Roadside　Units　(RSUs)　deployment　optimization　for　vehicle-to-road　communication　is　crucial　in　improving　the　performance　of　the　vehicular　and　transportation　networks.　Current　researches　on　RSU　deployment　optimization　overlooks　the　inequal　supply-demand　relationship　of　vehicular　communications　caused　by　unbalanced　vehicular　density.　In　addition,　the　impact　of　traffic　safety　risks　on　RSU　optimization　is　not　considered.　To　cope　with　these　challenges,　this　paper　presents　a　data-driven　and　multi-factor　RSU　deployment　strategy,　which　can　be　divided　into　two　stages.　The　first　stage　adopts　a　data-driven　approach　to　analyze　vehicular　density　using　realistic　vehicle　trajectory　data,　which　helps　identify　preliminary　deployment　positions　based　on　unbalanced　vehicular　density.　In　the　second　stage,　an　RSU　deployment　model　is　constructed　that　considers　RSU　deployment　costs,　geographical　environment　constraints,　road　coverage,　and　traffic　safety　risks.　This　model　calculates　the　cumulative　Poisson　probability　of　simple　and　general　traffic　crashes　to　evaluate　the　traffic　safety　risks　within　the　RSU　coverage　range.　Afterward,　this　paper　proposes　an　improved　genetic　algorithm　to　obtain　the　optimal　position　of　RSUs　within　the　deployment　area.　The　algorithm　employs　a　new　method　for　population　initialization　and　adopts　a　linear　ranking-based　selection　mechanism.　Finally,　we　conduct　a　joint　simulation　of　transportation　and　communication　networks　by　linking　SUMO　with　OMNET++　through　the　TraCI　interface　and　Veins　framework.　The　results　evaluate　and　demonstrate　the　performance　of　the　proposed　method　in　vehicle　coverage,　traffic　safety　risk　coverage,　and　average　notification　time　in　comparison　with　the　existing　schemes.