ACP Based Large-Scale Coordinated Route Planning: From Perspective of Cyber-Physical-Social Systems

Traffic congestion is deteriorating the sustainability and livability of the metropolis. However, most existing solutions are expected utility-based, i.e., making routing decisions based on statistical expectation, which would lead to restricted efficiency in alleviating the congestion problem. Building upon cyber-physical-social systems (CPSS), this paper proposes ParallelRoute that exploits computational experiments in ACP theory to coordinate the large-scale routes considering the human use in ITS, efficiently alleviating the congestion problem. Parallel Route adopts a congestion game theory for coordinating the route decisions to bridge user fairness and system efficiency and further supports the simulation over the future to capture the traffic dynamics, leading to coordinated, effective, and informed route decisions. Meanwhile, an ACP-based tree search algorithm is proposed to intertwine evaluations and prune, effectively handling the exponential number of states during the simulation. Comprehensive experiments on a real-world road network are conducted to evaluate the performance, and results show that Parallel Route achieves the lowest travel time, and is effective for coordinating large-scale routes and alleviating the traffic congestion problem.