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(a) Distribution of all traffic flows N (i, j, d) in loglog. The line is a power law fit of the form N −α with exponent α = 2.27 with fitting method described in [26]. (b) Average and standard deviation of the flows N (i, j, d) averaged over traffic flows versus the date d (from 1st January to 12th February).
Source publication
Understanding how interurban movements can modify the spatial distribution of the population is important for transport planning but is also a fundamental ingredient for epidemic modeling. We focus here on vacation trips (for all transportation modes) during the Chinese Lunar New Year and compare the results for 2019 with the ones for 2020 where tr...
Contexts in source publication
Context 1
... first consider the distribution of all flows of individuals N (i, j, d) for all cities i and j and all days d and which is shown in Fig. 1 (a). The maximum flow is of order 10 5 and the average of order 10 3 indicating a broad distribution. A power law fit is consistent with this picture with an exponent α ≈ 2.3 ( Fig. 1 (a)). This heterogeneity is confirmed in Fig. 1 (b) which shows both the average value µ d and the standard deviation σ d computed over all inter-city flows ...
Context 2
... maximum flow is of order 10 5 and the average of order 10 3 indicating a broad distribution. A power law fit is consistent with this picture with an exponent α ≈ 2.3 ( Fig. 1 (a)). This heterogeneity is confirmed in Fig. 1 (b) which shows both the average value µ d and the standard deviation σ d computed over all inter-city flows (for each day d). ...
Context 3
... the distribution of all flows of individuals N (i, j, d) for all cities i and j and all days d and which is shown in Fig. 1 (a). The maximum flow is of order 10 5 and the average of order 10 3 indicating a broad distribution. A power law fit is consistent with this picture with an exponent α ≈ 2.3 ( Fig. 1 (a)). This heterogeneity is confirmed in Fig. 1 (b) which shows both the average value µ d and the standard deviation σ d computed over all inter-city flows (for each day d). We see that for most days the relative dispersion σ d /µ d is of order 5 − 10. This heterogeneity is probably due to the large diversity of cities that can serve as origins or destinations of flows (see below for ...
Context 4
... deviation σ d computed over all inter-city flows (for each day d). We see that for most days the relative dispersion σ d /µ d is of order 5 − 10. This heterogeneity is probably due to the large diversity of cities that can serve as origins or destinations of flows (see below for further analysis). An important feature that we can observe on Fig. 1 (b) is the sharp drop of the standard deviation after Jan. 25th, the Lunar New Year (LNY), which we will see below is mainly due to the travel ban (see also Fig. S1, S3 in SI for a detailed ...
Context 5
... is probably due to the large diversity of cities that can serve as origins or destinations of flows (see below for further analysis). An important feature that we can observe on Fig. 1 (b) is the sharp drop of the standard deviation after Jan. 25th, the Lunar New Year (LNY), which we will see below is mainly due to the travel ban (see also Fig. S1, S3 in SI for a detailed ...
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... migration index reflecting the size of the population moving into or out from a city/province, and p(i, j, d) is migration ratio capturing the proportion of each origins and destination. However, the migration ratio is unavailable for 2019. We apply the data of p(i, j, d) for 2020 to the computation of N (i, j, d) for 2019, with results shown in Fig. S1. This result exhibits large heterogeneity of flows and displays a localized drop around ...
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... and city population in Fig. S2 which indicates that the larger a city and the more flows it carries. Compared to 2019, the differences between the scatter points for incoming flows corresponding to days before and after LNY are much larger in 2020. This result emphasizes again that travel ban causes indeed the sharp drop of standard deviation in Fig. 1 (b) of the main text rather than the low travel intention during the Spring ...
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... show the relative standard deviation of N over flows versus time with an order around 2.96 in Fig. S10 (a) and the distribution of the relative standard deviation of N over time concentrating around 0.64 in Fig. S10 (b). Large heterogeneity of traffic flows between provinces confirms the difficulty of modeling these flows. The relative standard ...
Context 9
... show the relative standard deviation of N over flows versus time with an order around 2.96 in Fig. S10 (a) and the distribution of the relative standard deviation of N over time concentrating around 0.64 in Fig. S10 (b). Large heterogeneity of traffic flows between provinces confirms the difficulty of modeling these flows. The relative standard ...
Context 10
... first consider the distribution of all flows of individuals N (i, j, d) for all cities i and j and all days d and which is shown in Fig. 1 (a). The maximum flow is of order 10 5 and the average of order 10 3 indicating a broad distribution. A power law fit is consistent with this picture with an exponent α ≈ 2.3 ( Fig. 1 (a)). This heterogeneity is confirmed in Fig. 1 (b) which shows both the average value µ d and the standard deviation σ d computed over all inter-city flows ...
Context 11
... maximum flow is of order 10 5 and the average of order 10 3 indicating a broad distribution. A power law fit is consistent with this picture with an exponent α ≈ 2.3 ( Fig. 1 (a)). This heterogeneity is confirmed in Fig. 1 (b) which shows both the average value µ d and the standard deviation σ d computed over all inter-city flows (for each day d). ...
Context 12
... the distribution of all flows of individuals N (i, j, d) for all cities i and j and all days d and which is shown in Fig. 1 (a). The maximum flow is of order 10 5 and the average of order 10 3 indicating a broad distribution. A power law fit is consistent with this picture with an exponent α ≈ 2.3 ( Fig. 1 (a)). This heterogeneity is confirmed in Fig. 1 (b) which shows both the average value µ d and the standard deviation σ d computed over all inter-city flows (for each day d). We see that for most days the relative dispersion σ d /µ d is of order 5 − 10. This heterogeneity is probably due to the large diversity of cities that can serve as origins or destinations of flows (see below for ...
Context 13
... deviation σ d computed over all inter-city flows (for each day d). We see that for most days the relative dispersion σ d /µ d is of order 5 − 10. This heterogeneity is probably due to the large diversity of cities that can serve as origins or destinations of flows (see below for further analysis). An important feature that we can observe on Fig. 1 (b) is the sharp drop of the standard deviation after Jan. 25th, the Lunar New Year (LNY), which we will see below is mainly due to the travel ban (see also Fig. S1, S3 in SI for a detailed ...
Context 14
... is probably due to the large diversity of cities that can serve as origins or destinations of flows (see below for further analysis). An important feature that we can observe on Fig. 1 (b) is the sharp drop of the standard deviation after Jan. 25th, the Lunar New Year (LNY), which we will see below is mainly due to the travel ban (see also Fig. S1, S3 in SI for a detailed ...
Context 15
... migration index reflecting the size of the population moving into or out from a city/province, and p(i, j, d) is migration ratio capturing the proportion of each origins and destination. However, the migration ratio is unavailable for 2019. We apply the data of p(i, j, d) for 2020 to the computation of N (i, j, d) for 2019, with results shown in Fig. S1. This result exhibits large heterogeneity of flows and displays a localized drop around ...
Context 16
... and city population in Fig. S2 which indicates that the larger a city and the more flows it carries. Compared to 2019, the differences between the scatter points for incoming flows corresponding to days before and after LNY are much larger in 2020. This result emphasizes again that travel ban causes indeed the sharp drop of standard deviation in Fig. 1 (b) of the main text rather than the low travel intention during the Spring ...
Context 17
... show the relative standard deviation of N over flows versus time with an order around 2.96 in Fig. S10 (a) and the distribution of the relative standard deviation of N over time concentrating around 0.64 in Fig. S10 (b). Large heterogeneity of traffic flows between provinces confirms the difficulty of modeling these flows. The relative standard ...
Context 18
... show the relative standard deviation of N over flows versus time with an order around 2.96 in Fig. S10 (a) and the distribution of the relative standard deviation of N over time concentrating around 0.64 in Fig. S10 (b). Large heterogeneity of traffic flows between provinces confirms the difficulty of modeling these flows. The relative standard ...
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