I developed an agent-based modeling that represents a synthetic population in a neighborhood. A micro network is created for each household based on the locations people typically travel every day (work, day care center, grocery stores, etc.). People interact with the infrastructure in the neighborhood. Presence of electric chargers at home, work or public as well as hydrogen stations influence their purchase behavior.
The model predicts the purchase probability based on a multinomial logit approach in real-time based on the daily driving pattern of the neighborhood. The model runs for a year.
Click here for more details. The model file is also available to download from the Github link.
A bi-objective algorithm that optimizes time and money is developed for the modal choices in the San Francisco Bay Area region. Here, the users choose between driving and BART during the peak and non-peak hours. The model also estimates the cost and emissions of their choices.
Click here to learn more.