Accurate　traffic　speed　forecasting　not　only　can　help　traffic　management　departments　make　better　judgments　and　improve　the　efficacy　of　road　monitoring　but　also　can　help　drivers　plan　their　driving　routes　and　arrive　safely　and　smoothly　at　their　destination.　This　paper　focuses　on　the　lack　of　traffic　speed　data　and　proposes　a　method　for　traffic　speed　forecasting　based　on　the　multitemporal　traffic　flow　volume　of　the　previous　and　later　moment　states.　First,　according　to　traffic　flow　volume　data,　the　different　traffic　patterns　of　previous　and　later　moment　states　were　extracted.　Second,　the　performance　of　five　forecasting　models,　namely,　long　short-term　memory　(LSTM),　backpropagation　(BP),　classification　and　regression　trees,　k-nearest　neighbor,　and　support　vector　regression,　were　compared.　Finally,　the　model　with　the　best　prediction　results　was　used　to　conduct　sensitivity　analysis　experiments　for　different　traffic　patterns.　Through　a　real-data　case　study,　we　found　that　the　LSTM　model　has　the　highest　prediction　accuracy　compared　to　other　models　in　both　time　and　space.　This　traffic　pattern　＇previous　=　3　and　later　=　3＇　can　forecast　traffic　speed　more　accurately,　and　its　forecasting　ability　is　robust　across　a　range　of　scenarios.　©　2013　IEEE.