Establishing　a　green　and　efficient　travel　service　system　is　an　important　part　of　China＇s　Green　Travel　Action　Plan.　It　is　necessary　to　consider　the　heterogeneity　of　job-housing　status　and　commuting　mode　in　different　levels　of　cities.　Based　on　1788　valid　questionnaires　collected　from　three　types　of　cities,　the　SEM-MNL　model　is　constructed　to　quantitatively　analyze　the　comprehensive　impact　of　job-housing　status,　commuting　attributes　and　personal　economic　characteristics　on　the　choice　of　commuting　modes　in　various　types　of　cities.　The　findings　reveal　that　the　latent　variable　commuting　attribute　is　the　key　factor　affecting　the　travel　mode,　and　the　restrictive　effect　is　more　prominent　in　ordinary　cities　than　in　first-tier　and　new　first-tier　cities.　Job-housing　status　indirectly　affects　commuting　mode　choice　through　commuting　attributes.　The　path　coefficients　of　three　classes　of　cities　are　0.83,　0.89,　and　0.93,　respectively.　The　effects　of　residential　type　on　commuting　distance　and　mode　choice　show　an　opposite　trend　in　first-tier　cities　and　ordinary　cities.　Highly　educated　travelers　in　first-tier　cities　prefer　green　travel　modes,　while　in　non-first-tier　cities,　the　result　is　reversed.　In　new　first-tier　cities,　residents　with　short　commute　distances　have　the　highest　proportion　of　renting,　nearly　half　of　them　choose　slow-speed　transportation.　Adjusting　the　job-housing　distribution　to　increase　the　proportion　of　short-distance　commuting　can　raise　the　share　of　green　travel　mode.　As　the　city　level　declines,　the　feedback　sensitivity　of　regulation　increases.　The　research　results　provide　differentiated　policy　recommendations　for　job-housing　balance　and　transportation　infrastructure　planning　in　multiple　types　of　cities.　The　results　are　conducive　to　promoting　the　low-carbon　travel　and　contribute　to　the　balance　of　urban　transportation　supply　and　demand　and　thus　sustainable　development.　©　2025　Science　Press.　All　rights　reserved.