Understanding Bicycle Dynamics and Cyclist Behavior From Naturalistic Field Data (November 2012)

Abstract

As technology advances, motorized vehicles employ newer, more intelligent systems to improve drivers' safety and mobility. The evolution of these systems is supported by increasingly accurate models for driver behavior and vehicle dynamics. Despite the significant role of nonmotorized vehicles such as bicycles in traffic accidents, the evolution of in-vehicle intelligent systems has no counterpart for bicycles. Part of the reason is that, to date, models for bicyclist behavior are absent and models for bicycle dynamics are limited. This paper presents a platform for collecting field data from bicycles and shows how such data can support the development of intelligent systems by offering novel insights into bicycle dynamics and bicyclist behavior.

Evaluation of e-bike accidents in Switzerland

“…First naturalistic driving studies (e.g. Twisk et al, 2013;Dozza and Fernandez, 2013) emphasize these findings by indicating higher velocities in e-bikers compared to bicyclists.…”

“…Furthermore, first results of naturalistic driving observations focusing on speeds, mental workload and travel behaviour of e-bikers were recently published (e.g. Twisk et al, 2013;Dozza and Fernandez, 2013). Amongst other results Twisk et al found and Dozza et al assumed, based on their observations that e-bikers tend to drive faster than bicyclists.…”

A context aware and video-based risk descriptor for cyclists

“…Despite the aforementioned technical hurdles, some recent studies address cycling experience by equipping bicycles with on-board sensors. Aiming at specialized cycling intelligent systems, [17] proposes a framework to understand bicycle dynamics and cyclist behavior. Such framework and collected data can be seen as an important pre-requisite to the development of bicycle suited applications.…”

Modeling cyclist acceleration process for bicycle traffic simulation using naturalistic data

“…Owing to the abundant data, an intelligent system called Bicycle Monitoring Index (BMI) which can be used to evaluate the safety and mobility of the bicycle environment was developed using the fault tree analysis (FTA) technique. Later, Dozza and Fernandez (2014) applied an advanced IPB with multiple sensors to study bicycle dynamics and cyclist behavior. Longitudinal, lateral, and vertical accelerations were measured and further processed by a specialized software.…”