For each tour, the Metrolina model predicts the choice of forward and
return time periods with the choices being Peak (PK) or Off-peak (OP).
Therefore, every tour falls under one of four categories depending on
the forward and return period, namely PK_PK, PK_OP, OP_PK, OP_OP. The
previous version of the model reverts to a trip-based approach after
destination choice. PK and OP matrices were generated by purpose on
which the mode choice models were evaluated. A TOD model then
subsequently splits the PK and OP mode choice output matrices by using
PA/AP factors to generate subperiod (AM, MD, PM and NT) matrices.
The updated Metrolina model deviates slightly from this approach.
Like in the previous model, the forward and return period (PK/OP) is
predicted for each tour. However, the mode choice is also tour-based,
and the time-of-day model is applied just before the creation of the OD
matrices. Therefore, there is a need to generate 4 sets of matrices (AM,
MD, PM and NT) from the tour level output.
This is done by analyzing the survey to apportion the appropriate
combination of PK_PK, PK_OP, OP_PK and OP_OP tours into sub-periods. For
example, the PK_PK combination has three possibilities (AM_AM, AM_PM and
PM_PM). The survey was used to obtain percentages of tours in each
subcategory by purpose. These are directly applied in the model.
The tables containing these factors are shown below.
Peak to Peak factors
|
|
Tour Distribution
|
|
|
Work
|
School
|
University
|
Shop
|
Other
|
AtWork
|
|
AM_AM
|
21,450
|
43,915
|
1,073
|
12,444
|
162,307
|
4,376
|
|
AM_PM
|
245,819
|
117,975
|
11,933
|
14,554
|
103,305
|
919
|
|
PM_PM
|
5,490
|
3,523
|
139
|
47,762
|
144,099
|
3,304
|
|
Total
|
272,759
|
165,413
|
13,145
|
74,759
|
409,711
|
8,599
|
|
|
|
|
|
|
|
|
|
Percentage Distribution
|
|
AM_AM
|
7.9%
|
26.5%
|
8.2%
|
16.6%
|
39.6%
|
50.9%
|
|
AM_PM
|
90.1%
|
71.3%
|
90.8%
|
19.5%
|
25.2%
|
10.7%
|
|
PM_PM
|
2.0%
|
2.1%
|
1.1%
|
63.9%
|
35.2%
|
38.4%
|
Peak to Off-Peak factors
|
|
Tour Distribution
|
|
|
Work
|
School
|
University
|
Shop
|
Other
|
AtWork
|
|
AM_MD
|
50,499
|
58,609
|
10,349
|
43,509
|
135,633
|
2,785
|
|
AM_NT
|
99,474
|
13,508
|
5,368
|
13,297
|
39,825
|
36
|
|
PM_NT
|
19,378
|
4,182
|
4,959
|
52,959
|
193,032
|
564
|
|
Total
|
169,350
|
76,299
|
20,675
|
109,765
|
368,490
|
3,386
|
|
|
|
|
|
|
|
|
|
Percentage Distribution
|
|
AM_MD
|
29.8%
|
76.58%
|
50.1%
|
39.6%
|
36.8%
|
82.3%
|
|
AM_NT
|
58.7%
|
17.7%
|
26.0%
|
12.1%
|
10.8%
|
1.1%
|
|
PM_NT
|
11.4%
|
5.5%
|
24.0%
|
48.2%
|
52.4%
|
16.7%
|
Off Peak to Peak factors
|
|
Tour Distribution
|
|
|
Work
|
School
|
University
|
Shop
|
Other
|
AtWork
|
|
MD_PM
|
32,206
|
3,243
|
1,002
|
62,524
|
155,120
|
5,407
|
|
NT_AM
|
7,567
|
1,866
|
568
|
2,653
|
15,894
|
72
|
|
NT_PM
|
76,713
|
13,621
|
133
|
5,241
|
28,077
|
0
|
|
Total
|
116,486
|
18,730
|
1,703
|
70,417
|
199,091
|
5,480
|
|
|
|
|
|
|
|
|
|
Percentage Distribution
|
|
MD_PM
|
27.6%
|
17.3%
|
58.8%
|
88.8%
|
77.9%
|
98.7%
|
|
NT_AM
|
6.5%
|
10.0%
|
33.3%
|
3.8%
|
8.0%
|
1.3%
|
|
NT_PM
|
65.9%
|
72.7%
|
7.8%
|
7.4%
|
14.1%
|
0.0%
|
Off Peak to Off Peak factors
|
|
Tour Distribution
|
|
|
Work
|
School
|
University
|
Shop
|
Other
|
AtWork
|
|
MD_MD
|
11,955
|
6,686
|
2,167
|
156,742
|
310,301
|
57,697
|
|
MD_NT
|
45,843
|
788
|
2,958
|
17,405
|
53,755
|
32
|
|
NT_MD
|
22,337
|
4,046
|
425
|
751
|
10,608
|
34
|
|
NT_NT
|
53,916
|
3,380
|
4,681
|
48,629
|
187,114
|
1,950
|
|
Total
|
134,052
|
14,900
|
10,232
|
223,527
|
561,779
|
59,714
|
|
Percentage Distribution
|
|
|
|
|
|
|
|
|
|
MD_MD
|
8.9%
|
44.9%
|
21.2%
|
70.1%
|
55.2%
|
96.6%
|
|
MD_NT
|
34.2%
|
5.3%
|
28.9%
|
7.8%
|
9.6%
|
0.1%
|
|
NT_MD
|
16.7%
|
27.2%
|
4.2%
|
0.3%
|
1.9%
|
0.1%
|
|
NT_NT
|
40.2%
|
22.7%
|
45.7%
|
21.8%
|
33.3%
|
3.3%
|
External Trip Fractions for Commercial Vehicles & Trucks
Table: External Trip
Fractions for Commercial Vehicles & Trucks by Time of Day
|
Time of Day (TOD)
|
EI
|
IE
|
EE
|
|
AM Peak
|
0.125
|
0.125
|
0.25
|
|
PM Peak
|
0.125
|
0.125
|
0.25
|
|
Midday
|
0.150
|
0.150
|
0.30
|
|
Night
|
0.100
|
0.100
|
0.20
|