Tunnels　are　an　engineering　solution　that　has　gained　prominence　for　constructing　freeways　in　mountainous　regions.　The　length　of　the　tunnels　can　vary,　depending　on　the　geological　conditions,　engineering　requirements,　and　budget　constraints.　Car-following　is　the　predominant　driving　behavior　observed　in　tunnels,　and　understanding　how　drivers　follow　each　other　in　different　types　of　tunnels　is　crucial　for　ensuring　smooth　traffic　flow　and　safety.　Each　type　of　tunnel　environment　can　uniquely　impact　car-following　behavior,　which　allows　for　targeted　studies　to　optimize　traffic　management.　In　this　research,　natural　driving　data　in　freeway　tunnels　were　collected　through　a　driving　experiment　conducted　on　the　Baomao　Freeway　in　Chongqing,　China.　Then,　the　correlations　and　differences　in　car-following　data　between　various　tunnels　and　sections　were　analyzed.　Finally,　car-following　models　were　developed　considering　various　tunnel　scenarios,　and　the　influence　of　tunnel　types　on　traffic　flow　was　analyzed　by　simulation.　The　study　revealed　notable　variations　in　car-following　behavior　across　different　types　of　tunnels,　as　well　as　within　consecutive　sections　of　the　same　tunnel.　As　tunnel　length　increased,　the　driving　stability　of　following　vehicles　decreased,　but　the　level　of　driving　safety　risk　was　not　positively　correlated　with　tunnel　length.　Significant　vehicle　trajectory　oscillation　was　observed　within　the　inner　sections　of　long　and　extra-long　tunnels,　and　a　significant　relationship　between　the　acceleration　of　following　vehicles　and　the　location　within　the　tunnel　section　was　found.　Additionally,　the　longer　the　tunnel,　the　greater　the　fluctuations　in　traffic　flow,　and　the　negative　impact　of　the　tunnel　environment　on　traffic　flow　stability　increased　periodically　downstream.　These　findings　offer　valuable　insights　for　understanding　and　modeling　car-following　behavior　in　freeway　tunnels,　which　ultimately　facilitate　traffic　safety　and　mobility.　©　2023　Elsevier　Ltd