3 |
3D Traffic Scenario | Monaco SUMO Traffic (MoST) Scenario: A 3D Mobility Scenario for Cooperative ITS |
A |
Activity based demand | Road network extraction with OSMNx and SUMOPy |
Activity-based model | Generating activity based, multi-modal travel demand for SUMO |
Adaptive Cruise Control (ACC) | Assessment of ACC and CACC systems using SUMO |
Advanced Driving Assistance Systems | Coupling traffic and driving simulation: Taking advantage of SUMO and SILAB together |
autonomous driving | Simulating the Impact of Shared, Autonomous Vehicles on Urban Mobility – a Case Study of Milan Simulation of Autonomous RoboShuttles in Shared Space |
autonomous vehicles | Flow: Deep Reinforcement Learning for Control in SUMO |
C |
communication | A new strategy for synchronizing traffic flow on a distributed simulation using SUMO |
Connected and Automated Vehicle | Assessment of ACC and CACC systems using SUMO |
Cooperative Adaptive Cruise Control (CACC) | Assessment of ACC and CACC systems using SUMO |
Cooperative Intelligent Transportation Systems | Monaco SUMO Traffic (MoST) Scenario: A 3D Mobility Scenario for Cooperative ITS |
coupled simulation | Simulating a multi-airport region on different abstraction levels by coupling several simulations |
D |
data-fusion in ITS | Calibrating Traffic Simulation Models in SUMO Based upon Diverse Historical Real-Time Traffic Data – Lessons Learned in ITS Upper Austria |
Deep Reinforcement Learning | Flow: Deep Reinforcement Learning for Control in SUMO |
Dijikstra SUMO topographic Road Netwoks OSM | Dynamic Route Optimization for Heterogeneous Agent Envisaging Topographic of Maps |
distributed traffic simulation | A new strategy for synchronizing traffic flow on a distributed simulation using SUMO |
driver model | Multi-Level-Validation of Chinese traffic in the ChAoS framework |
Driving Simulation | Coupling traffic and driving simulation: Taking advantage of SUMO and SILAB together |
Dyanic Route Optimization | Dynamic Route Optimization for Heterogeneous Agent Envisaging Topographic of Maps |
E |
Emergency Vehicles | Analysis of the traffic behavior of emergency vehicles in a microscopic traffic simulation |
H |
heterogeneous agent | Dynamic Route Optimization for Heterogeneous Agent Envisaging Topographic of Maps |
Heterogeneous traffic | Multi-Level-Validation of Chinese traffic in the ChAoS framework |
I |
Intermodal Mobility | Monaco SUMO Traffic (MoST) Scenario: A 3D Mobility Scenario for Cooperative ITS |
L |
Learning and Adaptive Systems | Flow: Deep Reinforcement Learning for Control in SUMO |
M |
Mesoscopic Traffic Simulation | Calibrating Traffic Simulation Models in SUMO Based upon Diverse Historical Real-Time Traffic Data – Lessons Learned in ITS Upper Austria |
micro-simulation | Generating activity based, multi-modal travel demand for SUMO |
microscopic modelling | Analysis of the traffic behavior of emergency vehicles in a microscopic traffic simulation |
microscopic simulation | Assessment of ACC and CACC systems using SUMO |
microscopic traffic simulation | Coupling traffic and driving simulation: Taking advantage of SUMO and SILAB together |
microsimulation | Road network extraction with OSMNx and SUMOPy |
multi-airport region | Simulating a multi-airport region on different abstraction levels by coupling several simulations |
N |
network flow | Route estimation based on network flow maximization |
network performance | Improving SUMO's Signal Control Programs by Introducing Route Information |
NetworkX | Road network extraction with OSMNx and SUMOPy |
O |
operation strategy | Simulation of Autonomous RoboShuttles in Shared Space |
Optimode.net | Simulating a multi-airport region on different abstraction levels by coupling several simulations |
OSM | Road network extraction with OSMNx and SUMOPy |
OSMnx | Road network extraction with OSMNx and SUMOPy |
R |
Rescue lanes | Analysis of the traffic behavior of emergency vehicles in a microscopic traffic simulation |
ride-sharing | Simulating the Impact of Shared, Autonomous Vehicles on Urban Mobility – a Case Study of Milan |
road network | Chula-SSS: Developmental Framework for Signal Actuated Logics on SUMO Platform in Over-saturated Sathorn Road Network Scenario |
Route estimation | Route estimation based on network flow maximization |
Routing | Route estimation based on network flow maximization |
S |
shared space | Simulation of Autonomous RoboShuttles in Shared Space |
Signal Adaptation | Improving SUMO's Signal Control Programs by Introducing Route Information |
SUMO | Road network extraction with OSMNx and SUMOPy Generating activity based, multi-modal travel demand for SUMO Simulation of Autonomous RoboShuttles in Shared Space |
synchronization | A new strategy for synchronizing traffic flow on a distributed simulation using SUMO |
T |
traffic data | Calibrating Traffic Simulation Models in SUMO Based upon Diverse Historical Real-Time Traffic Data – Lessons Learned in ITS Upper Austria |
traffic microsimulation | Flow: Deep Reinforcement Learning for Control in SUMO |
traffic signal control | Chula-SSS: Developmental Framework for Signal Actuated Logics on SUMO Platform in Over-saturated Sathorn Road Network Scenario |
traffic simulation | Analysis of the traffic behavior of emergency vehicles in a microscopic traffic simulation Calibrating Traffic Simulation Models in SUMO Based upon Diverse Historical Real-Time Traffic Data – Lessons Learned in ITS Upper Austria Chula-SSS: Developmental Framework for Signal Actuated Logics on SUMO Platform in Over-saturated Sathorn Road Network Scenario Simulating the Impact of Shared, Autonomous Vehicles on Urban Mobility – a Case Study of Milan |
traffic valiadtion | Multi-Level-Validation of Chinese traffic in the ChAoS framework |
U |
urban mobility | Simulating the Impact of Shared, Autonomous Vehicles on Urban Mobility – a Case Study of Milan |
V |
vehicle dynamics | Flow: Deep Reinforcement Learning for Control in SUMO |
W |
Webster | Improving SUMO's Signal Control Programs by Introducing Route Information |