Local administrations keep creating policies to promote cycling among citizens. However, these initiatives seem to be often counterproductive for the targeted objectives because of the increase opportunities for bike-theft. So, this research aimed at exploring Milan (IT) as a case study to address temporal distribution of bike-theft at the meso-level while controlling for seasonal variation. The occurrence of certain timeframes was evaluated against the distribution of bicycle theft between 2011 and 2015 and predictive risk effects were estimated using the Aoristic analysis. The latter provided a temporal weight and gave an indication of the probability that the events occurred within a defined period between 2016 and 2018. Within this timeframe, the Aoristic analysis showed a higher level of risk stemming from specific days of the week and times of the day, which indeed were responsible of 76% of the registered bike-thefts. The results of this study point to temporal elements that need to be taken into consideration while formulating policies that promote an urban crime-controlled environment for cyclists in Milan. Indeed, since many common crimes are aoristic, techniques such as the Aoristic analysis is crucial for ensuring an effective deployment of resources at the right time to prevent crime.