TY - JOUR
T1 - Optimizing urban rail timetable under time-dependent demand and oversaturated conditions
AU - Niu, Huimin
AU - Zhou, Xuesong
N1 - Funding Information:
This research work of the first author was supported, in part, by the National Natural Science Foundation of China (Nos. 50968009 and 71261014 ) and the Research Fund for the Doctoral Program of Higher Education of China (No. 20096204110003 ). The work presented in this paper remains the sole responsibility of the authors.
PY - 2013/11
Y1 - 2013/11
N2 - This article focuses on optimizing a passenger train timetable in a heavily congested urban rail corridor. When peak-hour demand temporally exceeds the maximum loading capacity of a train, passengers may not be able to board the next arrival train, and they may be forced to wait in queues for the following trains. A binary integer programming model incorporated with passenger loading and departure events is constructed to provide a theoretic description for the problem under consideration. Based on time-dependent, origin-to-destination trip records from an automatic fare collection system, a nonlinear optimization model is developed to solve the problem on practically sized corridors, subject to the available train-unit fleet. The latest arrival time of boarded passengers is introduced to analytically calculate effective passenger loading time periods and the resulting time-dependent waiting times under dynamic demand conditions. A by-product of the model is the passenger assignment with strict capacity constraints under oversaturated conditions. Using cumulative input-output diagrams, we present a local improvement algorithm to find optimal timetables for individual station cases. A genetic algorithm is developed to solve the multi-station problem through a special binary coding method that indicates a train departure or cancellation at every possible time point. The effectiveness of the proposed model and algorithm are evaluated using a real-world data set.
AB - This article focuses on optimizing a passenger train timetable in a heavily congested urban rail corridor. When peak-hour demand temporally exceeds the maximum loading capacity of a train, passengers may not be able to board the next arrival train, and they may be forced to wait in queues for the following trains. A binary integer programming model incorporated with passenger loading and departure events is constructed to provide a theoretic description for the problem under consideration. Based on time-dependent, origin-to-destination trip records from an automatic fare collection system, a nonlinear optimization model is developed to solve the problem on practically sized corridors, subject to the available train-unit fleet. The latest arrival time of boarded passengers is introduced to analytically calculate effective passenger loading time periods and the resulting time-dependent waiting times under dynamic demand conditions. A by-product of the model is the passenger assignment with strict capacity constraints under oversaturated conditions. Using cumulative input-output diagrams, we present a local improvement algorithm to find optimal timetables for individual station cases. A genetic algorithm is developed to solve the multi-station problem through a special binary coding method that indicates a train departure or cancellation at every possible time point. The effectiveness of the proposed model and algorithm are evaluated using a real-world data set.
KW - Genetic algorithm
KW - Oversaturated condition
KW - Time-dependent demand
KW - Train timetable
KW - Transit service optimization
KW - Urban rail line
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U2 - 10.1016/j.trc.2013.08.016
DO - 10.1016/j.trc.2013.08.016
M3 - Article
AN - SCOPUS:84885233325
VL - 36
SP - 212
EP - 230
JO - Transportation Research Part C: Emerging Technologies
JF - Transportation Research Part C: Emerging Technologies
SN - 0968-090X
ER -