An acceptability-based multi-objective traffic flow adjustment method for environmental sustainability and equity

As a product of digital development, connected autonomous vehicles (CAVs) offer a unique prospective solution to alleviate the possible performance deterioration of road networks under the mixed environment of humandriven vehicles (HVs) and CAVs. In this paper, we propose a traffic flow adjustment method (TFAM) that treats CAVs as mobile regulators with the purpose to reshape traffic flow distribution on road networks by guiding rather than controlling CAVs. More specifically, we deploy subsidy nodes to briefly outline travel routes and achieve higher acceptability than traditional route-based control schemes. The TFAM is a multi-objective bilevel programming problem where the upper-level problem optimizes the network performance through regulating location and subsidy on the subsidy nodes. The lower-level problem is a dual dynamic traffic assignment (DDTA) model. Apart from the total travel time cost (TTTC), total emission cost (TEC) and network equity (NE) are also introduced as optimization objectives to highlight environmental sustainability and acceptability. To obtain the Pareto solution frontier, a meta-heuristic algorithm with an improved encoding process is proposed. Results of two numerical case studies demonstrate the effects of TFAM on traffic flow distribution and network performance, which yields valuable insights on the optimization of urban traffic systems.