@unpublished{TakayasuWeiklDahmenetal., author = {Takayasu, Anna and Weikl, Simone and Dahmen, Victoria and Bogenberger, Klaus}, title = {Impact of Travel Stress and Infrastructure Quality on Cycling Mode Share: Insights From Multimodal Trajectory and Survey Data Analysis}, publisher = {Researchgate}, doi = {10.13140/RG.2.2.30696.85766}, abstract = {This study explores the relationship between travel stress, infrastructure quality, and cycling mode share. Utilizing multimodal trajectory data, survey responses on travel stress, and detailed road network information with geological and bicycle facility data, the study analyzes correlations between actual and perceived infrastructure quality, travel stress, and cycling mode share, as well as the impact of travel time and distance on cycling mode share. The results indicate significant correlations between perceived infrastructure quality and travel stress levels, and between travel stress levels and cycling mode share, particularly among regular bike users. However, clear correlations between actual and perceived infrastructure quality, and between perceived infrastructure quality and cycling mode share, were not observed. Additionally, direct effects of travel time and distance on cycling mode share were not evident. From these findings, three main insights emerge. Firstly, accurately estimating cycling mode share requires considering multiple parameters beyond travel time and distance. Secondly, to implement effective measures for bicycle facilities, a detailed analysis of perceived infrastructure quality, incorporating actual infrastructure design, is crucial. Lastly, travel stress, when carefully assessed with consideration of various factors beyond infrastructure design, emerges as a key determinant of travel mode choice. These insights have significant implications for future cycling research, including the development of travel mode choice models and estimating cycling demand. The study's valuable contributions advance efforts to promote cycling as a sustainable and preferred mode of transportation.}, language = {en} } @article{DahmenBogenbergerWeikl, author = {Dahmen, Victoria and Bogenberger, Klaus and Weikl, Simone}, title = {Modellierung des Verkehrsmittelwahlverhaltens mittels erkl{\"a}rbarem Maschinellem Lernen}, series = {Straßenverkehrstechnik}, volume = {69}, journal = {Straßenverkehrstechnik}, number = {2}, publisher = {Kirschbaum}, issn = {0039-2219}, doi = {10.53184/SVT2-2025-3}, pages = {96 -- 102}, abstract = {Die Modellierung der Verkehrsmittelwahl ist f{\"u}r die Vorhersage und das Verst{\"a}ndnis des Mobilit{\"a}tsverhaltens unerl{\"a}sslich. Hierbei wurden in den letzten Jahren mit maschinellem Lernen vielversprechende Ergebnisse erzielt, insbesondere f{\"u}r XGBoost- und Random-Forest-Modelle. Aufgrund der zunehmenden Verwendung von Tracking-basierten Smartphone-Apps zur Aufzeichnung des Mobilit{\"a}tsverhaltens, wenden wir diese Modelle auf einen solchen Datensatz an. Zudem analysieren wir eingehend deren Interpretierbarkeit. Wir kommen zu dem Schluss, dass das XGBoost-Modell am leistungsst{\"a}rksten und dennoch erkl{\"a}rbar ist. Die von solchen Modellen gewonnenen Erkenntnisse k{\"o}nnen beispielsweise genutzt werden, um die Verkehrsmittelwahl f{\"u}r beliebige Quelle-Ziel-Paare vorherzusagen.}, language = {de} }