Accurate　and　real-time　tracking　for　real-world　urban　logistics　has　become　a　popular　research　topic　in　the　field　of　intelligent　transportation.　While　the　routing　of　urban　logistic　service　is　usually　accomplished　via　complex　mathematical　and　analytical　methods.　However,　the　nature　and　scope　of　real-world　urban　logistics　are　highly　dynamic,　and　the　existing　optimization　technique　cannot　precisely　formulate　the　dynamic　characteristics　of　the　route.　To　ensure　customers’　demands　are　met,　planners　need　to　respond　to　these　changes　quickly　(sometimes　instantaneously).　This　paper　proposes　the　formulation　of　a　novel　deep　reinforcement　learning　framework　to　solve　a　dynamic　and　uncertain　vehicle　routing　problem　(DU-VRP),　whose　objective　is　to　meet　the　uncertain　servicing　needs　of　customers　in　a　dynamic　environment.　Considering　uncertain　information　about　the　demands　of　customers　in　this　problem,　the　partial　observation　Markov　decision　process　is　designed　to　frequently　observe　the　changes　in　customers’　demands　in　a　real-time　decision　support　system　that　consists　of　a　deep　neural　network　with　a　dynamic　attention　mechanism.　Besides,　a　cutting-edge　reinforcement　learning　algorithm　is　presented　to　control　the　value　function　of　the　DU-VRP　for　better　training　the　routing　process’s　dynamics　and　uncertainty.　Computational　experiments　are　conducted　considering　different　data　sources　to　obtain　satisfactory　solutions　of　the　DU-VRP.　©　2022,　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature.