EvoTSC: An evolutionary computation-based traffic signal controller for large-scale urban transportation networks

Abstract

Traffic Signal Control (TSC) is a crucial component in the modern intelligent transportation systems. Typically, the TSC can be formulated as a bilevel optimization problem, which is comprised of the signal timing and the traffic assignment modules. The problem is challenging that the existing approaches usually endure a huge computational cost. As a result, many TSC approaches focus on relatively simple and small transportation networks, which do not satisfy the practical situations. To address above issues, this paper proposes an evolvable TSC (EvoTSC) system, which adopts nature-inspired techniques to realize the global optimization of the TSC in large-scale urban transportation networks. Particularly, it involves two evolutionary computation components. The first component is an Adaptive Differential Evolution (ADE) to optimize the signal timing. Meanwhile, the traffic assignment process is included in the solution evaluation of the ADE to react to the traffic flow dynamics. The second component is an off-line Niching Ant Colony Optimization (NACO), which aims to provide the traffic assignment with sets of multiple promising routes beforehand. This way, the EvoTSC system avoids repeatedly building candidate routes for the traffic assignment, which can greatly save the computational cost of the ADE to evaluate each solution in a large-scale transportation network. In experiments, we carry out comparisons of different TSC approaches on both synthetic and practical transportation networks. The experimental results validate the effectiveness of the proposed EvoTSC system.