Wrong-way　riding　(WWR)　behavior　of　electric　bikes　(e-bikes)　is　a　hazardous　practice　that　frequently　threatens　traffic　safety.　It　is　imperative　to　accurately　detect　instances　of　WWR　and　analyze　the　various　factors　contributing　to　this　behavior　to　develop　and　implement　effective　management　policies.　The　emergence　of　shared　e-bikes　and　advancements　in　machine　learning　have　opened　up　new　avenues　for　accurately　examining　the　determinants　of　WWR　behavior.　Therefore,　this　study　utilizes　a　large-scale　dataset　of　shared　e-bike　trajectory　data　to　establish　a　framework　for　detecting　WWR　behavior.　Subsequently,　association　rule　mining　investigates　the　correlation　among　road,　traffic,　environmental　factors,　and　the　likelihood　of　higher　and　lower　WWR　behavior.　The　results　indicate　that:　(1)　In　the　association　rules　for　higher　WWR　frequency,　the　absence　of　central　separation　facilities　and　bike　lane　separation　facilities　on　the　tertiary　road,　as　well　as　lower　land　use　density　and　the　lack　of　public　transportation　facilities,　enhanced　the　likelihood　of　WWR　occurrence;　(2)　In　the　association　rules　for　lower　WWR　frequency,　longer　crossing　facility　distance,　physical　bike　lane　separation,　along　with　higher　land　use　mixing　and　the　presence　of　public　transportation　facilities,　reduce　the　probability　of　WWR　occurrence;　(3)　Time　conditions　display　spatial　heterogeneity,　and　different　land　use　and　road　condition　modulate　the　impact　of　time　factors　on　rider＇　intention　for　WWR.　Finally,　based　on　the　study　findings,　adequate　policy　recommendations　are　proposed　for　urban　management　agencies　to　reduce　WWR　behavior　and　enhance　the　safety　of　the　e-bike　traffic　system.