Kellermann, RobinSivizaca Conde, DanielRößler, DavidKliewer, NataliaDienel, Hans-Liudger2023-01-262023-01-262022-08-11https://depositonce.tu-berlin.de/handle/11303/18074https://doi.org/10.14279/depositonce-16867The COVID-19 pandemic marked a global disruption of unprecedented scale which was closely associated with human mobility. Since mobility acts as a facilitator for spreading the virus, individuals were forced to reconsider their respective behaviors. Despite numerous studies having detected behavioral changes during the first lockdown period (spring 2020), there is a lack of longitudinal perspectives that can provide insights into the intra-pandemic dynamics and potential long-term effects. This article investigates COVID-19-induced mobility-behavioral transformations by analyzing travel patterns of Berlin residents during a 20-month pandemic period and comparing them to the pre-pandemic situation. Based on quantitative analysis of almost 800,000 recorded trips, our longitudinal examination revealed individuals having reduced average monthly travel distances by ∼20%, trip frequencies by ∼11%, and having switched to individual modes. Public transportation has suffered a continual regression, with trip frequencies experiencing a relative long-term reduction of ∼50%, and a respective decrease of traveled distances by ∼43%. In contrast, the bicycle (rather than the car) was the central beneficiary, indicated by bicycle-related trip frequencies experiencing a relative long-term increase of ∼53%, and travel distances increasing by ∼117%. Comparing behavioral responses to three pandemic waves, our analysis revealed each wave to have created unique response patterns, which show a gradual softening of individuals’ mobility related self-restrictions. Our findings contribute to retracing and quantifying individuals’ changing mobility behaviors induced by the pandemic, and to detecting possible long-term effects that may constitute a “new normal” of an entirely altered urban mobility landscape.en304 Das Sozialverhalten beeinflussende FaktorenCOVID-19Travel behaviorMode choiceTravel patternsQuantitative analysisLongitudinal analysisArticle2590-1982