Wisconsin Study Predicts Impacts of Potential Shift to Automated Vehicles as 5.6 Percent Increase in Energy Consumption.
A Survey-based Approach Was Used to Understand the Impact of Automated Vehicles on Mode Choice and Resulting Environmental Impacts Using a Scenario Based Methodology.
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Autonomous Vehicle Adoption: Assessing Operational and Environmental Impacts

Summary Information

The introduction of automated vehicles (AV) is expected to change the demand for transit, along with safety, congestion, and other travel behaviors. This study sought to model the potential shifts in transit ridership due to the adoption and use of AV (both personal and in a shared capacity) and their environmental impacts. AVs in this study were considered as fully automated vehicles that could provide door-to-door transportation. The case study was based out of Madison Wisconsin, a midsized city in the Midwest that has an extensive bus system and prevalent bicycle culture. The impact of AV usage was determined by assessing the shift in demand for different transportation modes and categorizing environmental impacts through greenhouse gas emission, particulate matter and energy consumption. It was expected that fully AVs both personal and in shared capacity could cause shifts in transit ridership (both increase and decrease). The study analyzed the environmental impact of the usage phase of different transportation options, neglecting the raw materials, manufacturing, and end of life.


The demand shifts due to AV adoption was estimated from a stated-preference survey that presented the respondents with varying attributes (such as travel time, wait time, walk time, cost, etc.) about their choice of transportation modes in different scenarios. Survey experiments were designed to depict realistic travel scenarios for a range of commuters in the city of Madison, Wisconsin. The survey represented a young population in the age-group of 15-24 years. Majority of the respondents owned a bus pass, and a bicycle. The survey data were utilized to develop two different statistical models. The estimates from these models were used to explain the travel behavior of commuters under the adoption of AV and to predict the demand for each mode. The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model was used to estimate the environmental impacts of different transportation modes. GREET used a life cycle assessment approach, considering well- to-wheel impact of each transportation mode. AVs, personal vehicles and busses were assumed to be compression ignition direct injection (CIDI) vehicles powered by an internal combustion engine (ICE) running on a mixture of 20 percent biodiesel and 80 percent conventional diesel by volume. The environmental impacts were computed per mile bases for AVs and personal vehicles, and per passenger mile for buses. 

One of the developed statistical models was adopted to predict the percentage distribution of the transportation modes (vehicle, AV, bus, bicycle) for the survey population under seven hypothetical scenarios. These scenarios, listed below, were developed to gather insights on potential policies that could affect the transportation system in the future:

  • Scenario-I: 20 percent increase in AV cost
  • Scenario I-: 20 percent decrease in AV cost 
  • Scenario-III: 20 percent decrease in bus time access
  • Scenario-IV: 20 percent increase in car cost
  • Scenario-V: 20 percent decrease in bus travel time
  • Scenario-VI: 20 percent increase in personal vehicle and AV travel time and
  • Scenario-VII: 10 percent increase in AV cost with 20 percent decrease in travel time


  • It was found that the introduction of fully automated taxis would increase the environmental impact of the transportation system due to users switching from less environmentally intensive travel options, such as busses, to the autonomous taxis. The results showed an expected 5.6 percent increase in energy consumption (kj/mile), 5.7 percent increase in Greenhouse Gas (GHG) emissions (kg/mile) and 8 percent increase in particulate matter (mg/mile) as compared to current situation.
  • Among all the tested scenarios, Scenario-I had the lowest energy consumption (3.63 percent increase as compared to current situation) and GHG (3.67 percent increase as compared to current situation). Scenario-V was predicted to have the lowest PM 2.5 impacts (2.55 percent increase in PM 2.5).
  • Analysis showed that Scenario-II where there was a decrease in the cost of AVs led to a significant mode shift from buses to AV causing the highest environmental impact:  8.34 percent increase in energy consumption, 8.46 percent increase in GHG, and 12.18 percent increase in particulate matter (PM 2.5).
  • According to the predicted results, it was noticed that a decrease in travel time of bus (Scenario-V) was environmentally more effective compared to the Scenario III where there was a decrease in bus access time (1 .01, 1.16, and 4.81 percent point decrease in energy consumption, GHG and PM 2.5, respectively). A predicted decrease in travel time for bus under Scenario-V was an effective incentive for commuters to favor the bus over the other modes of transportation leading to an overall positive environmental impact.
  • Model results also indicated that those with high frequency usage of a ride hailing application were more likely to choose AVs as a mode of transportation over other modes. 
Results Type