Semi-dynamic traffic assignment with residual demand

Features

• Quasi-equilibrium traffic assignment
• Efficient routing for millions of trips using contraction hierarchy and priority-queue based Dijkstra algorithm sp
• Temporal dynamics with residual demand, with time step of a few minutes
• Compatible with road network retrieved from OSMnx

Use cases

• Calculating network traffic flow for small and large road networks (10 to 1,000,000 links) at sub-hourly time steps
• Visualizing traffic congestion dynamics throughout the day
• Analyzing traffic-induced carbon emissions (emission factor model)
• Assessing regional mobility and resilience with disruptions (e.g., road closure, seismic damages)

Getting started

1. Clone the repository git clone https://github.com/cb-cities/residual_demand.git
2. Create a new Python 3.8 virtual environment and install dependencies conda env create -f environment.yml
   • Active the environment conda activate residual_demand
   • Install pandana from Github. This is the contraction hierarchy code.
3. Run the test example python scripts/run_simulation_template.py
4. Examine the outputs in the output data (projects/test/simulation_outputs) and visualization (projects/test/visualization_outputs) folders
5. Run for your own problem by following the test example