TY - GEN
T1 - Fast train
T2 - 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014
AU - Meng, Lingyun
AU - Zhou, Xuesong
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/11/14
Y1 - 2014/11/14
N2 - A number of tactical and operational applications, such as train timetabling and dispatching, require sophisticated and computationally efficient software to optimize train schedules. An open-source train scheduling package, namely FastTrain has been designed and implemented to generate feasible schedules and optimality gap estimates for trains on a generic rail network with both single and double tracks. This paper describes its two major modelling components: (1) a network cumulative flow model to capture the complex safety rules of infrastructure resources, and (2) a Lagrangian relaxation solution framework which efficiently decomposes the original multi-trains complex model to a set of single train oriented subproblems. Two experimental cases based on the adapted datasets released by INFORMS RAS are conducted to demonstrate the effectiveness and efficiency of the developed algorithm under different network and data availability conditions.
AB - A number of tactical and operational applications, such as train timetabling and dispatching, require sophisticated and computationally efficient software to optimize train schedules. An open-source train scheduling package, namely FastTrain has been designed and implemented to generate feasible schedules and optimality gap estimates for trains on a generic rail network with both single and double tracks. This paper describes its two major modelling components: (1) a network cumulative flow model to capture the complex safety rules of infrastructure resources, and (2) a Lagrangian relaxation solution framework which efficiently decomposes the original multi-trains complex model to a set of single train oriented subproblems. Two experimental cases based on the adapted datasets released by INFORMS RAS are conducted to demonstrate the effectiveness and efficiency of the developed algorithm under different network and data availability conditions.
KW - Lagrangian Relaxation
KW - Resource Constraints
KW - Shortest Path Algorithm
KW - Train Routing
KW - Train Scheduling
UR - http://www.scopus.com/inward/record.url?scp=84937147655&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84937147655&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2014.6958077
DO - 10.1109/ITSC.2014.6958077
M3 - Conference contribution
AN - SCOPUS:84937147655
T3 - 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014
SP - 2416
EP - 2421
BT - 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 8 October 2014 through 11 October 2014
ER -