Transcript Document

TRIP GENERATION
The Conventional “Four Step” Modelling Process
Hutchinson, 1973
• Shall I travel somewhere?
– The Trip Generation Step
• Where shall I go?
– The Trip Distribution Step
• Which mode of transport shall I use?
– The Modal Choice Step
• Which route shall I take?
– The Traffic Assignment Step
4 Tipe Pergerakan
• Eksternal  Eksternal, zona asal dan tujuan
berada diluar daerah kajian;
• Internal  Internal, salah satu zona asa atau
tujuan berada diluar daerah kajian;
• Internal  Internal, zona asal dan tujuan
berada didalam daerah kajian;
• Intrazona, zona asal dan tujuan berada
didalam satu zona tertentu.
Model Bangkitan Pergerakan
• Tujuan  menghasilkan model hubungan
yang mengaitkan parameter fungsi lahan
dengan jumlah pergerakan yang menuju dan
meninggalkan suatu zona
• Zona asal dan tujuan pergerakan biasanya
dikenal dengan istilah TRIP END
• Menggunakan data berbasis zona untuk
memodelkan besarnya pergerakan yang
terjadi.
TRIP-END DEFINITIONS
[Tamin, Ofyar Z, 2000]
TRIP-END DEFINITIONS
[Papacostas & Prevedouros, 1993]
Zone j
Zone i
RESIDENTIAL
RESIDENTIAL
Food Mart
NON RESIDENTIAL
Two trip ends; one origin
and one destination,
or two attractions
NON RESIDENTIAL
Two trip ends; one origin
and one destination,
or two productions
Klasifikasi Pergerakan Berdasarkan:
1. Tujuan
•
Kerja, sekolah, belanja, rekreasi
2. Waktu
•
Pagi, siang, dan sore hari
3. Karakteristik Individu
•
Tingkat pendapatan, pemilikan kendaraan,
ukuran dan struktur rumah tangga
Faktor yang Mempengaruhi:
1. Bangkitan Pergerakan untuk Manusia
•
Pendapatan, pemilikan kendaraan, ukuran &
struktur rumah tangga, nilai lahan, kepadatan
penduduk, aksesibilitas.
2. Tarikan Pergerakan untuk Manusia
•
Luas lantai kegiatan, jumlah lapangan
pekerjaan, aksesibilitas.
3. Bangkitan dan Tarikan Pergerakan untuk
Barang
•
Jumlah lapangan pekerjaan, jumlah tempat
pemasaran, luas lahan industri.
Alur Pemodelan Regresi:
The selected explanatory variables:
1. Must be linearly related to the dependent
variable,
2. Must be highly correlated with the
dependent variable,
3. Must not be highly correlated between
themselves,
4. Must lend themselves to relatively easy
projection.
Regression Models:
Simple
Y = a + bX
Linear
Y = a + bX
REGRESSION
Multiple
Y = a + b1X1 + … + bnXn
Non-Linear
Y = a + bx + cx2
Y = aXb
Regression Models:
Regression Models:
Regression Models:
Regression Models:
Regression Models:
Regression Models:
Regression Models:
Regression Models:
Regression Models:
Regression Models:
Regression Models:
Regression Models:
Regression Models:
Regression Models:
Regression Models:
Regression Models:
Regression Models:
Pearson Correlation Matrix:
Y
X1
X2
X3
X4
Y
X1
X2
X3
X4
1,00
0,32
0,92
0,95
0,62
1,00
0,25
0,19
0,03
1,00
0,99
0,29
1,00
0,33
1,00
Alternative Regression Models:
1. Y = a0 + a2X2
2. Y = b0 + b3X3
3. Y = c0 + c4X4
4. Y = d0 + d2X2 + d4X4
5. Y = e0 + e3X3 + e4X4
TRIP-RATE Analysis
[Papacostas & Prevedouros, 1993]
Trip-rate analysis refers to several models
that are based on the determination of the
average trip production or trip attraction
rates associated with the important trip
generators within the region.
TRIP-RATE Analysis
[Tamin, Ofyar Z, 2000]
Waktu
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
Perkantoran (smp/100m2)
Masuk
Keluar
Total
0.73
0.27
1.00
0.26
0.16
0.42
0.25
0.18
0.43
0.22
0.16
0.38
0.23
0.22
0.45
0.19
0.23
0.42
0.23
0.19
0.42
0.17
0.17
0.34
0.19
0.18
0.37
0.20
0.51
0.71
0.10
0.34
0.44
0.00
0.00
0.00
0.00
0.00
0.00
Pertokoan (smp/100m2)
Masuk
Keluar
Total
0.04
0.02
0.06
0.08
0.04
0.12
0.55
0.15
0.70
0.80
0.42
1.22
0.78
0.65
1.43
0.60
0.56
1.16
0.65
0.59
1.24
0.57
0.70
1.27
0.61
0.68
1.29
0.50
0.95
1.45
0.45
0.58
1.03
0.00
0.00
0.00
0.00
0.00
0.00
Hotel (smp/100m2)
Masuk
Keluar
Total
0.00
0.00
0.00
0.41
0.23
0.64
0.46
0.35
0.81
0.41
0.26
0.67
0.30
0.27
0.57
0.24
0.27
0.51
0.34
0.33
0.67
0.32
0.37
0.69
0.31
0.45
0.76
0.29
0.32
0.61
0.29
0.31
0.60
0.39
0.32
0.71
0.36
0.32
0.68
TRIP-RATE Analysis
[Tamin, Ofyar Z, 2000]
Perkantoran  42.250m2 , Pertokoan  30.250m2 , Hotel 16.200m2
Waktu
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
Perkantoran
Masuk
308
110
106
93
97
80
97
72
80
85
42
0
0
Keluar
114
68
76
68
93
97
80
72
76
215
144
0
0
Pertokoan
Masuk
12
24
166
242
236
182
197
172
185
151
136
0
0
Keluar
6
12
45
127
197
169
178
212
206
287
175
0
0
Hotel
Masuk
0
66
75
66
49
39
55
52
50
47
47
63
58
Keluar
0
37
57
42
44
44
53
60
73
52
50
52
52
Total
Masuk
321
200
347
401
382
301
349
296
315
283
225
63
58
Keluar
120
117
178
237
333
310
312
344
355
555
369
52
52
Parkir
201
284
453
617
666
657
694
646
606
334
190
201
207
TRIP-RATE Analysis
[Tamin, Ofyar Z, 2000]
800
Jumlah Kendaraan (SMP)
700
600
500
400
300
200
100
0
7:00
8:00
9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00
Waktu
Masuk
Keluar
Parkir
Cross-Classification Models
[Papacostas & Prevedouros, 1993]
Cross-classification (or category analysis)
models may be thought of as extensions
of the simple trip-rate models.
Although they can be calibrated as areaor zone-based models, in trip-generation
studies they are almost exclusively used
as disaggregate models.
Number of Trips per Household Size by Auto
Ownership obtained from Regional Study
Household Size
Auto Ownership
0
HH
1
Trips
HH
2+
Trips
HH
Trips
1
1,200
2,520
2,560
6,144
54
130
2
874
2,098
3,456
9,676
5,921
20,165
3+
421
1,137
2,589
8,026
8,642
33,704
Household Size
Trip Rates obtained from previous Matrix
Auto Ownership
0
1
2+
1
2.10
2.40
2.41
2
2.40
2.80
3.41
3+
2.70
3.10
3.90
Household Size
Forecast Number of Household in Study
Zone by Auto Ownership and Household Size
Auto Ownership
0
1
2+
1
25
125
3
2
32
175
254
3+
10
89
512
Household Size
Forecast Number of Trips in Zone determined
by multiplying Trip Rates by number of
Households in category
Auto Ownership
0
1
2+
1
53
300
7
2
77
490
865
3+
27
276
1,997
4,091
TRIP GENERATION