The Internet of Things (IoT) is poised to deliver connected automation for monitoring, optimization, and control of processes and systems in sectors such as the automotive, healthcare and security. In this way, the IoT promises to make better use of the available resources, enable real-time interaction, and support innovative applications. Under this regime, the present work details an event-triggering technique (ETT) for efficient and effective tracking of road vehicles in the realm of intelligent transportation systems. The technique assumes that both the local and remote host follow a predetermined mobility model to approximate the actual vehicle location. Upon detection of deviation from this model, the local host triggers an update event to resynchronize the remote host. Within the proposed ETT, both the mobility model, as well as the detection thresholds are calculated solely based on sensory data, making the whole system both scalable and robust to dynamic changes. Experimental results demonstrate the many-fold improvements in efficiency of the proposed solution compared to traditional periodic tracking. These improvements are also validated in practice through an implementation on an operating bus route service.