Aiming　for　accurate　data-driven　predictions　for　the　passenger　walking　time,　this　study　proposes　a　novel　neuron-network-based　mixture　probability　(NNBMP)　model　with　repetition　learning　(RL)　to　estimate　the　probability　density　distribution　of　passenger　walking　time　(PWT)　in　the　metro　station.　Our　conducted　experiments　for　Fuzhou　metro　stations　demonstrate　that　the　proposed　NNBMP-RL　model　achieved　the　mean　absolute　error,　mean　square　error,　and　mean　absolute　percentage　error　of　0.0078,　1.33　x　10(-4),　and　19.41%,　respectively,　and　it　outperformed　all　the　seven　compared　models.　The　developed　NNBMP　model　fitting　accurately　the　PWT　distribution　in　the　metro　station　is　readily　applicable　to　the　microscopic　analyses　of　passenger　flow.