Research Publications

Automated vehicles (AVs) are becoming commercial reality, expected to revolutionize transportation sector and the way vehicles interact with their environment. Similar to other emerging technologies, they are likely to be first introduced in public fleet. Robotaxis are fully automated electric taxis, which downsized for serving up to two travelers. These vehicles are expected to have substantially lower energy consumption and entail zero tailpipe emissions. This results in reduction or displacing emissions from metropolitan areas with low air quality. Additionally, the smaller vehicle size as well as vehicle-to-vehicle and vehicle-to-infrastructure communications may lead to congestion mitigate and better routing, while maintaining the service level. Deployment of robotaxi fleets for high density municipalities is particularly profitable, because of high public transport utilization (less vehicle wait for next customer). We investigate a robotaxi fleet for providing sustainable transportation services in an urban region with fixed population. The study offers a framework for optimization of fleet design. It follows by a comparative environmental impact assessment of optimized robotaxi fleet with conventional taxis. The objective is to maximize energy savings. This ensures reducing fleet energy consummation and corresponding life cycle environmental implications. The operation of robotaxi fleet accounts for travel demand, vehicle assignment strategy, number and location of charging station depots, and charging schedule. As a case study, we consider the implementation of robotaxi fleet for fulfilling the travel demand in Beijing. The individual trip data is supplied from É__big-dataÉ_ù mining of real-time vehicle trajectory data, compromising of 10,375 taxis in this city over one week. The optimized operation robotaxi fleet is based on vehicle travel behavior and charging opportunities utilization. Then it is used to evaluate the sustainability factors of robotaxis in comparison with conventional taxi fleet. The assessment factors are energy consumption, life cycle environmental impacts, and human health risk mitigation. Although the results are based on a specific fleet, this study is generalizable and demonstrates the benefit of using large-scale individual-based trajectory data (big data) to inform better decision making on robotaxi fleet deployment and better understanding of its environmental implications and trade-offs.