A　human-built　urban　street　canyon　is　typically　characterized　by　the　presence　of　buildings　on　both　sides　of　a　road　in　which　the　air　pollutants,　especially　those　resulting　from　road　traffic,　may　pose　a　potential　threat　to　human　health.　Artificial　Neural　Network　(ANN)　differs　from　parametric　model　in　that　it　is　trained　to　learn　a　solution　rather　than　being　programmed　to　model　a　specific　problem　with　a　normal　way.　Therefore,　they　would　be　able　to　offer　better　practical　skill　to　predict　pollutant.　Because　pollutant　distribution　is　often　greatly　influenced　by　the　terrain　and　meteorological　factors,　establishing　ANN　has　to　consider　effective　parameters　as　the　input　neurons.　In　this　study,　traffic-related　nitrogen　oxides　was　predicted　by　combining　a　genetic　algorithm-back　propagation　ANN　and　two　parametric　models　(STREET　model　and　OSPM　model).　This　study　took　those　independent　parameters　or　components　in　the　two　parametric　models　into　account　as　the　input　neurons.　These　input　neurons　are　likely　to　enable　ANN　to　reach　desire　simulation　accuracy.　Results　indicated　that　ANN　had　better　performance　than　the　parametric　models.　Further　study　illustrated　that　the　simulation　had　higher　squared　Pearson　correlation　coefficient　(R-2)　up　to　0.73　for　validation,　less　simulation　error,　and　better　trend　description　in　measured　data　than　measured　mean.　Overall,　this　study　allowed　the　use　of　ANN　as　a　viable　option　for　forecasting　the　traffic-related　air　pollutants　within　a　street　canyon.　Copyright　(c)　2015　Turkish　National　Committee　for　Air　Pollution　Research　and　Control.　Production　and　hosting　by　Elsevier　B.V.　All　rights　reserved.