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Research articles · Dec 31, 2015

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

Le-le Cao, Xiao-xue Li, Fen-ni Kang, Chang Liu, Fu-chun Sun, Ramamohanarao Kotagiri

State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Computer Science and Technology, Tsinghua University, China; Department of Computing and Information Systems, The University of Melbourne, Australia; School of Accountancy, Shanghai University of Finance and Economics, China; Lab MAS, France

Volume 8(3) Winter 2015

Cite 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

Abstract

Multi-agent microsimulation, as a third way of doing science other than induction and deduction methods, is explored to aid subway carriage design in this paper. Realizing that passenger behavior shapes the environment and in turn is shaped by the environment itself, we intend to model this interaction and examine the effectiveness and usability of the proposed model. We address our micro-model from essential aspects of environment space, agent attributes, agent behaviors, simulation process, and global objective/convergence function. Based on the real and simulated data, we evaluate our model with a combination of quantitative and qualitative procedures. For quantitative approach, we proposed two evaluation paradigms (i.e. “unified multinomial classifier” and “one-vs.-all binary classifiers”) using the state-of-the-art machine learning techniques and frameworks; and we manage to show from various perspectives that our model matches the reality in the majority of cases. For qualitative verification, we present a small-scale case study to evaluate different seat layouts in a subway carriage, and identify their advantages and disadvantages with little effort. By enriching microsimulation theory with innovative techniques, our research aims at promoting its acceptance level in design communities by means of avoiding costly creation of real-world experiments.

This article contains 17 figures and 3 tables.

Figures

Figure 1. The essential components and concepts of a typical multi-agent microsimulation model.
Figure 2. The interior layout and exterior appearance of the SL Bombardier C20 subway carriage.
Figure 3. The manual zone partition (No. 1 ∼ 19) of the focused part of an SL C20 carriage.
Figure 4. The measurements of the simulated environment space E and the agent attributes p, q.
Figure 5. An calculation example of dynamic comfort distance qn(t): auto-adaptation according to Nt.
Figure 6. Illustration of agent turning angle that is selected obeying a discrete Gaussian distribution.
Figure 7. The heterogeneous agent moving speeds are mapped to groups of Swedish population.
Figure 8. The observation/simulation time (blue line) and the corresponding cubic interpolations (3D surface) as a function of existing and entering passengers/agents.
Figure 9. 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. 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).
Figure 11. The quantitative evaluation results using receiver operating characteristics (ROC) graph:
Figure 12. A screen-shot of “left-right sub-carriage imbalance” in the SL C20 subway carriage.
Figure 13. A screen-shot of “doorway jam” phenomenon in the SL C20 subway carriage.
Figure 14. A new design of seat layout by emptying wall areas in the SL C20 subway carriage.
Figure 15. An example of “pivot blocking” in the SL C20 carriage with the new seat distribution.
Figure 16. The “left-right sub-carriage imbalance” problem caused by “vertical detour difficulty”.

Tables

Table 1.
di Initial Status (entering|existing passengers) Real-data Dimensions (i.e. di,1, di,2,…, di,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.
d^i Model Initialization (entering|existing agents) Features of Simulation Data (d^i,1,…,d^i,23)
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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 & 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
Lk 103 102 500 103 2,000 200 200 103 50 500 N/A
Ck (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