An　input　vector　composed　of　various　features　plays　an　important　role　in　short-term　traffic　forecasting.　However,　there　is　limited　research　on　the　optimal　feature　selection　of　an　input　vector　for　a　certain　forecasting　task.　To　fill　the　gap,　this　paper　proposes　a　cohesion-based　heuristic　feature　selection　method　by　analyzing　the　nature　of　the　forecasting　methods.　This　method　is　able　to　determine　which　features　should　be　contained　in　an　input　vector　to　make　a　forecasting　algorithm　perform　better.　The　proposed　method　is　demonstrated　in　two　experiments　based　on　the　empirical　traffic　flow　data.　The　results　show　that　the　method　is　able　to　improve　the　performances　of　the　short-term　traffic　forecasting　algorithms.　It　is　then　suggested　to　consider　the　proposed　method　as　a　preprocessing　procedure　in　practical　forecasting　applications.