Traffic　video　question　answering　(TrafficVQA)　constitutes　a　specialized　VideoQA　task　designed　to　enhance　the　basic　comprehension　and　intricate　reasoning　capacities　of　videos,　specifically　focusing　on　traffic　events.　Recent　VideoQA　models　employ　pretrained　visual　and　textual　encoder　models　to　bridge　the　feature　space　gap　between　visual　and　textual　data.　However,　in　addressing　the　unique　challenges　inherent　to　the　TrafficVQA　task,　three　pivotal　issues　must　be　addressed:　(i)　Dimension　Gap:　Between　the　pretrained　image　(appearance　feature)　and　video　(motion　feature)　models,　there　exists　a　conspicuous　dimension　difference　in　static　and　dynamic　visual　data;　(ii)　Scene　Gap:　The　common　real-world　datasets　and　the　traffic　event　datasets　differ　in　visual　scene　content;　(iii)　Modality　Gap:　A　pronounced　feature　distribution　discrepancy　emerges　between　traffic　video　and　text　data.　To　alleviate　these　challenges,　we　introduce　the　coarse-fine　multimodal　contrastive　alignment　network　(CFMMC-Align).　This　model　leverages　sequence-level　and　token-level　multimodal　features,　grounded　in　an　unsupervised　visual　multimodal　contrastive　loss　to　mitigate　dimension　and　scene　gaps　and　a　supervised　visual-textual　contrastive　loss　to　alleviate　modality　discrepancies.　Finally,　the　model　is　validated　on　the　challenging　public　TrafficVQA　dataset　SUTD-TrafficQA　and　outperforms　the　state-of-the-art　method　by　a　substantial　margin　(50.2%　compared　to　46.0%).　The　code　is　available　at　https://github.com/guokan987/CFMMC-Align.　IEEE