Control Strategies for Connected and Automated Vehicles During an Intercity Trip

Abstract

This dissertation is about knowledge development of driving strategies for Connected and Automated Vehicles (CAVs) along an intercity trip from a freeway to a signalized urban corridor. AVs and non-AVs, trucks, and passenger cars are considered in the model. First, a model is proposed to optimize mobility while ensuring traffic stability when vehicles are traveling on freeways; The method can reduce delays by 23.19% and increase the average speed by 9.09%. Second, models are developed for vehicles at signals along the urban corridors. An eco-driving strategy is developed for heavy-duty (truck) CAVs at signals. The results show that the method could reduce NOx emission by an amount from 4.8% (truck penetration rate 5%) to 6.3% (truck penetration rate 30%) and CO2 emission by an amount from 2.1 % (truck penetration rate 5%) to 6.1% (truck penetration rate 30%). Another trajectory control method is developed to smooth CAV trajectories along multiple intersections, the time saving can be up to 29.33 % using this method. Third, CAVs can be assigned proper times by using coordination to avoid collisions when they are making turns. When CAV's final sequences are not fixed, the method can optimize the signal control strategy to minimize total travel time with travel time benefits from 8.85% to 11.86%. Multiple strategies are made to significantly contribute to comprehensive and detailed driving strategies for CAVs. The limitations and constraints of the proposed methods are discussed to ensure the validity and reliability of the findings. Feasible solutions to overcome these limitations are suggested.