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The quantitative and qualitative evaluation of a multi-agent microsimulation model for subway carriage design

Tsinghua University, China
The University of Melbourne, Australia
Shanghai University of Finance and Economics, China
Lab MAS, France

Research article Dec 31, 2015

Volume 8(3) Winter 2015

Cite this article as: L. Cao, X. Li, F. Kang, C. Liu, F. Sun, R. Kotagiri; 2015; The quantitative and qualitative evaluation of a multi-agent microsimulation model for subway carriage design; International Journal of Microsimulation; 8(3); 6-40. doi: 10.34196/ijm.00120

17 figures and 3 tables

Figures

Figure 1

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The essential components and concepts of a typical multi-agent microsimulation model.

Figure 2

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The interior layout and exterior appearance of the SL Bombardier C20 subway carriage.

Figure 3

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The manual zone partition (No. 1 ∼ 19) of the focused part of an SL C20 carriage.

Figure 4

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The measurements of the simulated environment space E and the agent attributes p, q.

Figure 5

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An calculation example of dynamic comfort distance $q_{n}^{(t)}$ : auto-adaptation according to *N_t*.

Figure 6

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Illustration of agent turning angle that is selected obeying a discrete Gaussian distribution.

Figure 7

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The heterogeneous agent moving speeds are mapped to groups of Swedish population.

Figure 8

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The observation/simulation time (blue line) and the corresponding cubic interpolations (3D surface) as a function of existing and entering passengers/agents.

*Data source*: Table 1 and 2.

Figure 9

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The structure of a typical ELM network with one hidden layer (with L nodes), one input layer (with 23 nodes), and one output layer (with 10 nodes representing 10 categories).

Figure 10

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The evaluation results obtained via an unified multinomial classifier: (a) the performance sensitivity obtained via grid searching; (b) the class-wise confusion matrix of prediction accuracy.

(18)

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Figure 11

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The quantitative evaluation results using receiver operating characteristics (ROC) graph:

Figure 12

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A screen-shot of “left-right sub-carriage imbalance” in the SL C20 subway carriage.

Figure 13

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A screen-shot of “doorway jam” phenomenon in the SL C20 subway carriage.

Figure 14

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A new design of seat layout by emptying wall areas in the SL C20 subway carriage.

Figure 15

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An example of “pivot blocking” in the SL C20 carriage with the new seat distribution.

Figure 16

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The “left-right sub-carriage imbalance” problem caused by “vertical detour difficulty”.

Tables

Table 1

Real data collected from in-field observation.

d_i	Initial Status (entering\|existing passengers)	Real-data Dimensions (i.e. d_i,1, d_i,2,…, d_i,23)
d₁	17-01,19-03\|16:1,17:2,18:1,19:1	1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,1,2,1,08,0,1,0
d₂	17-03,19-00\|14:1,16:3,17:2,18:2,19:4	0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,1,2,4,4,12,4,3,0
d₃	16-01,18-03\|9:1,14:3,15:3,16:4,17:3,18:2,19:2	1,1,1,0,0,0,0,0,1,0,0,0,0,3,3,2,4,3,3,18,1,1,0
d₄	16-02,18-05\|1:1,14:3,15:3,16:4,17:2,18:4,19:3	3,2,3,0,0,0,0,0,0,0,0,0,0,4,4,3,3,2,3,20,3,1,1
d₅	16-07,18-05\|1:1,2:2,14:3,15:2,16:5,17:5,18:5,19:3	4,2,3,0,0,1,0,0,0,0,0,0,0,5,7,4,2,6,4,31,6,3,2
d₆	17-09,19-08\|1:2,2:4,14:2,15:2,16:7,17:3,18:4,19:4	3,3,2,0,0,1,1,0,0,0,1,0,0,6,7,6,5,4,6,28,3,1,1
d₇	16-10,18-06\|1:4,3:1,4:1,7:1,14:4,15:2,16:5,17:4,18:4,19:4	3,2,2,0,0,0,1,0,0,0,0,0,0,5,9,7,6,4,7,30,7,4,3
d₈	17-08,19-10\|1:2,2:2,3:2,4:1,8:1,14:2,15:2,16:5,17:2,18:6,19:5	4,4,3,1,0,0,0,1,0,0,0,0,0,5,8,3,8,7,4,35,0,3,5
d₉	17-11,19-13\|1:2,2:2,3:1,6:1,8:1,14:3,15:3,16:4,17:3,18:2,19:3	2,4,2,0,1,0,0,0,1,1,0,0,0,6,8,6,5,5,8,40,3,3,4
d₁₀	16-10,18-11\|1:3,2:4,3:2,6:1,7:1,10:1,14:5,15:4,16:5,17:4,18:4,19:5	4,6,3,0,0,1,1,1,0,1,0,0,0,8,9,8,7,5,6,46,4,6,5

Table 2

Partial simulation data samples generated from 100 valid micro-model executions.

${\hat{d}}_{i}$	Model Initialization (entering\|existing agents)	Features of Simulation Data $({\hat{d}}_{i, 1}, \dots, {\hat{d}}_{i, 23})$
${\hat{d}}_{1}$	17–01,19–03\|16:1,17:2,18:1,19:1	1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,3,1,05,00,1,1
${\hat{d}}_{2}$	17–01,19–03\|16:1,17:2,18:1,19:1	1,0,1,1,0,0,0,0,0,0,0,0,0,1,0,1,1,0,3,10,00,0,4
${\hat{d}}_{3}$	17–01,19–03\|16:1,17:2,18:1,19:1	1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,3,1,1,1,13,00,2,3
${\hat{d}}_{4}$	17–01,19–03\|16:1,17:2,18:1,19:1	1,0,3,1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,18,04,4,4
${\hat{d}}_{5}$	17–01,19–03\|16:1,17:2,18:1,19:1	1,0,2,1,0,0,0,0,0,0,0,0,0,1,0,1,1,1,1,10,02,3,5
${\hat{d}}_{6}$	17–01,19–03\|16:1,17:2,18:1,19:1	1,0,3,1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,09,03,3,4
${\hat{d}}_{7}$	17–01,19–03\|16:1,17:2,18:1,19:1	1,0,3,1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,10,02,2,4
${\hat{d}}_{8}$	17–01,19–03\|16:1,17:2,18:1,19:1	1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,3,08,01,1,3
${\hat{d}}_{9}$	17–01,19–03\|16:1,17:2,18:1,19:1	1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,3,1,05,00,1,2
${\hat{d}}_{10}$	17–01,19–03\|16:1,17:2,18:1,19:1	1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,3,07,00,1,1
${\hat{d}}_{20}$	17–03,19–00\|14:1,16:3,17:2,18:2,19:4	1,0,1,0,1,0,0,0,0,0,0,0,1,0,0,3,4,2,2,23,05,5,0
${\hat{d}}_{30}$	16–01,18–03\|9:1,14:3,15:3,16:4,17:3,18:2,19:2	1,1,1,0,1,0,0,0,0,0,0,0,1,1,2,5,1,4,4,27,02,3,1
${\hat{d}}_{40}$	16–02,18–05\|1:1,14:3,15:3,16:4,17:2,18:4,19:3	3,2,2,0,0,1,0,0,0,0,0,1,0,4,4,2,3,2,3,43,05,1,1
${\hat{d}}_{50}$	16–07,18–05\|1:1,2:2,14:3,15:2,16:5,17:5,18:5,19:3	3,3,2,0,0,1,0,0,1,0,1,0,1,5,5,4,3,6,3,46,07,2,2
${\hat{d}}_{60}$	17–09,19–08\|1:2,2:4,14:2,15:2,16:7,17:3,18:4,19:4	4,2,3,0,1,2,0,0,0,1,0,0,0,6,5,7,5,6,3,47,05,2,1
${\hat{d}}_{70}$	16–10,18–06\|1:4,3:1,4:1,7:1,14:4,15:2,16:5,17:4,18:4,19:4	3,1,2,1,0,1,0,1,0,1,0,0,0,4,7,8,6,5,6,52,10,3,4
${\hat{d}}_{80}$	17–08,19–10\|1:2,2:2,3:2,4:1,8:1,14:2,15:2,16:5,17:2,18:6,19:5	3,5,3,0,2,0,1,0,0,1,0,0,1,5,6,4,6,6,5,56,01,3,5
${\hat{d}}_{90}$	17–11,19–13\|1:2,2:2,3:1,6:1,8:1,14:3,15:3,16:4,17:3,18:2,19:3	2,5,3,0,0,1,1,0,1,0,0,1,1,7,5,6,5,5,6,63,04,2,4
${\hat{d}}_{100}$	16–10,18–111:3,2:4,3:2,6:1,7:1,10:1,14:5,15:4,16:5,17:4,18:4,19:5	5,6,2,0,2,1,1,0,1,0,1,0,1,7,7,8,6,6,6,69,04,5,5

Table 3

Parameters and measures of 10 one-vs.-rest binary classifiers (200 trials); the scores labeled with bold, asterisk, and underline represent the best, 2nd best, and the worst results respectively.

Parameters & Measures	Class. k=1	Class. k=2	Class. k=3	Class. k=4	Class. k=5	Class. k=6	Class. k=7	Class. k=8	Class. k=9	Class. k=10	Mean
Parameters & Measures	Lk	10³	10²	500	10³	2,000	200	200	10³	50	Mean	500	N/A
C_k (baseline)	10⁶	10³	10²	10³	10³	10²	10²	1	10	10²	N/A
Mean Acc.	0.879	0.916	0.897	0.996∗	0.991	0.906	0.992	0.998	0.872	0.996∗	0.944
Dev.	0.074	0.036	0.015∗	0.021	0.029	0.037	0.027	0.012	0.074	0.02	0.034
Precision	0.852	1	0.1	1	0.942	0.643	1	0.985∗	0.291	1	0.781
Recall	0.799	0.16	0.005	0.965	0.97∗	0.135	0.92	1	0.195	0.955	0.61

Figures

The essential components and concepts of a typical multi-agent microsimulation model.

The interior layout and exterior appearance of the SL Bombardier C20 subway carriage.

The manual zone partition (No. 1 ∼ 19) of the focused part of an SL C20 carriage.

The measurements of the simulated environment space E and the agent attributes p, q.

An calculation example of dynamic comfort distance $q_{n}^{(t)}$ : auto-adaptation according to N_t.

Illustration of agent turning angle that is selected obeying a discrete Gaussian distribution.

The heterogeneous agent moving speeds are mapped to groups of Swedish population.

The observation/simulation time (blue line) and the corresponding cubic interpolations (3D surface) as a function of existing and entering passengers/agents.

The structure of a typical ELM network with one hidden layer (with L nodes), one input layer (with 23 nodes), and one output layer (with 10 nodes representing 10 categories).

The evaluation results obtained via an unified multinomial classifier: (a) the performance sensitivity obtained via grid searching; (b) the class-wise confusion matrix of prediction accuracy.

The quantitative evaluation results using receiver operating characteristics (ROC) graph:

A screen-shot of “left-right sub-carriage imbalance” in the SL C20 subway carriage.

A screen-shot of “doorway jam” phenomenon in the SL C20 subway carriage.

A new design of seat layout by emptying wall areas in the SL C20 subway carriage.

An example of “pivot blocking” in the SL C20 carriage with the new seat distribution.

The “left-right sub-carriage imbalance” problem caused by “vertical detour difficulty”.

Tables

Download links

The essential components and concepts of a typical multi-agent microsimulation model.

The interior layout and exterior appearance of the SL Bombardier C20 subway carriage.

The manual zone partition (No. 1 ∼ 19) of the focused part of an SL C20 carriage.

The measurements of the simulated environment space E and the agent attributes p, q.

An calculation example of dynamic comfort distance qn(t)<math id="inf10"><mrow><msubsup><mi>q</mi><mi>n</mi><mrow><mo stretchy="false">(</mo><mi>t</mi><mo stretchy="false">)</mo></mrow></msubsup></mrow></math>: auto-adaptation according to Nt.

Illustration of agent turning angle that is selected obeying a discrete Gaussian distribution.

The heterogeneous agent moving speeds are mapped to groups of Swedish population.

The observation/simulation time (blue line) and the corresponding cubic interpolations (3D surface) as a function of existing and entering passengers/agents.

The structure of a typical ELM network with one hidden layer (with L nodes), one input layer (with 23 nodes), and one output layer (with 10 nodes representing 10 categories).

The evaluation results obtained via an unified multinomial classifier: (a) the performance sensitivity obtained via grid searching; (b) the class-wise confusion matrix of prediction accuracy.

The quantitative evaluation results using receiver operating characteristics (ROC) graph:

A screen-shot of “left-right sub-carriage imbalance” in the SL C20 subway carriage.

A screen-shot of “doorway jam” phenomenon in the SL C20 subway carriage.

A new design of seat layout by emptying wall areas in the SL C20 subway carriage.

An example of “pivot blocking” in the SL C20 carriage with the new seat distribution.

The “left-right sub-carriage imbalance” problem caused by “vertical detour difficulty”.

An calculation example of dynamic comfort distance $q_{n}^{(t)}$ : auto-adaptation according to N_t.