To　provide　a　better　understanding　of　spatiotemporal　characteristics　of　vehicle　trajectories　in　connected　vehicle　environment,　a　driving　simulation　study　was　designed　and　conducted　with　an　extra-long　tunnel　scenario.　35　drivers　were　recruited　to　participate　in　the　driving　experiment.　To　evaluate　the　spatiotemporal　characteristics　of　vehicles　with　and　without　a　warning　system,　objective　measures　were　analyzed,　including　a　spatiotemporal　diagram　of　the　curvature　of　obtained　data　and　speed　adjustment　behaviors.　This　article　also　evaluated　the　impacts　of　connected　vehicles　on　the　traffic　capacity　based　on　the　converging　pattern　mining　method.　The　results　indicated　that　the　in-vehicle　human-machine　interface　(HMI)　improved　driving　behavior　and　traffic　capacity.　Notably,　the　in-vehicle　HMI　helped　drivers　better　prepare　for　speed　adjustments　when　approaching　the　tunnel　and　when　the　vehicle　in　front　of　the　study　vehicle　made　a　sudden　operational　change.　Moreover,　the　system　contributed　to　a　more　stable　operation　speed,　especially　near　the　tunnel　entrance,　than　that　without　the　system.　The　findings　suggest　that　connected　vehicle　environments　enable　drivers　to　change　from　traditional　visual　stimuli　response　behaviors　to　proactive　response　behaviors　based　on　psychological　expectations.　Besides,　based　on　the　best-converging　patterns　from　the　spatiotemporal　trajectories　of　35　drivers,　the　results　revealed　that　the　traffic　capacity　could　be　improved　by　22.19%　under　the　experimental　traffic　flow　conditions.　Moreover,　the　differences　in　the　benefits　of　the　in-vehicle　HMI　among　individuals　were　found　to　be　statistically　significant.