Glance　behavior　is　significant　because　whether　and　how　the　driver　is　scanning　and　observing　the　driving　scene　is　closely　related　to　driving　safety.　This　paper　aims　to　improve　the　accuracy　of　glance　behavior　modeling　and　realize　the　spatiotemporal　representation　and　visualization　of　glance　behavior.　Forty　subjects　were　recruited　to　perform　a　freeway　driving　task　using　a　driving　simulator.　The　vehicle　data　were　collected　by　the　simulator.　Drivers＇　gaze　points　were　collected　by　an　eye　tracker.　The　prior　knowledge　on　gaze　points　obtained　through　a　statistical　analysis　were　provided　for　K-means　(KM)　to　form　a　semi-supervised　K-means　(SSKM),　which　classifies　gaze　points　into　different　fixation　zones.　The　classification　results　were　compared　with　the　results　of　KM.　Furthermore,　a　clustering　center　based　piecewise　aggregate　representation　(CCPAR)　was　proposed　to　characterize　glance　behavior.　Maneuvers　identification　was　taken　as　a　case　to　evaluate　the　proposed　method.　The　k-nearest　neighbour　(KNN)　based　on　the　similarity　of　CCPAR　identified　driving　maneuvers　into　lane-keeping,　left　lane　change,　and　right　lane　change.　The　identification　results　were　compared　with　the　results　of　the　Hidden　Markov　Model　(HMM).　The　average　classification　accuracies　of　KM　and　SSKM　were　55.28%　and　94.75%,　respectively.　The　accuracies　of　maneuvers　identified　by　CCPAR-KNN　and　by　HMM　were　87.50%　and　85.83%,　respectively.　The　results　indicate　that　SSKM　and　CCPAR　are　feasible　for　glance　behavior　modeling.　SSKM　eliminates　the　randomness　of＇　initial　cluster　center　selection　and　improves　the　accuracy　of　gaze　points　classification.　CCPAR　is　intuitive　and　convenient　to　describe　and　visualize　the　spatiotemporal　characteristics　of　glance　behavior.