Optimizing urban rail timetable under time-dependent demand and oversaturated conditions

•Develop integer programming models to design train timetables in a heavily congested urban rail corridor.•Analytically calculate effective passenger loading time periods under dynamic demand patterns.•Calculate time-dependent waiting times under oversaturated conditions.•Propose gradient-based and...

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Veröffentlicht in:Transportation research. Part C, Emerging technologies Jg. 36; S. 212 - 230
Hauptverfasser: Niu, Huimin, Zhou, Xuesong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier India Pvt Ltd 01.11.2013
Elsevier
Schlagworte:
Applied sciences
Exact sciences and technology
Genetic algorithm
Ground, air and sea transportation, marine construction
Oversaturated condition
Railway transportation and traffic
Time-dependent demand
Train timetable
Transit service optimization
Transportation planning, management and economics
Urban rail line
Time-dependent demand
Genetic algorithm
Oversaturated condition
Urban rail line
Transit service optimization
Train timetable
Transport demand
Supersaturation
Scheduling
Running of trains
Modeling
Optimization
Integer programming
Time dependence
Urban transportation
Example
Timetabling problem
Numerical simulation
Strategy
Rail transportation
ISSN:0968-090X, 1879-2359
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Zusammenfassung:•Develop integer programming models to design train timetables in a heavily congested urban rail corridor.•Analytically calculate effective passenger loading time periods under dynamic demand patterns.•Calculate time-dependent waiting times under oversaturated conditions.•Propose gradient-based and genetic algorithms for optimizing timetables. 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.
ISSN:0968-090X
1879-2359
DOI:10.1016/j.trc.2013.08.016