bbjubjub2494 / cssm_project

Complex Social Systems: Modeling Agents, Learning, and Games 2022

• Group Name: Traffic is our Jam
• Group participants names: Louis Bettens, Manuel Dublanc, Carolin Heinzler
• Project Title: Traffic flow with bicycle lanes and bike boxes: a cellular automaton

General Introduction

In this class report, we evaluate the influence of advanced stop lines (also called bike boxes) on urban traffic, using the established Nagel Schreckenberg model to simulate traffic. With this, we get quantitative results on the traffic flow and average speed which can be leveraged in the discussion of safe cycling in cities and city planning.

The Model

We implemented an cellular automaton based on the original Nagel Schreckenberg model. The model consists of heterogeneous vehicles which live on a 2 dimensional lattice. We implemented bike lanes and advanced stop lines at intersections to understand the parameters traffic flow and average speed achieved for different configurations (namely a shared road, bicycle lanes and bicycle lanes with advanced stop lines). For further details and the results of this analysis, see the attached report.

Fundamental Questions

We hope to contribute to the application of Cellular Automata for traffic behaviour, by implementing a basic model which exhibits the basic interactions between cars and bicycles on a shared road with no bicycle lane. Furthermore, we want to incorporate a bicycle lane together with ASLs to have a more thorough understanding of how this might influence the traffic flow of cars and bicycles respectively.

As far as our research goes, the topic of modelling traffic with ASLs has not been explored in research yet, however we deem this necessary, as simulations of traffic flow are an important and recognized tool in decision making for urban traffic planning. Furthermore, increasing safety and visibility of bikes needs to be prioritized in city planning to make the bike more attractive.

Expected Results

By comparing the different configurations of shared road, bicycle lanes and bicycle lanes with advanced stop lines, we expected to observe that the shared road is worst case in terms of average speed and traffic flow, for cars and bikes respectively. Similarly, we expected the road with bicycle lane to be the best case. For the implementation of advanced stop lines next to the bicycle lanes, we expected that this would be best case for the bikes and non-optimal for the cars as they have to wait longer at a red light. An overview of the actual results can be found in the attached report.

References

See the attached report in this repository for an exhaustive list of our literature references.

In the code we also relied on these sources:

• projectmesa: "Introductory Tutorial": https://mesa.readthedocs.io/en/latest/tutorials/intro_tutorial.html
• projectmesa: "Advanced Tutorial": https://mesa.readthedocs.io/en/latest/tutorials/adv_tutorial.html
• projectmesa: "API": https://mesa.readthedocs.io/en/latest/apis/api_main.html
• projectmesa: "Examples": https://github.com/projectmesa/mesa/tree/v1.1.1/examples
• Chengarda: "Simple Pan and Zoom Canvas": https://codepen.io/chengarda/pen/wRxoyB
• icomonstr: "Car 1": https://iconmonstr.com/car-1-svg/
• icomonstr: "Bicycle 5": https://iconmonstr.com/bicycle-5-svg/

Research Methods

Traffic Cellular Automata, Agent-Based Model, Nagel Schreckenberg Model, Traffic Planning