Travel　time　prediction　has　important　influence　on　the　overall　control　of　urban　Intelligent　Transportation　Systems　(ITS).　Urban　arterial　networks　are　typically　composed　of　links　and　intersections,　where　each　link　or　intersection　can　be　regarded　as　a　spatial　node　within　the　network.　However,　existing　researches　predominantly　focus　on　modeling　spatial　nodes　in　the　link　modality　to　predict　travel　times　in　urban　arterial　networks,　neglecting　the　potential　correlations　among　heterogeneous　modal　nodes.　To　overcome　these　limitations,　we　propose　a　Heterogeneous　Multi-Modal　Graph　Neural　Network　(HMGNN)　specifically　tailored　for　travel　time　prediction　in　arterial　networks.　Specifically,　we　innovatively　construct　spatial　correlation　graphs　that　capture　the　unique　traffic　characteristics　of　intersection　modal　nodes.　Furthermore,　we　design　a　cross-modal　graph　generator　that　captures　the　latent　spatiotemporal　features　between　spatial　nodes　of　distinct　modalities,　resulting　in　the　generation　of　heterogeneous　modal　graphs.　Finally,　our　proposed　HMGNN　model　incorporates　tailored　network　structures　for　graphs　of　varying　complexities,　enabling　targeted　mining　of　their　inherent　information　to　derive　the　final　prediction　results.　Extensive　experiments　conducted　using　real-world　traffic　data　from　Zhangzhou,　China,　demonstrate　that　our　HMGNN　model　achieves　significant　improvements　in　prediction　accuracy.