Objective:　Guaranteeing　a　safe　and　comfortable　driving　workload　can　contribute　to　reducing　traffic　injuries.　In　order　to　provide　safe　and　comfortable　threshold　values,　this　study　attempted　to　classify　driving　workload　from　the　aspects　of　human　factors　mainly　affected　by　highway　geometric　conditions　and　to　determine　the　thresholds　of　different　workload　classifications.　This　article　stated　a　hypothesis　that　the　values　of　driver　workload　change　within　a　certain　range.Methods:　Driving　workload　scales　were　stated　based　on　a　comprehensive　literature　review.　Through　comparative　analysis　of　different　psychophysiological　measures,　heart　rate　variability　(HRV)　was　chosen　as　the　representative　measure　for　quantifying　driving　workload　by　field　experiments.　Seventy-two　participants　(36　car　drivers　and　36　large　truck　drivers)　and　6　highways　with　different　geometric　designs　were　selected　to　conduct　field　experiments.　A　wearable　wireless　dynamic　multiparameter　physiological　detector　(KF-2)　was　employed　to　detect　physiological　data　that　were　simultaneously　correlated　to　the　speed　changes　recorded　by　a　Global　Positioning　System　(GPS)　(testing　time,　driving　speeds,　running　track,　and　distance).　Through　performing　statistical　analyses,　including　the　distribution　of　HRV　during　the　flat,　straight　segments　and　P-P　plots　of　modified　HRV,　a　driving　workload　calculation　model　was　proposed.　Integrating　driving　workload　scales　with　values,　the　threshold　of　each　scale　of　driving　workload　was　determined　by　classification　and　regression　tree　(CART)　algorithms.Results:　The　driving　workload　calculation　model　was　suitable　for　driving　speeds　in　the　range　of　40　to　120km/h.　The　experimental　data　of　72　participants　revealed　that　driving　workload　had　a　significant　effect　on　modified　HRV,　revealing　a　change　in　driving　speed.　When　the　driving　speed　was　between　100　and　120km/h,　drivers　showed　an　apparent　increase　in　the　corresponding　modified　HRV.　The　threshold　value　of　the　normal　driving　workload　K　was　between　-0.0011　and　0.056　for　a　car　driver　and　between　-0.00086　and　0.067　for　a　truck　driver.Conclusion:　Heart　rate　variability　was　a　direct　and　effective　index　for　measuring　driving　workload　despite　being　affected　by　multiple　highway　alignment　elements.　The　driving　workload　model　and　the　thresholds　of　driving　workload　classifications　can　be　used　to　evaluate　the　quality　of　highway　geometric　design.　A　higher　quality　of　highway　geometric　design　could　keep　driving　workload　within　a　safer　and　more　comfortable　range.　This　study　provided　insight　into　reducing　traffic　injuries　from　the　perspective　of　disciplinary　integration　of　highway　engineering　and　human　factor　engineering.