System Dynamics Model Sensitivity Analysis in a Generic Rural Area Revealed Up to a 67 Percent Reduction in Traveler Wait Time with Automated Services Compared to Human-Driven Vehicles.

USDOT Study Conducted Sensitivity Analyses to Compare the Performance of Human Driven Services to Two Automated Vehicle Scenarios in a Simulated Rural Setting.

Date Posted
07/27/2023
Identifier
2023-B01778
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System Dynamics Models of Automated Vehicle Impacts

Summary Information

In rural areas, travel mode choice can often be constrained to privately owned vehicles because transit and Transportation Network Company (TNC) services often do not function in sparsely populated environments. By removing human drivers, automated vehicles could reduce the barrier to sustainability for businesses such that they could operate in low density conditions. System dynamics (SD) models are a tool that can be used to bring different stakeholders together to quantify important considerations so that the evaluation of scenarios given different assumptions is possible. This study adopted SD to understand the potential impacts of a shared mobility service from both views of service provider and household. Specifically, this study constructed and calibrated a car service model for urban and suburban areas using TNC data from Massachusetts and Chicago in 2019. Additionally, a sensitivity analysis in rural areas was performed to find where automated driver services could outperform current human services.

METHODOLOGY

The sensitivity analysis assumed little transit and no TNC services in rural areas. The analysis considered three scenarios including human driven vehicles (HDV), automated vehicles (AV), and automated vehicles plus the induced demand created by them. This study conducted 600 runs of car service model looking at population densities from 13 to 398 people per square mile. Model parameters were set at an average trip distance of 10 miles, an average speed of 30 mph, and a human-driven fare of $20 whereas the AV fare was $5. 

FINGDINGS

  • AV service improved wait times by 30 to 67 percent due to the ability to more efficiently assign trips.
  • A threshold of 30 people/square mile was needed for viability and positive revenue.
  • Because of the lower fare, AV usage rates were higher than human driven vehicles (around 100:1). This could lead to larger AV fleets under higher usage outcomes.

 

Goal Areas
Results Type