Optimization of Short-Term On-Street Park-Pay License Plate Surveying

Abstract

Many cities in urban and suburban areas are characterized by parking space problems, especially during peak hours and special events. To overcome these problems, some local governments initiate on-street short-term pay programs to limit lengths of stay and generate revenue. The problem lies in determining which data should be collected, how frequently, and for how long to produce significant results. This study applied power analysis in analyzing rate of turnover and utilization levels to develop optimum data collection using license plate surveying methodology. A case involving downtown areas, shopping centers, and beach parks is presented. Analysis evaluated the probability of detecting desired changes, minimum detectable changes (MDC), and coefficient of variation (CV) for different data monitoring frequencies over desired data collection periods. Three critical license plate monitoring intervals for downtown parking were identified to be between 5–20 min, 30–40 min, and 40–60 min, with each group having different characteristics compared with others. There was a slight overlap in transitional monitoring time intervals. Analysis found that, to detect all possible optimal parking characteristics and cost-efficient surveying, license plates should be monitored at intervals less than every 30 min for downtown and 60 min for beach parks.