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EVS-EN 12896-4:2019

Public transport - Reference data model - Part 4: Operations monitoring and control

General information

Valid from 01.10.2019
Base Documents
EN 12896-4:2019
Directives or regulations
None

Standard history

Status
Date
Type
Name
01.10.2019
Main
1.1  General Scope of the Standard
The main objective of the present standard is to present the Reference Data Model for Public Transport, based on:
-  the Reference Data Model, EN 12896, known as Transmodel V5.1;
-  EN 28701:2012, Intelligent transport systems - Public transport - Identification of Fixed Objects in Public Transport (IFOPT), although note that this particular standard has been withdrawn as it is now included within Parts 1 and 2 of this standard (EN 12896-1:2016 and EN 12896-2:2016) following their successful publication;
incorporating the requirements of:
-  EN 15531-1 to -3 and CEN/TS 15531-4 and -5: Public transport - Service interface for real-time information relating to public transport operations (SIRI);
-  CEN/TS 16614-1 and -2: Public transport - Network and Timetable Exchange (NeTEx), in particular the specific needs for long distance train operation.
Particular attention is drawn to the data model structure and methodology:
-  the data model is described in a modular form in order to facilitate the understanding and the use of the model;
-  the data model is entirely described in UML.
The following functional domains are considered:
-  Network Description: routes, lines, journey patterns, timing patterns, service patterns, scheduled stop points and stop places;
-  Timing Information and Vehicle Scheduling (runtimes, vehicle journeys, day type-related vehicle schedules);
-  Passenger Information (planned and real-time);
-  Fare Management (fare structure, sales, validation, control);
-  Operations Monitoring and Control: operating day-related data, vehicle follow-up, control actions;
-  Driver Management:
-  Driver Scheduling (day-type related driver schedules),
-  Rostering (ordering of driver duties into sequences according to some chosen methods),
-  Driving Personnel Disposition (assignment of logical drivers to physical drivers and recording of driver performance);
-  Management Information and Statistics (including data dedicated to service performance indicators).
The data modules dedicated to cover most functions of the above domains will be specified.
Several concepts are shared by the different functional domains. This data domain is called "Common Concepts".
1.2  Functional Domain Description
The different functional domains (enumerated above) taken into account in the present document, and of which the data have been represented as the reference model, are described in EN 12896-1:2016, Public transport - Reference data model - Part 1: Common concepts.
1.3  Particular Scope of this Document
The present document entitled Public transport - Reference data model - Part 4: Operations monitoring and control incorporates the following data packages:
-  Dated Production Components MODEL;
-  Call MODEL;
-  Production Plan MODEL;
-  Detecting and Monitoring MODEL;
-  Control Action MODEL;
-  Event and Incident MODEL;
-  Messaging MODEL;
-  Situation MODEL; and
-  Facility Monitoring and Availability MODEL.
The data structures represented in this part form descriptions of data that are specific to operations for an operational day (as opposed to those planned for day types). They reference to structures as described in EN 12896-1:2016, such as version frames or generic grouping mechanisms, but also to EN 12896-2:2016 and EN 12896-3:2016.
This document itself is composed of the following parts:
-  Main document (normative) presenting the data model for the domain Operations Monitoring and Control;
-  Annex A (normative), containing the data dictionary, i.e. the list of all the concepts and attribute tables present in the main document together with the definitions;
-  Annex B (normative), providing a complement to EN 12896-1:2016, particularly useful for parts 4 to 8 of the Public Transport Reference Data Model;
-  Annex C (informative), indicating the data model evolutions; and
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