Abstract: Vehicle movement data can be utilized for a number of spatio-temporal applicatio ...
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Abstract: Vehicle movement data can be utilized for a number of spatio-temporal applications, which have been investigated mainly for urban settings. Yet, such applications are also valuable for nature parks that are constrained by sparse data and dynamic conditions. Accordingly, we design and develop an IoT-based DSS to support decision-making and analytics for the key stakeholders i.e., park administrators, visitors and vendors, in a nature park. Our design is based on sparse vehicle movement data collected at various gates within the park, using RFID tags on visitor vehicles and IoT sensors placed on the gates. Using graph analytics, visualization, and forecasting techniques, we develop capabilities for park management by administrators, as well as features for vendors to publish offers, and visitors to locate paths and vendors. Our preliminary investigations using vehicle movement data from a wildlife park in Thailand illustrate the initial contributions of our DSS and approach.
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Semantic filters:
holiday accommodation
Topics:
decision support system mobile system internet of things geographic information system Python
Methods:
design artifact machine learning time series analysis longitudinal research autoregressive integrated moving average
