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Article 2: Putting Fatbikes to the Test: A Large-Scale Controlled Experiment on Riding Behavior and Safety
Fatbikes have rapidly become a visible, and sometimes controversial, part of the urban mobility landscape, particularly in the Netherlands. While they are legally classified as e-bikes, their vehicle characteristics, acceleration, riding behavior and user profiles can differ substantially from those of conventional bicycles. At the same time, concerns are growing about their interactions with pedestrians, cyclists and other users of increasingly busy active-mode infrastructure. Yet, surprisingly little empirical evidence exists on a fundamental question: How do fatbike riders actually behave and interact with others in traffic?
To address this gap, our Active-Mode Lab at TU Delft, together with research partners from several universities in China, recently organized what we believe to be the first controlled experimental study specifically designed to investigate fatbike riding behavior and its interaction with other road users. The experiment was conducted in China following more than half a year of preparation, including experimental design, vehicle and equipment preparation, pilot testing, participant recruitment, safety assessment and scenario development.
From individual riding to complex interactions
Rather than studying fatbike riders in isolation, we designed the experiment around the interactions that occur in real active-mode traffic. Fatbike riders, conventional cyclists and pedestrians were asked to move continuously through a controlled experimental environment, allowing us to observe a range of naturally emerging behaviors. These included overtaking, yielding, merging, crossing interactions, counterflow encounters and pedestrian–cyclist interactions.
We also varied elements of the cycling environment, including the presence or absence of a center line, to investigate how relatively small changes in infrastructure may influence lateral positioning, negotiation and interaction behavior. A further experimental component explored auditory interventions. Different sound conditions were introduced to examine whether auditory information can influence riding behavior, attention and interaction patterns. This provides an interesting opportunity to explore whether sound may function not only as information, but potentially also as a behavioral nudge in active-mode traffic.
Looking beyond cyclists
Another major part of the experiment focused on pedestrian–micromobility interaction. Pedestrians interacted with fatbikes and conventional bicycles under different flow and density conditions, including same-direction and opposing movements. These experiments allow us to investigate not only how pedestrians react to approaching micromobility users, but also how pedestrian groups reorganize themselves when sharing limited space with faster-moving traffic. Some experiments additionally focused on individual pedestrian responses within a moving pedestrian stream, providing a closer look at how local interactions may influence movement through a group.
A multi-layered dataset
One of the most exciting outcomes of the experiment is the richness of the collected data. High-resolution video recordings will be processed to derive individual trajectory data, allowing us to analyze speed, acceleration, lateral movement, spacing, overtaking, yielding and interaction patterns at a microscopic level. For selected participants and scenarios, eye-tracking data provide an additional layer of information about visual attention during interactions. Finally, questionnaires collected information about participants' perceived safety, interaction experience and perceptions of different parts of the experimental environment.
Together, these datasets give us the opportunity to study traffic interaction from several perspectives: what people do, what they look at, and how safe they feel.
What comes next?
The experimental campaign is only the beginning. The next stage will focus on processing and synchronizing the different datasets and translating the observations into scientific insights. We are particularly interested in understanding how fatbike behavior differs from conventional cycling, how infrastructure and environmental interventions influence interactions, how pedestrians respond to micromobility traffic, and how objective movement behavior relates to subjective perceptions of safety.
Combining trajectories, visual attention and perception data may also open up new ways of understanding the behavioural mechanisms behind interactions—beyond what can be observed from trajectories alone. Ultimately, the experimental data will support the development and calibration of behavioural models for cyclists, fatbike riders and pedestrian–micromobility interactions. The findings can provide an empirical basis for micromobility policy, traffic safety measures and the design of cycling and shared active-mode infrastructure.
As fatbikes and other emerging micromobility modes continue to reshape urban mobility, evidence from controlled experiments can help move the discussion from assumptions and anecdotes towards measurable behaviour and data-driven solutions.
Interested in working with the data?
MSc students interested in fatbike behavior, cycling safety, pedestrian–micromobility interaction, trajectory analysis, eye-tracking, behavioral modelling or related topics are welcome to contact me to discuss possible thesis projects.
Article 1: "Navigating the Micromobility Maze: A Controlled Study of Urban Riders' Interactions"
Travel patterns and lifestyles in cities around the world have changed in recent years due to the strong growth of travel modes commonly referred to as micromobility. Micromobility vehicles include all easy-to-carry or easy-to-push vehicles allowing for the augmentation of pedestrians. They range from the lightest rollers and skis to the heaviest two-wheeled self-balancing personal transporters. They can be motorized or non-motorized, shared or privately owned. Among them are electric scooters, electric bicycles, and electric mopeds, whose number and use have increased dramatically over the past few years due to reduced purchase costs, enhanced motor efficiency, and lightweight designs, as well as a growing focus on sustainability. However, the detailed behavior and how these modes and their users interact with each other on the dedicated infrastructure (e.g., a bicycle path, a shared space) remains to be studied.
In order to obtain a clear picture of these movement and interactive dynamics, Wenyi Zhang, a Master student of TUD, and Yufei Yuan, a researcher in the field of active-mode research, have organized a controlled experiment across two days, conducted in a campus in a Chinese university. The experiment is under the umbrella of an ongoing educational collaboration between BJTU and TUD. It is a joint educational program for BSc students that has been running for years.
The study is to collect data through a controlled experiment and then analyze the data on the individual riding behavior of three selected types of micromobility (e-scooter, e-moped, and e-bike) users as they travel in a bike lane while interacting with bicycles, static obstacles, and pedestrians as well as within the three investigated micromobility modes.
In a simulated environment, around 50 participants, divided into 5 travel modes, joined the study. During the experiment, participants ride a micromobility vehicle that they are familiar with on a designed bicycle track along a certain route. Firstly, they ride in a non-interactive setting. Then they ride and interact with other riders, pedestrians, or static obstacles, including avoiding obstacles and overtaking low-speed vehicles, and driving through bottlenecks where the number of bike lanes is reduced, crossing at 90-degree intersections, and zebra crossings with pedestrians.
