Delay propagation data examples simulated by LinTim software

`delayAth`

Delay propagation data generated on the Athens metro network by LinTim software

`delayGoe`

Delay propagation data generated on the Goettingen bus system by LinTim software

Public transportation network datasets are generated by LinTim software (Integrated Optimization in Public Transportation; https://www.lintim.net/index.php?go=data&lang=en).

`delayAth`

Delay data on the Athens metro network. Propagation simulation under consideration of secruity distances and fixed-waiting time delay management. 'data.frame' with 510 observations (10 sequential time pictures for delay spreading pattern from 51 stations) of 53 variables (`k0`

true source, `time`

, delays at 51 stations).

`delayGoe`

Delay data on the directed Goettingen bus system. Progation simulation under consideration of secruity distances and fixed-waiting time delay management. 'data.frame' with 2570 observations (10 sequential time pictures for delay spreading pattern from 257 stations) of 259 variables (`k0`

true source, `time`

, delays at 257 stations).

Manitz, J., J. Harbering, M. Schmidt, T. Kneib, and A. Schoebel (2017): Source Estimation for Propagation Processes on Complex Networks with an Application to Delays in Public Transportation Systems. Journal of Royal Statistical Society C (Applied Statistics), 66: 521-536.

Jonas Harbering

if (FALSE) { # compute effective distance data(ptnAth) athnet <- igraph::as_adjacency_matrix(ptnAth, sparse=FALSE) p <- athnet/rowSums(athnet) eff <- eff_dist(p) # apply source estimation if (requireNamespace("aplyr", quietly = TRUE)) { data(delayAth) res <- alply(.data=delayAth[,-c(1:2)], .margins=1, .fun=origin_edm, distance=eff, silent=TRUE, .progress='text') perfAth <- ldply(Map(performance, x = res, start = as.list(delayAth$k0), list(graph = ptnAth))) } } if (FALSE) { # compute effective distance data(ptnGoe) goenet <- igraph::as_adjacency_matrix(ptnGoe, sparse=FALSE) p <- goenet/rowSums(goenet) eff <- eff_dist(p) # apply source estimation if (requireNamespace("aplyr", quietly = TRUE)) { data(delayGoe) res <- alply(.data=delayGoe[,-c(1:2)], .margins=1, .fun=origin_edm, distance=eff, silent=TRUE, .progress='text') perfGoe <- ldply(Map(performance, x = res, start = as.list(delayGoe$k0), list(graph = ptnGoe))) } }