Public transport in Switzerland and in Latin America is organised differently and operated with different levels of reliability, innovation priorities, and acceptance of digital support solutions by passengers. The common element is that in both regions, operators struggle with optimising the offered services to further contribute to sustainable urban mobility. Operators and city planners require more expertise in capturing accurate real data from vehicle and passenger movement, and even more with processing the amounting heaps of data of mixed input quality. This poses an enormous problem in upcoming data-centric approaches, and raises the question on whether potential solutions to that problem based on novel algorithms and technologies are universal and could also bring benefits in systems of different scale. In this binational Swiss-Ecuador project, local fieldwork is conducted related to data acquisition, consensus and analytics, as well as data exploration methods to get more accurate views on movement patterns based on socially and environmentally sustainable sensing. The two principal methods to reach this objective of extracting high-quality movement pattern data are cache-enabled serverless data analytics and data-centric consensus.