In recent years, there has been a considerable growth in electric vehicles (EV), autonomous vehicles (AV), and ride sharing (RS). These technologies have the potential to improve transportation sustainability. The existing literature has three major gaps: (1) the adoption of these three technologies need to be evaluated together, (2) existing models do not evaluate systems on a common ground, and (3) the heterogeneous preferences of riders towards these emerging technologies are not incorporated. To address these gaps, this work studies and quantifies the environmental and efficiency gains of these emerging transportation technologies by developing a Parameterized Preference Based Shared Autonomous Electric Vehicle (PP-SAEV) agent-based model. The model is then applied to a case study of New York City (NYC) taxis to make inferences regarding system performance with increasing AV, EV, and RS adoption.

The outputs from the PP-SAEV model show that replacing taxi cabs in NYC with AVs along with RS potentially can reduce CO2 emissions by 866 metric Tones per day and increase average vehicle occupancy from 1.2 to 3 persons in vehicles with seating capacity of 4. A prediction model based on the PP-SAEV output recommends that 6000 vehicles are needed to maintain the current level of service with 100% AV and RS adoption using capacity 4 taxis. Taxi fleets with capacity 4 with high RS and low AV adoption are also found to have the least CO2 emissions. Because the heterogeneous sharing preferences of riders have shown as the major limiting factor to ride sharing, these heterogeneous sharing preferences are further modelled. It is seen that high service levels are achieved when all the riders are open to sharing, and the maximum service level is reached when 30% of riders will only accept shared rides and 70% of the riders are either indifferent to sharing or prefer to use ride sharing over riding alone. Additionally, the service level and waiting time of riders that are inflexible (will accept only shared or non-shared rides) are greatly impacted by varying mix of riders with different sharing preference. Finally, an optimization model was built to site charging stations in a system with continually increasing EV adoption. Using the best charging station locations, transforming a fleet of autonomous or traditional vehicles to electric vehicles does not significantly change the system service level. The results show that increasing the EV adoption in fleets with 100% RS and AV adoption reduced the daily CO2 emissions by about 861 Tones and transforming a fleet of traditional taxi cabs to electric taxi cabs reduced the daily CO2 emissions by 1100 Tones.

 

In summary, this dissertation evaluates the growth of autonomous vehicles, ride sharing, and electric vehicles in systems where riders may have heterogeneous sharing preferences, from a system performance perspective and from an environmental perspective. The insights gained can inform policy makers to develop sustainable transportation systems that are able to support rising demand and reduce CO2 emissions.