School of Architecture and Civil Engineering

Danil Belikhov

Research Focus

 - Microscopic Traffic Modeling
 - Travel Behavior
 - Active Modes of Mobility
 - Data Analytics and Machine Learning

General information

Current Courses


Professional Activities

Since 05/2023: Research Associate
Chair of Bicycle Traffic, Bergische Universität Wuppertal

06/2021 - 02/2023: Research Assistant
Chair of Traffic Engineering and Control, Technical University of Munich

03/2019 - 12/2020: Design Engineer
Gidroteck Group, Russia


10/2020 - 01/2023: Master of Science in Transportation Systems
School of Engineering and Design, Technical University of Munich

09/2016 - 07/2020: Bachelor of Science in Civil Engineering
Institute of Civil Engineering, Peter the Great St.Petersburg Polytechnic University


Master thesis

  • 2023: Belikhov, D. Development of a machine learning model for the extrapolation of short-term bicycle counts with the inclusion of meteorological data. Master's Thesis, Technical University of Munich (TUM)

Peer-Reviewed Conference Papers

  • 2023: Sautter, Natalie; Kessler, Lisa; Belikhov, Danil; Bogenberger, Klaus: A Multimodal Performance Index for the Evaluation of Urban Traffic Control Measures. 2023 8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)
  • 2022: Sautter, Natalie; Kessler, Lisa; Belikhov, Danil; Bogenberger, Klaus: Multimodal Performance Evaluation of Urban Traffic Control: A Microscopic Simulation Study. 4th Symposium on Management of Future Motorway and Urban Traffic Systems 2022 (MFTS2022)

Using environment and physical and physiological parameters of bicyclists to improve their speed choice in traffic modeling.

Bicycling, bicycle traffic, speed choice, traffic modeling, discrete choice analysis, power output, e-bike

Bicycling involves human energy expenditure, however, minimal research focuses on speed choice models that account for bicyclist's energy spending and bicycle type. Existing microscopic traffic simulations inadequately represent cyclist parameters and power outputs, hindering proper infrastructure planning and further research on inclusive urban traffic, especially with rising e-bike use. This study aims to integrate energy aspects into bicyclists' speed choice modeling, enhancing simulation accuracy. This involves developing and testing speed choice models for bicycle traffic in microscopic traffic simulation. Models include environmental parameters, cyclist power reserves, and travel time. Results will provide a more accurate bicyclist behavior model, promoting inclusive urban traffic planning, active mobility, safety, and public health.

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