IoT-Based Technological Retrofitting of Urban Collective Public Transport in Intermediate Cities of the Global South: Edge–Cloud Architecture, Field Validation and a Digital Twin in Tunja, Colombia

Intermediate cities of the Global South concentrate a growing share of the urban population but lack the financial and institutional capacity to renew their urban collective public transport (UCPT) systems according to smart-city references conceived in the Global North. This article presents the design, implementation and validation of an intelligent system for UCPT based on the technological retrofitting of existing elements (seats, buses, bus-stops and pedestrian crossings), developed within a research macro-project with a case study in Tunja, Colombia. The system derives its requirements from a citizen-centred multidimensional evaluation model of sustainable and smart mobility and is structured in the three layers of an IoT architecture: four low-cost perception modules (i-Seat, i-Bus, i-Bus-Stop and i-Crossing/Traffic-Light), a hybrid Edge–Cloud telematic system with LoRaWAN as the main technology and LTE/4G as backup, and the MueveT platform, which integrates telemetry, estimated time of arrival, detection of presumed infringements, device monitoring and service quality indicators. Validation was carried out with prototypes installed in a private vehicle that emulated UCPT journeys and with a usability evaluation completed by project researchers as a proof of concept. On a 3.72 km route, the mobile node recorded packet error rates of 38.7% with DR1 and 40.4% with DR2, the latter with half the time on air; on a 6.53 km route with four gateways, the fixed bus-stop node recorded no LoRaWAN losses, and the LTE link delivered the samples with a median latency of 272 ms to the platform. A digital twin fed with these records projected deliveries above 85% for 25 buses per gateway, and the platform achieved user experience ratings between 4.25 and 5.00. A comparative analysis with similar developments shows that the main contribution lies in the explicit traceability between the evaluation model, the captured data and the functions, providing a basis for daily operation and evidence for the maturity level of urban mobility.