Texas A&M University researchers have developed a conceptual model to identify systematically the potential health impacts of self-driving vehicles.
They identified 32 transportation-related risk factors that affected health and concluded that 17 could negatively impact public health, while eight could have a positive impact. There were seven areas of uncertain implications that require further investigation.
The researchers recently published their findings in the December issue of Sustainable Cities and Society.
A survey on the receptiveness of autonomous vehicles’ impacts showed that there is a lack of awareness of the potential health impacts of AVs and low perceptions of the importance of AV health benefits. On the other hand, there are some unintended consequences of AVs’ implementation that need to be studied before AVs find their way onto the road.Soheil Sohrabi, lead author
Sohrabi, with Dr. Dominque Lord, professor in the civil and environmental engineering department, A.P. Wiley Faculty Fellow and Texas A&M Transportation Institute (TTI) associate research scientist, and Dr. Haneen Khreis with TTI created a conceptual model to identify the pathways through which AVs can affect public health.
The proposed model summarizes the potential changes in transportation after AV implementation into seven points of impact: transportation infrastructure; land use and the built environment; traffic flow; transportation mode choice; transportation equity; and jobs related to transportation and traffic safety.
Then, the team outlined transportation-related risk factors that affect health. Finally, the researchers consolidated information from the first two steps and formulated the potential pathways between AVs and public health.
Based on the proposed model, we found that AVs can impact public health through 32 pathways, of which 17 can adversely impact health, eight can positively impact health, and seven are uncertain. The health impacts of AVs are contingent upon supporting policies. Equipping AVs with electric motors, regulating urban area development, implementing traffic demand management strategies, controlling AV ownership, and imposing ride-sharing policies are some strategies that can reinforce the positive impacts of AVs on public health.—Sohrabi et al.
Sohrabi et al.
In optimistic views, AVs are expected to prevent 94% of traffic crashes by eliminating driver error, but AVs’ operation introduces new safety issues such as the potential of malfunctioning sensors in detecting objects (pedestrians, bikes and cyclists, vehicles, obstacles, etc.); misinterpretation of data; and poorly executed responses, which can jeopardize the reliability of AVs and cause serious safety consequences in an automated environment.
Another possible safety consideration is the riskier behavior of users because of their overreliance on AVs—for example, neglecting the use of seatbelts due to an increased false sense of safety.
AVs have the potential to shift people from public transportation and active transportation such as walking and biking to private vehicles in urban areas, which can result in more air pollution and greenhouse gas emissions and create the potential loss of driving jobs for those in the public transit or freight transport industries.
The model serves researchers in the fields of transportation engineering and urban planning as well as automotive makers, health sectors and policymakers to identify the potential pathways through which AVs can affect public health, and to investigate the impacts, quantify them and develop policies to mitigate them.
Sohrabi said more research is needed to clarify public health impacts of AVs more accurately. This study was primarily focused on urban areas and does not take into account the affects of AVs in rural areas.
The discussion about the health implications of AVs is new and limited. Next, we will be working on quantifying the health implications of AVs.—Soheil Sohrabi
Soheil Sohrabi, Haneen Khreis, Dominique Lord (2020), “Impacts of Autonomous Vehicles on Public Health: A Conceptual Model and Policy Recommendations,” Sustainable Cities and Society, doi: 10.1016/j.scs.2020.102457