Transferring knowledge from monitored to unmonitored areas for forecasting parking spaces
Smart cities around the world have begun monitoring parking areas in order to estimate available parking spots and help drivers looking for parking. The current results are promising, indeed. However, existing approaches are limited by the high cost of sensors that need to be installed throughout the city in order to achieve an accurate estimation. This work investigates the extension of estimating parking information from areas equipped with sensors to areas where they are missing. To this end, the similarity between city neighborhoods is determined based on background data, i.e., from geographic information systems. Using the derived similarity values, we analyze the adaptation of occupancy rates from monitored- to unmonitored parking areas.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning
Most environmental data come from a minority of well-monitored sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unmonitored sites. Here, we demonstrate a novel t…
Meta-LearningTransfer LearningZero-shot Microclimate Prediction with Deep Learning
Weather station data is a valuable resource for climate prediction, however, its reliability can be limited in remote locations. To compound the issue, making local predictions often relies on sensor data that may not be…
Deep LearningPredictionWeather ForecastingZero-Shot LearningAIREX: Neural Network-based Approach for Air Quality Inference in Unmonitored Cities
Urban air pollution is a major environmental problem affecting human health and quality of life. Monitoring stations have been established to continuously obtain air quality information, but they do not cover all areas. …
Air Quality InferenceMixture-of-ExpertsDistributed Coverage Hole Prevention for Visual Environmental Monitoring with Quadcopters via Nonsmooth Control Barrier Functions
This paper proposes a distributed coverage control strategy for quadcopters equipped with downward-facing cameras that prevents the appearance of unmonitored areas in between the quadcopters' fields of view (FOVs). We de…
Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resources
Prediction of dynamic environmental variables in unmonitored sites remains a long-standing challenge for water resources science. The majority of the world's freshwater resources have inadequate monitoring of critical en…
Time SeriesTime Series PredictionTransfer Learning