Research field B: (De-⁠)centralized cooperative traffic management

Research field B put emphases on key models and key methods of (de-⁠)centralized cooperative traffic management which are investigated in the research areas (De-⁠)centralized cooperative traffic management (B1) and Distributed intelligent and cooperative systems (B2).

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B1. Sustainable cooperative traffic management for urban environments (Prof. Friedrich)


B1.1 Qinrui Tang: Left Turn Prohibition Problem combining Traffic Assignment

B1.2 Aleksandar Trifunović: (Re)design of public transport network to integrate shared modes of transport

B2. Distributed intelligent and cooperative systems (Prof. Müller)


B2.1 Malte Aschermann: Automated Mechanisms of Cooperative Traffic Flow Coordination: Fairness vs. Efficiency

Figure B2.1.1: Coordination of Vehicles by Means of Policies
Cooperative Lane Management and Traffic flow Optimisation

To answer my research questions regarding coordination mechanisms on 2+1 roadways, I developed the platform Cooperative Lane Management and Traffic flow Optimisation (CoLMTO).

Execution Model

The execution model of CoLMTO, developed to conduct my simulation studies, is depicted in the following figure:

Figure B2.1.2: The CoLMTO Execution Model


The software architecture is structured as follows:

Figure B2.1.2: The CoLMTO Simulation Architecture as of release v0.1.1

Source Code Statistics

  • license
  • CircleCI  Codecov  Codacy
  • html documentation



Since 2015, I am working together with Philipp Kraus and Sophie Dennisen on a joint project called LightJason/AgentSpeak(L++).

As described on our website

LightJason is a concurrent BDI multi-agent framework for creating a multi-agent systems with Java. The project is inspired by AgentSpeak(L) and Jason, but designed and implemented from scratch. LightJason is fine-tuned to concurrent plan execution suitable for distributed computing environments and aims at efficient and scalable integration with existing platforms.

Our goal is to provide researchers a scalable platform to simulate multi-agent environments with concurrent execution in mind.

  • Platooning models for autonomous vehicles in SUMO (GitHub)
  • Vehicle following models with social force (BSc thesis, GitHub)
  • Agent-based simulation of cooperative driving manoeuvres of autonomous cars (BSc thesis, GitHub)
  • Aufdeckung von regelwidrigem Verhalten von Agenten im Verkehrskontext (Revealing rule-adverse behaviour of agents in traffic context) (BSc thesis)
  • Entwicklung eines institutionellen Frameworks für multiagentenbasierte Verkehrssimulationen (Developing an institutional framework for multiagent-based traffic simulations) (MSc thesis)


B2.2 Sophie Dennisen: Collective Decision-Making Mechanisms in Urban Traffic

Considered Scenario

In my research, I consider an intraurban (future) scenario where visitors of a city form travel groups at pre-defined locations and need to agree on locations to visit together.

Figure B2.2.1: Phases for Collective Decision Making in urban traffic

To answer my research questions regarding the suitability of different voting mechanisms for collective decision making in urban traffic, I developed the simulation tool LightVoting which is based on LightJason developed by my colleagues Philipp Kraus and Malte Aschermann.

Execution Model

The following figure depicts the execution model of LightVoting:

Figure B2.2.2: Execution Model for LightVoting

Simulation Architecture

The following figure gives an overview of the LightVoting simulation architecture:

Figure B2.2.3: LightVoting Simulation Architecture

Source Code Statistics

  • license
  • CircleCI
  • html documentation



B2.3 Fatema Tuj Johora: Scalable micro-simulation of Mixed Traffic based on cognitive and hybrid models

B2.4 Sinziana Sebe: Collective decision-making for online coordination of same day delivery with mixed autonomous fleets

Research field A ◀ previous page