Parking Availability Prediction with Coarse-Grained Human Mobility Data

نویسندگان

چکیده

Nowadays, the anticipation of parking-space demand is an instrumental service in order to reduce traffic congestion levels urban spaces. The purpose our work study, design and develop a parking-availability predictor that extracts knowledge from human mobility data, based on anonymized displacements area, also weather conditions. Most existing solutions for this prediction take as contextual data current road-traffic state defined at very high temporal or spatial resolution. However, access type fine-grained location usually quite limited due several economic privacy-related restrictions. To overcome limitation, proposal uses areas are low We conducted experiments using three Artificial Neural Networks: Multilayer Perceptron, Gated Recurrent Units bidirectional Long Short Term Memory networks we tested their suitability different combinations inputs. Several metrics provided sake comparison within study between other studies. solution has been evaluated real-world testbed city Murcia (Spain) integrating open human-mobility dataset showing accuracy. A MAPE 4% 10% was reported horizons 1 3 h.

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ژورنال

عنوان ژورنال: Computers, materials & continua

سال: 2022

ISSN: ['1546-2218', '1546-2226']

DOI: https://doi.org/10.32604/cmc.2022.021492