 2,672 3, % 201 2,264 1,105 (1,160) -51% 202 2,285 1,055 (1,229) -54% (47) -18% 301 2,821 3, % 302 2,479 1,645 (835) -34% 303 4,468 4,082 (385) -9% , % 305 2,006 2, % , % %

37 Route Name observed estimated difference % Difference 309 2,204 2, % % ,267 1, % % (125) -50% % ,061 1, % ,801 1, % % % (168) -65% , % 350 2,447 2,203 (244) -10% 351 1, (57) -6% 352 1, (172) -17% , % % % % (317) -55% % % % % 701 2,579 2,521 (58) -2% 704 1,735 2, % 705 1,225 1, % % 709 4,441 6,320 1,879 42% 712 3,799 4, % 815 1, (135) -13% 816 1,645 2, % % % %

38 Route Name observed estimated difference % Difference % % 848 1,392 1, % (33) -7% 854 1,027 1, % 855 1,255 1, % 856 2,964 3, % 864 1,487 2, % % % % (115) -12% 901 3,868 6,812 2,944 76% % 905 2,116 4,295 2, % 906 4,027 4, % (98) -21% (207) -41% 921 1,665 2,700 1,035 62% 923 1,123 1, % 928 1,339 1, % 929 8,444 6,531 (1,913) -23% 932 4,837 3,128 (1,709) -35% 933 3,711 4, % 934 3,885 3, % 936 2,059 1,631 (428) -21% 955 6,519 5,367 (1,152) -18% % 961 2,476 3, % 962 1,968 1,634 (334) -17% (364) -38% , % (120) -31% % (103) -35%

39 Route Name observed estimated difference % Difference % % (22) -22% % 992 1, (466) -34% COASTER 5,482 5,472 (10) 0% 398 5,482 5,472 (10) 0% SPRINTER/TROLLEY 123, ,517 (15,212) -12% 399 8,947 9, % ,582 55,939 (8,643) -13% ,423 21,124 (9,299) -31% ,777 22,195 2,418 12% PREMIUM EXPRESS 1,430 1,034 (396) -28% (199) -28% (127) -59% (137) -80% (61) -40% % % TOTAL 347, ,543 (11,533) -3% Table 18: Comparison of Transit Trip Mode by Model Component Model Component Local Express LRT 2010 Commuter Rail Resident Airport Visitor Cross Border Internal External Resident Airport Visitor Cross Border Internal External

40 - 39 -

41 3. Conclusions The overall highway and transit assignment results remained similar to the 2010 validation. The following conclusions can be made about the 2012 validation: The overall highway VMT matched within 1% while the transit boardings matched within - 3%. The highway assignment looks better but the volumes on SR-125 need to be examined further. Perhaps the model is missing the commercial vehicle trips coming from the border since the estimated volumes at the southern end of the facility were extremely low compared to the northern end. A more detailed analysis of SR125 is currently being done under the ABM Maintenance contract. The transit assignment still looks good, but the light rail volumes are slightly more underestimated compared to And part of this is due to the reduction in the total border crossings input. There were still some traffic counts missing for many screenline links so an analysis could not be done to check for VMT across all 24 screenlines. The express bus riders transferring to and/or from LRT should be examined further to determine if this does occur