Travel time is an integral part of all human activities. In transport planning, travel time is one of the most widely used and significant parameters in the design of transport systems. However, people's ability to estimate travel time and time, in general, is limited. The aim of this thesis is to broaden the knowledge on the perception of travel time of active modes. More specifically, this thesis's objective is to provide insight into the deviation of perceived travel time to the actual trip duration of cycling and walking trips and to identify the factors that influence it. Individual characteristics and emotional state, trip characteristics, and external conditions such as the weather are considered. To reach the research aim, data are collected on the subjective and objective travel time and on the possible determinants. A mobile application is developed to serve as the single data collection tool. The app incorporates a location (GPS) data source and multiple surveys. The trip duration, which is reported by the survey respondents right after the completion of a trip, is considered as the subjective travel time. Data are collected for two weeks in the Netherlands in June 2020. An analysis is performed on the obtained dataset in terms of descriptive statistics and regression analysis. The former reveals that, on average, people underestimate the duration of trips conducted by bike or on feet. Via linear and logistic regression models, the decisive factors of travel time misperception are pointed out. Disappointing weather, high physical effort demand, and compulsory trip purposes prolong the perceived trip duration. On the contrary, the usage of an information source for planning a trip leads to a shorter subjective travel time. The overall underestimation of travel time by active modes indicates that the perceived disutility due to travelling could also be lower than the objectively measured one when travelling by these modes. The findings of the study are discussed in regards to recommendations to practice and future research. Furthermore, the thesis provides suggestions for further usages of mobile applications for the collection of data on travel time distortion and on travel experience, in general.