Traffic　state　estimation　(TSE),　which　reconstructs　the　traffic　variables　(e.g.,　speed,　flow)　on　road　segments　using　partially　observed　data,　plays　an　essential　role　in　intelligent　transportation　systems.　Generally,　traffic　estimation　problems　can　be　divided　into　two　categories:　model-driven　approaches　and　data-driven　approaches.　The　model-driven　method　is　commonly　used　to　solve　TSE　efficiently　and　calibrate　the　parameters　of　these　models.　The　data-driven　method　requires　a　large　amount　of　historical　observed　traffic　data　in　order　to　improve　performance　accurately.　In　order　to　combine　the　advantages　of　model-driven　and　data-driven　methods,　this　paper　proposed　a　hybrid　framework　incorporating　the　traffic　flow　model　into　deep　learning　(TFMDL)　modeling　that　contains　both　model-driven　and　data-driven　components.　This　paper　focuses　on　highway　TSE　with　observed　data　from　loop　detectors.　We　build　a　hybrid　cost　function　to　adjust　the　weights　of　model-driven　and　data-driven　proportions.　We　then　evaluate　the　proposed　framework　using　the　open-access　performance　measurement　system　(PMS)　dataset　on　a　corridor　of　US　I-405　in　Los　Angeles,　California.　The　experimental　results　show　the　advantages　of　the　proposed　TFMDL　approach　in　performing　better　than　several　benchmark　models　in　terms　of　estimation　accuracy　and　data　efficiency.