This　paper　presents　results　of　an　exploratory　study　on　building　up　the　car-following　model　under　emergency　evacuation　situations.　A　simulated　scenario　was　used　to　create　a　driving　environment　in　emergency　evacuation　conditions,　and　a　questionnaire　investigation　at　the　Miriam　ECG　was　used　to　verify　the　validity　of　the　driving　environment　from　both　the　subjective　and　objective　aspects.　A　BP　Neural　Network　was　designed,　and　a　gray-correlation　analysis　was　conducted　to　determine　which　factors　have　more　impact　on　the　acceleration　of　the　following　car.　Acceleration　of　the　following　vehicle　was　set　as　the　mother　factors　series,　and　speed　of　the　following　vehicle,　speed　of　the　leading　vehicle,　speed　difference　between　vehicles,　headway　between　vehicles,　and　acceleration　of　the　leading　vehicle　were　set　as　the　sub-factors　series.　Finally,　acceleration　of　the　leading　vehicle,　speed　difference　between　vehicles,　and　speed　of　following　vehicle　were　determined　to　be　the　three　most　important　factors.　These　three　factors　were　selected　as　input　variables.　The　acceleration　of　the　following　car　was　selected　as　the　output　variable.　Simulation　of　the　BP　Neural　Network　using　data　collected　from　the　driving　simulator　revealed　that　the　BP　Neural　Network　has　a　high　precision　in　the　prediction　of　the　car-following　model.　©　2010　ASCE.