In　the　area　of　urban　transportation　networks,　a　growing　number　of　day-to-day　(DTD)　traffic　dynamic　theories　have　been　proposed　to　describe　the　network　flow　evolution,　and　an　increasing　amount　of　laboratory　experiments　have　been　conducted　to　observe　travelers＇　behavior　regularities.　However,　the　`communication＇　between　theorists　and　experimentalists　has　not　been　well　made.　This　paper　devotes　to　1)　detecting　unanticipated　behavior　regularities　by　conducting　a　series　of　laboratory　experiments,　and　2)　improving　existing　DTD　dynamics　theories　by　embedding　the　observed　behavior　regularities　into　a　route　choice　model.　First,　312　subjects　participated　in　one　of　the　eight　decision-making　scenarios　and　make　route　choices　repeatedly　in　congestible　parallel-route　networks.　Second,　three　route-switching　behavior　patterns　that　cannot　be　fully　explained　by　the　classic　route-choice　models　are　observed.　Third,　to　enrich　the　explanation　power　of　a　discrete　route-choice　model,　behavioral　assumptions　of　route-dependent　attractions,　i.e.,　route-dependent　inertia　and　preference,　are　introduced.　An　analytical　DTD　dynamic　model　is　accordingly　proposed　and　proven　to　steadily　converge　to　a　unique　equilibrium　state.　Finally,　the　proposed　DTD　model　could　satisfactorily　reproduce　the　observations　in　various　datasets.　The　research　results　can　help　transportation　science　theorists　to　make　the　best　use　of　the　laboratory　experimentation　and　to　build　network　equilibrium　or　DTD　dynamic　models　with　both　real　behavioral　basis　and　neat　mathematical　properties.