The　road-crossing　of　pedestrians　at　unsignalized　crosswalks　is　a　major　concern　for　road　safety.　Previous　studies　focused　on　explaining　of　the　mechanism　underlying　this　behavior,　but　a　framework　of　prediction　is　missing.　To　predict　this　behavior,　only　variables　measured　before　the　decision　is　made　should　be　considered.　To　explore　whether　historical　data　is　able　to　predict　the　behavior,　this　paper　investigates　pedestrians＇　wait-or-go　(WOG)　behavior　based　on　trajectory　data　and　a　machine　learning　method,　both　of　which　have　been　rarely　applied　by　previous　studies.　The　use　of　trajectory　data　enables　the　analysis　of　several　influential　factors　related　to　moving　characteristics,　which　are　critical　for　pedestrians＇　decision　making.　The　framework　based　on　machine　learning,　combined　with　trajectory　data,　achieves　good　explanatory　power　and　predictability　of　pedestrians＇　WOG　behavior.　Moreover,　a　possible　application　of　this　study　is　the　prediction　of　pedestrian　road-crossing　intention　in　the　context　of　autonomous　cars.