SUMO 2018- Simulating Autonomous and Intermodal Transport Systems

Papers

Pages 1-13
Pages 14-24
Pages 25-42
Pages 43-55
Pages 56-66
Pages 67-81
Pages 82-93
Pages 94-110
Pages 111-117
Pages 118-133
Pages 134-151
Pages 152-161
Pages 162-172
Pages 173-182
Pages 183-193
Pages 194-205
Pages 206-217

Keyphrases

3D Traffic Scenario, Activity based demand, Activity-based model, Adaptive Cruise Control (ACC), Advanced Driving Assistance Systems, autonomous driving2, autonomous vehicles, communication, Connected and Automated Vehicle, Cooperative Adaptive Cruise Control (CACC), Cooperative Intelligent Transportation Systems, coupled simulation, data-fusion in ITS, Deep Reinforcement Learning, Dijikstra SUMO topographic Road Netwoks OSM, distributed traffic simulation, driver model, Driving Simulation, Dyanic Route Optimization, Emergency Vehicles, heterogeneous agent, Heterogeneous traffic, Intermodal Mobility, Learning and Adaptive Systems, Mesoscopic Traffic Simulation, micro-simulation, microscopic modelling, microscopic simulation, microscopic traffic simulation, microsimulation, multi-airport region, network flow, network performance, NetworkX, operation strategy, Optimode.net, OSM, OSMnx, Rescue lanes, ride-sharing, road network, Route estimation, Routing, shared space, Signal Adaptation, SUMO3, synchronization, traffic data, traffic microsimulation, traffic signal control, traffic simulation4, traffic valiadtion, urban mobility, vehicle dynamics, Webster