Redirigiendo al acceso original de articulo en 18 segundos...
ARTÍCULO
TITULO

Investigating cyclist interaction behavior through a controlled laboratory experiment

Yufei Yuan    
Winnie Daamen    
Bernat Goñi-Ros    
Serge Hoogendoorn    

Resumen

Nowadays, there is a need for tools to support city planners in assessing the performance of cycling infrastructure and managing bicycles and mixed flows. Microscopic and macroscopic bicycle traffic models can be used to fulfill this need. However, fundamental knowledge on individual cyclist interaction behavior (which should underpin these models) is hardly available in literature. Detailed bicycle traffic data are necessary if we want to gain insight into cyclist interaction behavior and develop sound behavioral theories and models. Laboratory experiments have been proven to be one of the most effective ways to collect detailed traffic data. For this reason, a controlled experiment aimed to investigate cyclist interaction behavior has been carried out at Delft University of Technology. This paper describes the experimental design, the resulting microscopic bicycle trajectories, and some preliminary results regarding one of the most common interaction situations: the bidirectional interaction. The preliminary results reveal how and to what extent cyclists interact in bidirectional cycling. It is found that cyclists perform a clearly-visible evading (collision avoidance) maneuver when they have face-to-face encounters. During these maneuvers, changes in speed and displacements in the lateral direction are observed. Cyclists start to deviate from their original path when they are around 30 m from each other, and they strongly prefer passing on the right-hand side. Moreover, the expectation of gender differences in cycling behavior reported in the literature is confirmed: our results show that women generally cycle more slowly than men and deviate more from their intended paths in face-to-face encounters. More observations will be available in the next stage of data analysis. These findings can be used to formulate improved microscopic bicycle traffic models for infrastructure design and policy development.

 Artículos similares

       
 
Yiwen Tang, Jiaxin Zhang, Runjiao Liu and Yunqin Li    
Streets are an important component of urban landscapes and reflect the image, quality of life, and vitality of public spaces. With the help of the Google Cityscapes urban dataset and the DeepLab-v3 deep learning model, we segmented panoramic images to ob... ver más

 
Xiangyu Li, Gobi Krishna Sinniah, Ruiwei Li and Xiaoqing Li    
As a form of rapid mass transportation, urban rail systems have always been widely used to alleviate urban traffic congestion and reconstruct urban structures. Land use characteristics are indispensable to this system and correlate with urban ridership. ... ver más

 
Faheem Ahmed Malik, Laurent Dala, Muhammad Khalid and Krishna Busawon    
This paper develops an intelligent real-time learning framework for the last-mile delivery of mobility as a service in city planning, based upon safe infrastructure use. Through a hybrid approach integrating statistics and supervised machine learning tec... ver más
Revista: Applied Sciences

 
Dana Kaziyeva, Martin Loidl and Gudrun Wallentin    
Transport planning strategies regard cycling promotion as a suitable means for tackling problems connected with motorized traffic such as limited space, congestion, and pollution. However, the evidence base for optimizing cycling promotion is weak in mos... ver más

 
Maria Ciesla    
This article presents the issues and needs for modern solutions in building urban infrastructure, based on the smart city idea to improve the living standards of residents. Particular attention is paid to one of the most important aspects of life, relate... ver más
Revista: Infrastructures