Publication Detail
Tolling lessons learned for road usage charge
UCD-ITS-RR-23-17 Research Report
Available online at
https://doi.org/10.7922/G23R0R6M
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Suggested Citation:
Chakraborty, Debapriya, Alan Jenn, Jean Ji, Marcus Chan (2023) Tolling lessons learned for road usage charge. Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-23-17
In 2021, the federal gasoline tax raised about $32.8 billion which accounted for about 70% of the Federal Highway Trust Fund’s expenditures, with a shortfall of $14 billion (FHWA, 2021). In response, many states have launched pilot or full-scale programs of road-usage charge (RUC) as an alternative transportation funding source. One of the fundamental challenges of RUC is the high cost of implementation compared to a traditional motor fuel tax (Caltrans, 2017). To address this, states look to leverage existing vehicle-level pricing programs, such as road tolling to learn possible synergies between RUC and tolling. In this paper, the authors conducted semi-structured interviews with experts from tolling programs across the U.S. to identify areas of overlap between tolling and RUC. Consequently, they built upon the interview findings with a multi-criteria decision analysis (MCDA) to evaluate how ready the RUC pilot programs are for implementation. The results demonstrated that there are numerous lessons that the RUC pilots can learn from the tolling industry and develop an integrated system—tolling hub operations, methods to maintain data privacy, technology, etc. RUC programs can benefit from integration with tolling from the increased scale of operations which would largely reduce administrative costs. Lastly, ensuring equity in RUC rate design to alleviate any potential financial burdens on low-income populations and ensuring that unbanked and underbanked populations have access to the system is important.
Key words: Tolling, Road User Charge, Expert Interview, Multi-criteria Analysis
Key words: Tolling, Road User Charge, Expert Interview, Multi-criteria Analysis