Public transportation network scan for rapid surveillance

Figures & data

Figure 1. Examples of detected clusters by each algorithm: Proposed (Top), Circular-scan (Bottom left), and Flex-scan (Bottom right).

Figure 2. JR network in Tokyo: black line is JR railway line, and gray circle is 800-m ball centered at each station.

Table 1. Number of potential clusters created in each algorithm.

K	PTNS	Circular	Flex
1	142	142	142
2	240	240	227
3	344	366	365
4	449	488	505
5	559	602	673
6	661	721	935
7	766	841	1288
8	870	960	1846
9	966	1086	2627
10	1064	1206	4083
11	1156	1331	6169
12	1240	1452	9714
13	1326	1574	16443
14	1407	1695	26744
15	1483	1818	40987
16	1554	1940	68974
17	1621	2063	107915
18	1683	2185	183180
19	1742	2302	302465
20	1798	2420	513992

Figure 3. Simulation results: Accuracy, Sensitivity, Positive predicted value (PPV) and calculation time (second) of proposed (PTNS), Circular-, and Flex-scan approaches.

Data availability statement

In this study, we do not use real data.