Applying ANN and TM to Build a Prediction Model for the Site Selection of a Convenience Store
Abstract
:1. Introduction
2. Literature Review
3. Methodology
3.1. BPN Network
- Yj: output data;
- f: transfer function;
- Wij: weighting of connecting node i to node j;
- Xi: input data;
- θj: threshold.
3.2. TM
4. Case Study
4.1. Data Collection
4.2. Neural Network Parameter Design
- Neural network design (number of units in the hidden layer)
- 2.
- Learning iterations
- 3.
- Learning rate and momentum term
- 4.
- Transfer function
4.3. TM Analytical Results
4.4. Experimental Results and Validation
4.5. Robustness Evaluation
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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No | Factor | Definition | |
---|---|---|---|
A | Customer source | A1 | Residential area (dominated by the residential population surrounding the site, with fixed consumption and higher turnover on holidays and non-work-days compared with weekdays) |
A0 | Business area (dominated by transient populations surrounding the site engaging in commercial activities in the area, with a higher number of customers on weekdays) | ||
B | Competitiveness of other CVSs | The number of other chain CVSs within a radius of 200 M + self-owned CVSs within a radius of 100 M | |
C | Competitiveness of supermarkets | The number of supermarkets and other types of retail stores within a radius of 200 M | |
D | Customer flow | Pedestrian flow through the site + the number of motorcycles passing through the site | |
E | Major customer type | (1) Workers (2) Students (3) Pedestrian flow (4) Nearby residents | |
F | The number of households | The number of effective households within a radius of 100M | |
G | Store location | (1) Intersection of two parkways (between main streets) (2) Intersection of one parkway and byway (between minor streets) (3) Off intersection | |
H | Visibility | The signboard is visible from 100 m away | |
I | Usable area | The area of the store (greater than 75 m2 or not) |
No | A1 | A2 | B | C | D | E | F | G | H | I | Predict | No | A1 | A2 | B | C | D | E | F | G | H | I | Predict |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 1 | 0 | 100 | 60 | 15.16 | 100 | 17.16 | 80 | 50 | 1.1 | 70,400 | 49 | 1 | 0 | 100 | 100 | 14.58 | 80 | 21.32 | 80 | 50 | 1.1 | 52,790 |
2 | 1 | 0 | 20 | 100 | 14.83 | 80 | 30.52 | 50 | 100 | 1.1 | 60,300 | 50 | 1 | 0 | 10 | 20 | 7.53 | 100 | 3.28 | 80 | 100 | 1.1 | 47,500 |
3 | 1 | 0 | 65 | 55 | 12.83 | 80 | 14.44 | 80 | 50 | 1.1 | 43,040 | 51 | 1 | 0 | 100 | 100 | 13.44 | 0 | 5.4 | 70 | 100 | 1.1 | 60,800 |
4 | 1 | 0 | 10 | 100 | 5.54 | 100 | 8.56 | 80 | 100 | 1.1 | 49,700 | 52 | 1 | 0 | 20 | 100 | 9.13 | 100 | 15.48 | 50 | 100 | 1.1 | 73,200 |
5 | 1 | 0 | 100 | 40 | 0.05 | 80 | 2.88 | 100 | 100 | 1.1 | 65,000 | 53 | 1 | 0 | 100 | 100 | 10.07 | 100 | 6.48 | 80 | 100 | 1.1 | 69,600 |
6 | 1 | 0 | 100 | 100 | 10.30 | 100 | 28.92 | 80 | 100 | 1.1 | 87,200 | 54 | 1 | 0 | 10 | 0 | 11.41 | 80 | 13.88 | 80 | 100 | 1.1 | 33,500 |
7 | 0 | 1 | 20 | 100 | 11.76 | 80 | 6.75 | 80 | 50 | 1.1 | 32,550 | 55 | 1 | 0 | 65 | 100 | 6.30 | 80 | 7.96 | 80 | 100 | 1.1 | 63,000 |
8 | 1 | 0 | 15 | 5 | 12.73 | 100 | 3.68 | 50 | 100 | 1.1 | 34,700 | 56 | 1 | 0 | 100 | 50 | 7.89 | 80 | 5.96 | 50 | 100 | 1.1 | 39,020 |
9 | 1 | 0 | 100 | 100 | 1.54 | 80 | 0 | 100 | 100 | 1.1 | 47,050 | 57 | 1 | 0 | 20 | 75 | 6.84 | 80 | 26.88 | 80 | 50 | 1.1 | 54,600 |
10 | 1 | 0 | 0 | 50 | 5.44 | 80 | 4.12 | 50 | 0 | 1.1 | 32,900 | 58 | 1 | 0 | 30 | 100 | 9.49 | 100 | 4.28 | 50 | 100 | 1.1 | 44,200 |
11 | 1 | 0 | 100 | 100 | 4.06 | 80 | 3.12 | 50 | 50 | 1.1 | 55,000 | 59 | 1 | 0 | 40 | 100 | 12.70 | 100 | 7.04 | 50 | 50 | 1.1 | 39,100 |
12 | 1 | 0 | 30 | 100 | 14.80 | 80 | 28.2 | 80 | 50 | 1.1 | 68,000 | 60 | 1 | 0 | 60 | 100 | 6.69 | 100 | 50.08 | 80 | 50 | 1.1 | 85,200 |
13 | 1 | 0 | 10 | 50 | 6.15 | 80 | 32.04 | 20 | 50 | 1.1 | 33,000 | 61 | 1 | 0 | 30 | 75 | 9.03 | 100 | 13.72 | 50 | 50 | 1.1 | 49,000 |
14 | 1 | 0 | 100 | 100 | 3.21 | 80 | 4.16 | 50 | 0 | 1.1 | 52,600 | 62 | 1 | 0 | 5 | 75 | 5.35 | 80 | 19.44 | 50 | 100 | 1.1 | 43,800 |
15 | 1 | 0 | 25 | 100 | 10.50 | 100 | 4.8 | 50 | 50 | 1.1 | 40,700 | 63 | 1 | 0 | 10 | 100 | 7.10 | 80 | 10.8 | 80 | 50 | 1.1 | 32,520 |
16 | 1 | 0 | 25 | 100 | 24.80 | 100 | 27.48 | 80 | 100 | 1.1 | 49,080 | 64 | 1 | 0 | 10 | 100 | 5.63 | 80 | 4.96 | 80 | 50 | 1.1 | 50,000 |
17 | 1 | 0 | 10 | 35 | 14.48 | 100 | 13.44 | 80 | 50 | 1.1 | 49,000 | 65 | 1 | 0 | 100 | 100 | 6.91 | 80 | 20.68 | 80 | 50 | 0.9 | 46,750 |
18 | 1 | 0 | 20 | 60 | 12.59 | 100 | 19.92 | 80 | 100 | 1.1 | 59,100 | 66 | 1 | 0 | 10 | 100 | 5.48 | 80 | 14.28 | 50 | 100 | 1.1 | 41,500 |
19 | 1 | 0 | 100 | 100 | 11.05 | 80 | 17.8 | 50 | 50 | 0.9 | 50,000 | 67 | 1 | 0 | 15 | 100 | 13.49 | 100 | 14.72 | 100 | 50 | 1.1 | 43,300 |
20 | 0 | 1 | 10 | 100 | 10.08 | 100 | 13.25 | 50 | 100 | 1.1 | 45,000 | 68 | 1 | 0 | 100 | 60 | 10.54 | 100 | 6 | 20 | 100 | 1.1 | 51,100 |
21 | 1 | 0 | 100 | 100 | 6.50 | 100 | 13.24 | 50 | 100 | 1.1 | 61,600 | 69 | 1 | 0 | 100 | 100 | 3.91 | 100 | 6.92 | 80 | 100 | 1.1 | 63,200 |
22 | 1 | 0 | 10 | 100 | 6.06 | 100 | 5.24 | 80 | 50 | 1.1 | 43,800 | 70 | 1 | 0 | 0 | 45 | 6.71 | 100 | 10.96 | 70 | 50 | 1.1 | 38,500 |
23 | 1 | 0 | 30 | 75 | 4.56 | 0 | 52.4 | 80 | 50 | 1.1 | 74,500 | 71 | 1 | 0 | 20 | 100 | 10.17 | 80 | 36.96 | 50 | 50 | 1.1 | 59,100 |
24 | 1 | 0 | 20 | 80 | 2.81 | 100 | 2.08 | 50 | 100 | 1.1 | 42,500 | 72 | 1 | 0 | 20 | 100 | 4.38 | 100 | 8.56 | 80 | 100 | 1.1 | 55,200 |
25 | 1 | 0 | 10 | 100 | 9.20 | 100 | 13.48 | 80 | 50 | 1.1 | 45,100 | 73 | 1 | 0 | 40 | 100 | 4.74 | 0 | 15.68 | 80 | 50 | 1.1 | 45,700 |
26 | 1 | 0 | 100 | 100 | 24.48 | 100 | 25.88 | 80 | 100 | 1.1 | 73,700 | 74 | 1 | 0 | 20 | 55 | 5.51 | 0 | 25.76 | 80 | 100 | 1 | 41,800 |
27 | 1 | 0 | 30 | 100 | 8.80 | 80 | 22.56 | 80 | 100 | 1.1 | 68,400 | 75 | 1 | 0 | 40 | 100 | 8.65 | 80 | 23.2 | 80 | 50 | 1.1 | 63,200 |
28 | 1 | 0 | 40 | 55 | 6.17 | 80 | 11 | 50 | 100 | 1.1 | 44,100 | 76 | 1 | 0 | 20 | 100 | 8.75 | 80 | 16.76 | 50 | 100 | 1.1 | 51,000 |
29 | 1 | 0 | 20 | 55 | 7.43 | 100 | 14.32 | 80 | 100 | 1.1 | 56,100 | 77 | 1 | 0 | 10 | 65 | 9.47 | 0 | 42 | 20 | 100 | 1.1 | 39,500 |
30 | 1 | 0 | 20 | 100 | 2.62 | 100 | 10.64 | 80 | 100 | 1.1 | 51,300 | 78 | 1 | 0 | 20 | 100 | 10.31 | 80 | 19.72 | 80 | 100 | 1.1 | 50,700 |
31 | 1 | 0 | 100 | 100 | 7.24 | 100 | 5.44 | 50 | 100 | 1.1 | 52,100 | 79 | 1 | 0 | 100 | 100 | 6.91 | 80 | 22.72 | 80 | 0 | 1.1 | 64,800 |
32 | 1 | 0 | 30 | 100 | 8.32 | 80 | 8.52 | 50 | 50 | 1.1 | 70,100 | 80 | 0 | 1 | 25 | 35 | 10.27 | 100 | 27.95 | 20 | 100 | 1.1 | 44,500 |
33 | 1 | 0 | 10 | 60 | 3.68 | 80 | 3.12 | 80 | 50 | 1.1 | 40,800 | 81 | 1 | 0 | 0 | 100 | 13.06 | 80 | 15.48 | 50 | 50 | 1.1 | 40,700 |
34 | 1 | 0 | 10 | 100 | 9.82 | 80 | 17.64 | 80 | 100 | 1 | 33,870 | 82 | 0 | 1 | 20 | 55 | 3.35 | 100 | 12.4 | 20 | 100 | 1.1 | 40,000 |
35 | 1 | 0 | 15 | 100 | 20.45 | 100 | 1.44 | 100 | 100 | 1.1 | 64,200 | 83 | 1 | 0 | 100 | 100 | 4.75 | 0 | 4.24 | 80 | 100 | 1.1 | 42,000 |
36 | 1 | 0 | 15 | 100 | 3.86 | 100 | 27.8 | 20 | 100 | 1 | 24,800 | 84 | 1 | 0 | 15 | 100 | 14.95 | 100 | 17.16 | 50 | 50 | 1.1 | 43,600 |
37 | 1 | 0 | 40 | 100 | 4.15 | 100 | 7.92 | 50 | 100 | 1.1 | 44,000 | 85 | 1 | 0 | 100 | 100 | 6.28 | 80 | 9.04 | 50 | 50 | 1.1 | 52,600 |
38 | 1 | 0 | 100 | 100 | 1.21 | 0 | 37.08 | 50 | 50 | 1.1 | 73,600 | 86 | 1 | 0 | 100 | 90 | 13.03 | 80 | 15.88 | 50 | 100 | 1.1 | 45,460 |
39 | 1 | 0 | 30 | 100 | 15.83 | 80 | 17.85 | 80 | 50 | 1.1 | 57,200 | 87 | 1 | 0 | 15 | 100 | 8.33 | 0 | 41 | 80 | 100 | 1.1 | 60,100 |
40 | 1 | 0 | 40 | 75 | 2.52 | 100 | 29.6 | 20 | 50 | 1.1 | 39,100 | 88 | 1 | 0 | 10 | 85 | 6.28 | 80 | 1.4 | 50 | 100 | 1.1 | 34,900 |
41 | 1 | 0 | 20 | 100 | 9.21 | 100 | 10 | 80 | 100 | 1.1 | 43,300 | 89 | 1 | 0 | 30 | 100 | 7.88 | 100 | 9.28 | 100 | 50 | 1.1 | 59,300 |
42 | 1 | 0 | 100 | 100 | 22.02 | 80 | 5.84 | 50 | 50 | 1 | 36,280 | 90 | 1 | 0 | 40 | 100 | 18.76 | 100 | 18.72 | 80 | 50 | 1.1 | 66,300 |
43 | 1 | 0 | 20 | 35 | 12.96 | 80 | 4.72 | 80 | 50 | 1.1 | 48,000 | 91 | 1 | 0 | 40 | 50 | 8.64 | 80 | 28.76 | 80 | 100 | 1.1 | 60,900 |
44 | 1 | 0 | 10 | 100 | 15.92 | 80 | 12.72 | 50 | 100 | 1.1 | 35,600 | 92 | 1 | 0 | 55 | 55 | 8.80 | 80 | 17.32 | 80 | 50 | 1.1 | 62,000 |
45 | 1 | 0 | 100 | 100 | 7.18 | 80 | 13.84 | 50 | 50 | 1.1 | 71,000 | 93 | 1 | 0 | 30 | 40 | 20.87 | 100 | 29.6 | 80 | 50 | 1.1 | 40,100 |
46 | 1 | 0 | 30 | 100 | 9.44 | 80 | 16.16 | 50 | 100 | 1.1 | 46,000 | 94 | 1 | 0 | 10 | 100 | 25.23 | 80 | 3.2 | 80 | 100 | 1.1 | 51,100 |
47 | 0 | 1 | 20 | 40 | 18.97 | 80 | 24.12 | 100 | 100 | 1.1 | 55,000 | 95 | 1 | 0 | 100 | 100 | 7.06 | 80 | 39.92 | 80 | 100 | 1.1 | 96,900 |
48 | 1 | 0 | 5 | 10 | 7.98 | 100 | 14.68 | 80 | 100 | 1.1 | 43,800 | 96 | 1 | 0 | 100 | 80 | 12.81 | 100 | 15.88 | 50 | 50 | 1.1 | 44,320 |
Factors | Base | Score | ||||||
---|---|---|---|---|---|---|---|---|
B: Competitiveness of other CVSs | ||||||||
BQ1 | Competitive store category | Major competitive CVS (0) | Minor competitive CVS (20) | Local chain CVS (30) | Self-operated CVS (40) | None (100) | 1 | 100 |
BQ2 | Relative distance | <50 m (0) | 50–100 m (5) | 100–200 m (10) | >200 m (20) | NA | ||
BQ3 | Competitive store proximity | good (0) | acceptable (10) | poor (20) | NA | |||
BQ4 | Competition store opening hours | 24 h | not 24 h | NA | ||||
C: Competitiveness of Supermarkets: | ||||||||
CQ1 | Number of stores | ≧4 (0) | 3 (5) | 2 (20) | 1 (40) | none (100) | 20 | |
CQ2 | Relative distance | <50 m (0) | 50–100 m (10) | 100–200 m (15) | >200 m (35) | 35 | ||
CQ3 | Competition store closed hours | after 22:00 (0) | 21:00–22:00 (5) | 20:00–21:00 (10) | before 20:00 (15) | 5 | ||
D: People flow | ||||||||
DQ1 | Survey time | 07:00–09:00 | 12:00–14:00 | 17:00–19:00 | 21:00–23:00 | |||
DQ2 | Pedestrian | 264 | 40 | 264/40 = 6.6 | ||||
DQ3 | Motorcycle | 2568 | 500 | 2568/500 = 5.1 | ||||
E: Major customer type | ||||||||
EQ1 | Single source (0) | Two sources (80) | >Two sources (100) | 100 | ||||
F: Number of households | ||||||||
FQ2 | Number of households | 429 | 25 | 429/25 = 17.2 | ||||
G: Store location | ||||||||
GQ1 | Store location | Off intersection (0) | Between minor streets (30) | Between main streets (50) | 30 | |||
GQ2 | Width | <6 m (0) | 6–10 m (20) | >10 m (50) | 50 | |||
H: Visibility | ||||||||
HQ1 | Visibility | <50 m (0) | 50–100 m (50) | >100 m (100) | 50 | |||
I: Area | ||||||||
IQ1 | Usable area | <50 m2 (0.9) | 50–100 m2 (1.0) | >100 m2 (1.1) |
Factor | Score | Weighting of Residential Area (Factor A) | Sub-Sum |
---|---|---|---|
B | 100.0 | 0.29% | 288.5 |
C | 60.0 | 0.06% | 34.6 |
D | 11.7 | 0.67% | 79.0 |
E | 100.0 | 0.08% | 76.9 |
F | 17.2 | 0.58% | 99.0 |
G | 80.0 | 0.38% | 307.7 |
H | 50.0 | 0.06% | 28.8 |
Sum | 914.5 |
Store | Predict. | Actual | Difference | Ratio | Success | Store | Predict | Actual | Difference | Ratio | Success |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 70,400 | 55,023 | 15,377 | 0.78 | 49 | 52,790 | 47,007 | 5783 | 0.89 | ||
2 | 60,300 | 46,923 | 13,377 | 0.78 | 50 | 47,500 | 37,819 | 9681 | 0.80 | ||
3 | 43,040 | 39,375 | 3665 | 0.91 | 1 | 51 | 60,800 | 40,152 | 20,648 | 0.66 | |
4 | 49,700 | 47,214 | 2486 | 0.95 | 1 | 52 | 73,200 | 46,710 | 26,490 | 0.64 | |
5 | 65,000 | 39,669 | 25,331 | 0.61 | 53 | 69,600 | 61,930 | 7670 | 0.89 | ||
6 | 87,200 | 91,004 | 3804 | 1.04 | 1 | 54 | 33,500 | 67,507 | 34,007 | 2.02 | 1 |
7 | 32,550 | 42,914 | 10,364 | 1.32 | 1 | 55 | 63,000 | 64,056 | 1056 | 1.02 | 1 |
8 | 34,700 | 53,743 | 19,043 | 1.55 | 1 | 56 | 39,020 | 36,379 | 2641 | 0.93 | 1 |
9 | 47,050 | 43,513 | 3537 | 0.92 | 1 | 57 | 54,600 | 35,050 | 19,550 | 0.64 | |
10 | 32,900 | 44,322 | 11,422 | 1.35 | 1 | 58 | 44,200 | 30,780 | 13,420 | 0.70 | |
11 | 55,000 | 56,081 | 1081 | 1.02 | 1 | 59 | 39,100 | 37,799 | 1301 | 0.97 | 1 |
12 | 68,000 | 48,007 | 19,993 | 0.71 | 60 | 85,200 | 46,564 | 38,636 | 0.55 | ||
13 | 33,000 | 35,872 | 2872 | 1.09 | 1 | 61 | 49,000 | 55,184 | 6184 | 1.13 | 1 |
14 | 52,600 | 39,851 | 12,749 | 0.76 | 62 | 43,800 | 39,196 | 4604 | 0.89 | ||
15 | 40,700 | 33,121 | 7579 | 0.81 | 63 | 32,520 | 70,667 | 38,147 | 2.17 | 1 | |
16 | 49,080 | 52,619 | 3539 | 1.07 | 1 | 64 | 50,000 | 42,152 | 7848 | 0.84 | |
17 | 49,000 | 40,460 | 8540 | 0.83 | 65 | 46,750 | 50,033 | 3283 | 1.07 | 1 | |
18 | 59,100 | 48,155 | 10,945 | 0.81 | 66 | 41,500 | 46,384 | 4884 | 1.12 | 1 | |
19 | 50,000 | 53,140 | 3140 | 1.06 | 1 | 67 | 43,300 | 80,205 | 36,905 | 1.85 | 1 |
20 | 45,000 | 53,561 | 8561 | 1.19 | 1 | 68 | 51,100 | 41,517 | 9583 | 0.81 | |
21 | 61,600 | 33,410 | 28,190 | 0.54 | 69 | 63,200 | 40,175 | 23,025 | 0.64 | ||
22 | 43,800 | 44,622 | 822 | 1.02 | 1 | 70 | 38,500 | 43,788 | 5288 | 1.14 | 1 |
23 | 74,500 | 37,861 | 36,639 | 0.51 | 71 | 59,100 | 44,298 | 14,802 | 0.75 | ||
24 | 42,500 | 47,955 | 5455 | 1.13 | 1 | 72 | 55,200 | 62,976 | 7776 | 1.14 | 1 |
25 | 45,100 | 43,360 | 1740 | 0.96 | 1 | 73 | 45,700 | 30,051 | 15,649 | 0.66 | |
26 | 73,700 | 53,278 | 20,422 | 0.72 | 74 | 41,800 | 45,711 | 3911 | 1.09 | 1 | |
27 | 68,400 | 67,508 | 892 | 0.99 | 1 | 75 | 63,200 | 47,535 | 15,665 | 0.75 | |
28 | 44,100 | 40,352 | 3748 | 0.92 | 1 | 76 | 51,000 | 36,745 | 14,255 | 0.72 | |
29 | 56,100 | 42,921 | 13,179 | 0.77 | 77 | 39,500 | 48,996 | 9496 | 1.24 | 1 | |
30 | 51,300 | 57,308 | 6008 | 1.12 | 1 | 78 | 50,700 | 73,138 | 22,438 | 1.44 | 1 |
31 | 52,100 | 49,883 | 2217 | 0.96 | 1 | 79 | 64,800 | 61,396 | 3404 | 0.95 | 1 |
32 | 70,100 | 50,055 | 20,045 | 0.71 | 80 | 44,500 | 20,763 | 23,737 | 0.47 | ||
33 | 40,800 | 51,543 | 10,743 | 1.26 | 1 | 81 | 40,700 | 43,101 | 2401 | 1.06 | 1 |
34 | 33,870 | 52,791 | 18,921 | 1.56 | 1 | 82 | 40,000 | 58,015 | 18,015 | 1.45 | 1 |
35 | 64,200 | 42,554 | 21,646 | 0.66 | 83 | 42,000 | 51,495 | 9495 | 1.23 | 1 | |
36 | 24,800 | 51,127 | 26,327 | 2.06 | 1 | 84 | 43,600 | 48,268 | 4668 | 1.11 | 1 |
37 | 44,000 | 50,130 | 6130 | 1.14 | 1 | 85 | 52,600 | 56,032 | 3432 | 1.07 | 1 |
38 | 73,600 | 44,734 | 28,866 | 0.61 | 86 | 45,460 | 45,805 | 345 | 1.01 | 1 | |
39 | 57,200 | 48,733 | 8467 | 0.85 | 87 | 60,100 | 52,036 | 8064 | 0.87 | ||
40 | 39,100 | 56,205 | 17,105 | 1.44 | 1 | 88 | 34,900 | 37,717 | 2817 | 1.08 | 1 |
41 | 43,300 | 38,257 | 5043 | 0.88 | 89 | 59,300 | 51,201 | 8099 | 0.86 | ||
42 | 36,280 | 45,635 | 9355 | 1.26 | 1 | 90 | 66,300 | 39,763 | 26,537 | 0.60 | |
43 | 48,000 | 43,643 | 4357 | 0.91 | 1 | 91 | 60,900 | 65,757 | 4857 | 1.08 | 1 |
44 | 35,600 | 22,774 | 12,826 | 0.64 | 92 | 62,000 | 45,233 | 16,767 | 0.73 | ||
45 | 71,000 | 47,499 | 23,501 | 0.67 | 93 | 40,100 | 44,463 | 4363 | 1.11 | 1 | |
46 | 46,000 | 40,230 | 5770 | 0.87 | 94 | 51,100 | 55,101 | −4001 | 1.08 | 1 | |
47 | 55,000 | 74,899 | 19,899 | 1.36 | 1 | 95 | 96,900 | 52,973 | 43,927 | 0.55 | |
48 | 43,800 | 50,339 | 6539 | 1.15 | 1 | 96 | 44,320 | 39,231 | 5089 | 0.89 | |
RMSE | 15,791 | Success | 53 | ||||||||
MAE | 12,155 | Success rate | 55% | ||||||||
MSE | 249,363,237 |
Experiment | A | B | C | D | E |
---|---|---|---|---|---|
1 | 1 | 1 | 1 | 1 | 1 |
2 | 1 | 2 | 2 | 2 | 2 |
3 | 1 | 3 | 3 | 3 | 3 |
4 | 1 | 4 | 4 | 4 | 4 |
5 | 2 | 1 | 2 | 3 | 4 |
6 | 2 | 2 | 1 | 4 | 3 |
7 | 2 | 3 | 4 | 1 | 2 |
8 | 2 | 4 | 3 | 2 | 1 |
9 | 3 | 1 | 3 | 4 | 2 |
10 | 3 | 2 | 4 | 3 | 1 |
11 | 3 | 3 | 1 | 2 | 4 |
12 | 3 | 4 | 2 | 1 | 3 |
13 | 4 | 1 | 4 | 2 | 3 |
14 | 4 | 2 | 3 | 1 | 4 |
15 | 4 | 3 | 2 | 4 | 1 |
16 | 4 | 4 | 1 | 3 | 2 |
Factor | Neural Network Design (A) | Learning Iteration (B) | Learning Rate (C) | Momentum Term (D) | Transfer Function (E) | |
---|---|---|---|---|---|---|
Level | ||||||
Level 1 | Five nodes, one hidden layer | 1000 | 0.1 | 0.1 | Sigmoid | |
Level 2 | Ten nodes, one hidden layer | 3000 | 0.4 | 0.4 | Gaussian | |
Level 3 | Five nodes in the first hidden layer; three nodes in the second hidden layer | 6000 | 0.7 | 0.7 | Hyperbolic Tangent | |
Level 4 | Ten nodes in the first hidden layer; three nodes in the second hidden layer | 10,000 | 0.9 | 0.9 | Hyperbolic Secant |
Exp. | Fold 1 | Fold 2 | Fold 3 | Average | St dev | S/N |
---|---|---|---|---|---|---|
1 | 0.122132 | 0.130395 | 0.123223 | 0.12525 | 0.004489 | 23.2695 |
2 | 0.147806 | 0.147416 | 0.148411 | 0.147878 | 0.000501 | 21.8307 |
3 | 0.114294 | 0.130201 | 0.114288 | 0.119594 | 0.009186 | 23.6575 |
4 | 0.114294 | 0.431601 | 0.409061 | 0.318319 | 0.17705 | 14.3572 |
5 | 0.409061 | 0.431601 | 0.409061 | 0.416574 | 0.013013 | 12.8321 |
6 | 0.166123 | 0.206958 | 0.173732 | 0.182271 | 0.021715 | 19.9735 |
7 | 0.186692 | 0.20525 | 0.197209 | 0.196384 | 0.009306 | 19.3602 |
8 | 0.167459 | 0.19631 | 0.166127 | 0.176632 | 0.017055 | 20.2605 |
9 | 0.114294 | 0.130202 | 0.1142 | 0.119565 | 0.009212 | 23.6565 |
10 | 0.147329 | 0.16983 | 0.114294 | 0.143818 | 0.027934 | 21.9647 |
11 | 0.161272 | 0.219702 | 0.159119 | 0.180031 | 0.034373 | 20.0176 |
12 | 0.158788 | 0.178615 | 0.180458 | 0.17262 | 0.012015 | 20.4729 |
13 | 0.114289 | 0.130207 | 0.114303 | 0.1196 | 0.009186 | 23.6571 |
14 | 0.212842 | 0.162019 | 0.12926 | 0.16804 | 0.042115 | 20.5424 |
15 | 0.226479 | 0.172241 | 0.202408 | 0.200376 | 0.027176 | 19.1389 |
16 | 0.231873 | 0.180894 | 0.188176 | 0.200314 | 0.027572 | 19.14 |
Neural Network Design | Learning Iteration | Learning Rate | Momentum Term | Transfer Function | |
---|---|---|---|---|---|
1 | 20.77873798 | 20.85457474 | 20.60016135 | 20.91125083 | 21.15840307 |
2 | 18.10658004 | 21.07781527 | 18.56867528 | 21.44147353 | 20.99761299 |
3 | 21.52867521 | 20.54355308 | 22.0299774 | 19.39858585 | 21.94028713 |
4 | 20.61961943 | 18.55766957 | 19.83479863 | 19.28230244 | 16.93730947 |
Effect | 3.422095173 | 2.520145701 | 3.461302121 | 2.159171086 | 5.002977668 |
Rank | 3 | 4 | 2 | 5 | 1 |
Best level | A3 | B2 | C3 | D2 | E3 |
Experiment | Fold 1 | Fold 2 | Fold 3 | S/N | Upper Bond | Lower Bond |
---|---|---|---|---|---|---|
Training 1–25 | 0.259155 | 0.13692 | 0.318543 | 14.2625 | ||
Training 26–50 | 0.125047 | 0.165635 | 0.201135 | 17.7714 | 18.415372 | 14.253997 |
Training 51–75 | 0.24473 | 0.143677 | 0.141125 | 16.9701 | ||
Retesting 76–96 | 0.15075 | 0.197292 | 0.236022 | 16.2946 |
Experiment | Fold 1 | Fold 2 | Fold 3 | Average | St. Dev | S/N |
---|---|---|---|---|---|---|
Combination of A3:B2:C3:D2:E3 | 0.15075 | 0.197292 | 0.236022 | 0.194688 | 0.042696 | 19.3049 |
Combination of A1:B3:C3:D3:E3 | 0.257151 | 0.242185 | 0.203655 | 0.23433 | 0.027599 | 17.7922 |
Store | Target | Prediction | Error Value | Prediction Rate | Accuracy | Store | Target | Prediction | Error Value | Prediction Rate | Accuracy |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 55,023 | 45,854 | 9169 | 1.20 | 1 | 49 | 47,007 | 49,091 | 2084 | 0.96 | 1 |
2 | 46,923 | 49,244 | 2321 | 0.95 | 1 | 50 | 37,819 | 45,503 | 7684 | 0.83 | |
3 | 39,375 | 45,845 | 6470 | 0.86 | 51 | 40,152 | 45,367 | 5215 | 0.89 | ||
4 | 47,214 | 58,166 | 10,952 | 0.81 | 52 | 46,710 | 47,061 | 351 | 0.99 | 1 | |
5 | 39,669 | 58,107 | 18,438 | 0.68 | 53 | 61,930 | 46,147 | 15,783 | 1.34 | 1 | |
6 | 91,004 | 87,454 | 3550 | 1.04 | 1 | 54 | 67,507 | 45,464 | 22,043 | 1.48 | 1 |
7 | 42,914 | 57,043 | 14,129 | 0.75 | 55 | 64,056 | 46,985 | 17,071 | 1.36 | 1 | |
8 | 53,743 | 46,913 | 6830 | 1.15 | 1 | 56 | 36,379 | 45,538 | 9159 | 0.80 | |
9 | 43,513 | 58,137 | 14,624 | 0.75 | 57 | 35,050 | 43,322 | 8272 | 0.81 | ||
10 | 44,322 | 41,168 | 3154 | 1.08 | 1 | 58 | 30,780 | 45,571 | 14,791 | 0.68 | |
11 | 56,081 | 52,364 | 3717 | 1.07 | 1 | 59 | 37,799 | 45,458 | 7659 | 0.83 | |
12 | 48,007 | 45,539 | 2468 | 1.05 | 1 | 60 | 46,564 | 48,266 | 1702 | 0.96 | 1 |
13 | 35,872 | 41,905 | 6033 | 0.86 | 61 | 55,184 | 45,607 | 9577 | 1.21 | 1 | |
14 | 39,851 | 51,025 | 11,174 | 0.78 | 62 | 39,196 | 47,428 | 8232 | 0.83 | ||
15 | 33,121 | 42,860 | 9739 | 0.77 | 63 | 70,667 | 46,256 | 24,411 | 1.53 | 1 | |
16 | 52,619 | 56,793 | 4174 | 0.93 | 1 | 64 | 42,152 | 45,659 | 3507 | 0.92 | 1 |
17 | 40,460 | 47,130 | 6670 | 0.86 | 65 | 50,033 | 50,749 | 716 | 0.99 | 1 | |
18 | 48,155 | 55,865 | 7710 | 0.86 | 66 | 46,384 | 46,644 | 260 | 0.99 | 1 | |
19 | 53,140 | 41,386 | 11,754 | 1.28 | 1 | 67 | 80,205 | 51,606 | 28,599 | 1.55 | 1 |
20 | 53,561 | 57,164 | 3603 | 0.94 | 1 | 68 | 41,517 | 45,511 | 3994 | 0.91 | 1 |
21 | 33,410 | 51,917 | 18,507 | 0.64 | 69 | 40,175 | 47,094 | 6919 | 0.85 | ||
22 | 44,622 | 57,347 | 12,725 | 0.78 | 70 | 43,788 | 45,564 | 1776 | 0.96 | 1 | |
23 | 37,861 | 45,750 | 7889 | 0.83 | 71 | 44,298 | 52,693 | 8395 | 0.84 | ||
24 | 47,955 | 46,572 | 1383 | 1.03 | 1 | 72 | 62,976 | 49,760 | 13,216 | 1.27 | 1 |
25 | 43,360 | 54,439 | 11,079 | 0.80 | 73 | 30,051 | 45,444 | 15,393 | 0.66 | ||
26 | 53,278 | 48,830 | 4448 | 1.09 | 1 | 74 | 45,711 | 45,511 | 200 | 1.00 | 1 |
27 | 67,508 | 55,690 | 11,818 | 1.21 | 1 | 75 | 47,535 | 65,842 | 18,307 | 0.72 | |
28 | 40,352 | 41,985 | 1633 | 0.96 | 1 | 76 | 36,745 | 46,789 | 10,044 | 0.79 | |
29 | 42,921 | 56,473 | 13,552 | 0.76 | 77 | 48,996 | 45,413 | 3583 | 1.08 | 1 | |
30 | 57,308 | 58,213 | 905 | 0.98 | 1 | 78 | 73,138 | 60,417 | 12,721 | 1.21 | 1 |
31 | 49,883 | 53,856 | 3973 | 0.93 | 1 | 79 | 61,396 | 52,210 | 9186 | 1.18 | 1 |
32 | 50,055 | 42,082 | 7973 | 1.19 | 1 | 80 | 20,763 | 27,751 | 6988 | 0.75 | |
33 | 51,543 | 53,754 | 2211 | 0.96 | 1 | 81 | 43,101 | 45,484 | 2383 | 0.95 | 1 |
34 | 52,791 | 54,859 | 2068 | 0.96 | 1 | 82 | 58,015 | 46,198 | 11,817 | 1.26 | 1 |
35 | 42,554 | 58,567 | 16,013 | 0.73 | 83 | 51,495 | 45,388 | 6107 | 1.13 | 1 | |
36 | 51,127 | 41,989 | 9138 | 1.22 | 1 | 84 | 48,268 | 45,612 | 2656 | 1.06 | 1 |
37 | 50,130 | 48,448 | 1682 | 1.03 | 1 | 85 | 56,032 | 45,605 | 10,427 | 1.23 | 1 |
38 | 44,734 | 39,971 | 4763 | 1.12 | 1 | 86 | 45,805 | 45,853 | 48 | 1.00 | 1 |
39 | 48,733 | 45,646 | 3087 | 1.07 | 1 | 87 | 52,036 | 45,553 | 6483 | 1.14 | 1 |
40 | 56,205 | 42,024 | 14,181 | 1.34 | 1 | 88 | 37,717 | 45,467 | 7750 | 0.83 | |
41 | 38,257 | 57,763 | 19,506 | 0.66 | 89 | 51,201 | 50,338 | 863 | 1.02 | 1 | |
42 | 45,635 | 41,986 | 3649 | 1.09 | 1 | 90 | 39,763 | 46,545 | 6782 | 0.85 | |
43 | 43,643 | 44,194 | 551 | 0.99 | 1 | 91 | 65,757 | 65,938 | 181 | 1.00 | 1 |
44 | 22,774 | 37,175 | 14,401 | 0.61 | 92 | 45,233 | 47,671 | 2438 | 0.95 | 1 | |
45 | 47,499 | 45,448 | 2051 | 1.05 | 1 | 93 | 44,463 | 46,158 | 1695 | 0.96 | 1 |
46 | 40,230 | 43,343 | 3113 | 0.93 | 1 | 94 | 55,101 | 45,378 | 9723 | 1.21 | 1 |
47 | 74,899 | 58,563 | 16,336 | 1.28 | 1 | 95 | 52,973 | 49,800 | 3173 | 1.06 | 1 |
48 | 50,339 | 56,292 | 5953 | 0.89 | 96 | 39,231 | 41,638 | 2407 | 0.94 | 1 | |
RMSE | 9810 | Success | 62 | ||||||||
MAE | 7750 | Success rate | 65% | ||||||||
MSE | 96,229,425 |
Number of Training Instances | Success Rate |
---|---|
16 | 65% |
32 | 69% |
48 | 72% |
64 | 73% |
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Fu, H.-P.; Yeh, H.-P.; Chang, T.-H.; Teng, Y.-H.; Tsai, C.-C. Applying ANN and TM to Build a Prediction Model for the Site Selection of a Convenience Store. Appl. Sci. 2022, 12, 3036. https://doi.org/10.3390/app12063036
Fu H-P, Yeh H-P, Chang T-H, Teng Y-H, Tsai C-C. Applying ANN and TM to Build a Prediction Model for the Site Selection of a Convenience Store. Applied Sciences. 2022; 12(6):3036. https://doi.org/10.3390/app12063036
Chicago/Turabian StyleFu, Hsin-Pin, Hsiao-Ping Yeh, Tein-Hsiang Chang, Ying-Hua Teng, and Cheng-Chang Tsai. 2022. "Applying ANN and TM to Build a Prediction Model for the Site Selection of a Convenience Store" Applied Sciences 12, no. 6: 3036. https://doi.org/10.3390/app12063036
APA StyleFu, H.-P., Yeh, H.-P., Chang, T.-H., Teng, Y.-H., & Tsai, C.-C. (2022). Applying ANN and TM to Build a Prediction Model for the Site Selection of a Convenience Store. Applied Sciences, 12(6), 3036. https://doi.org/10.3390/app12063036