The　jam　flow　condition　is　one　of　the　main　traffic　states　in　traffic　flow　theory　and　the　most　difficult　state　for　sectional　traffic　information　acquisition.　Since　traffic　information　acquisition　is　the　basis　for　the　application　of　an　intelligent　transportation　system,　research　on　traffic　vehicle　counting　methods　for　the　jam　flow　conditions　has　been　worthwhile.　A　low-cost　and　energy-efficient　type　of　multi-function　wireless　traffic　magnetic　sensor　was　designed　and　developed.　Several　advantages　of　the　traffic　magnetic　sensor　are　that　it　is　suitable　for　large-scale　deployment　and　time-sustainable　detection　for　traffic　information　acquisition.　Based　on　the　traffic　magnetic　sensor,　a　basic　vehicle　detection　algorithm　(DWVDA)　with　less　computational　complexity　was　introduced　for　vehicle　counting　in　low　traffic　volume　conditions.　To　improve　the　detection　performance　in　jam　flow　conditions　with　a　＂tailgating　effect＂　between　front　vehicles　and　rear　vehicles,　an　improved　vehicle　detection　algorithm　(SA-DWVDA)　was　proposed　and　applied　in　field　traffic　environments.　By　deploying　traffic　magnetic　sensor　nodes　in　field　traffic　scenarios,　two　field　experiments　were　conducted　to　test　and　verify　the　DWVDA　and　the　SA-DWVDA　algorithms.　The　experimental　results　have　shown　that　both　DWVDA　and　the　SA-DWVDA　algorithms　yield　a　satisfactory　performance　in　low　traffic　volume　conditions　(scenario　I)　and　both　of　their　mean　absolute　percent　errors　are　less　than　1%　in　this　scenario.　However,　for　jam　flow　conditions　with　heavy　traffic　volumes　(scenario　II),　the　SA-DWVDA　was　proven　to　achieve　better　results,　and　the　mean　absolute　percent　error　of　the　SA-DWVDA　is　2.54%　with　corresponding　results　of　the　DWVDA　7.07%.　The　results　conclude　that　the　proposed　SA-DWVDA　can　implement　efficient　and　accurate　vehicle　detection　in　jam　flow　conditions　and　can　be　employed　in　field　traffic　environments.