Traffic　forecasting　plays　an　essential　role　in　modern　intelligent　transportation　systems　(ITS)　since　it　is　the　fundamental　component　of　a　wide　range　of　real-life　applications　like　alleviating　traffic　congestion　or　public　transportation　planning.　With　the　pervasive　use　of　sensors　deployed　in　the　cities　and　the　evolution　of　computing　resources,　it　is　necessary　to　carefully　explore　the　information　in　traffic　data　to　further　facilitate　traffic　applications.　However,　the　complex　spatial-temporal　dependency　of　traffic　can　hardly　be　fully　captured　by　the　classic　statistical　methods　and　traditional　machine　learning　methods.　Recently,　many　frameworks　based　on　Graph　Neural　Networks　(GNNs)　have　been　proposed　to　make　full　use　of　the　abundant　spatial　information　preserved　in　graph-structured　data,　and　plenty　of　emerging　GNN　frameworks　have　reached　state-of-the-art　performance.　One　key　advantage　of　GNNs　is　their　ability　to　model　irregular　graphs　with　local　aggregations.　Motivated　by　the　success　of　GNNs　achieved　in　traffic　prediction　tasks,　we　present　a　survey　in　this　paper　that　provides　a　summary　on　GNN-based　deep　learning　approaches　in　traffic　prediction　from　multiple　perspectives.　In　general,　traffic　prediction　methods　based　on　spatial　information　often　depend　on　graph　construction,　spatial　information　extraction,　and　spatial-temporal　information　fusion.　We　classify　current　methods　by　their　roles　in　these　steps　during　the　traffic　prediction　pipeline,　giving　the　advantages　of　these　methods　when　introducing　the　detailed　methods.　At　the　end,　research　gap　and　trend　of　this　discipline　of　study　is　summarised　to　be　provided　as　a　guideline　for　interested　researchers.