Knowing where vacant taxis are and will be at a given time and location helps the users in daily planning and scheduling, as well as the taxi service providers in dispatching. In this paper, we present a predictive model for the number of vacant taxis in a given area based on time of the day, day of the week, and weather condition. The history is used to build the prior probability distributions for our inference engine, which is based on the naïve Bayesian classifier with developed error-based learning algorithm and method for detecting adequacy of historical data using mutual information. Based on 150 taxis in Lisbon, Portugal, we are able to predict for each hour with the overall error rate of 0.8 taxis per 1x1 km 2 area.

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We present a model to implement digital twins in sustainable agriculture. Our two-year research project follows the design science research paradigm, aiming at the joint creation of physical and digital layers of IoT-enabled structures for vertical farming. The proposed model deploys IoT to (1) improve productivity, (2) allow self-configuration to environmental changes, (3) promote energy saving, (4) ensure self-protection with continuous structural monitoring, and (5) reach self-optimization learning from multiple data sources. Our model shows how the digital twins can contribute within the agrofood lifecycle of planning, operation, monitoring, and optimization. Moreover, it clarifies the interconnections between the goals, tasks, and resources of IoT-enabled structures for sustainable agriculture, which is one of the biggest human challenges in this century.