asu-trans-ai-lab
ASU Transportation AI Lab is a team led by Dr. Xuesong (Simon) Zhou, focusing on open-source tools for integrating AI methods with transportation systems.
Pinned Repositories
asu-trans-ai-lab.github.io
DLSim-MRM
Open-source Python package designed for integration of deep learning (first DL) and traffic simulation, by extending original C++ based structure of DTALite (second DL)
DTALite
grid2demand
A tool for generating zone-to-zone travel demand based on grid zones and gravity model
GTFS2GMNS
Integrated_modeling_GMNS
AMS data Hub that connects all transportation modeling tools based on GMNS format
NeXTA4GMNS
How to use NEXTA and QGIS to visualize and edit Analysis, Modeling, and Simulation (AMS) and General Travel Network Format Specification (GMNS) data sets.
OSM2GMNS
signal4gmns
GMNS based traffic signal timing generation tool for multi-resolution modeling
Traffic-Flow-Fundamental-Diagram
asu-trans-ai-lab's Repositories
asu-trans-ai-lab/DTALite
asu-trans-ai-lab/grid2demand
A tool for generating zone-to-zone travel demand based on grid zones and gravity model
asu-trans-ai-lab/GTFS2GMNS
asu-trans-ai-lab/DLSim-MRM
Open-source Python package designed for integration of deep learning (first DL) and traffic simulation, by extending original C++ based structure of DTALite (second DL)
asu-trans-ai-lab/Integrated_modeling_GMNS
AMS data Hub that connects all transportation modeling tools based on GMNS format
asu-trans-ai-lab/signal4gmns
GMNS based traffic signal timing generation tool for multi-resolution modeling
asu-trans-ai-lab/OSM2GMNS
asu-trans-ai-lab/NeXTA4GMNS
How to use NEXTA and QGIS to visualize and edit Analysis, Modeling, and Simulation (AMS) and General Travel Network Format Specification (GMNS) data sets.
asu-trans-ai-lab/Traffic-Flow-Fundamental-Diagram
asu-trans-ai-lab/QVDF
Queue based Volume Delay Function
asu-trans-ai-lab/CAMLite
We introduce a new virtual track-based framework and open-source tools for modeling partially schedulable connected and automated mobility (CAM) systems on layered networks considering the emerging trend of CAM system deployment.
asu-trans-ai-lab/EV_charging_network_planning
EV_charging_network_planning
asu-trans-ai-lab/utdf2gmns
synchro utdf format to gmns signal timing format at movement layer
asu-trans-ai-lab/CBI
Congestion Bottleneck Identification: Convert TMC identification and reading files to GMNS node and link files, and perform QVDF estimation
asu-trans-ai-lab/DTALite-MOVESLite
MOVESLite implementation in DTALite for regional emission analysis
asu-trans-ai-lab/img2net
The use of AI to process images and identify which network with different movement and lane configurations is more likely to be correct.
asu-trans-ai-lab/Learning_Quantum_Computing-QUBO_for_Multimodal_Transportation
Quadratic unconstrained binary optimization (QUBO)
asu-trans-ai-lab/open-science_for_network_modeling
data sets for teaching and learning GMNS network standard
asu-trans-ai-lab/Path4GMNS
asu-trans-ai-lab/plot4gmns
A visualization tool for visualizing and analyzing transportation network and demand files in GMNS format
asu-trans-ai-lab/recursive_logit_model
recursive logit model for traffic assignment
asu-trans-ai-lab/SHP2GMNS
Converting Planning GIS files in Shape Files to GMNS format
asu-trans-ai-lab/AI_traffic_flow
AI-Based Traffic Flow Theory
asu-trans-ai-lab/AVRLite
Autonomous vehcle routing lite (AVRLite)
asu-trans-ai-lab/CGLite
CGLite
asu-trans-ai-lab/LEA
Linear-Exponential Adaptation Model
asu-trans-ai-lab/openDTA
State-of-the-art open-source applications for Transportation Optimization, Modeling, and Simulation
asu-trans-ai-lab/pyDTALite
Python package for DTALite
asu-trans-ai-lab/smart_city_planning
medium-sized city planning procedure based on regional model and multiple data sources
asu-trans-ai-lab/Traffic_State_Estimation-Computational_Graph