Smart Poqueira: Predicting Rural Parking Lot Feasibility with Sensor-Questionnaire Integration

<p>We introduce a dataset comprising 525 instances of visitor behavior data from Pampaneira, Bubi&oacute;n, and Capileira in the Sierra Nevada National Park, Granada, Spain. Collected in January, March, and July 2023, the questionnaires excluded locals and residents. Conducted in parking lots, the questionnaires gathered information like license plate numbers, residential postcodes, visit frequency, and overnight stays. Additionally, data from four Hikvision license plate recognition (LPR) cameras tracking vehicle movement in each village during the same period supplement the dataset, enhancing understanding of individual mobility patterns. To further enrich the dataset, contextual details such as holiday days, vehicle provenance, and socio-demographic information, aiding in the validation and enhancement of questionnaire-derived data.</p> <p>The dataset comprises 26 variables, including: total_distance, nights, visits_dif_weeks, visits_dif_months, fidelity, total_entries, avg_nights, std_nights, total_holiday, avg_holiday, std_holiday, total_workday, avg_workday, std_workday, total_high_season, avg_high_season, std_high_season, total_low_season, avg_low_season, std_low_season, entry_in_holiday, entry_in_high_season, population, avg_gross_income, km_to_area, and park_price_will_affect_behaviour.</p>