The data model consists of the following key elements: trainrun, trainrun section, transition, trainrun connection, port and node.
The nodes in the data model represent the specific locations, such as stations or stops, where a trainrun can have different actions or events associated with it. These nodes serve as key points in the trainrun's route, determining where it stops, passes through, or starts and ends.
On the other hand, the trainrun section represents a specific segment or portion of a trainrun that connects two nodes. It encapsulates all the relevant information related to that particular section, including temporal details like departure and arrival times. Additionally, it also stores the journey time, which indicates the duration it takes for the trainrun to move from one node to another.
The trainrun connection signifies the point in the logistic network where two trains need to coordinated to ensure a smooth connection between them.
In addition, a trainrun has references to behaviour-related abstractions such as category, frequency and time category, which define the behaviour of a trainrun.
- Category specifies the type of trainrun, e.g. a regional train, an intercity train or a goods train.
- Frequency defines the frequency with which the trainrun is carried out, e.g. 1/4h, 1/2h or every hour.
- TimeCategory defines the time categorisation of the trainrun, e.g. peak times or off-peak times or occasional.
By combining the nodes and trainrun sections in this data model, we can create a representation of the trainrun network, mapping out the connections and relationships between the different stations and the time it takes to traverse between them. This enables us to analyze and visualize complex trainrun routes and schedules.
Thus, the data model, consisting of TrainrunSection and nodes, forms a network of edges and nodes, similar to an undirected graph. The TrainrunSections represent the connections between the nodes and enable the representation and analysis of complex relationships in the model.
- TrainrunSection corresponds to the edges between nodes in an undirected graph.
- Nodes represent the points in the graph, allowing the connection of trainrun section .
- A pin represents a port (point) in the graph where a trainrun section is connected to a node.
- The transition extends the graph within the node. A transition corresponds to an edge that connects two pins within the node, thereby connecting two trainrun section.
- If two trainruns should make a connection at a station, this can be defined using a connection. The trainrun connection links two pins, each have to be associated with a different train.
Together, these elements form an undirected graph consisting of edges (TrainrunSections, Transitions) and nodes (Ports).
The last key element is the trainrun. The trainrun consists of an ordered sequence of trainrun sections and transitions. This ordered sequence defines the route of the trainrun and establishes a direction in the undirected graph. This direction corresponds to the exact path of the train.
classDiagram
Node *-- Port
Node *-- Transition
Node *-- Connection
Trainrun *-- TrainrunSection
TrainrunSection --* Port
Transition -- Port
Connection -- Port
class Node {
betriebspunktName : string
fullName : string
...
ports: Port[]
transitions: Transition[]
connections: Connection[]
}
class Trainrun {
name : string
...
trainrunCategory: TrainrunCategory
trainrunFrequency: TrainrunFrequency
trainrunTimeCategory: TrainrunTimeCategory
}
class TrainrunSection {
...
trainrun : Trainrun
sourceNode : Node
targetNode : Node
}
class Port {
...
trainrunSection : TrainrunSection
}
class Transition {
...
port1 : Port
port2 : Port
}
class Connection {
...
port1 : Port
port2 : Port
}
More specific information about the business and technical data can be referenced in the data structure definitions within the source code.
The key concept of network visualization is to obtain a clear and automated representation of connections (lines). For this purpose, a routing heuristic is implemented, which arranges the lines in a visually organized manner.
To begin with, the heuristic focuses on the pair of nodes that are connected by the trainrun section. At each node, a decision is made regarding whether the line exits the node in an upward-downward direction or a left-right direction. This decision is based on the following logic: If the other node is positioned above, the edge will exit upwards; otherwise, it will exit downwards. If the other node is located to the right of the current node, the edge will exit the node to the right; conversely, if it is located to the left, the edge will exit to the left. The prioritization is given to the upward-downward direction over the left-right direction.
By implementing this routing heuristic, the network visualization achieves an organized representation of the lines, ensuring clarity and ease of understanding.
export enum PortAlignment {
Top, // 0
Bottom, // 1
Left, // 2
Right // 3
}See for more details VisAVisPortPlacement.placePortsOnSourceAndTargetNode(srcNode, targetNode).
The sorting heuristics description can be found in the chapter, it is worth taking a look there before diving into the source code Node.sortPorts() for detailed information.
Trainrun sections, connections, and transition all have the path to be drawn stored (pre-defined). This storage makes it very easy for the rendering to determine whether the path has changed or not. Once the path changes, it is redrawn. Therefore, as long as there are no changes, nothing is rendered, which greatly increases the interactivity of the application but also requires more storage space.
Additionally, using the native JSON format adds more complexity because the stored path must be provided during import.
More details can be found in the source code:
- Node.computeTransitionRouting()
- Node.computeConnectionRouting()
- TrainrunSection.routeEdgeAndPlaceText()
- TrainrunSectionService.initializeTrainrunSectionRouting()