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From page 357... ... 351 A P P E N D I X F Freeway Facilities Lane-by-Lane Analysis Data Collection The data collected for this part of the project include speed and flow data from selected detector station sensors in California, Virginia, Utah, Wisconsin, Minnesota, and Florida. These locations represent diverse operational and design conditions across the US. Read the entire page →
From page 358... ... 352 Table F-1 – Database used for determination of lane-by-lane flows – Basic freeway segments # lanes State Road Observation Period Detector Station IDs % Grade %HV # adjacent ramps 2L CA HWY 1 NB 2018 500014082 0.5 1.7 2 CA SR-132 EB 2018 10119910 -0.5 0.3 0 MN US-10 2017 - 18 946 0.3 1.4 1 MN I-694 NB 2017 1413 -1.2 8.6 1 UT SR-67 NB 2018 820 -0.3 18 0 VA I-66 WB 2018 19002981 0.5 1 2 VA I-64 EB 2018 64239221 -0.9 9 1 WI I-43 SB 2017 - 18 692 -0.02 6.5 0 3L CA I-205 WB 2018 - 19 1027310 -0.9 4.2 0 CA SR-85 NB 2018 - 19 407336 -0.8 5.6 1 FL I-4 WB 2017 4712 -0.2 10.8 0 MN I-94 NB 2017 - 18 1356 -1.7 11.8 0 UT I-215 SB 2016 - 17 82 -3.1 10.5 0 UT I-15 SB 2018- 19 963 1.45 32.9 1 VA I-64 EB 2017- 18 64055221 0.2 4 3 WI I-43 NB 2017 - 18 659 -0.6 10.2 1 4L CA I-80 2018 - 19 413373 0.4 6.8 1 CA SR-24 EB 2018 - 19 400532 1.7 14.1 0 CA I-80 EB 2018 - 19 413375 1.1 5 1 FL I-295W NB 2017 2220 0 13.6 1 FL I-275 SB 2016 - 17 3829 -1.1 4.4 0 FL I-275 NB 2016 - 17 3408 0 4.4 0 UT I-215 CW 2018 - 19 50 -0.8 28.2 1 VA I-295 EB 2018 - 19 4044741 -0.1 10 1 Read the entire page →
From page 359... ... 353 Table F-2 – Database used for determination of lane-by-lane flows – Merge segments # lanes State Road Observation Period Detector Station IDs % Grade %HV # adjacent ramps 2L UT SR-67 S 2018 872, 876, 1872 0 13 0 UT SR-67 N 2018 830, 834, 1830 0.1 17 0 FL I-295E NB 2018 10821, 10852, 10890 2.7 11.8 0 FL I-295E NB 2018 10854, 10841, 10879 1.5 11.8 0 3L CA I-5 SB 2018 - 19 10121110, 10121310, 1083110 -0.9 11.9 0 CA CA-99 WB 2018 10109610, 10109810, 10109710 0.1 11.23 1 MN I-694 2017 174, 172, 753 1.8 8.6 1 UT SR201-W 2018 - 19 348, 350, 1348 0 27.9 1 UT I-215 CCW 2017 173, 175, 1173 1.5 9 1 UT I-215S EB 2017 168, 169, 1168 0.2 11 0 UT I-215 2017 188, 190, 1188 0 10 0 UT I-80 2017 231, 232, 1231 1.32 14 0 4L CA I280 SB 2018 - 19 403908, 403328, 403909 0 1.3 1 CA 1-8 WB 2017 403908, 403328, 403909 2.71 2.1 1 Table F-3 – Database used for determination of lane-by-lane flows – Diverge segments # lanes State Road Observation Period Detector Station IDs % Grade %HV # adjacent ramps 2L CA US-101 SB 2018 – 19 406305, 406303 -0.3 5 1 CA I-8 WB 2016 - 17 1115624, 1122447 0.8 3.4 0 UT SR-67 NB 2017 810, 2810 0 9 0 FL I-295E NB 2017 10829, 10876 0 11.8 0 WI I-94 WB 2017 67663, 67662 -2 7.2 1 3L CA CA-242 2018 – 19 414251, 417124 0 2.43 0 CA I-5 NB 2018 – 19 10121410, 1090210 0 18.1 0 CA CA-73 2018 – 19 1208789, 1208940 -5.4 3.8 0 MN I-694 2017 151, 554 2.5 8.6 2 MN I-694 2017 171, 755 2.5 8.6 2 UT I-215 CCW 2017 112, 2114 0 17 0 UT I-215 CW 2018 – 19 22, 2024 -2.2 61.6 0 UT I-215 CW 2018 – 19 86, 2087 0 8 0 4L CA SR-242 NB 2018 – 19 414252, 418256 1.5 2.21 4 CA I-80 EB 2017 413375, 410766 0.7 5.1 1 Read the entire page →
From page 360... ... 354 Table F-4 – Database used for determination of lane-by-lane flows – Weaving segments # lanes State Road Observation Period Detector Station IDs % Grade %HV # adjacent ramps 2L CA CA-56 WB 2018 1125546, 1125575, 1125543, 1126293 0 2.03 0 MN I-694 NB 2018 1027, 1410, 5120, 6226 0.6 14.8 3 MN Lafayette Hwy 2017 S1169, S1170, 5626, 5629 0 8.81 0 MN US 52 2017 S1442, S1444, 6061, 6069 0 8.01 0 MN Lafayette Hwy 2017 S1159, S1160, 5581, 5584 0 8.81 0 MN MN-36 WB 2017 S616, S617, 2361, 2362 0.8 8.81 0 MN MN-36 EB 2017 S591, S592, 2256, 2257 0.8 8.81 0 3 CA I-80 SB 2018-2019 409107, 404408, 409108, 419489 1 15.8 3 UT I-215 CW 2013-2014 320, 322, 1320, 2322 0 14 1 UT I-215 CW 2015-16 344, 346, 1344, 2346 0 14 0 UT Belt Route 2011-2012 162, 165, 1162, 2165 1 14 1 4 CA SR4-EB 2018 416930, 414707, 416931, 414708 -0.5 3.3 2 CA SR4-EB 2018 400049, 405269, 418869, 406635 -0.8 4.8 0 CA I-680 SB 2018 407177, 407179, 409059, 407178 0.51 4.4 4 CA I-880 SB 2018 400949, 400678, 403028, 403030 0.1 6.1 0 UT I-80 EB 2015 228, 240, 1228, 2231 0 16 0 Speed and flow detector data were collected from online sources from the respective state agencies. Data were obtained by lane in 15-min intervals, over a 1-year period for each location. Read the entire page →
From page 361... ... 355 LFRn = share of the total flow on the leftmost lane (lane n) ; fa = adjustment factor for 𝑎 (Equations F-3, F-5, F-7) Read the entire page →
From page 362... ... 356 LS = length of the weaving segment (ft) fa,LS = adjustment factor for a due to length of the weaving segment fc,LS = adjustment factor for c due to length of the weaving segment VR = volume ratio (weaving volume/total volume) Read the entire page →
From page 363... ... 357 Table F-5 – Adjustment factors for lane flow distribution on basic, merge and diverge segments Lane # Parameter Basic segments Diverge segments Merge segments 2 lanes 3 lanes 4 lanes 2 lanes 3 lanes 4 lanes 2 lanes 3 lanes 4 lanes L1 a 0.17991 0.02708 0.06815 0.00969 -0.07503 0.30943 0.01501 0.00290 -0.07664 c 0.51747 0.27040 0.21903 0.44267 0.26667 0.24818 0.58644 0.28248 0.23621 fa,g 0.02397 0.02095 -0.01107 0.00969 0.00768 -0.03381 0.01501 -0.00290 -0.00302 fa,t -0.04821 -0.00364 -0.00209 -0.00928 0.00080 -0.05689 -0.00929 -0.00290 0.01110 fa,n -0.09525 -0.00829 -0.05870 -0.00969 0.01382 -0.02756 -0.00474 -0.00290 0.01449 fc,g 0.00301 0.00969 -0.03378 -0.00976 -0.00810 -0.00016 0.01965 0.03100 0.04041 fc,t 0.00788 -0.00289 0.00243 0.00775 0.00140 -0.01887 -0.01350 -0.00179 -0.02714 fc,n 0.00134 0.03222 -0.03481 0.00057 0.03129 0.00516 -0.03997 -0.04212 -0.04073 fa,vR -0.21359 -0.06664 -0.00871 -0.03477 -0.10409 0.02637 fc,vR -0.12519 0.01324 -0.02112 -0.07032 -0.02982 0.00914 L2 a -0.06337 -0.02491 0.00960 0.28585 -0.00816 -0.08022 c 0.31448 0.28769 0.33948 0.24967 0.37687 0.24498 fa,g -0.00596 0.00150 -0.00960 -0.03465 -0.00816 0.00048 fa,t 0.00113 0.00027 -0.00054 -0.05211 -0.00082 0.01250 fa,n 0.00368 -0.00845 -0.00960 -0.03023 -0.00261 0.01782 fc,g -0.01688 -0.02388 -0.00189 0.00189 0.00791 -0.01938 fc,t 0.00239 -0.00036 0.00089 -0.00408 -0.00048 -0.00670 fc,n 0.01139 -0.04134 0.00520 0.00437 -0.00597 0.00101 fa,vR -0.04766 -0.00652 -0.11832 -0.03270 fc,vR -0.07333 -0.00914 -0.03855 -0.01262 L3 a -0.04510 0.26611 0.02860 c 0.27607 0.25113 0.25373 fa,g -0.00171 -0.03618 -0.00169 fa,t 0.00213 -0.04404 -0.00579 fa,n 0.00808 -0.03444 -0.00678 fc,g 0.01052 0.00344 0.00060 fc,t -0.00112 0.00918 0.01424 fc,n 0.01485 0.00164 0.01764 fa,vR 0.02083 -0.07890 fc,vR -0.00644 -0.04144 Read the entire page →
From page 364... ... 358 The weaving LFRs are calculated at two locations: upstream of the on-ramp and within the weaving segment (Figure F-1) Read the entire page →
From page 365... ... 359 is included in the NWL. Figure F-3 provides two weaving example configurations with the respective NWL. Read the entire page →
From page 366... ... 360 The calculation steps are as follows: Step 1. Determine the number of upstream freeway weaving lanes (NWUP) Read the entire page →
From page 368... ... 362 For the weaving segments, capacity is estimated using the method proposed in HCM 6th Edition – Chapter 13 (Step 5, Equations 13-5 through 13-9) in passenger-cars per hour (pc/h) Read the entire page →
From page 369... ... 363 Figure F-6. Reasonableness check for lane capacity Model application examples Diverge segment A practical application of the LFR model is presented next for a 3-lane diverge segment (single period analysis) Read the entire page →
From page 370... ... 364 𝑓 = 0.3218 The lane flow ratio on lane 1 can then be obtained by: 𝐿𝐹𝑅 = −0.07779 ∙ ln 55003 ∙ 2050 + 0.3218 𝐿𝐹𝑅 = 33.0% The same procedure is applied to obtain the lane flow ratio on lane 2, using the respective coefficients from Table F-5: 𝑓 = 𝑎 + 𝐺 ∙ 𝑓 , + 𝑡 ∙ 𝑓 , + 𝑛 ∙ 𝑓 , + ∙ 𝑓 , 𝑓 = 0.0096 + 3 ∙ (−0.00960) Read the entire page →
From page 371... ... 365 • On-ramp demand flow rate (vR,m) : 428 veh/h • Freeway-to-freeway demand (vFF) Read the entire page →
From page 373... ... 367 𝐿𝐹𝑅 = − 0.00012 ∙ ln 45124 ∙ 2275.3 + 0.2310 𝐿𝐹𝑅 = 23.1% The same procedure is applied to obtain the LFR on lane 3, using the respective coefficients from Table F-6: 𝑓 , = 𝑎 + 𝐺 ∙ 𝑓 , + 𝑡 ∙ 𝑓 , + 𝐼𝐷 ∙ 𝑓 , + 𝑣 ,1000 ∙ 𝑓 , + 𝑣 ,1000 ∙ 𝑓 , + 𝐿 ∙ 𝑓 , + 𝑉𝑅 ∙ 𝑓 , 𝑓 , = 0.12 + (−0.5) Read the entire page →
From page 374... ... 368 Hence, the entire freeway-to-ramp flow will be located at the upstream freeway-weaving lane and v =0. Step 3: Calculate auxiliary lane flow (v0) Read the entire page →
From page 375... ... 369 Lane FFS Field observations have shown that operating speeds differ among lanes, and they are typically lower in shoulder lanes and higher in median lanes. Free-flow speeds were measured as the average speed for segment flow rates below 450 veh/h/ln. Read the entire page →
From page 376... ... 370 Based on the obtained results, models were developed to estimate individual lane FFS by applying a multiplying factor to the segment FFS. These models are shown in Figure F-8 for each lane. Read the entire page →
From page 377... ... 371 Figure F-9. Example of lane capacity estimation (French Camp, CA) Read the entire page →
From page 378... ... 372 Figure F-10. Relationship between segment capacity and individual lane capacity, by segment type and number of lanes For weaving segments, capacity distributions were observed to be significantly more complex and the breakdown observation method was not capable of providing reliable results from the selected dataset. Read the entire page →
From page 379... ... 373 Next, results were averaged by segment type and number of lanes. Figure F-11 presents the percent distribution of the total segment capacity across lanes (the numbers below the whisker boxes represent the average values of lane capacity) Read the entire page →
From page 380... ... 374 For each location from the dataset, base capacity was calculated using Equation F-28, as FFS is available from field measurements. Since this equation provides capacity values in passenger-car equivalents, a heavy vehicle factor fHV (as defined in HCM Equation 12-10) Read the entire page →
From page 381... ... 375 Figure F-12. Field measured and HCM estimated capacity values, for (a) Read the entire page →
From page 382... ... 376 With flow, capacity and FFS by lane determined, HCM equations can be used to estimate operating speeds on individual lanes. Segment-wise inputs of flow, capacity and FFS are based on the field measurements, and the developed methods previously described are applied to estimate their distribution among individual lanes. Read the entire page →
From page 383... ... 377 Next, the breakpoint values for each lane can be obtained: BP1 = [1000+ 40 x (75-FFS1) Read the entire page →

From page 357...

... 351 A P P E N D I X F Freeway Facilities Lane-by-Lane Analysis Data Collection The data collected for this part of the project include speed and flow data from selected detector station sensors in California, Virginia, Utah, Wisconsin, Minnesota, and Florida. These locations represent diverse operational and design conditions across the US.