Modeling the long-term regional impacts of autonomous vehicles: A case study of Victoria, Australia

Abstract

Driverless vehicles are expected to revolutionize existing transportation systems by improving safety, mobility, and sustainability. This study provides a framework that incorporates autonomous vehicles (AVs) into the transportation demand analysis and planning process of regional areas. To this end, we apply a nested logit model based on the probability distributions of the trip destination and mode choice, and formulate a multiclass traffic assignment to differentiate between AV and HV modes of transport. The proposed model is implemented in a conventional 4-step model developed for the state of Victoria, Australia. We study two different future scenarios in the year 2051 using various parameters including the value of travel time, vehicle operating cost, and toll rates to evaluate the impacts of different assumptions of AVs on Victoria’s future mobility. Our results show that travelers prefer to traverse longer distances with little to no difference in their associated general travel costs. This indicates that AVs provide users with more flexible residential, work, and school location choices. Choosing AVs for long distances rather than short distances is another finding of this study. Furthermore, as a result of the AVs deployment, up to 10% of new trips from public transport and active modes will be shifted to car trips. Empty vehicle travels by AV add around 20 percent to the vehicle kilometers traveled on Victoria’s roadway network which leads to a major reduction in the average speed and an increase in travel time delay.

Keywords:

• autonomous vehicle
• travel behavior
• travel demand modeling
• trip-base demand models

Acknowledgement

We are grateful to the Department of Transport (DoT) for providing the Victorian Integrated Transport Model (VITM) for use in this project. Views and conclusions in this paper are those of the authors and should not be interpreted as representing the policies, either expressed or implied, of the model owners.

Disclosure statement

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

Additional information

Funding

The research work presented in this paper was supported by the Australian Government-Department of Education under Research Training Program (RTP Stipend) award.