Accurately　predicting　truck　origin–destination　(OD)　flows　is　essential　for　optimizing　logistics　systems　and　promoting　coordinated　regional　development.　Existing　methods　typically　assume　a　monotonic　decrease　in　truck　OD　flows　with　increasing　geospatial　distance,　which　oversimplifies　the　complex　non-monotonic　distribution　patterns　observed　in　practice.　Moreover,　these　methods　overlook　interregional　socioeconomic　distances　and　their　interaction　with　geospatial　distances,　thereby　limiting　the　prediction　accuracy　and　reliability.　This　study　introduces　a　gravity-inspired　model　that　integrates　both　geospatial　and　socioeconomic　distances　(GSD-DG)　to　explicitly　represent　their　combined　influence　on　truck　OD　flows.　Specifically,　we　1)　develop　a　geospatial　distance　relation　graph　using　the　Weibull　function　to　model　the　complex　spatial　distribution　patterns　of　truck　OD　flows　with　varying　geospatial　distances;　2)　propose　a　gravity-inspired　representation　learning　method　based　on　graph　attention　mechanism　to　quantify　the　influence　of　socioeconomic　distance　on　truck　OD　flows;　and　3)　construct　a　deep　gravity　model　that　integrates　these　distances　and　their　interactions　to　capture　their　non-linear　relationship　with　truck　OD　flows.　Extensive　experiments　on　four　datasets　with　varying　spatial　scale　and　economic　development　levels　demonstrate　that　the　GSD-DG　model　improves　the　robustness　and　prediction　accuracy　across　diverse　spatial　distribution　patterns,　reducing　RMSE　by　14.2%–85.8%　and　MSE　by　23.5%–92.5%　compared　to　the　six　baseline　models.　Incorporating　socioeconomic　distance　and　its　interaction　with　geospatial　distance　further　reduces　RMSE　by　8.5%–36.0%.　Additionally,　explainable　artificial　intelligence　techniques　highlight　how　these　distances　affect　truck　OD　flows,　providing　valuable　policy　insights　for　logistics　planning　and　coordinated　regional　development.　©　2024　The　Authors