The short-run and long-run equilibria for commuting with autonomous vehicles

ABSTRACT

Recent empirical studies indicated that using autonomous vehicles (AVs) can reduce commuters' value of time. In this context, this paper investigates how variation in value of time for AVs will reshape the commuting dynamics in the short-run and the implication on AV-related policies in the long run. We find that in the short run, the adoption of AV can create more congestion delays since delay becomes cheaper for commuters. In the long run, a number of external factors such as ownership cost and safety concerns may affect commuters' preference for AVs as against to traditional vehicles (TVs). This will influence the AV penetration, which in turn affects the daily commuting equilibrium. Multiple long-run equilibria with different AV penetrations may exist, depending on the additional cost/benefit of AVs with respect to TVs. Government subsidies may be needed to drive the system from inefficient long-run equilibrium to a more efficient one.

Keywords:

• Short-run equilibrium
• long-run equilibrium
• autonomous vehicles (AV)
• AV penetration
• bottleneck model

Acknowledgments

The authors thank the anonymous reviewers very much for their thoughtful and constructive comments, which helped improve both the technical quality and exposition of this paper. This research was partly supported by funding from the Australian Research Council (DE200101793) and by funding from the UNSW Digital Grid Futures Institute, UNSW, Sydney, under a cross disciplinary fund scheme.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Funding information

This research was partly supported by funding from the Australian Research Council [grant number DE200101793] and by funding from the UNSW Digital Grid Futures Institute, UNSW, Sydney, under a cross disciplinary fund scheme.

Notes

1 If this is not the case, it means that commuters prefer staying in an AV rather than being early in the office. While this might be possible in some certain circumstances, we consider that in general people have more flexibility in an office or surrounding areas than in a car, and thus ${\alpha }_a>\beta$.

2 How to derive the toll is omitted here to save space while interested readers may refer to e.g. van den Berg and Verhoef (2011).

3 In addition to ‘price’ and ‘safety’, many other factors may play a role, such as energy, legislative, and privacy considerations. The variations of these exogenous factors are out of the scope of this paper.

4 There is a branch of studies examining the day-to-day evolution of traffic dynamics, e.g. Cascetta and Cantarella (1991), Guo and Liu (2011), Watling and Cantarella (2013), Xu, Lam, and Zhou (2014), Guo et al. (2015), where dynamical systems have been developed. This study, while focuses on evolution of AV penetration, follows a similar dynamical modeling framework to the literature.