{"id":46946,"date":"2026-06-17T12:12:31","date_gmt":"2026-06-17T10:12:31","guid":{"rendered":"https:\/\/optit.net\/cosa-facciamo\/casi-duso\/bomob\/"},"modified":"2026-07-15T17:36:46","modified_gmt":"2026-07-15T15:36:46","slug":"bomob","status":"publish","type":"caso-duso","link":"https:\/\/optit.net\/en\/what-we-do\/use-cases\/bomob\/","title":{"rendered":"Mathematical optimisation of urban parking enforcement scheduling"},"content":{"rendered":"<p>Managing <strong>paid on-street parking enforcement<\/strong> across a large urban area such as Bologna is a highly complex logistical and organisational challenge. On-the-ground checks, carried out by traffic control teams, must comprehensively cover parking meters and bays, based on rigid planning.<\/p>\n<p>Relying on empirical or static zoning creates significant structural inefficiencies, leaving some strategic areas under-resourced and others overloaded, which undermines service effectiveness and territorial control. Adding to this complexity is the need to plan the workforce while respecting binding operational constraints, national contracts and union agreements, all while ensuring a fair rotation of shifts.<\/p>\n<p>The challenge was to <strong>move beyond manual planning<\/strong> and <strong>turn historical data flows<\/strong> into balanced patrol zones and <strong>optimised, practical daily work schedules<\/strong>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Turning big data from urban mobility into balanced work shifts and predictive territorial clustering through Decision Science.<\/p>\n","protected":false},"featured_media":46947,"template":"","mercati":[818],"servizi":[908,820],"class_list":["post-46946","caso-duso","type-caso-duso","status-publish","has-post-thumbnail","hentry","mercato-manufacturing","servizio-ai-powered-consulting-en","servizio-custom-solutions"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.1 - 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