Road　freight　transportation　is　a　cornerstone　of　China＇s　economy,　enabling　efficient　commodity　circulation　and　network　consumption.　However,　the　high　rate　of　truck-related　accidents　and　fatalities　is　largely　influenced　by　individual　characteristics　and　company　management　practices.　This　study　investigates　the　impact　of　these　factors　on　driving　safety　using　social　cognitive　theory　as　the　framework.　A　questionnaire　was　designed　to　gather　empirical　data　on　truck　drivers’　individual　traits,　work　environments,　and　driving　behaviors.　Exploratory　factor　analysis　confirmed　the　reliability　and　validity　of　the　questionnaire.　The　association　rule　algorithm　was　then　used　to　uncover　correlations　among　drivers’　risk　behaviors,　personal　attributes,　and　work　environments.　A　structural　equation　model　(SEM)　was　constructed,　integrating　findings　from　the　association　rule　analysis　and　social　cognitive　theory　to　explore　causal　relationships　and　influence　paths.　This　approach　effectively　addressed　challenges　in　constructing　optimal　path　models　with　multivariable　relationships.　The　SEM　results　revealed　that　insomnia,　negative　affect,　burnout,　and　safety　climate　significantly　influence　driving　behavior.　Mediation　analysis　further　showed　that　well-being,　weekly　working　days,　and　salary　levels　indirectly　affect　driving　behavior　through　burnout　and　negative　affect.　These　findings　highlight　critical　targets　for　individual　and　organizational　interventions.　Freight　companies　can　implement　targeted　education　and　training　programs　addressing　risk　factors　and　enhance　safety　management.　This　research　offers　practical　insights　for　improving　truck　drivers’　behavior　and　promoting　road　freight　safety.　©　2025