CE252Project

Authors: Court Freund, Kaiqi Jiang, Sohel Dinnoo, Thibaud Cambronne

Date: 12/11/2025

Overview

This projected explored several road usage charge pricing schemes with different costs per mile based on income group. The goal of designing a new RUC system, was to achieve an effective a more equitable tax than the gas tax. The project analyzed data from the 2017 National Household Travel Survey (NHTS) to simulate the impact of different RUC pricing schemes on households across various income brackets in California. The analysis included visualizations to compare the effectiveness and equity of the proposed RUC systems against the traditional gas tax.

Content Descriptions

Raw Data

You can request access to our rawData folder on google drive here. Then move this folder into your local repo. The files were too large for Github.

NHTS 2017 Data

"Transportation Secure Data Center." 2017. National Laboratory of the Rockies. Accessed 2025: www.nrel.gov/tsdc.

California Census Data

Used for overal population by income bracket for all of California. Accessed 2025: https://nhts.ornl.gov/.

EPA Fuel Economy Data

Used for estimating vehicle fuel economy based on make, model, and year. Accessed 2025: https://www.fueleconomy.gov/feg/download.shtml.

Clean Data

Dataframes in csv format with cleaned data for analysis.

Scripts

anaysisNb.ipynb

Contains the main analysis and visualizations for the project. The first section was used to generate clean data and requires the raw data folder from google drive to be added to the local repo. The second section is based on the clean data and can be run without the raw data folder being downloaded.

data_utils.py

Contains functions for loading and processing the raw data.

price_functions.py

Contains functions for estimating price per mile based on various scenarios.

gas_tax_calculator.py

Contains functions for modeling the gas tax scenario.

Notebook.ipynb

Is used for testing coded and visualizations, it is not part of the final project.