01588954

Component

Cycling is a healthy and sustainable mode of transportation. However, various problems, such as bicycle congestion, cycling safety and accessibility issues have been frequently observed in cities like Stockholm due to the rapidly increasing number of cyclists and insufficient bicycle facilities. In comparison to the overwhelming research effort on vehicle traffic and driver behavior, the studies on bicycle movement and cyclist behavior are however still far behind. This paper therefore aims at bridging the gap by presenting a new methodology for investigating and modeling microscopic cyclist behavior. In particular, the paper focuses on representing bicycle movements when they are not interacting with others. The cyclist acceleration behavior is modeled using naturalistic GPS (Global Positioning System) data collected by eleven recruited commuter cyclists from Stockholm. After processing the large amount data, cyclist trajectories are obtained and acceleration profiles are selected. A mathematical model is proposed and then identified by the acceleration datasets using the maximum likelihood estimation methodology. The cross validation approach is conducted to compare different forms of the mathematical model. While the model with more parameters shows superior performance, the simplified ones are still capable of capturing the trends in the acceleration profiles. Moreover, model extension is also discussed to show the possibility of examining the impact of cyclist specific factors, such as age, gender and agility, on cyclist behavior by using the proposed model. Although the cyclist population investigated in the current study is still limited, it is believed that this research provides a unique insight into this non-motorized transportation mode and could promote future bicycle related studies.

This paper was sponsored by TRB committee ANF20 Standing Committee on Bicycle Transportation. Alternate title: Modeling of Cyclist Acceleration Behavior Using Commuter GPS Data

01584066

16-4194

English

14p

2016

Transportation Research Board 95th Annual Meeting

Digital/other

Figures; References; Tables

Acceleration (Mechanics); Automatic data collection systems; Behavior; Cyclists; Global Positioning System; Safety

Stockholm (Sweden)

Pedestrians and Bicyclists; Planning and Forecasting; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2016 Paper #16-4194

TRIS, TRB, ATRI

Jan 12 2016 5:51PM