Lane　imbalance　does　not　provide　sufficient　space　for　merging　vehicles　to　adjust　their　speed　and　change　lanes　smoothly.　This　leads　to　improper　driving　behavior　that　disrupts　mainline　traffic　flow　stability,　resulting　in　capacity　drops　and　increased　vehicle　emissions.　However,　quantitative　analyses,　specifically　the　effects　of　lane　imbalance　on　capacity　and　emissions,　remain　limited.　Existing　traffic　simulation　platforms　struggle　to　capture　the　effects　of　geometric　design　changes　on　capacity.　To　address　these　gaps,　we　developed　a　simulation　method　incorporating　interactions　between　geometric　design　and　traffic　flow　demand　into　an　XGBoost　model,　enhancing　the　predictive　accuracy　for　driving　behavior　parameters.　Implemented　within　the　TESS　NG　platform,　this　model　enables　real-time　adjustments　in　driving　behavior　parameters　as　traffic　demand　varies　under　different　lane　balance　conditions.　The　simulation　results　indicated　a　42.4%　capacity　drop　and　a　34.9%　increase　in　CO2　emissions　when　the　balanced　merging　area　was　shifted　to　lane　imbalance.　Conversely,　shifting　to　lane　balance　increases　capacity　by　8.2%　and　reduces　CO2　emissions　by　39.8%　under　severe　congestion　conditions.　Under　lane　imbalance,　vehicle　speeds　are　lower　across　all　traffic　demand　levels.　When　the　demand　exceeds　1300　pcu/h/ln,　lane　changes　occur　closer　to　the　end　of　the　acceleration　lane,　with　higher　speed　differentials.　These　insights　underscore　the　potential　of　lane　balance　optimization　to　mitigate　capacity　drops　and　emissions,　providing　a　valuable　simulation　approach　for　the　design　and　evaluation　of　merging　areas.