With　the　tide　of　electrifying　urban　transportation　systems　by　introducing　electric　vehicles,　the　differences　between　fuel　vehicles　and　electric　vehicles　in　driving　styles　and　strategies　to　achieve　eco-driving　have　become　a　burden　for　efficient　operations　of　urban　transportation　systems.　Most　of　the　previous　energy　management　strategies　have　sought　to　achieve　system　optimisation　at　a　single-vehicle　or　multi-vehicles　level,　and　failed　to　consider　the　vehicle-to-vehicle　and　vehicle-to-infrastructure　effects　in　a　global　optimisation　manner.　Furthermore,　as　a　typical　human-in-the-loop　cyber?physical　system,　the　mobility　behaviours　of　road　users　undoubtedly　play　a　vital　role　in　the　cooperative　and　green　operations　of　urban　transportation　systems.　Yet　little　research　has　dedicated　to　develop　means　to　incentivise　energy-saving　behaviours　in　transportation　systems.　Hence,　in　this　paper,　we　propose　a　unifying　queueing　and　neural　network　model　to　calculate　the　time　and　energy　efficient　course　of　actions　and　routes　for　different　types　of　road　users　within　an　urban　road　network　in　a　real　time　manner.　The　lower-level　queueing　model　captures　the　interactive　dynamics　of　road　users　and　solves　the　optimal　flow　ratio　at　each　intersection　while　the　upper-level　neural　network　model　further　customises　desired　routes　for　different　types　of　road　users.　In　addition,　an　incentive　mechanism　is　proposed　to　encourage　road　users　to　follow　the　optimal　actions　via　publishing　various　types　of　reward-gaining　tasks.　A　case　study　in　a　designated　area　of　Beijing　shows　that　the　use　of　the　bi-level　optimisation　algorithm　can　reduce　the　average　travel　time　by　approximately　20%　and　decrease　the　energy　consumption　by　10%　in　comparison　with　the　realistic　trip　data.