The　focus　of　this　paper　is　the　crash　risk　assessment　of　off-ramps　in　Xi＇an.　The　time-to-collision　(TTC)　is　used　for　the　measurement　and　cross-comparison　of　the　crash　risk　of　each　location.　Five　sites　from　the　urban　expressway　in　Xi＇an　were　selected　to　explore　the　TTC　distribution.　An　unmanned　aerial　vehicle　and　a　camera　were　used　to　collect　traffic　flow　data　for　20　min　at　each　site.　The　parameters,　including　speed,　deceleration　rate,　truck　percentage,　traffic　volume,　and　vehicle　trajectories,　were　extracted　from　video　images.　The　TTCs　were　calculated　for　each　vehicle.　The　Gaussian　mixture　model　(GMM)　was　proposed　to　predict　the　TTC　probability　density　functions　(PDFs)　and　cumulative　density　functions　(CDFs)　for　five　sites.　The　Kolmogorov-Smirnov　(K-S)　test　indicated　that　the　samples　followed　the　estimated　GMM　distribution.　The　relationship　between　the　crash　risk　level　and　influencing　factors　was　studied　by　an　ordinal　logistic　regression　model　and　a　naive　Bayesian　model.　The　results　showed　that　the　naive　Bayesian　model　had　an　accuracy　of　86.71%,　while　the　ordinal　logistic　regression　model　had　an　accuracy　of　84.81%.　The　naive　Bayesian　model　outperformed　the　ordinal　logistic　regression　model,　and　it　could　be　applied　to　the　real-time　collision　warning　system.　©　2020　by　the　authors.