Dynamic Traffic Assignment Model Based on GPS Data and Point of Interest (POI) in Shanghai
Abstract
:1. Introduction
2. Materials and Methods
- Determine the initial value. Initial point , given an error , ;
- Solve the approximate linear programming: , to obtain the optimal solution ;
- Construct feasible descent directions, let , if , stop the computation and output ; otherwise go to the next step.
- One-dimensional search: to get step . Let , updated to , go to the second step.
3. Experiments
3.1. Data Description
3.2. Performance Indexes
4. Interpretation of Results
4.1. The Results of POI Impact
4.1.1. Qualitative Analysis
4.1.2. Qualitative Analysis
4.2. The Results of User Equilibrium Model
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Figure 3 | Flow | Congestion Level | Description |
---|---|---|---|
(a) | 33 | Light | The red area of the link is small, and the road congestion is light. |
(b) | 65 | Heavy | The red area of the link is increasing, which means that parent pick-up vehicles are gathering. |
(c) | 52 | Heavy | The red area of the link decreases, because the parents’ pick-up vehicles are parked in front of the kindergarten waiting for the children to be released. The speed is null and is not counted in the track data, and this type of data points is not shown on the heat map. |
(d) | 71 | Heavy | The red area of the link increases as most of the parents have received their children. There is a short period of congestion caused by them leaving the link. |
(e) | 46 | Light | The red area of the link decreases, and the road is reopened. |
No. of Link | Link | Length (km) | Free Time (s) | Capacity | Flow | V/C |
---|---|---|---|---|---|---|
1 | [‘1’, ‘5’] | 0.41 | 0.0068 | 2000 | 213 | 0.106 |
2 | [‘1’, ‘6’] | 0.62 | 0.0103 | 2000 | 159 | 0.08 |
3 | [‘2’, ‘5’] | 0.84 | 0.014 | 2000 | 125 | 0.062 |
4 | [‘2’, ‘3’] | 0.64 | 0.0107 | 1000 | 104 | 0.104 |
5 | [‘3’, ‘2’] | 0.64 | 0.0107 | 1000 | 48 | 0.048 |
6 | [‘3’, ‘6’] | 0.93 | 0.0155 | 2000 | 75 | 0.038 |
7 | [‘3’, ‘4’] | 0.67 | 0.0112 | 1000 | 47 | 0.047 |
8 | [‘5’, ‘2’] | 0.84 | 0.014 | 2000 | 244 | 0.122 |
9 | [‘5’, ‘1’] | 0.41 | 0.0068 | 2000 | 89 | 0.044 |
10 | [‘5’, ‘6’] | 0.32 | 0.0053 | 1000 | 50 | 0.05 |
11 | [‘6’, ‘4’] | 0.55 | 0.0092 | 2000 | 95 | 0.048 |
12 | [‘6’, ‘1’] | 0.62 | 0.0103 | 2000 | 75 | 0.037 |
13 | [‘6’, ‘5’] | 0.32 | 0.0053 | 1000 | 0 | 0 |
14 | [‘6’, ‘3’] | 0.93 | 0.0155 | 2000 | 114 | 0.057 |
No. OD Pair | OD Pairs | Demand | No. of Path | Time [s] | Paths |
---|---|---|---|---|---|
0 | [‘1’, ‘1’] | 0 | 0 | 0 | [‘1’] |
1 | [‘1’, ‘2’] | 103 | 1 | 0.031 | [‘1’, ‘5’, ‘2’] |
2 | 0.062 | [‘1’, ‘5’, ‘6’, ‘3’, ‘2’] | |||
3 | 0.0465 | [‘1’, ‘6’, ‘5’, ‘2’] | |||
4 | 0.0465 | [‘1’, ‘6’, ‘3’, ‘2’] | |||
2 | [‘1’, ‘3’] | 114 | 5 | 0.0465 | [‘1’, ‘5’, ‘2’, ‘3’] |
6 | 0.0465 | [‘1’, ‘5’, ‘6’, ‘3’] | |||
7 | 0.062 | [‘1’, ‘6’, ‘5’, ‘2’, ‘3’] | |||
8 | 0.031 | [‘1’, ‘6’, ‘3’] | |||
3 | [‘1’, ‘4’] | 45 | 9 | 0.0775 | [‘1’, ‘5’, ‘2’, ‘3’, ‘6’, ‘4’] |
10 | 0.062 | [‘1’, ‘5’, ‘2’, ‘3’, ‘4’] | |||
11 | 0.0465 | [‘1’, ‘5’, ‘6’, ‘4’] | |||
12 | 0.062 | [‘1’, ‘5’, ‘6’, ‘3’, ‘4’] | |||
13 | 0.031 | [‘1’, ‘6’, ‘4’] | |||
14 | 0.0775 | [‘1’, ‘6’, ‘5’, ‘2’, ‘3’, ‘4’] | |||
15 | 0.0465 | [‘1’, ‘6’, ‘3’, ‘4’] | |||
4 | [‘1’, ‘5’] | 110 | 16 | 0.0155 | [‘1’, ‘5’] |
17 | 0.031 | [‘1’, ‘6’, ‘5’] | |||
18 | 0.062 | [‘1’, ‘6’, ‘3’, ‘2’, ‘5’] | |||
5 | [‘2’, ‘1’] | 44 | 19 | 0.031 | [‘2’, ‘5’, ‘1’] |
20 | 0.0465 | [‘2’, ‘5’, ‘6’, ‘1’] | |||
21 | 0.0465 | [‘2’, ‘3’, ‘6’, ‘1’] | |||
22 | 0.062 | [‘2’, ‘3’, ‘6’, ‘5’, ‘1’] | |||
6 | [‘2’, ‘2’] | 0 | 23 | 0 | [‘2’] |
7 | [‘2’, ‘3’] | 60 | 24 | 0.062 | [‘2’, ‘5’, ‘1’, ‘6’, ‘3’] |
25 | 0.0465 | [‘2’, ‘5’, ‘6’, ‘3’] | |||
26 | 0.0155 | [‘2’, ‘3’] | |||
8 | [‘2’, ‘4’] | 2 | 27 | 0.062 | [‘2’, ‘5’, ‘1’, ‘6’, ‘4’] |
28 | 0.0775 | [‘2’, ‘5’, ‘1’, ‘6’, ‘3’, ‘4’] | |||
29 | 0.0465 | [‘2’, ‘5’, ‘6’, ‘4’] | |||
30 | 0.062 | [‘2’, ‘5’, ‘6’, ‘3’, ‘4’] | |||
31 | 0.0465 | [‘2’, ‘3’, ‘6’, ‘4’] | |||
32 | 0.031 | [‘2’, ‘3’, ‘4’] | |||
9 | [‘2’, ‘5’] | 51 | 33 | 0.0155 | [‘2’, ‘5’] |
34 | 0.062 | [‘2’, ‘3’, ‘6’, ‘1’, ‘5’] | |||
35 | 0.0465 | [‘2’, ‘3’, ‘6’, ‘5’] | |||
10 | [‘3’, ‘1’] | 75 | 36 | 0.0465 | [‘3’, ‘2’, ‘5’, ‘1’] |
37 | 0.062 | [‘3’, ‘2’, ‘5’, ‘6’, ‘1’] | |||
38 | 0.031 | [‘3’, ‘6’, ‘1’] | |||
39 | 0.0465 | [‘3’, ‘6’, ‘5’, ‘1’] | |||
11 | [‘3’, ‘2’] | 18 | 40 | 0.0155 | [‘3’, ‘2’] |
41 | 0.062 | [‘3’, ‘6’, ‘1’, ‘5’, ‘2’] | |||
42 | 0.0465 | [‘3’, ‘6’, ‘5’, ‘2’] | |||
12 | [‘3’, ‘3’] | 0 | 43 | 0 | [‘3’] |
13 | [‘3’, ‘4’] | 45 | 44 | 0.0775 | [‘3’, ‘2’, ‘5’, ‘1’, ‘6’, ‘4’] |
45 | 0.062 | [‘3’, ‘2’, ‘5’, ‘6’, ‘4’] | |||
46 | 0.031 | [‘3’, ‘6’, ‘4’] | |||
47 | 0.0155 | [‘3’, ‘4’] | |||
14 | [‘3’, ‘5’] | 30 | 48 | 0.031 | [‘3’, ‘2’, ‘5’] |
49 | 0.0465 | [‘3’, ‘6’, ‘1’, ‘5’] | |||
50 | 0.031 | [‘3’, ‘6’, ‘5’] | |||
15 | [‘5’, ‘1’] | 45 | 51 | 0.062 | [‘5’, ‘2’, ‘3’, ‘6’, ‘1’] |
52 | 0.0155 | [‘5’, ‘1’] | |||
53 | 0.031 | [‘5’, ‘6’, ‘1’] | |||
16 | [‘5’, ‘2’] | 99 | 54 | 0.0155 | [‘5’, ‘2’] |
55 | 0.062 | [‘5’, ‘1’, ‘6’, ‘3’, ‘2’] | |||
56 | 0.0465 | [‘5’, ‘6’, ‘3’, ‘2’] | |||
17 | [‘5’, ‘3’] | 42 | 57 | 0.031 | [‘5’, ‘2’, ‘3’] |
58 | 0.0465 | [‘5’, ‘1’, ‘6’, ‘3’] | |||
59 | 0.031 | [‘5’, ‘6’, ‘3’] | |||
18 | [‘5’, ‘4’] | 50 | 60 | 0.062 | [‘5’, ‘2’, ‘3’, ‘6’, ‘4’] |
61 | 0.0465 | [‘5’, ‘2’, ‘3’, ‘4’] | |||
62 | 0.0465 | [‘5’, ‘1’, ‘6’, ‘4’] | |||
63 | 0.062 | [‘5’, ‘1’, ‘6’, ‘3’, ‘4’] | |||
64 | 0.031 | [‘5’, ‘6’, ‘4’] | |||
65 | 0.0465 | [‘5’, ‘6’, ‘3’, ‘4’] | |||
19 | [‘5’, ‘5’] | 0 | 66 | 0 | [‘5’] |
Link | [‘1’, ‘5’] | [‘1’, ‘6’] | [‘2’, ‘5’] | [‘2’, ‘3’] | [‘3’, ‘2’] | [‘3’, ‘6’] | [‘3’, ‘4’] | [‘5’, ‘2’] | [‘5’, ‘1’] | [‘5’, ‘6’] | [‘6’, ‘4’] | [‘6’, ‘1’] | [‘6’, ‘5’] | [‘6’, ‘3’] |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
213 | 159 | 125 | 104 | 48 | 75 | 47 | 244 | 89 | 50 | 95 | 75 | 0 | 114 | |
149 | 124 | 92 | 104 | 52 | 91 | 81 | 205 | 87 | 127 | 142 | 122 | 86 | 165 |
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Song, X.; Yang, Z.; Wang, T.; Li, C.; Zhang, Y.; Chen, G. Dynamic Traffic Assignment Model Based on GPS Data and Point of Interest (POI) in Shanghai. Sensors 2021, 21, 7341. https://doi.org/10.3390/s21217341
Song X, Yang Z, Wang T, Li C, Zhang Y, Chen G. Dynamic Traffic Assignment Model Based on GPS Data and Point of Interest (POI) in Shanghai. Sensors. 2021; 21(21):7341. https://doi.org/10.3390/s21217341
Chicago/Turabian StyleSong, Xueying, Zheng Yang, Tao Wang, Chaoyang Li, Yi Zhang, and Ganyu Chen. 2021. "Dynamic Traffic Assignment Model Based on GPS Data and Point of Interest (POI) in Shanghai" Sensors 21, no. 21: 7341. https://doi.org/10.3390/s21217341
APA StyleSong, X., Yang, Z., Wang, T., Li, C., Zhang, Y., & Chen, G. (2021). Dynamic Traffic Assignment Model Based on GPS Data and Point of Interest (POI) in Shanghai. Sensors, 21(21), 7341. https://doi.org/10.3390/s21217341