This　paper　is　based　on　resident　travel　survey　data　in　Jinan　City　using　adisaggregate　model　and　conducts　exploratory　research　related　to　the　travel　mode　choice　behavior　of　the　elderly.　The　results　show　that　age,　gender,　having　a　driver＇s　license　and　a　monthly　pass,　number　of　bikes　and　electric　cars　in　the　family,　travel　purpose,　travel　distance,　travel　time,　and　other　variables　have　an　important　impact　on　the　travel　mode　choice　of　the　elderly.　After　determining　the　characteristics　of　the　variables,　the　parameters　of　the　model　were　calibrated.　According　to　the　model　calibration　results,　combined　with　the　sample　data　used　in　the　parameter　calibration,　the　future　travel　choice　probability　of　the　elderly　was　predicted.　The　result　of　personal　choice　was　calculated　using　aggregate　analysis　after　predicting　the　average　probability　of　all　types　of　travel　modes　and　comparing　them　with　the　actual　survey　sample　selection　ratio.　The　final　model　errors　were　below　2　percent,　which　is　within　the　allowable　range,　so　the　model　parameter　calibration　is　effective.　©　2014　American　Society　of　Civil　Engineers.