Roads impact wildlife through a range of mechanisms from habitat loss and decreased landscape connectivity to direct mortality through wildlife-vehicle collisions (roadkill). These collisions have been rated amongst the highest modern risks to wildlife. With the development of "citizen science" projects, in which members of the public participate in data collection, it is now possible to monitor the impacts of roads over scales far beyond the limit of traditional studies. However, the reliability of data provided by citizen scientists for roadkill studies remains largely untested. This study used a dataset of 2,666 roadkill reports on national and regional roads in South Africa (total length ∼170,000 km) over 3 years. We first compared roadkill data collected from trained road patrols operating on a major highway with data submitted by citizen scientists on the same road section (431 km). We found that despite minor differences, the broad spatial and taxonomic patterns were similar between trained reporters and untrained citizen scientists. We then compared data provided by two groups of citizen scientists across South Africa: (1) those working in the zoology/conservation sector (that we have termed "regular observers," whose reports were considered to be more accurate due to their knowledge and experience), and (2) occasional observers, whose reports required verification by an expert. Again, there were few differences between the type of roadkill report provided by regular and occasional reporters; both types identified the same area (or cluster) where roadkill was reported most frequently. However, occasional observers tended to report charismatic and easily identifiable species more often than road patrols or regular observers. We conclude that citizen scientists can provide reliable data for roadkill studies when it comes to identifying general patterns and high-risk areas. Thus, citizen science has the potential to be a valuable tool for identifying potential roadkill hotspots and at-risk species across large spatial and temporal scales that are otherwise impractical and expensive when using standard data collection methodologies. This tool allows researchers to extract data and focus their efforts on potential areas and species of concern, with the ultimate goal of implementing effective roadkill-reduction measures. Keywords: spatial clusters, mammals, mortality, road ecology, wildlife-vehicle collisions.