Modeling Automated Vehicle Markets

The age of automated vehicles (AVs) presents new opportunities to improve accessibility, particularly in low-density suburbs where traditional public transportation often fails to deliver adequate service. However, the characteristics of AVs may adversely affect public transport equilibria by decreasing demand and service levels as a result of a reduction in frequency. In this research, we maximize the positive externalities due to AVs, such as increased mobility for under-served populations, whilst minimizing potential negative externalities, such as congestion, in order to ensure an equitable and inclusive transportation system. We develop a stylized suburban neighborhood model that is connected to a central business district. We compare the market structure results to an optimal social welfare outcome and derive subsidies and congestion charges. The results suggest that a duopoly market structure leads to reasonable service levels at a relatively low cost, which is close to the social optimum, but requires subsidizing AV services. Under political limitations on the ability to subsidize private transport services, the second-best solution implies that AV operators competing in different segments of the market would be a preferable alternative. The findings should aid policymakers when regulating new technology adoption in order to create equitable transport equilibria outcomes.

Part of the discission will also be research that examines the potential impact of autonomous vehicles on public transport and air services in the 300 to 700 km medium-haul market, which was assigned by the EU in 2010 as one of the targets for vehicle trip reductions through investments in rail transport infrastructure. Current data reveals that the private car remains a significant player in the medium-haul corridor due to flexibility of use and high door-to-door accessibility, despite the additional trip time and fatigue involved. To investigate the impact of the autonomous vehicle on these markets, we develop an applied game-theoretic model to analyze the potential transport equilibria outcome associated with profit-maximizing competitors, including high-speed railway operators, airlines and autonomous vehicle companies. The supply-side decision variables for the high-speed railway company and airline include fares and frequencies, whilst the autonomous car company’s decision variables are fares and fleet size. We model the demand-side using a multinomial nested logit market share function. We apply the formulation to a case study of corridors from Madrid to five destinations, using anonymized mobile phone records to estimate the demand matrix, and Google Maps API for supply-side data. Results suggest that autonomous vehicles will affect both high-speed railway fares and airfares. Furthermore, increased use of autonomous vehicles could potentially undo any gains that were expected to be achieved by the proposals included in the 2010 EU White Paper, “European transport policy for 2010: time to decide”, whose goals were to reduce vehicle trips by encouraging drivers to switch to other transport modes.

Prof. Adler is an internationally well renowend scientist in Transportation Economics, Operations Research and Aviation Economics. She works with methods of OR, modelling game theoretical approaches and performing efficiency and producitivity studies. Prof. Adler is currently a Dresden Senior Fellow at the Chair of Economics, esp. Transport Policy and Spatial Economics.
More, see: https://bschool-en.huji.ac.il/nicole-adler or https://scholar.google.co.il/citations?user=JALBSH4AAAAJ&hl=en