There are two reasons for implementing a toll road. The first is to recover roadway construction costs, which can include allowing private lenders to collect tolls to recoup their investment. The second reason is to use road pricing to help with congestion. Roads are designated as toll roads during heavy traffic periods to reduce the number of drivers on the road. These tolls are generally regressive and rise and fall based on usage, with the highest rates occurring during heavy traffic periods. These regressive tolls can be particularly onerous on low-income individuals. They are either impacted by the high toll-to-income ratio or are forced to switch to alternate routes with generally longer travel times. Understanding the impact across income brackets and offering good travel alternatives, such as high-quality public transportation, would make road pricing more equitable.
Researchers Donghyung Yook, Kangsoo Kim, and Kevin Heaslip present a methodology for measuring spatial distributional effects caused by road pricing in a disaggregated manner to inform policies to alleviate regressivity. In their research. “A Disaggregated Approach to Estimate the Spatial Distributional Effects of Implemented Toll Road Systems,” they investigate the theoretical conditions that lead to regressivity and introduce the method to express distributional effects spatially disaggregated. To assess this effect on the toll road network, the authors used a South Korea highway as a case study. Learn more about how this research can help mitigate the welfare loss by toll roads in the Journal of Transportation Engineering, Part A: Systems at https://doi.org/10.1061/JTEPBS.TEENG-7804. The abstract is below.
Abstract
This study presents a methodology in a disaggregated manner to assess the spatial distributional effects of implemented toll road networks. Existing zone-based research focuses only on revealing the existence of regressivity. This approach provides limited information in understanding the spatial distribution of travelers from low-income groups known to have suffered welfare loss due to toll roads. For road pricing to be a win-win strategy for all income groups, the revenue from toll roads should be used to improve the mobility of low-income groups. Therefore, it is desirable to have more detailed location information and regressivity effects for each income group due to toll roads. This study proposes a new method to better represent the spatial distributional effects by utilizing disaggregated and recently available data (e.g., smartphone navigation data), enabling analysts to easily estimate the welfare loss of low-income groups due to toll roads. Applying the proposed method in Chungcheongnam-do, Korea, reveals that installing toll roads would be regressive in terms of income. However, the analysis results provide sufficient information to interpret spatial distributional effects, current status, and causes. This method improves the existing zone-based analysis, which cannot distinguish who has greater welfare loss among high- and low-income groups in a particular zone. This information would be extremely useful in establishing mitigation policies for low-income groups affected by installing toll roads in the future.
Learn more about how to analyze the effects of tolls on lower incomes in the ASCE Library: https://doi.org/10.1061/JTEPBS.TEENG-7804.
