india_bus_routes — Bus networks of Indian cities (2016)
- Description
Networks of the bus routes of six major Indian cities, compiled by A. Chatterjee, M. Manohar and G. Ramadurai from the route information published by the public bus operators: Ahmedabad Bus Rapid Transit System (
ahmedabad), Metropolitan Transport Corporation, Chennai (chennai), Delhi Transport Corporation (delhi), Andhra Pradesh State Road Transport Corporation, Hyderabad (hyderabad), Calcutta State Transport Corporation, Kolkata (kolkata), and Brihanmumbai Electricity Supply and Transport, Mumbai (mumbai). Nodes are bus stops, and an undirected edge connects two stops that are consecutive on at least one bus route, withweightequal to the number of routes that overlap on that link (i.e. that serve the two stops in succession).In the source files the links are listed as source/target pairs. For Ahmedabad, Delhi and Kolkata, some pairs of stops are listed twice, once in each order and usually with different weights (presumably the numbers of routes in each travel direction); these are kept as parallel edges. Some links are self-loops. The authors treat the networks as undirected, as buses run in both directions.
The networks have the following properties:
name: the name of the bus stop (ahmedabad,delhi,hyderabad,kolkata), or its numeric code (chennai,mumbai), as given in the source files.weight: the number of overlapping routes. One link ofdelhihas no weight in the source file, and hasweightset to -1.
- Tags
- Transportation Public Transport Weighted Multigraph Metadata
- Citation
-
- A. Chatterjee, M. Manohar, G. Ramadurai, "Statistical analysis of bus networks in India." PLoS ONE 11(12), e0168478 (2016) 10.1371/journal.pone.0168478 Sci-Hub
- Upstream URL OK
- https://github.com/achatterjee3/Dataset
- Networks
-
Tip: click on a column header to sort the list, or hover over it for a description.
Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties
Name Nodes Edges $\left<k\right>$ $\sigma_k$ $\lambda_h$ $\tau$ $r$ $c$ $\oslash$ $S$ Kind Mode NPs EPs gt GraphML GML csv ahmedabad 1,103 2,582 4.68 4.26 7.03 36.65 0.12 0.14 16 1.00 Undirected Unipartite name weight 14 KiB 22 KiB 21 KiB 19 KiB chennai 1,009 1,610 3.19 2.23 5.28 100.02 0.13 0.10 27 1.00 Undirected Unipartite name weight 7 KiB 11 KiB 11 KiB 11 KiB delhi 1,554 4,285 5.51 5.28 9.41 358.76 0.10 0.14 29 1.00 Undirected Unipartite name weight 22 KiB 34 KiB 33 KiB 29 KiB hyderabad 1,086 2,953 5.44 8.44 15.92 17.14 -0.04 0.10 11 1.00 Undirected Unipartite name weight 16 KiB 25 KiB 24 KiB 21 KiB kolkata 519 884 3.41 2.63 4.44 39.75 -0.01 0.07 16 1.00 Undirected Unipartite name weight 6 KiB 9 KiB 9 KiB 8 KiB mumbai 2,266 3,042 2.68 1.34 3.94 3095.63 0.18 0.19 98 1.00 Undirected Unipartite name weight 12 KiB 20 KiB 19 KiB 23 KiB