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• Vehicle, cyclist & pedestrian count data from 44 neighboring or collocated stations were analyzed. • Pre & post-pandemic datasets showed pattern changes due to Covid-19 restrictions were mode-specific. • Land-use restrictions and altered trip purposes impacted daily and weekly travel patterns. • Cross-correlation, clustering, and Bayesian change-point analyses showed congruent inferences. • Timeline of pandemic restrictions could be matched using statistical analyses on traffic counts. The spread of Covid-19 and implementation of various restrictions changed how and why people travel. The present study analyzed three different modes of transportation, to understand the impact of Covid-19 mobility and land-use restrictions on chosen neighbourhoods in Dublin City, Ireland. Classification analysis, Spatial correlation analysis, and Bayesian change point analysis had been conducted using vehicle, cyclist & pedestrian count data from 44 neighbouring or collocated stations to explore the statistical changes in the traffic system characteristics. Apart from reduction in traffic counts, the other impacts were modal shift, rise in cyclist numbers, and similarity between weekday & weekend patterns observed. Analyses could identify that changes in the statistical aspects of traffic system are congruent with the changes in lockdown measures. Overall, this study presented a set of tools to identify the existence and degree of changes in traffic patterns over time due to any mobility or land-use policy changes. [ABSTRACT FROM AUTHOR]