Measuring reliability of transportation networks using snapshots of movements in the network – An analytical and empirical study

In this paper we introduce an analytical framework based on discrete Likelihood Maximization techniques that provides estimates of operational level data of Queuing models and Transportation networks based on snapshots of data on movements of commodities in a network. We apply our methodology to detailed data on movements of containers imported from S.E. Asian ports to marine ports on the west coast of Canada, unloaded at these ports, moved to rail cars, and transported by rail to destinations in U.S. and Canada. We show how one can estimate operational level parameters such as the number of servers at the ports, schedules of departure and capacity of trains, and even speed of trains based on only snapshots of container movements in the network. Subsequently, we were able to calibrate the entire inter-continental transportation network, were able to identify the sources of variability in the network and were able to measure the reliability of the network to shocks.