Impacts of autonomous on-demand mobility service: A simulation experiment in the City of Athens

ABSTRACT

High-capacity Connected and Autonomous Vehicles (CAVs) are expected to be extensively utilized by on-demand services. This paper aims to assess the impacts of large-scale autonomous on-demand mobility services on traffic, environment, and road safety, under various service specifications using microsimulation. To that end, an urban on-demand shuttle service was designed, optimized, based on a variation of the Dial-a-Ride optimization problem (DARP), and implemented in the road network of the city of Athens to serve different portions of demand with various capacity specifications. It was then investigated through forty mobility scenarios, with differences in policy implementation and market penetration rate of CAVs. Findings show that it led to improved network level traffic conditions, as delays decreased, and that traffic impacts evolve with fleet capacity and served demand. Furthermore, the number of conflicts decreased and the environmental conditions significantly improved, with CAVs in the network, while the traveled distance increased.

KEYWORDS:

• CAVs
• shuttle service
• DARP
• optimization
• traffic microsimulation
• impact assessment

Acknowledgments

The present research was carried out within the research project “LEVITATE - Societal Level Impacts of Connected and Automated Vehicles,” which received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 824361.

Disclosure statement

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