Travel　time　distribution　estimation　is　fundamentally　important　for　the　evaluation　of　travel　time　variability　and　reliability.　For　urban　roads,　signal　delays　are　key　components　of　travel　time.　They　are　stochastic　and　differ　for　vehicular　movements　due　to　different　signal　timings　for　through,　left-turning,　and　right-turning　vehicles.　To　better　assist　travelers　in　making　trip　decisions　under　connected　environments,　this　study　seeks　to　investigate　lane-level　travel　time　distributions　for　signalized　arterial　roads　by　specifically　considering　the　impacts　of　both　the　link　travel　time　and　the　vehicle　movement-based　signal　delays　at　an　intersection.　A　simulation　testbed　based　on　the　VISSIM　microscopic　traffic　simulator　and　a　Java　plugin　is　developed　to　mimic　the　traffic　flow　dynamics　of　a　signalized　arterial,　El　Camino　Real,　in　Palo　Alto,　California,　under　a　connected　environment.　The　detailed　vehicle　trajectory　data　obtained　from　the　simulation　can　be　leveraged　to　obtain　lane-level　travel　time　information.　Typical　normal,　lognormal,　gamma,　and　Weibull　distributions,　as　well　as　kernel　density　estimation　(KDE),　are　adopted　to　calibrate　the　lane-level　travel　time　distributions.　The　estimation　results　demonstrate　that　conventional　distribution　models　are　suitable　for　estimating　the　travel　time　distributions　of　only　a　few　road　segments,　while　KDE　fully　captures　travel　time　reliability　metrics　such　as　the　buffer　time　index,　skewness,　and　width　of　the　travel　time　distributions　for　all　road　segments.　This　result　will　help　traffic　managers　and　engineers　carry　out　effective　traffic　management　and　control　to　optimize　the　operation　of　arterial　roads.