Description
Transport network models derived from digital sources provide a higher level of detail and complexity. In particular, network graphs have become more complex due to features such as directionally separated lanes and the inclusion of ramps at intersections. Additionally, the availability of link attributes such as lane counts or speed limits results in links being segregated each time an attribute changes. Consequently, complex transport networks feature a multitude of nodes.
The Access Node Deriver is a tool designed to simplify the representation of complex intersections in the network graph by introducing singular access nodes. This is achieved through an algorithmic analysis of the network graph that considers the topology of the nodes and the existing road hierarchies of the adjacent links to identify nodes belonging to intersections (Intersection Nodes). Using spatial buffers, the identified Intersection Nodes are grouped into clusters, where each cluster represents an individual intersection. For each cluster identified in this way, a new node (Access Node) is inserted to provide access to all directions served by this intersection. The original topology remains with a few added nodes and links.
The Access Nodes can be used to auto-generate connectors, which are particularly useful in a network structuring task as described in [1]. Refer to the README file in the linked GitHub repository for further details. We are currently preparing an affiliated publication in the Transportation Research Record and will share the link to the paper here upon its release.
[1] Friedrich, M. Functional Structuring of Road Networks. Transportation Research Procedia, Vol. 25, 2017, pp. 568–581, doi: 10.1016/j.trpro.2017.05.439.
Notes
Currently, the code only supports transport networks in the software PTV Visum. In the future, this may be implemented differently. Please note that there is no support or liability provided by the chair.
Developer: Yannik Wohnsdorf, M. Sc.
Download
You can find the Python source code, including an example for testing purposes (PTV Visum 23), at the following link: https://github.com/Verkehrsplanung-und-Verkehrsleittechnik/access_node_deriver
CoEXist & CIVITAS SATELLITE Webinar 19.11.2019
Automation-ready transport modelling tools: including CAVs in your traffic flow and travel demand simulations.
Description
Many transport planning decisions affecting urban mobility and road infrastructure are based on the results of traffic flow and travel demand modelling. Within the H2020 CoEXist project, vital progress has been made on the micro- and macroscopic simulation capabilities to model Connected and Automated Vehicles (CAVs) and their interactions with conventional vehicles and other road users, within PTV’s Vissim and Visum software.
How can these tools be used to enable informed decision-making about Cooperative, Connected and Automated Mobility? To answer these questions, CoEXist, in cooperation with the CIVITAS SATELLITE project, is organising a webinar on automation-ready transport modelling tools and its application in urban mobility planning.
Notes
There is no support and no liability on the part of the chair
Developer: Jörg Sonnleitner
Download
CoEXist Webinar - Macroscopic Travel Demand Modelling Tools.zip
Description
State of the art travel demand models for urban areas typically distinguish four or five main modes: walking, cycling, public transport and car. The mode car can be further split into car-driver and car-passenger. As the importance of ridesharing may increase in the coming years, ridesharing should be addressed as an additional sub or main mode in travel demand modeling. This requires an algorithm for matching the trips of suppliers (typically car drivers) and demanders (travelers of non-car modes).
Therefore a matching algorithm is necessary, which can be integrated in existing travel demand models. The algorithm works likewise with integer demand, which is typical for agent-based microscopic models, and with non-integer demand occurring in travel demand matrices of a macroscopic model. The algorithm compares two path sets of suppliers and demanders. The representation of a path in the road network is reduced from a sequence of links to a sequence of zones. The zones act as a buffer along the path, where demanders can be picked up.
Notes
There is no support and no liability on the part of the chair
Developer:
Download
Here you can find the tool including an example for testing purposes (PTV VISUM 16): Application
Description
To minimize the number of vehicles required for on-demand services (carsharing or ridesharing), empty vehicles need to be reallocated to places with current demand. This requires a vehicle scheduling (or vehicle blocking) process simulating the dispatching of shared vehicle fleets. The vehicle scheduling algorithm determines the number of required vehicles and the origins, destinations and times of empty vehicle trips. It uses time-dependent demand matrices (=service trips) as input and determines time-dependent empty trip matrices and the number of required vehicles as a result.
The algorithm can be applied to integer and non-integer demand matrices and is therefore particularly suitable for macroscopic travel demand models.
Notes
There is no support and no liability on the part of the chair
Developer:
Download
Here you can find the algorithm, including an example implementation for scheduling electric scooters on the University Campus Stuttgart-Vaihingen.

Markus Friedrich
Prof. Dr.-Ing.Head of Chair for Transport Planning and Traffic Engineering