Optimising Train Priorities To Support The
Regulation Of Train Services With The Assistance
Of Active And Deductive Databases
Author(s)
C. Sakowitz & E. Wendler
Abstract
Maximisation of revenues is a fundamental goal of any business-driven railway
infrastructure company. In order to achieve this target in the context of traffic
regulation, it must try to avoid delays and ensure scheduled connections.
However, nominally equal delays to two different trains are not equal in value
from an economic point of view in most cases. Moreover, some connections
between trains might be more important in this sense than others. There are
complex interdependencies and reciprocal effects in railway traffic. Considering
these effects, a dispatcher must evaluate possible forms of conflict resolution and
the waiting times these give rise to and select the best solution possible. This is
not achievable where a time-critical conflict arises at short notice. Even closed
mathematical optimisation algorithms encounter limits in the case of larger
railway networks due to the enormous number of constraints to be considered.
This paper will therefore propose that the optimisation process be separated from
the train regulation process. Instead, economically evaluated train priorities for
conflict situations are to be determined with the help of active, deductive and
normative rules. Existing concepts of \“smart” database management systems
(DBMS) with integrated active and deductive database functionalities can be
used for this application. An active DBMS allows the definition of reactions to
be automatically initiated by the DBMS in response to the detection of given
database-related events. A deductive DBMS allows new, deducible facts to be
specified, administered and specially derived from explicitly introduced facts.
Train priorities are generated and assigned in detachment from day-to-day
operations for lightly and heavily disrupted railway traffic respectively.
Long-term optimisation of these priorities is effected by evaluating past
operational data.
Keywords: traffic regulation, real-time rescheduling, decision support system,
train priorities, active databases, deductive databases.
Keywords
traffic regulation, real-time rescheduling, decision support system,
train priorities, active databases, deductive databases.
Related Book
Other papers in this volume
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