File(s) under embargo
until file(s) become available
Environmental Impacts of Private and Shared Autonomous Vehicles: Integrated Modeling Considering Individual Preferences from a Life Cycle Perspective
The transportation sector is witnessing rapid development of autonomous vehicle (AV) technology. While an AV can be more energy efficient than a conventional human-driven vehicle, their environmental impacts at the fleet and city level could be either significantly better or worse than the traditional systems, depending on how people use them – adopting AVs as privately-owned AVs (PAV) or centrally-managed shared AVs (SAV) will result in very different fleet size, vehicle-miles-travelled (VMT), and carbon emissions. To understand the environmental impacts of AVs at the city level, it is critical to consider who are likely to adopt which types of AVs, their travel demands, and the associated AV operation. Previous studies evaluating the potential impacts of AVs on the environment are limited by the existing travel demand models, which do not have sociodemographic information linked to the travel demands to support modeling of AV adoption or only generate trip origin and destination at the zonal level that is insufficient to support modeling of shared AV use. Additionally, existing research mainly focused on SAV systems and did not consider the potential competition between SAV and PAV. It is necessary to compare the system performance between the privately-owned AV system and the centrally-managed shared AV system and under the scenarios that both systems co-exist to inform AV system development. Furthermore, although AVs can help reduce fleet size through shared use, each vehicle will be used more intensively due to empty VMT, resulting in acceleration of vehicle replacement and increased need for vehicle production. To fully quantify the environmental impacts of a city’s AV system, it is also important to take a life-cycle perspective, considering not only vehicle use but also upstream vehicle manufacturing and downstream vehicle disposal with fleet replacement.
To address these gaps, this work proposed an integrated agent-based model to quantify the environmental impacts of PAV and SAV. The integrated model includes four key components: 1) a travel demand generation model that links high resolution individual and household travel demand with socio-demographics information, 2) an AV adoption model that evaluates individual’s and household’s likelihood to accept AV and preference to use PAV, SAV or conventional vehicle, 3) an AV operation model to simulate the system performance of different AV fleets, and 4) an AV life cycle model that assesses different AV systems’ emissions considering vehicle replacement. Applying the proposed integrated model to a case study of Miami, the results have presented that the existing studies may overestimate AV systems’ environmental benefits, due to lack of travel demand data that can support the proposed integrated modeling, inconsideration of individual and household AV adoption decisions, and/or biased evaluation that does not account for all phases in AV system’s life-cycle. Case study results have showed that SAVs are more environmentally beneficial than PAVs but are less likely to be adopted by travelers and households, due to low cost of PAV use based on existing AV survey findings and current AV pricing knowledge. To promote SAV adoption to gain more positive environmental impacts, it is crucial to optimize SAV’s vehicle and system design to reduce service fee, waiting time, and in-vehicle value of time. The case study also found that due to more frequent vehicle replacement resulted from more intensively vehicle utilization, an AV systems’ environmental benefits from the operation phase can be counterbalanced by the impacts from other life-cycle phases. To achieve a life-cycle emission breakeven point, SAVs and PAVs need to improve fuel efficiency during the operation phase by 5% and 16% or reduce per-vehicle manufacture and disposal emissions by 36% and 5%. The proposed models and findings of this work can inform decision making for SAV operators, policy makers, and transportation planners.