How closely this study's model reproduces what was observed: traffic
counts, federal traffic volumes, commuting and transit ridership. Checked
on the baseline scenario, the
study's baseline.
Published guidelines are shown for reference: they are guidelines, not
pass/fail standards, and the manuals that publish them say so.
Independent check the model never saw this dataPartly independent some of this data was used to fit the modelConsistency only the model was fitted to this dataNo observed data a check on the run itself
Across 39 count locations in the study area, the model carries 84 % of the counted traffic in the analysis hour.
Technical detail
Independent check: The model never sees traffic counts: demand comes from population, jobs and travel costs, not from the counts.
By count class
Count class
N
Geh lt5 pct
Geh lt10 pct
Median geh
Sum ratio
Rmse
Pct rmse
no_partner
0
—
—
—
—
—
—
count_defect
0
—
—
—
—
—
—
reversible_closed
0
—
—
—
—
—
—
express_hov
0
—
—
—
—
—
—
wrong_road
0
—
—
—
—
—
—
out_of_area
6
66.7
66.7
0.63
0.92
153
13.8
clean
39
25.6
46.2
10.3
0.84
928
38.2
Clean counts by road type
Road type
N
Geh lt5 pct
Geh lt10 pct
Median geh
Sum ratio
Rmse
Pct rmse
freeway
29
17.2
34.5
13.1
0.83
1,068
35.2
multilane_arterial
4
25
50
10.9
0.95
349
27.7
smaller_arterial
6
66.7
100
3.55
0.97
72.7
26.6
Headline uses the clean counts; all counts together: 0.85×.
Sources
Traffic counts (2025-06-04 to 2025-06-25; 45 counts)
Percent RMSE by volume group
Independent check
38 %
Typical error at a single count location, as a share of the average count.
Published guideline. FHWA (2010) Figure 9.8 compiles %RMSE targets by volume group from Florida, Ohio and Oregon: from about 100 % on links under 5,000 vehicles/day down to about 20 % above 50,000/day (values read off the published chart). Those groups are daily volumes (AADT); this model's counts are one analysis hour, so its groups are not the same links and the comparison is indicative only.
FHWA, Travel Model Validation and Reasonableness Checking Manual, 2nd ed. (FHWA-HEP-10-042, 2010), §9.1.4, Figure 9.8
Technical detail
Independent check: The model never sees traffic counts: demand comes from population, jobs and travel costs, not from the counts.
Clean counts
Volume group (veh/h)
Counts
%RMSE
RMSE (veh/h)
Modelled ÷ counted
Median GEH
<500
6
26.6
72.7
0.97
3.55
500-1000
3
35.1
299
0.71
12.5
1000-2000
12
32.5
459
0.86
8.61
2000-4000
9
47.5
1,252
0.92
22.2
>=4000
9
24.6
1,359
0.81
12.5
All counts
Volume group (veh/h)
Counts
%RMSE
RMSE (veh/h)
Modelled ÷ counted
<500
8
47.9
146
0.77
500-1000
3
35.1
299
0.71
1000-2000
16
27.9
398
0.90
2000-4000
9
47.5
1,252
0.92
>=4000
9
24.6
1,359
0.81
Sources
Traffic counts (2025-06-04 to 2025-06-25; 45 counts)
Modelled vs observed, by count
Independent check
45 counts: 6 out of study area, 39 clean.
Clean countsOther classes (left out of the headlines)
Technical detail
Independent check: The model never sees traffic counts: demand comes from population, jobs and travel costs, not from the counts.
Modelled side: assigned demand + background preload (truck_and_band); two-way counts sum the edge and its partner (reverse edge or opposite carriageway); a one-way street is scored alone.
Count classes (each count gets the first that applies)
Count class
Counts
What it means
Two-way, no partner carriageway
0
A count of both directions with no opposite carriageway to pair it with: a ramp, a freeway with none found, or a divided road turning undivided at the count. It is not scored, because the model could only compare it with part of the road.
Faulty count
0
The count's own records show a fault: one lane reads 0 in every hour, so the total misses it, or the count is doubled (every value even, or more vehicles in one lane than a lane can carry).
Reversible lane, closed
0
A lane closed in this direction at the count hour, such as a reversible express lane, so there is no traffic to compare.
Express / HOV lane
0
On or beside an express or HOV lane. Their traffic depends on tolls and occupancy rules, so they are scored apart.
Matched to the wrong road
0
Matched to the wrong road: more traffic than the matched road can carry, or a station whose directions matched different roads.
Out of study area
6
Outside the study area, where most traffic comes from outside the model.
Clean
39
Everything else: the fair test of the model, used in the headlines.
Counts left out of the headlines (6)
Count
Class
Why it is left out
Counted
Modelled
tmas:000508:198981409-7904578673-0
Out of study area
1.9 km outside the study area
1,571
1,597
tmas:000508:7904578674-198980959-0
Out of study area
1.9 km outside the study area
1,852
1,826
tmas:00N210:197356522-1717464295-0
Out of study area
4.1 km outside the study area
1,254
1,258
tmas:00N210:375468894-1717472846-0
Out of study area
4.1 km outside the study area
1,204
1,216
tmas:00N312:1887637316-359963416-0
Out of study area
3.6 km outside the study area
405
138
tmas:00N312:528365952-1887637321-0
Out of study area
3.6 km outside the study area
400
138
Largest differences (GEH)
Count
Counted
Modelled
GEH
Class
tmas:000121:229613443-229584346-0
3,594
6,362
39.2
clean
tmas:000121:229598250-229627704-0
5,585
3,102
37.7
clean
tmas:00N221:1837763255-349011994-0
6,541
4,630
25.6
clean
tmas:00N102:229873892-229594682-0
5,812
4,075
24.7
clean
tmas:00N101:5233358972-229626111-0
2,755
1,406
29.6
clean
Sources
Traffic counts (2025-06-04 to 2025-06-25; 45 counts)
Peak-direction share at paired stations
Independent check
58.4 % vs 56.3 %
At 19 count stations with both directions clean, 56.3 % of the counted traffic runs in the busier (peak) direction; the model puts 58.4 % there. Typical station error 10.4 percentage points.
ModelCounted
Technical detail
Independent check: The model never sees traffic counts: demand comes from population, jobs and travel costs, not from the counts.
Difference, model − counted
2.1 pp
Modelled ÷ counted, peak direction
0.88×
Modelled ÷ counted, counter-peak direction
0.80×
Two-way counts (no split)
0
The peak direction is the one with more counted traffic. The two ratios tell a level error (both off together) from a split error (the two apart).
By road type
Road type
Stations
Counted peak share
Modelled peak share
Difference (pp)
Median station error (pp)
Modelled ÷ counted, peak
Modelled ÷ counted, counter-peak
Freeways
14
56.2 %
57.3 %
1.13
8.68
0.85
0.81
Multilane arterial
2
59.6 %
73.5 %
13.9
13.8
1.18
0.63
Arterials
3
53.9 %
62.9 %
9.05
10.9
1.14
0.78
Paired stations
Station
Road type
Peak / counter
Counted peak
Counted counter
Modelled peak
Modelled counter
Counted peak share (%)
Modelled peak share (%)
Error (pp)
000053
Arterials
5 / 1
180
156
203
61.1
53.6
76.8
23.3
000121
Freeways
3 / 7
5,585
3,594
3,102
6,362
60.8
32.8
-28.1
000155
Freeways
3 / 7
2,236
1,734
2,036
1,014
56.3
66.7
10.4
000502
Freeways
7 / 3
2,282
1,396
2,610
1,198
62
68.5
6.50
000515
Arterials
3 / 7
217
146
229
189
59.8
54.8
-5
000531
Freeways
7 / 3
1,188
1,084
1,480
694
52.3
68.1
15.8
000539
multilane_arterial
7 / 3
1,859
1,231
2,279
788
60.2
74.3
14.1
000622
Freeways
7 / 3
993
753
978
345
56.9
73.9
17
000628
Freeways
7 / 3
1,614
1,257
1,786
670
56.2
72.7
16.5
000707
Freeways
7 / 3
2,126
1,422
2,631
1,322
59.9
66.5
6.60
000784
Arterials
5 / 1
488
455
575
343
51.7
62.6
10.9
000N97
Freeways
5 / 1
2,524
2,300
1,523
1,231
52.3
55.3
3
000N99
Freeways
1 / 5
4,672
2,944
3,864
1,710
61.3
69.3
8
00N100
Freeways
3 / 7
6,065
4,024
6,614
3,511
60.1
65.3
5.20
00N101
Freeways
5 / 1
2,974
2,755
2,226
1,406
51.9
61.3
9.40
00N102
Freeways
1 / 5
5,812
5,335
4,075
4,232
52.1
49.1
-3.10
00N214
Freeways
5 / 1
1,774
1,258
793
1,260
58.5
38.6
-19.9
00N221
Freeways
1 / 5
6,772
6,541
5,970
4,630
50.9
56.3
5.50
00N310
multilane_arterial
1 / 5
1,142
804
1,254
486
58.7
72.1
13.4
Directional stations not paired (4)
Source
Station
Why it is not paired
tmas
000508
partner not clean (out_of_area)
tmas
00N106
partner not matched
tmas
00N210
partner not clean (out_of_area)
tmas
00N312
partner not clean (out_of_area)
Sources
Traffic counts (2025-06-04 to 2025-06-25; 45 counts)
GEH statistic
Independent checkAdvanced
26 % below 5
Share of clean count locations whose GEH is below 5.
Published guideline. GEH below 5 on more than 85 % of links. TAG applies this to models whose trip tables may have been adjusted to counts (matrix estimation); a demand model without that step should not be expected to reach it.
UK DfT, TAG Unit M3.1 Highway Assignment Modelling (May 2026), §3.3.10–3.3.11, Table 2
Technical detail
Independent check: The model never sees traffic counts: demand comes from population, jobs and travel costs, not from the counts.
GEH below 10, clean
46 %
Median GEH, clean
10.3
By count class
Count class
N
Geh lt5 pct
Geh lt10 pct
Median geh
Sum ratio
Rmse
Pct rmse
no_partner
0
—
—
—
—
—
—
count_defect
0
—
—
—
—
—
—
reversible_closed
0
—
—
—
—
—
—
express_hov
0
—
—
—
—
—
—
wrong_road
0
—
—
—
—
—
—
out_of_area
6
66.7
66.7
0.63
0.92
153
13.8
clean
39
25.6
46.2
10.3
0.84
928
38.2
Clean counts by road type
Road type
N
Geh lt5 pct
Geh lt10 pct
Median geh
Sum ratio
Rmse
Pct rmse
freeway
29
17.2
34.5
13.1
0.83
1,068
35.2
multilane_arterial
4
25
50
10.9
0.95
349
27.7
smaller_arterial
6
66.7
100
3.55
0.97
72.7
26.6
Sources
Traffic counts (2025-06-04 to 2025-06-25; 45 counts)
TAG flow criteria
Independent checkAdvanced
33 %
Share of the 39 clean counts within TAG's flow criteria.
Published guideline. More than 85 % of links within 100 veh/h below 700 veh/h, within 15 % at 700–2,700 veh/h, and within 400 veh/h above 2,700 veh/h. Defined on hourly flows, the model's own unit. As with GEH, TAG's models may have had matrix estimation.
UK DfT, TAG Unit M3.1 Highway Assignment Modelling (May 2026), §3.3.10–3.3.11, Table 2
Technical detail
Independent check: The model never sees traffic counts: demand comes from population, jobs and travel costs, not from the counts.
All counts
38 %
Sources
Traffic counts (2025-06-04 to 2025-06-25; 45 counts)
Vehicle-miles travelled
Regional VMT vs HPMS
Independent check
0.79×
Modelled vehicle-km on freeways, arterials and major collectors, against federal HPMS traffic volumes.
Published guideline. Florida's guideline, quoted by FHWA (2010): regional VMT within ±5 % (acceptable) or ±2 % (preferable). Freeways, arterials and major collectors only; local roads and ramps have no HPMS counterpart.
FHWA, Travel Model Validation and Reasonableness Checking Manual, 2nd ed. (FHWA-HEP-10-042, 2010), §9.1.4
Technical detail
Independent check: HPMS traffic volumes are not used to build or calibrate travel demand. They set the background through-traffic and truck layer, which is counted on both sides.
Model
3,693,492 veh-km per analysis hour
HPMS
4,701,862 veh-km per analysis hour
Passenger demand only
0.71×
Observed side uses TMAS-measured per-cell K-factors from this study's own volumes (provenance.hour_factor). Trucks still carry the assumed truck_hour_factor. Caveat: the measurement uses whatever months observed_counts fetched — early summer on studies to date — and summer mornings carry no school traffic, so school-year K is likely somewhat higher. Direction known, size unmeasured.
Sources
FHWA Highway Performance Monitoring System (HPMS) (vintage 2024)
Hour factor (AADT → analysis hour) (measured)
VMT by road type vs HPMS
Independent check
0.83×, 0.73×
Modelled ÷ HPMS vehicle-km, by road type (freeways and expressways; arterials and major collectors), on the matched basis; the thinner bar is the netted ratio.
Matched (headline)Netted
* No HPMS counterpart for most of its length; left out of the regional ratio.
Published guideline. FHWA (2010) Table 9.2 compiles VMT guidelines by functional class from five agencies: freeways within 6–7 %; principal arterials 7–15 %; minor arterials 10–15 %; collectors 15–25 %. This comparison groups principal arterials, minor arterials and major collectors into one cell, so its guideline is the range across those classes. HPMS does not inventory local roads; that row is a coverage artifact, not a finding.
FHWA, Travel Model Validation and Reasonableness Checking Manual, 2nd ed. (FHWA-HEP-10-042, 2010), §9.1.4, Table 9.2
Technical detail
Independent check: HPMS traffic volumes are not used to build or calibrate travel demand. They set the background through-traffic and truck layer, which is counted on both sides.
Road type
Model (veh-km per analysis hour)
HPMS
Ratio
Netted
Gross
Passenger only
Freeways and expressways
2,093,334
2,460,610
0.83
0.85
0.77
0.87
Arterials and major collectors
1,829,993
2,241,252
0.73
0.82
0.81
0.82
Local roads and unclassified
247,299
59,971
0.65
4.12
3.78
4.18
Observed side uses TMAS-measured per-cell K-factors from this study's own volumes (provenance.hour_factor). Trucks still carry the assumed truck_hour_factor. Caveat: the measurement uses whatever months observed_counts fetched — early summer on studies to date — and summer mornings carry no school traffic, so school-year K is likely somewhat higher. Direction known, size unmeasured.
N sections cached
N sections in area
N edges model
N edges wholly outside area
Model vmt share in area
N edges bound
N edges full basis
N edges pruned floor
N edges eligible
Bind rate
Preload available
Disposition available
Coverage
34,348
21,513
87,131
8,170
0.93
36,171
214,391
127,260
47,724
0.76
yes
yes
Sources
FHWA Highway Performance Monitoring System (HPMS) (vintage 2024)
Hour factor (AADT → analysis hour) (measured)
Congestion
Volume-to-capacity distribution
No observed dataAdvanced
0.2 %
Share of directional road-km at a volume-to-capacity ratio of 0.85 or more in the analysis hour. A reasonableness check: no observed data.
Technical detail
No observed data: A reasonableness check: there is no observed side.
Road type
Directional km
V/C ≥ 0.85
V/C ≥ 1.0
collector
13,911
0.001
0.000
freeway
1,855
0.020
0.003
local
10,619
0.001
0.000
multilane_arterial
1,901
0.004
0.002
ramp
42.9
0.003
0.000
smaller_arterial
2,013
0.003
0.001
all
30,342
0.002
0.001
directional road-km; assigned + background vehicles over directional capacity.
Trip lengths
Work trip lengths vs LODES
UnavailableAdvanced
The model's work trips by origin and destination were not saved for this study ([demand.balancing] is off).
Commute mode share
Commute mode share across the region
Partly independent
r = 0.77
How closely the model's car share of work trips follows the ACS from tract to tract (correlation weighted by commuters). Calibration fits the region's shares, not this pattern.
Car r = 0.77
Transit r = 0.69
Walk r = 0.70
Bicycle r = 0.36
Work-trip mode share by residential density (people per sq mi), model vs ACS, with the ACS 90 % margin of error.
ModelACS
Technical detail
Partly independent: Calibration fits the region's work mode shares to this same ACS table, so the regional level is consistency-only. How the shares vary from tract to tract is not fitted.
Mode
Model modes
Weighted correlation
Tracts
car
CAR, CAR_NONHH
0.77
521
transit
TRANSIT
0.69
521
walk
WALK
0.70
521
bicycle
BICYCLE
0.36
521
Mode
People / sq mi
Tracts
Model share
ACS share
ACS ± (90 %)
ACS commuters
car
<1,000
152
0.99
0.97
0.023
223,269
car
1,000–2,500
87
0.98
0.97
0.034
152,479
car
2,500–5,000
150
0.96
0.95
0.025
279,667
car
5,000–10,000
115
0.94
0.93
0.030
200,415
car
10,000–20,000
13
0.87
0.87
0.071
28,312
car
≥20,000
6
0.77
0.54
0.052
10,273
transit
<1,000
152
0.001
0.003
0.001
223,269
transit
1,000–2,500
87
0.007
0.010
0.002
152,479
transit
2,500–5,000
150
0.014
0.013
0.002
279,667
transit
5,000–10,000
115
0.029
0.028
0.003
200,415
transit
10,000–20,000
13
0.047
0.043
0.012
28,312
transit
≥20,000
6
0.070
0.075
0.018
10,273
walk
<1,000
152
0.006
0.012
0.002
223,269
walk
1,000–2,500
87
0.011
0.012
0.002
152,479
walk
2,500–5,000
150
0.020
0.020
0.002
279,667
walk
5,000–10,000
115
0.025
0.026
0.003
200,415
walk
10,000–20,000
13
0.078
0.067
0.012
28,312
walk
≥20,000
6
0.15
0.36
0.027
10,273
bicycle
<1,000
152
0.001
0.001
0.001
223,269
bicycle
1,000–2,500
87
0.002
0.001
0.001
152,479
bicycle
2,500–5,000
150
0.003
0.002
0.001
279,667
bicycle
5,000–10,000
115
0.004
0.005
0.001
200,415
bicycle
10,000–20,000
13
0.006
0.013
0.005
28,312
bicycle
≥20,000
6
0.009
0.017
0.009
10,273
Car
ACS drove alone + carpooled + motorcycle vs model car + car as passenger (CAR_NONHH)
Weighting
tract ACS commuters × share of the tract inside the zones
Density
2020 Census population per square mile of land
Sources
ACS 5-year estimates, census tracts (B08301) (vintage 2018–2022)
2020 Census block population (density, crosswalk) (vintage 2020)
Transit
Transit boardings vs NTD
Independent check
1.37×
Modelled boardings in the analysis period ÷ NTD's average weekday × the share of weekday transit trips in that period (11.3 %). 6,182 modelled, 4,528 observed.
ModelNTD × period share
Published guideline. Florida's guideline, quoted by FHWA (2010): regional estimated-over-observed transit boardings within ±9 % (acceptable) or ±3 % (preferable). FHWA adds that when the trips come from the model's own mode choice rather than an observed rider survey, as here, "desired criteria might be less stringent" (§9.2.4).
FHWA, Travel Model Validation and Reasonableness Checking Manual, 2nd ed. (2010), §9.2.4, Table 9.9
2016–17 peaking, before the pandemic; transit peaks have flattened since, so the share may run high
NTD report year
2,025
Left out
demand response, vanpool, taxi, jitney (the model does not route them)
transit trips × rides on the routed path; NTD counts unlinked trips (boardings)
Sources
National Transit Database (FTA), annual Service by mode and time period (vintage report year 2025; data.transportation.gov wwdp-t4re; fetched 2026-11-07)
NTD agency match (ntd_match.toml) (vintage 2026-11-06; connector-rollout session, on Taylor's go-ahead)
Transit passenger-miles vs NTD
Independent check
1.09×
Modelled passenger-miles in the analysis period ÷ NTD's average weekday × the share of weekday transit trips in that period (11.3 %). 22,038 modelled, 20,224 observed.
ModelNTD × period share
Published guideline. FHWA (2010) suggests the regional VMT guideline "might be considered" for passenger-miles: e.g. ±5 %. FHWA adds that when the trips come from the model's own mode choice rather than an observed rider survey, as here, "desired criteria might be less stringent" (§9.2.4).
FHWA, Travel Model Validation and Reasonableness Checking Manual, 2nd ed. (2010), §9.2.4
2016–17 peaking, before the pandemic; transit peaks have flattened since, so the share may run high
NTD report year
2,025
Left out
demand response, vanpool, taxi, jitney (the model does not route them)
Routing keeps each ride's in-vehicle time, not its distance. Ride miles are that time × the route's average scheduled speed, so a ride on the route's slow downtown end is counted a little long, and one on its fast outer end a little short.
Sources
National Transit Database (FTA), annual Service by mode and time period (vintage report year 2025; data.transportation.gov wwdp-t4re; fetched 2026-11-07)
NTD agency match (ntd_match.toml) (vintage 2026-11-06; connector-rollout session, on Taylor's go-ahead)
Average transit trip length
Independent checkAdvanced
3.56 vs 4.47 mi
Average miles per boarding, model vs NTD (passenger-miles ÷ boardings).
ModelNTD
Technical detail
Independent check: the model never saw this data.
Mode group
Model (mi)
NTD (mi)
Includes partial agencies
bus
3.56
4.47
no
A ratio, so it needs no period scaling; partial-coverage agencies are included and flagged.
Sources
National Transit Database (FTA), annual Service by mode and time period (vintage report year 2025; data.transportation.gov wwdp-t4re; fetched 2026-11-07)
ACS commute times are self-reported and door to door. They cluster at 15, 30 and 60 minutes and run above a network travel time, which for car leaves out parking search and the walk at each end. Read the shape before the level.
ACS car is car, truck or van (drove alone and carpooled); its bicycle time is lumped with taxi, motorcycle and other means, so there is no bicycle row. The ACS means are county-level: tract-level aggregate times are mostly suppressed.
Share of work trips by commute time, per mode. Coincidence is the overlap of the two distributions (1 = identical).
Car coincidence 0.83
Transit coincidence 0.44
Walk coincidence 0.43Consistency only
ModelACS
Technical detail
Partly independent: Calibration fits work walk durations to this same ACS table, so the walk rows are consistency-only; car and transit are independent.
Mode
Coincidence
ACS workers
Independence
car
0.83
847,575
independent
transit
0.44
13,476
independent
walk
0.43
20,711
consistency
ACS commute times are self-reported and door to door. They cluster at 15, 30 and 60 minutes and run above a network travel time, which for car leaves out parking search and the walk at each end. Read the shape before the level.
Sources
ACS 5-year estimates, census tracts (B08134) (vintage 2018–2022)
Run quality
Assignment convergence
No observed data
1.2×10-6
Relative gap when the assignment stopped, after 2 iterations of tapb; target 1.0×10-5. Converged.
Published guideline. A relative gap of 10⁻⁴ (0.01 %) for link flows stable enough to compare scenarios; TAG asks for below 0.1 % on a base-year model.
Boyce, Ralevic-Dekic & Bar-Gera, "Convergence of Traffic Assignments: How Much Is Enough?", Journal of Transportation Engineering 130(1):49–55 (2004); UK DfT, TAG Unit M3.1 Highway Assignment Modelling (May 2026), Table 4
Technical detail
No observed data: a check on the run itself.
Relative gap as the assignment solver reports it: total travel time on the loaded network minus the travel time if every trip took its current shortest path, divided by the former. Zero is a perfect user equilibrium.
Demand-feedback convergence
No observed data
Converged at iteration 2
Demand and assignment re-run until travel times and the trip table stop changing.
Change in the trip table between outer iterations (RMSE ÷ mean), log scale.
Technical detail
No observed data: a check on the run itself.
Iteration
Assignment gap
Travel-time change
Trip-table change
Total-trip change
Criteria met
1
0.000
0.000
0.028
0.001
no
2
0.000
0.000
0.000
0.000
yes
Time gap tol
0.025
Od rmse tol
0.005
Total trips tol
0.005
Max outer
10
Min outer
2
Noise floor
Unavailable
The noise floor was not measured for this study ([assignment.feedback] measure_noise_floor is off).
Mode-choice calibration residual
Consistency only
residual 0.046
Converged at iteration 2 of 2; tolerance 0.050. Detail on the Calibration page.
Technical detail
Consistency only: Calibration fits these targets by construction.
Coverage
No observed data
822 zones
13 of 835 zones were too sparse to model; their residents and destinations moved to a neighbouring zone, not lost. Every home-based trip was assigned a destination. Every trip home was placed.
Technical detail
No observed data: a check on the run itself.
N zones original
835
N zones retained
822
N zones dropped
13
Pop before
2,071,589
Pop after
2,071,589
Snap method
nearest_zone
Generation pool period
247,786
Assigned person trips period
247,786
Unassigned person trips period
-0.003
Relative gap
0.000
Status
ok
Generated
54,609
Distributed
54,609
Unreachable
-0.001
Unreachable share
-0.000
Max column gap rel
0.000
What this was checked against
No observed data
12 observed sources.
Source
Vintage
From
To
Used by
Traffic counts
—
2025-06-04
2025-06-25
A3, A5, A7, A8, A1, A2, A9
FHWA Highway Performance Monitoring System (HPMS)
2024
—
—
B1, B2
Hour factor (AADT → analysis hour)
—
—
—
B1, B2
ACS 5-year estimates, census tracts (B08301)
2018–2022
—
—
E2
2020 Census block population (density, crosswalk)
2020
—
—
E2
National Transit Database (FTA), annual Service by mode and time period