Metro traffic regulation from the passenger perspective

Abstract

Maintaining metro ridership in the future will require an increasing focus on customer perceptions of the service quality. This work outlines a process whereby passenger expectations may be encapsulated into a mathematical evaluation function that can then be used in an on-line optimization procedure. The function proposed penalizes excess waiting time, travelling time and congestion following a disturbance such as a platform delay. With the function defined, the problem resolves into finding a set of optimum arrival and departure times (the decision variables) that minimizes this penalty function. Ultimately, the concept is intended to form part of an on-line, real-time traffic controller residing on the despatching computer in a control centre. An embedded simulation is used to perform the optimization calculations, and several techniques are described that reduce the computing time needed to a level feasible for on-line use. Comparisons are made with some previous control algorithms. Using data based on the Hong Kong Island Line, it is shown that the new controller performs well, albeit when using the newly proposed evaluation function as the test criterion.

Automatic train regulation with energy saving using dual heuristic programming

“…Let a ob,k W diag a k−1 ob,1 · · · a k−N ob,N and a ob,k W diag a k−1 ob,1 · · · a k−N ob,N , then the state space representation of (11) with N stations in sequence can be expressed as follows E aux,k+1 = (P ac a ob,k+1 + P lc I)(t k+1 − (Lt k + T 0,k )) (14) where…”

“…Each sample was to construct an environment for the ATR performing train regulation by using (7), (14) and (19) with daily passenger-flow distribution taken from real O/D data gathered through the automatic fare collection system. The duration of each sample is 06:00-22:00, which covers peak hours and off-peak hours.…”

“…In general, designing ATR is an optimisation problem with large scale, high non-linearity, heavy constraints and stochastic characteristics. Various optimisation methodologies such as GA [11], linear quadratic programming [12,13], constrained non-linear programming [14] and fuzzy expert system [15] were investigated to implement the traffic regulation function of ATR. In order to improve real-time operability, some of the methodologies are implemented with techniques to reduce computation time, such as receding horizon and reducing search space, yet the computation time is still long and the optimality of regulation performance would be degraded to some extent.…”

An optimisation-based traffic regulator for metro lines

“…Thus, different time deviations from nominal operation can be defined. Time deviation from nominal departure of train i at platform k is defined as (5). Time deviation from nominal arrival of train i at platform k is defined as (6).…”

“…Other predictive strategies try to minimise a cost function not based on the 'system' performance but on the passengers disturbance due to train delays. In [5] the cost function is independent on the timetable, and it is minimised using non-linear programming. A similar approach is found in [6] to schedule commercial timetables, minimising the passengers disturbance along a planning horizon (typically a whole operation day), using linear programming.…”