Travel time data and variability of travel times can be used to quantify the network performance as an important aspect of sustainable development in modern cities. We introduce a data collection and analysis framework to be employed as an integral part of a neighbourhood sustainability assessment (NSA). The proposed framework performs a targeted selection and evaluation of indicators based on publicly available traffic data, specifically addressing short-term observations of travel times and traffic counts. Entailing a hybrid and modular approach, we demonstrate the applicability of travel time reliability measures by combining different datasets, various variability estimators, and visualisation methods, to advance the interpretation strength of the assessment. Implemented are regression methods based on cosinor models, which allow us to analyse the rhythmic behaviour of travel time trends. These models can be applied to assess the differential rhythmicity among different factors, like route, weather conditions and type of day (i.e., weekend or workday). Further, the same datasets are used to evaluate two reliability metrics, i.e., a travel time index and a planning time index. We demonstrate the application of the suggested framework on several strategic routes associated with three testbed neighbourhoods in Ljubljana, Slovenia. The results show that average travel times and their variations are affected by route, day of the week, and weather conditions, and correlate with vehicles count data. Analyses of rhythmicity give straightforward insights into travel time trends, appropriate for comparative analysis and thus well-suited for use in NSA and monitoring practices. To the best of our knowledge, this work presents the first study quantifying reliability indicators intended for integration with NSA standards. •Travel time distributions are analysed to formulate indicators for the neighbourhood sustainability assessment (NSA)•We enhance NSA methods based on short-term observations of travel times and traffic counts•Cosinor regression is applied for detection and characterization of rhythmicity of travel times•The interchangeable use and integration of two data sources, Google Direction API and roadway counters, is examined•The study provides recommendations for the straightforward analysis and presentation of travel times and their variability