In　facing　＂big-city　diseases＂,　using　urban　underground　space　to　build　a　stratified　transportation　system　is　crucial　for　the　sustainable　development　of　the　social　economy.　In　recent　years,　underground　logistics　systems　(ULS)　receive　broad　attention　but　developing　a　purpose-built　ULS　in　megacities　faces　difficulties　due　to　high　costs　and　risks.　As　a　cost-effective　alternative,　the　metro-based　ULS　(M-ULS)　initiatives　provide　new　visions　for　the　city-wide　underground　transport　integration　of　passengers　and　freight.　This　paper　explores　the　decision-making　mechanisms　of　M-ULS　network　expansion　based　on　multi-source　data　collection　and　mathematical　program-ming.　A　model　with　three　interrelated　modules　is　proposed　and　applied　in　the　Beijing　city　case.　First,　a　monetary　evaluation　framework　with　13　flow-based　indicators　is　established　to　quantify　the　comprehensive　benefits　of　M-ULS　in　terms　of　logistics　economics,　environment,　transportation,　urban　space　expansion,　and　emergency　sup-plies.　Second,　correlations　between　metro　freight　service　pricing　and　market　purchase　willingness　are　formulated　based　on　surveys　and　interviews.　Investment　budgets　and　government　subsidies　are　modeled　as　scenario　vari-ables　affected　by　the　project＇s　benefits　and　operating　profits.　Third,　a　mixed　integer　programming　model　is　developed　to　optimize　the　multi-period　facility　location,　allocation,　and　pricing　during　network　expansion.　Simulation　results　show　that　M-ULS　has　the　ability　to　fully　eliminate　the　road-based　e-commerce　distribution　in　city　centers.　The　urban　underground　logistics　businesses　are　expected　to　make　metro　operations　profitable　and　recover　the　construction　costs　of　both　M-ULS　and　original　metro　lines.　The　project＇s　external　benefits　are　equivalent　to　0.8%　of　the　city＇s　GDP,　where　the　most　salient　contributions　come　from　breaking　freight　access　restriction,　energy-saving,　efficiency　upgrading,　pollutant　reduction,　and　traffic　mobility　improvement.　Increasing　funding　support　at　the　project＇s　early　stage　can　significantly　accelerate　the　system　expansion.　More　findings　related　to　M-ULS　facility　deployment,　pricing　mechanisms,　and　incentive　strategies　are　disclosed.