A Realistic Cyclist Model for SUMO Based on the SimRa Dataset

TL;DR

This paper develops a more accurate cyclist model for the SUMO traffic simulation tool by analyzing real-world cyclist behavior from the SimRa dataset, aiming to improve urban planning and safety assessments.

Contribution

It introduces a novel cyclist model for SUMO based on empirical data, enhancing the realism of bicycle traffic simulation compared to previous simplified models.

Findings

Derived cyclist acceleration and velocity profiles from real data

Improved intersection left-turn behavior modeling

Enhanced simulation accuracy for bicycle traffic

Abstract

Increasing the modal share of bicycle traffic to reduce carbon emissions, reduce urban car traffic, and to improve the health of citizens, requires a shift away from car-centric city planning. For this, traffic planners often rely on simulation tools such as SUMO which allow them to study the effects of construction changes before implementing them. Similarly, studies of vulnerable road users, here cyclists, also use such models to assess the performance of communication-based road traffic safety systems. The cyclist model in SUMO, however, is very imprecise as SUMO cyclists behave either like slow cars or fast pedestrians, thus, casting doubt on simulation results for bicycle traffic. In this paper, we analyze acceleration, velocity, and intersection left-turn behavior of cyclists in a large dataset of real world cycle tracks. We use the results to derive an improved cyclist model and implement it in SUMO.