Accurate　and　robust　short-term　traffic　forecasting　is　a　critical　issue　in　intelligent　transportation　systems　and　realtime　traffic-related　applications.　Existing　short-term　traffic　forecasting　approaches　adopt　fixed　model　structures　and　assume　traffic　correlations　between　adjacent　road　segments　within　assigned　time　periods.　Due　to　the　inherent　spatial　heterogeneity　of　city　traffic,　it　is　difficult　for　these　approaches　to　obtain　stable　and　satisfying　results.　To　overcome　the　problems　of　fixed　model　structures　and　quantitatively　unclear　spatiotemporal　dependency　relationships,　this　paper　proposes　an　adaptive　spatiotemporal　k-nearest　neighbor　model　(adaptive-STKNN)　for　short-term　traffic　forecasting.　It　comprehensively　considers　the　spatial　heterogeneity　of　city　traffic　based　on　adaptive　spatial　neighbors,　time　windows,　spatiotemporal　weights　and　other　parameters.　First,　for　each　road　segment,　we　determine　the　sizes　of　spatial　neighbors　and　the　lengths　of　time　windows　for　traffic　influence　using　cross-correlation　and　autocorrelation　functions,　respectively.　Second,　adaptive　spatiotemporal　weights　are　introduced　into　the　distance　functions　to　optimize　the　candidate　neighbor　search　mechanism.　Next,　we　establish　adaptive　spatiotemporal　parameters　to　reflect　continuous　changes　in　traffic　conditions,　including　the　number　of　candidate　neighbors　and　the　weight　allocation　parameter　in　the　predictive　function.　Finally,　we　evaluate　the　adaptive-STKNN　model　using　two　vehicular　speed　datasets　collected　on　expressways　in　California,　U.S.A.,　and　on　city　roads　in　Beijing,　China.　Four　traditional　prediction　models　are　compared　with　the　adaptive-STKNN　model　in　terms　of　forecasting　accuracy　and　generalization　ability.　The　results　demonstrate　that　the　adaptive-STKNN　model　outperforms　those　models　during　all　time　periods　and　especially　the　peak　period.　In　addition,　the　results　also　show　the　generalization　ability　of　the　adaptive-STKNN　model.