Vulnerability Analysis and Passenger Source Prediction in Urban Rail Transit Networks

<div><p>Based on large-scale human mobility data collected in San Francisco and Boston, the morning peak urban rail transit (URT) ODs (origin-destination matrix) were estimated and the most vulnerable URT segments, those capable of causing the largest service interruptions, were identified. In both URT networks, a few highly vulnerable segments were observed. For this small group of vital segments, the impact of failure must be carefully evaluated. A bipartite URT usage network was developed and used to determine the inherent connections between urban rail transits and their passengers' travel demands. Although passengers' origins and destinations were easy to locate for a large number of URT segments, a few show very complicated spatial distributions. Based on the bipartite URT usage network, a new layer of the understanding of a URT segment's vulnerability can be achieved by taking the difficulty of addressing the failure of a given segment into account. Two proof-of-concept cases are described here: Possible transfer of passenger flow to the road network is here predicted in the cases of failures of two representative URT segments in San Francisco.</p></div

Locating major passenger sources (MPS) and major passenger destination (MPD).

<p>(a) In the San Francisco URT network, the census tracts in yellow, orange, and red indicate the MPS passenger production values of the most vulnerable segment. The census tracts shown in shades of blue represent the segments' MPD passenger attraction values. Black links indicate the connections between each selected URT segment and its MPS. (b) In the Boston URT network, the census tracts in yellow, orange, and red indicate the MPS values of the most vulnerable segment. The census tracts shown in shades of blue represent the segments' MPD values. Black links indicate the connections between each selected URT segment and its MPS. (c) The color of each segment represents its number of MPS (). (d) The color of each segment represents its number of MPS (). (e) The distribution of the number of major sources of passengers in San Francisco. (f) The distribution of the number of major passenger destinations in San Francisco. (g) The distribution of the number of major sources of passengers in Boston. (h) The distribution of the number of major passenger destinations in Boston.</p

Alternative routes in the road network.

<p>(a) The color of a road segment represents the potential passenger flows transferring to it when the URT segment from Market St. and Buchanan St. to Market St. and South Van Ness Ave. fails. (b) Same as (a) but for an actual disconnection of the URT segment from Duboce Ave. and Noe St. to Church St. and Duboce Ave. during a maintenance project in San Francisco. (c) The potential alternative bus routes when the URT segment from Market St. and Buchanan St. to Market St. and South Van Ness Ave. breaks down (red lines) and the potential alternative bus routes that were available during the maintenance project (blue lines). (d) The distribution of passenger flow indicates different patterns of travel demand put on the San Francisco road network.</p

Passenger flow in the URT networks.

<p>(a) In the San Francisco URT network, the colors indicate the passenger flow of URT segment . (b) Same as (a) but for the Boston URT network. (c) The passenger flow follows a power-law distribution () with () in San Francisco (Boston). (d) In San Francisco, the betweenness centrality can be approximated by a Gaussian distribution (). In Boston, the betweenness centrality can also be approximated by a Gaussian distribution (). (e) Low correlations were observed between passenger flow and the betweenness centrality . The topology of the Boston URT network was found to have a greater effect on shaping the passenger flow distribution than that of the San Francisco URT network did (<i>PCC</i> = 0.79).</p

The URT network data and the human mobility data.

<p>(a) The black arrow points to the geographical center of the San Francisco URT network. The URT segments with directions heading to the geographical center are here considered “inbound” segments, and the URT segments with directions leaving away from the geographical center are considered “outbound” segments. (b) The geographic center and outbound segments in the Boston URT network. (c) The blue polygons on the San Francisco map indicate the census tracts, and the light gray dots indicate the centers of the city blocks. The San Francisco daily commuting OD data are recorded in a city-block resolution. (d) The blue polygons on the Boston map indicate the census tracts, and the light gray dots indicate the locations of mobile phone users detected during the three-week observational period.</p

Distribution of the trip travel time.

<p>In San Francisco, the duration of a URT trip showed a double Gaussian distribution (). In Boston, the duration of a URT trip showed a Gaussian distribution ().</p

Additional file 1 of ETV2 promotes osteogenic differentiation of human dental pulp stem cells through the ERK/MAPK and PI3K-Akt signaling pathways

Additional file 1: Figure S1. The mRNA level of CBFA1 in qRT-PCR (a) and RNA-Seq (b). *P < 0.05

Additional file 3 of ETV2 promotes osteogenic differentiation of human dental pulp stem cells through the ERK/MAPK and PI3K-Akt signaling pathways

Additional file 3: Figure S3. HE (scale bar = 200 μm) and Masson (scale bar = 50 μm) staining in the rat calvarial defect model (n = 6)