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 92101 Prepared by: PB Americas, Inc. 303 2nd Avenue, Suite 700 North San Francisco, CA 94107 November 2013
Table of Contents Table of Contents... i List of Tables... ii List of Figures... iii 1. Introduction... 4 2. 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... 33 Transit Boarding Summaries... 33 3. Conclusions... 40 - i -
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... 10 Table 9. Daily Traffic at Key Count Locations After Adjustments... 10 Table 10. Percent RMSE by MSA After Adjustments... 11 Table 11. Daily Screenline Comparisons After Adjustments... 11 Table 12. Daily Screenline Comparisons by Link ID After Adjustments... 12 Table 13. Daily Traffic on SR-125... 23 Table 14: Final Transit Boardings by Mode... 33 Table 15: Comparison of Transit Boardings by Mode... 33 Table 16: Estimated Transit Boardings - Access Mode and Line Haul Mode by Aggregate Mode... 33 Table 17: Transit Boardings by Mode... 34 Table 18: Comparison of Transit Trip Mode by Model Component... 38 - ii -
List of Figures Figure 1. SANDAG Key Count Locations... 24 Figure 2. Scatterplot of Daily Observed Counts by Daily Estimated Volumes and by Count Source. 25 Figure 3: SANDAG Screenline Map... 26 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)... 27 Figure 5: AM Period Drive Alone Toll Cost to La Mesa (blue star)... 28 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)... 29 Figure 7: AM Period Drive Alone Toll Cost to SR-125 at Otay Lakes (blue star)... 30 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)... 31 Figure 9: AM Period Drive Alone Toll Cost to SR-125 at Otay Mesa (blue star)... 32 - iii -
1. Introduction This document describes the San Diego Association of Governments (SANDAG) Activity-Based Model (ABM) validation for 2012. The two available sources of data for validation were: 1. 2012 traffic counts from CalTrans, PeMS Traffic Research Associates (TRA), and local jurisdictions. Counts were also available for SR125 (toll road). 2. 2012 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 1.4. 2012 airport enplanements were increased from 11,441,719 in 2010 to 11,673,771. 2012 airport transfers were increased from 463,624 in 2010 to 473,027 in 2012. 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 1.02028. 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. - 4 -
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 0 0 0 0 0 0 TOLL 0 0 0 0 0 0 New Constants HOV 0 0 0 0 0 0 TOLL 0 0 20 20 20 20 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 -8 0-30 -17-20 -3 TOLL -11 0-10 -6-6 0 New Constants HOV 0 0 0 0 0 0 TOLL 0 0 10 10 10 10 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 1,381,827 37.8% 1,361,070 37.7% -20,757-0.1% Drive Alone Pay 90,151 2.5% 100,461 2.8% 10,310 0.3% Shared 2 Free 767,480 21.0% 753,700 20.9% -13,780-0.1% Shared 2 HOV 56,313 1.5% 50,840 1.4% -5,473-0.1% Shared 2 Pay 3,330 0.1% 3,999 0.1% 669 0.0% Shared 3+ Free 770,374 21.1% 757,368 21.0% -13,006-0.1% Shared 3+ HOV 39,048 1.1% 35,960 1.0% -3,088-0.1% Shared 3+ Pay 1,343 0.0% 1,336 0.0% -7 0.0% Walk 347,793 9.5% 344,562 9.5% -3,231 0.0% Bike 35,435 1.0% 34,869 1.0% -566 0.0% Walk to Local 40,884 1.1% 41,158 1.1% 274 0.0% - 5 -
Walk to Express 103 0.0% 111 0.0% 8 0.0% Walk to Light Rail 25,484 0.7% 25,492 0.7% 8 0.0% Walk to Commuter Rail 496 0.0% 500 0.0% 4 0.0% Park-Ride to Local 1,702 0.0% 1,698 0.0% -4 0.0% Park-Ride to Express 114 0.0% 121 0.0% 7 0.0% Park-Ride to Light Rail 10,450 0.3% 10,243 0.3% -207 0.0% Park-Ride to Commuter Rail 1,620 0.0% 1,606 0.0% -14 0.0% Kiss-Ride to Local 2,400 0.1% 2,378 0.1% -22 0.0% Kiss-Ride to Express 22 0.0% 19 0.0% -3 0.0% Kiss-Ride to Light Rail 5,469 0.1% 5,459 0.2% -10 0.0% Kiss-Ride to Commuter Rail 282 0.0% 270 0.0% -12 0.0% School Bus 76,965 2.1% 77,104 2.1% 139 0.0% Total 3,659,085 100.0% 3,610,324 100.0% -48,761 0.0% 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 4,963,738 52.9% 4,829,420 52.2% -134,318-0.8% Drive Alone Pay 47,653 0.5% 130,555 1.4% 82,902 0.9% Shared 2 Free 1,808,167 19.3% 1,736,364 18.8% -71,803-0.5% Shared 2 HOV 22,833 0.2% 56,111 0.6% 33,278 0.4% Shared 2 Pay 1,494 0.0% 3,957 0.0% 2,463 0.0% Shared 3+ Free 1,246,706 13.3% 1,205,893 13.0% -40,813-0.3% Shared 3+ HOV 10,234 0.1% 27,472 0.3% 17,238 0.2% Shared 3+ Pay 686 0.0% 1,602 0.0% 916 0.0% Walk 864,520 9.2% 856,965 9.3% -7,555 0.0% Bike 78,864 0.8% 77,690 0.8% -1,174 0.0% Walk to Local 90,970 1.0% 91,249 1.0% 279 0.0% Walk to Express 265 0.0% 262 0.0% -3 0.0% Walk to Light Rail 51,688 0.6% 51,913 0.6% 225 0.0% Walk to Commuter Rail 946 0.0% 939 0.0% -7 0.0% Park-Ride to Local 3,558 0.0% 3,575 0.0% 17 0.0% Park-Ride to Express 135 0.0% 125 0.0% -10 0.0% Park-Ride to Light Rail 15,983 0.2% 15,714 0.2% -269 0.0% Park-Ride to Commuter Rail 2,983 0.0% 2,864 0.0% -119 0.0% Kiss-Ride to Local 3,404 0.0% 3,373 0.0% -31 0.0% Kiss-Ride to Express 24 0.0% 26 0.0% 2 0.0% Kiss-Ride to Light Rail 7,886 0.1% 7,869 0.1% -17 0.0% Kiss-Ride to Commuter Rail 575 0.0% 578 0.0% 3 0.0% School Bus 153,930 1.6% 154,208 1.7% 278 0.0% Total 9,377,242 100.0% 9,258,724 100.0% -118,518 0.0% - 6 -
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 7,744 4.6669 Final Calibrated Crossings 4,352 3.6201 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 2012. 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 1,730,914 113,803 7% Central 12,086,035 12,479,387 393,352 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 6,578,118 100,231 2% North County West 8,258,592 8,478,942 220,350 3% North County East 8,563,272 9,194,727 631,455 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 2013. 3 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. - 7 -
East County 137,862 225,803 87,941 64% Total 61,810,474 65,369,906 3,559,432 5.8% 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 135,004 132,395-2,609-2% # 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 118,519 125,272 6753 6% # 4 - I-15S at CARMEL MOUNTAIN ROAD 117,785 127,073 9,288 8% # 5 - I-5N at CARMEL MOUNTAIN ROAD 108,264 111,116 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. 163 165,551 174,745 9,194 6% # 8 - I-15S at JCT. RTE. 163 152,191 170,170 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 104,943 102,796-2,147-2% # 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. 805 16,461 24,327 7,866 48% # 13 - I-5S at SOUTH JCT. RTE. 805 19,442 19,974 532 3% - 8 -
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 -
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-125 4. 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 11,845,340-240,695-2% North City 20,829,984 21,873,766 1,043,782 5% South Suburban 3,839,731 3,504,477-335,254-9% East Suburban 6,477,887 6,235,726-242,161-4% North County West 8,258,592 8,174,715-83,877-1% North County East 8,563,272 8,910,338 347,066 4% East County 137,862 195,578 57,716 42% Total 61,810,474 62,353,049 542,575 0.9% 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 135,004 135,125 121 0% # 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 98,817 951 1% # 3 - I-15S at VALLEY PARKWAY 96,115 96,967 852 1% # 4 - I-15N at CARMEL MOUNTAIN ROAD 118,519 122,377 3858.25 3% # 4 - I-15S at CARMEL MOUNTAIN ROAD 117,785 123,157 5,372 5% # 5 - I-5N at CARMEL MOUNTAIN ROAD 108,264 104,391-3,873-4% # 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. 163 165,551 169,503 3,952 2% # 8 - I-15S at JCT. RTE. 163 152,191 166,410 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. 805 16,461 19,904 3,443 21% # 13 - I-5S at SOUTH JCT. RTE. 805 19,442 16,290-3152.1-16% 4 A more detailed analysis of SR125 is currently being done under the ABM Maintenance contract. - 10 -
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 1 137,780 136,256 (1,524) -1.1% 2 - - - 0.0% 3 182,726 114,568 (68,158) -37.3% 4 - - - 0.0% 5 31,300 50,236 18,936 60.5% 6 252,912 336,802 83,890 33.2% 7 - - - 0.0% 8 12,200 12,654 454 3.7% 9 6,585 11,067 4,482 68.1% 10 302,369 352,898 50,529 16.7% 11 33,000 31,874 (1,126) -3.4% 12 449,841 506,108 56,267 12.5% 13 337,134 308,970 (28,164) -8.4% 14 121,200 111,275 (9,925) -8.2% 15 245,363 208,940 (36,423) -14.8% 16 - - - 0.0% 17 159,067 138,509 (20,558) -12.9% 18 13,900 19,038 5,138 37.0% 19 427,270 394,653 (32,617) -7.6% 20 118,090 116,658 (1,432) -1.2% 21 288,664 294,148 5,484 1.9% 22 121,800 125,343 3,543 2.9% 23 223,409 222,474 (935) -0.4% 24 10,035 6,804 (3,231) -32.2% - 11 -
Table 12. Daily Screenline Comparisons by Link ID After Adjustments Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume 1 137780 136256.0711 360,118 6334 0 0 2,586 8061 1915 1464.973434 1,465 8574 0 0 934 12444 0 0 2,211 12769 0 0 726 12772 0 0 139 24685 0 0 28,131 24686 0 0 61,000 27195 67427 67320.41704 67,320 27196 68438 67470.68065 67,471 27519 0 0 47,595 27520 0 0 49,102 37734 0 0 10,746 37735 0 0 20,212 38532 0 0 478 2 0 0 99,154 6506 0 0 3,616 12711 0 0 21,461 12732 0 0 6,520 12735 0 0 56,825 17180 0 0 10,118 20474 0 0 158 26669 0 0 430 38560 0 0 26 3 182726 114567.6404 231,461 1512 0 0 1,324 3633 0 0 1,197 4853 0 0 2,942 5778 0 0 3,625 5788 0 0 1,861 7227 0 0 25,954 7626 0 0 9,770 7745 49500 25519.63509 25,520 9653 0 0 10,885 12547 64901 42236.41745 42,236 16489 68325 46811.58789 46,812 17201 0 0 22,326 17836 0 0 1,324 19373 0 0 15,997 19374 0 0 4,386 19375 0 0 4,504 5 Estimated volumes in this table are only shown where there is an observed count. - 12 -
Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume 21010 0 0 1,247 21014 0 0 315 21026 0 0 64 22714 0 0 361 22720 0 0 679 24720 0 0 729 25775 0 0 120 27103 0 0 3,326 27104 0 0 2,626 39750 0 0 1,330 4 0 0 248,888 6822 0 0 19,727 8839 0 0 54,050 8840 0 0 52,915 10053 0 0 4,192 12317 0 0 13,371 14690 0 0 44,188 14866 0 0 12,721 20633 0 0 2,483 22410 0 0 1,969 22796 0 0 966 25802 0 0 23,601 25804 0 0 611 25805 0 0 2,033 27250 0 0 6,181 27251 0 0 7,913 36014 0 0 1,778 39680 0 0 190 5 31300 50235.56888 101,835 3474 0 0 6,272 7212 0 0 17,201 11457 0 0 16,739 13369 0 0 3,712 13379 1600 6532.994644 6,533 16312 29700 43702.57424 43,703 20468 0 0 563 22927 0 0 4,204 24751 0 0 42 25841 0 0 6 30402 0 0 1,443 36630 0 0 660 39885 0 0 306 39959 0 0 451 40003 0 0-40005 0 0 - - 13 -
Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume 6 252912 336801.9594 682,459 514 0 0 32,661 1219 36600 59558.67933 59,559 4906 0 0 16,723 8770 0 0 92,367 8771 0 0 88,675 8807 108664 140890.7552 140,891 8808 107648 136352.5249 136,353 9804 0 0 19,204 16236 0 0 63,226 22309 0 0 6,445 23053 0 0 26,355 7 0 0 448,983 2053 0 0 14,807 8776 0 0 115,876 13851 0 0 24,189 13875 0 0 19,826 14246 0 0 116,602 23431 0 0 246 23433 0 0 2,570 25381 0 0 15,381 25387 0 0 117,352 25973 0 0 1,695 29575 0 0 15,500 30342 0 0 1,141 37717 0 0 3,798 8 12200 12654.22187 288,827 5407 0 0 1,697 10765 0 0 6,425 11426 12200 12654.22187 12,654 11677 0 0 17,644 13604 0 0 18,993 14347 0 0 63,535 14362 0 0 75,549 16350 0 0 34,542 16351 0 0 32,749 22038 0 0 1,281 23544 0 0 1,137 23720 0 0 4,583 31250 0 0 17,748 36589 0 0 291 9 6585 11067.20934 11,222 2586 0 0 40 7742 1317 2807.949181 2,808 8260 5268 8259.260159 8,259-14 -
Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume 36441 0 0 115 10 302369 352897.9332 931,079 460 0 0 24,372 528 0 0 28,362 789 0 0 128,340 6050 1227 2009.292043 2,009 8698 614 1984.210427 1,984 8699 0 0 705 8706 1682 4232.04622 4,232 10858 0 0 13,151 11210 0 0 34,848 11441 29768 57277.29904 57,277 12017 0 0 35,382 14264 8200 14844.01321 14,844 14265 0 0 53,425 14385 0 0 15,077 14386 115944 131860.1696 131,860 14667 0 0 13,151 17361 10697 10002.16588 10,002 19295 1115 1371.261267 1,371 19714 17920 13554.82048 13,555 20146 0 0 21,948 22115 0 0 2,580 22601 0 0 5,241 23522 0 0 1,840 23534 0 0 4,356 24793 0 0 4,697 26950 0 0 1,199 28792 0 0 53,472 28795 0 0 47,616 28796 0 0 58,486 29559 0 0 16,877 29676 14209 22377.9577 22,378 30776 100162 93374.03081 93,374 30805 0 0 9,135 33264 831 10.666528 11 38901 0 0 2,867 39152 0 0 1,054 11 33000 31873.74914 234,072 1224 0 0 4,969 3882 0 0 4,796 4489 0 0 46,199 15055 12700 10127.5439 10,128 15146 0 0 2,346 15148 20300 21746.20524 21,746-15 -
Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume 16506 0 0 53,174 22012 0 0 1,993 22018 0 0 1,137 23684 0 0 8,686 23691 0 0 5,780 26039 0 0 9,313 28280 0 0 33,948 28281 0 0 29,859 12 449841 506108.4481 950,088 760 0 0 97,859 771 0 0 100,186 1755 14715 38673.92108 38,674 1960 5110 5821.418 5,821 1965 0 0 10,863 3975 3187 10559.34486 10,559 5073 6367 11873.70879 11,874 5188 22100 25574.04992 25,574 8027 6883 13235.74386 13,236 8378 14003 29469.00672 29,469 9346 0 0 14,312 9942 18700 6715.355594 6,715 15194 0 0 10,159 15195 0 0 10,377 15208 0 0 81,361 15209 0 0 84,927 17959 0 0 1,462 22743 78386 81897.48843 81,897 23707 0 0 4,655 23710 0 0 4,069 23723 0 0 3,127 23726 0 0 13,099 24795 0 0 251 26014 101208 95501.19153 95,501 26065 100340 98700.87956 98,701 28899 78842 88086.33976 88,086 28900 0 0 7,274 13 337134 308970.338 418,641 285 19522 21029.40952 21,029 3513 40097 25796.66275 25,797 7377 0 0 45,637 8052 18686 21646.78883 21,647 8244 2152 2192.153804 2,192 11260 5685 11458.03906 11,458 11266 117367 103381.7388 103,382 11268 112370 103835.6892 103,836-16 -
Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume 12974 1818 704.591141 705 12982 2617 3148.278528 3,148 15286 5988 4597.885555 4,598 18043 0 0 8,600 19975 3965 6806.900348 6,807 20124 3097 1261.962153 1,262 21912 0 0 1,110 24068 0 0 7,858 24076 0 0 698 24082 0 0 9 26057 0 0 1,359 27241 0 0 44,399 29805 3770 3110.238253 3,110 39496 0 0-14 121200 111275.035 1,253,748 3954 0 0 283 4181 0 0 20,751 7874 0 0 75,966 7933 25800 25597.08663 25,597 8855 36800 39036.92999 39,037 8895 0 0 19,645 9318 0 0 23,448 11969 0 0 36,304 14767 0 0 17,682 14771 0 0 17,771 14772 0 0 17,508 15721 0 0 28,483 15722 0 0 76,397 15723 0 0 14,769 15769 0 0 22,886 15773 0 0 41,132 16015 0 0 5,284 16019 0 0 4,383 16059 0 0 120,336 18048 0 0 4,790 19844 0 0 23,406 20034 58600 46641.01841 46,641 20639 0 0 78,221 20685 0 0 62,666 20755 0 0 73,697 20782 0 0 74,621 21822 0 0 57,597 22622 0 0 2,074 24000 0 0 3,550 24001 0 0 12,523-17 -
Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume 24385 0 0 20,980 24500 0 0-25101 0 0 24,131 26648 0 0 14,241 26649 0 0 1,777 28074 0 0 19,757 28075 0 0 20,607 28993 0 0 31,706 28996 0 0 34,779 30711 0 0 25,597 30832 0 0 12,698 38754 0 0 21 39145 0 0 3 15 245363 208939.7458 315,914 389 0 0 10,026 6432 0 0 1,647 7084 0 0 28,433 8935 0 0 28,963 8969 0 0 14,928 10996 121299 98062.7511 98,063 10998 124064 110876.9947 110,877 12844 0 0 18,208 20618 0 0 2,592 24315 0 0 518 29341 0 0 1,660 16 0 0 70,077 5557 0 0 20,272 5560 0 0 5,052 11807 0 0 5,634 21828 0 0 889 22022 0 0 763 28231 0 0 18,634 28232 0 0 18,672 39484 0 0 161 17 159067 138508.98 453,794 896 3764 1161.954161 1,162 1935 1328 113.19461 113 3459 5397 2469.081883 2,469 3461 6496 8296.897809 8,297 5850 4578 6143.112799 6,143 8703 4098 3147.897752 3,148 10401 14151 31484.05471 31,484 16580 0 0 98,612 16596 18649 8369.819755 8,370 16768 57942 52191.94251 52,192-18 -
Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume 16770 8792 8167.115018 8,167 16773 11500 5806.293429 5,806 17722 15700 4190.384628 4,190 18118 6672 6967.230969 6,967 20696 0 0 88,334 21850 0 0 16,695 24957 0 0 6,852 25062 0 0-29076 0 0 43,389 30903 0 0 56,907 37237 0 0 437 37238 0 0 3,780 38440 0 0 279 18 13900 19038.00613 412,247 943 0 0 989 1110 0 0 8,033 2155 0 0 7,627 4348 0 0 6,037 5307 0 0 3,239 6586 0 0 13,477 10341 13900 19038.00613 19,038 11155 0 0 2,641 16707 0 0 4,340 16795 0 0 5,156 22595 0 0 291 24963 0 0 1,151 25411 0 0 1,842 27789 0 0 72,709 27790 0 0 101,394 28461 0 0 27,031 29060 0 0 35,743 30904 0 0 65,295 31211 0 0 36,215 19 427270 394652.8194 907,190 876 0 0 26,665 2215 0 0 27 2418 0 0 195 2647 0 0 3,209 2833 16700 17916.1233 17,916 2835 0 0 319 4253 0 0 27,400 5693 8900 3567.780234 3,568 5719 0 0 5,976 5916 0 0 1,599 6424 0 0 15,293-19 -
Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume 6729 0 0 23,759 6841 0 0 25,467 7519 0 0 1,080 8494 0 0 6,035 9790 0 0 17,790 10428 0 0 174 14142 61395 49829.62669 49,830 14143 58862 47283.42475 47,283 14819 0 0 990 15442 113884 102666.2749 102,666 15443 116329 108955.3827 108,955 15448 0 0 385 16099 27600 39006.52234 39,007 16101 0 0 5,687 17300 0 0 63,208 17717 0 0 21,496 19837 0 0 18,046 21435 0 0 709 21739 0 0 5,253 21740 0 0 9,942 21787 0 0 5,519 21788 0 0 1,276 23879 0 0 12,613 24008 0 0 690 24158 0 0 59,405 24281 0 0 3,236 24297 0 0 1,472 25656 0 0 3,753 26468 0 0 2,112 26510 0 0 244 26583 0 0 4,394 26826 0 0 190 27051 0 0 940 29016 0 0 64,019 29025 0 0 64,352 29730 0 0-29731 0 0-30975 23600 25427.68453 25,428 31027 0 0 439 37086 0 0 1,744 39156 0 0 2,006 39213 0 0 1,932 39325 0 0 139 39362 0 0 1,358 20 118090 116657.8282 482,472-20 -
Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume 711 0 0 88,169 2846 16200 14188.17383 14,188 5864 0 0 6,509 5867 0 0 3,578 7018 0 0 10,061 7807 0 0 7,271 8603 0 0 905 19061 0 0 2,797 19065 0 0 2,533 20790 101890 102469.6544 102,470 27090 0 0 362 27114 0 0 657 27332 0 0 3,090 27440 0 0 3,580 27489 0 0-27647 0 0 1,268 27648 0 0-28696 0 0 104,237 28701 0 0 104,732 28754 0 0 15,623 37911 0 0 2,600 39196 0 0 7,150 39998 0 0 693 21 288664 294147.8431 561,724 501 0 0 3,097 837 0 0 119,318 2297 6944 6998.24796 6,998 2392 4338 4557.68061 4,558 3053 3691 1762.525471 1,763 3071 3715 2115.915008 2,116 5516 16058 26731.78138 26,732 6008 22033 16546.38603 16,546 7017 11035 1691.734457 1,692 9012 0 0 4,690 9788 0 0 16,104 10257 15838 6606.231389 6,606 11152 0 0 10,505 13995 2615 2151.833658 2,152 14100 32000 45033.15823 45,033 27068 0 0 804 27069 0 0 3,792 27942 84424 86617.75265 86,618 27943 85973 93334.59623 93,335 27967 0 0 18,582 28387 0 0 1,510-21 -
Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume 28673 0 0 75,572 29536 0 0 95 29558 0 0 3,158 29910 0 0 2,792 29916 0 0 5,430 29925 0 0 1,889 37084 0 0 237 22 121800 125342.8687 285,174 6481 0 0 2,487 7133 0 0 5,275 14007 0 0 6,477 14111 0 0 61,631 16247 59900 61576.25733 61,576 19101 0 0 69,057 19102 61900 63766.61137 63,767 30000 0 0 8,780 30020 0 0 4,121 30062 0 0 341 30068 0 0 1,662 23 223409 222473.9776 368,910 689 0 0 50,392 853 0 0 56,370 9085 5132 10513.378 10,513 9799 6636 941.276876 941 10036 8092 13127.69936 13,128 10415 10502 10409.00945 10,409 13939 17527 23525.46705 23,525 15640 14900 17989.41313 17,989 27845 75165 69979.89381 69,980 27914 76024 75811.6909 75,812 29133 0 0 16,777 29137 0 0 15,868 30161 0 0 1,067 30188 0 0 98 30225 0 0 15 30230 0 0 968 30233 0 0 3,678 31013 4125 88.829289 89 31014 5306 87.319733 87 36977 0 0 231 38720 0 0 956 40000 0 0 15 24 10035 6804.282714 75,095 4114 10035 6804.282714 6,804 9930 0 0 34,342-22 -
Screenline / Link ID Observed Count Estimated Volume 5 Estimated Volume 27741 0 0 33,949 30543 0 0 - Grand Total 3474645 3499274.526 9,780,924 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 6,205 215 (5,990) -97% Olympic Pkwy to Birch 4,776 129 (4,647) -97% Birch to Otay Mesa 3,820 87 (3,733) -98% South to North Otay Mesa to Birch 3,943 89 (3,854) -98% Birch to Olympic Pkwy 4,776 125 (4,651) -97% Olympic Pkwy to Otay Lakes 7,120 218 (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% - 23 -
Figure 1. SANDAG Key Count Locations - 24 -
MODEL ESTIMATED Figure 2. Scatterplot of Daily Observed Counts by Daily Estimated Volumes and by Count Source 160000 140000 120000 100000 45 Degree Line 80000 ARTERIALS PEMS COUNT 60000 CALTRANS TRA COUNTS 40000 20000 0 0 20000 40000 60000 80000 100000 120000 140000 160000 OBSERVED COUNTS - 25 -
Figure 3: SANDAG Screenline Map - 26 -
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) - 27 -
Figure 5: AM Period Drive Alone Toll Cost to La Mesa (blue star) - 28 -
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) - 29 -
Figure 7: AM Period Drive Alone Toll Cost to SR-125 at Otay Lakes (blue star) - 30 -
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) - 31 -
Figure 9: AM Period Drive Alone Toll Cost to SR-125 at Otay Mesa (blue star) - 32 -
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 216,435 223,901 7,466 3% Express 1,430 1,034 (396) -28% Light Rail 123,729 108,517 (15,212) -12% Commuter Rail 5,482 5,472 (10) 0% Total 347,076 338,924 (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 96,608 119,827 109,752 114,149 13,144 (5,678) 14% -5% Express 1,367 63 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 1,588 111 (121) 3% -7% Total 160,778 186,298 167,476 171,448 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 - 33 -
Trip Table Line Haul Mode 10-Local 4-Coaster 5-Sprinter /Trolley 8-Premium Express 1-Walk Access 204,764 1,968 83,536 666 290,934 1-Local 131,459 - - - 131,459 2-Express 312-1 278 591 3-Light Rail 70,472-82,113 381 152,966 4-Commuter Rail 2,521 1,968 1,422 7 5,918 2-Park and Ride 10,580 2,866 16,478 272 30,196 1-Local 3,949 - - - 3,949 2-Express 132 - - 125 257 3-Light Rail 5,303-16,044 132 21,479 4-Commuter Rail 1,196 2,866 434 15 4,511 3-Kiss and Ride 8,557 638 8,503 96 17,794 1-Local 3,861 - - - 3,861 2-Express 34 - - 26 60 3-Light Rail 4,283-8,353 67 12,703 4-Commuter Rail 379 638 150 3 1,170 Total 223,901 5,472 108,517 1,034 338,924 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 216,435 220,520 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 2,174 16 1% 7 12,344 7,144 (5,200) -42% - 34 -
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% 14 390 800 410 105% 15 5,584 5,035 (549) -10% 18 251 286 35 14% 20 4,468 4,888 419 9% 25 457 1,091 634 139% 27 1,470 1,407 (63) -4% 28 1,549 1,705 156 10% 30 7,316 8,009 693 9% 31 379 728 349 92% 35 1,843 1,870 27 1% 41 4,676 5,998 1,322 28% 44 4,991 6,019 1,028 21% 50 1,146 1,401 255 22% 83 177 186 9 5% 84 163 435 272 167% 88 413 288 (125) -30% 105 1,449 1,159 (291) -20% 115 1,263 4,006 2,743 217% 120 3,947 4,464 517 13% 150 2,672 3,047 374 14% 201 2,264 1,105 (1,160) -51% 202 2,285 1,055 (1,229) -54% 210 258 211 (47) -18% 301 2,821 3,004 183 6% 302 2,479 1,645 (835) -34% 303 4,468 4,082 (385) -9% 304 923 1,469 546 59% 305 2,006 2,515 509 25% 306 834 1,246 412 49% 308 537 776 239 45% - 35 -
Route Name observed estimated difference % Difference 309 2,204 2,934 730 33% 313 283 729 446 158% 315 481 2,267 1,787 372% 316 13 173 160 1194% 318 252 127 (125) -50% 323 106 280 174 164% 325 594 2,061 1,466 247% 332 673 1,801 1,128 168% 333 403 706 303 75% 334 631 802 172 27% 340 257 89 (168) -65% 347 299 1,245 945 316% 350 2,447 2,203 (244) -10% 351 1,024 966 (57) -6% 352 1,021 849 (172) -17% 354 809 1,102 293 36% 356 601 643 41 7% 358 145 394 249 172% 359 170 412 241 142% 388 575 258 (317) -55% 395 195 769 574 294% 444 31 104 73 236% 445 95 323 228 241% 446 2 36 34 1697% 701 2,579 2,521 (58) -2% 704 1,735 2,207 472 27% 705 1,225 1,293 68 6% 707 290 748 458 158% 709 4,441 6,320 1,879 42% 712 3,799 4,237 438 12% 815 1,064 929 (135) -13% 816 1,645 2,049 404 25% 832 208 842 634 305% 833 578 733 155 27% 834 88 304 216 245% - 36 -
Route Name observed estimated difference % Difference 844 174 373 199 115% 845 675 986 311 46% 848 1,392 1,852 460 33% 851 477 444 (33) -7% 854 1,027 1,260 233 23% 855 1,255 1,379 124 10% 856 2,964 3,548 584 20% 864 1,487 2,238 751 50% 871 221 273 52 24% 872 234 256 22 9% 874 858 952 94 11% 875 995 880 (115) -12% 901 3,868 6,812 2,944 76% 904 71 73 2 2% 905 2,116 4,295 2,179 103% 906 4,027 4,316 289 7% 916 475 377 (98) -21% 917 505 298 (207) -41% 921 1,665 2,700 1,035 62% 923 1,123 1,783 660 59% 928 1,339 1,878 539 40% 929 8,444 6,531 (1,913) -23% 932 4,837 3,128 (1,709) -35% 933 3,711 4,038 327 9% 934 3,885 3,908 23 1% 936 2,059 1,631 (428) -21% 955 6,519 5,367 (1,152) -18% 960 383 688 305 80% 961 2,476 3,177 701 28% 962 1,968 1,634 (334) -17% 963 968 604 (364) -38% 964 415 1,340 925 223% 965 380 260 (120) -31% 967 247 248 1 0% 968 298 195 (103) -35% - 37 -
Route Name observed estimated difference % Difference 972 163 185 22 13% 973 146 186 40 27% 978 99 77 (22) -22% 979 82 168 86 105% 992 1,380 914 (466) -34% COASTER 5,482 5,472 (10) 0% 398 5,482 5,472 (10) 0% SPRINTER/TROLLEY 123,729 108,517 (15,212) -12% 399 8,947 9,259 312 3% 510 64,582 55,939 (8,643) -13% 520 30,423 21,124 (9,299) -31% 530 19,777 22,195 2,418 12% PREMIUM EXPRESS 1,430 1,034 (396) -28% 810 714 515 (199) -28% 820 216 89 (127) -59% 850 171 34 (137) -80% 860 153 92 (61) -40% 870 64 179 115 180% 880 112 125 13 12% TOTAL 347,076 335,543 (11,533) -3% Table 18: Comparison of Transit Trip Mode by Model Component Model Component Local Express LRT 2010 Commuter Rail Resident 95959 2570 72189 3714 Airport 117 9 176 40 Visitor 300 8 381 6 Cross Border 3208 12 28384 316 Internal External 60 0 1231 384 2012 Resident 101594 413 76639 4405 Airport 107 7 175 61 Visitor 261 0 357 6 Cross Border 2228 0 23396 532 Internal External 47 0 1160 467-38 -
- 39 -
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 2010. 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. - 40 -