Chair for Transport Planning and Traffic Engineering

Tools

A selection of the tools developed at our chair.

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: 

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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.

 

This image shows Markus Friedrich
Prof. Dr.-Ing.

Markus Friedrich

Head of Chair for Transport Planning and Traffic Engineering

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