day of data • Extract lat,lng pairs (>=1 per street) • Count start and end usage events • Model change by hour • Note over-usage (council gets free cash!)
which bays are heavily used or lightly used from Cashless Meters • We know when fines get handed out (and where if geocoding were added) • We have a pretty good idea about uneven resource usage • Where do the parking bays cluster?
of nearby (probably) underused spaces • Slice by time of day, warn of the number of fines issued nearby at this time of day • Integrate paid local advertising with useful Council messages in the App? • Next diagram white == low occupancy
parking cheaper and easier to find • Encourage drivers to park in less-used streets (it relieves congestion) • Encourage Traffic Management Agents to target trouble areas (increases income)