Inefficient　and　unreliable　public　transportation　systems　remain　a　significant　challenge　in　growing　cities,　with　bus　bunching　being　a　key　contributor　to　passenger　dissatisfaction.　Despite　numerous　proposed　holding　strategies　to　mitigate　this　issue,　there　is　a　lack　of　a　standardized　testbed　for　their　comprehensive　evaluation.　This　paper　presents　an　open-source,　extensible　simulation　platform　that　enables　the　development　and　benchmarking　of　bus　holding　strategies　in　a　unified　environment.　It　accommodates　both　model-based　and　model-free　reinforcement　learning　(RL)　control　strategies,　providing　a　systematic　approach　to　assess　their　performance　under　various　operating　conditions.　Holding　control　strategies　can　be　customized　by　users　within　our　platform,　provided　they　create　a　class　that　fulfills　the　basic　requirements　of　the　exposed　application　programming　interface　(API).　The　platform　is　designed　to　be　easily　extensible,　allowing　users　to　incorporate　real-world　datasets　and　customize　detailed　operational　features.　We　demonstrate　the　platform＇s　capabilities　by　comparing　three　holding　strategies:　a　modelbased　forward　headway　control　method　and　two　RL-based　approaches.　Experimental　results　highlight　the　importance　of　comprehensive　evaluations,　as　the　relative　performance　of　different　strategies　varies　under　different　holding　time　budgets.　The　proposed　simulation　platform　aims　to　facilitate　more　robust,　comparable,　and　reproducible　research　in　bus　operation　control　strategies,　ultimately　leading　to　improved　bus　service　reliability　in　real-world　implementations.