Microscopic Traffic Flow Simulator VISSIM

By M. Fellendorf, P. Vortisch. 2010

555 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Ocean Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/601f44abb1008fd0cb40dfc524a1d4ea58641678

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In Control and Systems Engineering

In Ocean Engineering

In Transportation

Citations in the corpus

Incorporating human-factors in car-following models : a review of recent developments and research needs (2014, methodology): “A modified version of the original Wiedemann model has been used in the commercial microsimulation software VISSIM (Fellendorf and Vortisch, 2010). Several calibration attempts for VISSIM model exist in the literature. For example, Park and Qi (2006) used Genetic Algorithm (Goldberg, 1989) to estima”
A Communications-Oriented Perspective on Traffic Management Systems for Smart Cities: Challenges and Innovative Approaches (2015, background): “There has also been roughly 3800 downloads of the VISSIM demo in 2012 [94].”
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario (2019, unknown): “world behavior [1, 3].”
Flow: Architecture and Benchmarking for Reinforcement Learning in Traffic Control (2017, background): “Traffic Simulators: Traffic microsimulators include Quadstone Paramics [78], VISSIM [79], [80], AIMSUN [81], MATSIM [82], POLARIS [83], and SUMO [43].”
Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles (2020, methodology): “Another popular microscopic simulator that has been used commercially and for research also is VISSIM [30].”
Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey (2020, methodology): “VISSIM [55] is a simulator preferred by researchers due to its interaction with MATLAB, similar to AIMSUN.”
Simulation of Urban Mobility (2013, methodology): “According to traffic laws [6, 7] it is forbidden to enter an intersection if there is a danger of not being able to pass the intersection and consequently blocking cross traffic.”
Benchmarks for reinforcement learning in mixed-autonomy traffic (2018, methodology): “This has spurred interest in the development of several frameworks dedicated to accurate microscopic traffic reconstruction, with prominent simulators including propriety software such as Aimsun [25] and VISSIM [26], and open-source libraries such as SUMO [27].”
Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys (2023, methodology): “more crucial for developers, and the simulation platform such as Carla [131], Vissim [132], PerScan, AirSim [133], Udacity,”