Abstract
This paper proposes a novel model to optimize the first train timetables for urban rail transit networks, with the goal of maximizing passengers’ transfer waiting time satisfaction. To build up the relationship of transfer waiting time and passenger satisfaction, a reference-based piecewise function is formulated with the consideration of passengers’ expectations, tolerances and dissatisfaction on “just miss”. In order to determine the parameters of zero waiting satisfaction rating, the most comfortable waiting time, and the maximum tolerable waiting time in time satisfaction function, a stated preference survey is conducted in rail transit transfer stations in Shanghai. An artificial bee colony algorithm is developed to solve the timetabling model. Through a real-world case study on Shanghai’s urban rail transit network and comparison with the results of minimizing the total transfer time, we demonstrate that our approach performs better in decreasing extremely long wait and “just miss” events and increasing the number of passengers with a relatively comfortable waiting time in [31s, 5min). Finally, four practical suggestions are proposed for urban rail transit network operations.
Highlights
Timetabling is one of the most challenging issues in public transit operation and management
Compared to the original timetables, the first train originating times of six line directions are brought forward, and the other two are postponed. By means of these adjustments, the total waiting time satisfaction of all the first train transfer passengers is increased by 44.31%, and 71.88% of all the transfer relationships are improved in terms of time satisfaction
Data on the passenger volume of the first train transfer passengers are provided by the Beijing rail transit company
Summary
Timetabling is one of the most challenging issues in public transit operation and management. Aiming at determining a periodic or non-periodic schedule for a set of trains subject to some train operational constraints, train timetabling models with different optimization objectives have been proposed, e.g., minimizing total transfer cost [2], minimizing supplier and user costs [3,4], minimizing passenger waiting time [5,6,7], minimizing wasted capacity [7], minimize the total travel time/cost [8,9], minimizing energy consumption [8,10] and so on. Xue et al [7] formulated a nonlinear integer programming model to determine an optimal timetable for an urban rail transit network, thereby reducing the wasted capacity at a constant departure frequency with a slight increase in passenger waiting time.
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