The　corrosion　rate　of　reinforcing　steel　is　an　important　factor　to　determine　the　corrosion　propagation　of　reinforced　concrete　structures　in　the　chloride-laden　environments.　Since　the　corrosion　rate　of　reinforcing　steel　is　affected　by　several　coupled　parameters,　the　efficient　prediction　of　which　remains　challenging.　In　this　study,　a　total　of　156　experimental　data　on　corrosion　rate　from　the　literature　were　collected　and　compared.　Seven　empirical　models　for　predicting　the　corrosion　rate　were　reviewed　and　investigated　using　the　collected　experimental　data.　Based　on　the　investigations,　a　new　empirical　model　is　proposed　for　predicting　the　corrosion　rate　in　corrosion-affected　reinforced　concrete　structures　considering　parameters　including　concrete　resistivity,　temperature,　relative　humidity,　corrosion　duration　and　concrete　chloride　content.　The　comparison　between　the　experimental　data　and　those　predicted　using　the　new　empirical　model　demonstrates　that　the　new　model　gives　a　good　prediction　of　the　corrosion　rate.　Furthermore,　the　uncertainty　and　probability　characteristics　of　these　empirical　models　are　also　investigated.　It　is　found　that　the　probability　distributions　of　the　model　errors　can　be　described　as　lognormal,　normal,　Weibull　or　Gumbel　distributions.　As　a　result,　the　new　empirical　model　can　provide　an　efficient　prediction　of　the　corrosion　rate　of　reinforcing　steel,　and　the　model　error　analysis　results　can　be　utilized　for　reliability-based　service　life　prediction　of　reinforced　concrete　structures　under　chloride-laden　environments.