v1.3.1

This is a maintenance release which updates CGAL to 6.0 because users reported compilation errors with certain compilers and older versions of CGAL.

v1.3.0

Release Notes

This release introduces a new model, deprecations, and bug fixes.

Anticipation Velocity Model (AVM)

The AVM models pedestrian anticipation in three phases:

1. Perception of the current situation.
2. Prediction of future movement.
3. Strategy selection leading to action.

By incorporating these steps, the AVM quantitatively reproduces bidirectional pedestrian flow. It accounts for:

• Anticipation of changes in neighboring pedestrians' positions.
• The strategy of following others' movement.

More details on the AVM: Anticipation Velocity Model

Deprecations

CamelCase Naming Deprecated

In our last releases several properties slipped into the release that did not follow PEP8 naming conventions, this has been corrected and the camel case style names have been deprecated.

v0 renamed to desired_speed

This update also deprecates v0 in all models and replaces it with the much clearer desired_speed.

e0 renamed to desired_direction

This update also deprecates e0 in all models and replaces it with the much clearer desired_direction.

Bug fixes

• Fixed #1324
• Fixed #1413
• Fixed #1449

v1.2.1

What's Changed

• Fixed WaitingSet: In specific cases the wrong waiting position was returned, this should work as expected now.
• Fixed serialisation issue: The serialisation is writing out a bounding box over the union of all geometries, min/max values had been swapped.

v1.2.0

This release contains two new features

New Model - Social Force Model

JuPedSim now implements another microscopic model, the Social Force Model as described by Helbing, D., Farkas, I., Vicsek, T. (2000). Simulating dynamical features of escape panic.

New route planning method - Direct Steering

Direct Steering allows the user to set each agents target individually and at any time. Agents will then use normal way findig to navigate to the set position. This should allow for rapid prototyping of high level agent behaviour such as waiting or queueing. The intention here is that user will be able to use their own logic written in python to do route planning. While this needs extra coding and might be runtime intensive it allows full flexibility.

v1.1.1

What's Changed

• The SQL output does now properly include all geometry variants when geometries are switched at runtime.

v1.1.0

What's Changed

• Add possibility to switch geometry at runtime.
• Add new iteration of the Collision Free Speed Model (CollisionFreeSpeedModelV2), in this update formerly global repulsion parameters have become agent specific parameters. Thus allowing to model individual agents to react with different strength to geometry / neighbouring agents.

v1.0.6

This is a bugfix release:

• Fix typo in documentation
• Address address sanitiser finding when freeing memory for journeys

v1.0.5

Changes

• Update dependencies
• Add support for Python 3.12

v1.0.4

Changes

• Check if added stages are inside walkable area by
• Allow v0 to be zero
• Explicitly mark supported Python versions

Documentation fixes

• Add missing queues notebook
• Improve online documentation
• Some editing of routing page

v1.0.3

Changes

• x86_64 wheels for macOS are now published to pypi
• Fixed missing file in source distribution
• Lowered minimum required macOS version to 11

Documentation Fixes

• Fix broken link to GitHub repository
• Fix link to Zenodo, now links to JuPedSim concept