delay-data {NetOrigin}

Delay propagation data examples simulated by LinTim software

Description

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

Details

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).

Author(s)

Jonas Harbering

Source

Public transportation network datasets are generated by LinTim software (Integrated Optimization in Public Transportation; https://lintim.net/).

References

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.

See Also

ptn-data

Examples

## Not run:  
# compute effective distance
data(ptnAth)
athnet <- igraph::as_adjacency_matrix(ptnAth, sparse=FALSE)
p <- athnet/rowSums(athnet)
eff <- eff_dist(p)
# apply source estimation
data(delayAth)
res <- plyr::alply(.data=delayAth[,-c(1:2)], .margins=1, .fun=origin_edm, distance=eff,
             silent=TRUE, .progress='text')
perfAth <- plyr::ldply(Map(performance, x = res, start = as.list(delayAth$k0),  
                     list(graph = ptnAth)))

## End(Not run)
## Not run:  
# compute effective distance
data(ptnGoe)
goenet <- igraph::as_adjacency_matrix(ptnGoe, sparse=FALSE)
p <- goenet/rowSums(goenet)
eff <- eff_dist(p)
# apply source estimation
data(delayGoe)
res <- plyr::alply(.data=delayGoe[,-c(1:2)], .margins=1, .fun=origin_edm, distance=eff,
             silent=TRUE, .progress='text')
perfGoe <- plyr::ldply(Map(performance, x = res, start = as.list(delayGoe$k0), 
                     list(graph = ptnGoe)))

## End(Not run)