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Urban network traffic is a complex, nonlinear, unstable system and is significantly affected by immeasurable factors. This paper presents an optimal model to the application of signal coordination for urban network based on real-time traffic volumes. It divides a large signalized network into several subgroups. The number of subgroups, phase-time and offsets can be optimized to achieve an optimal...
An optimization model to the application of signal coordination for traffic arterials based on multi-group partition is proposed. The approach divides a large signalized arterial into subgroups with three to five signals in each subgroup according to actual traffic conditions. In each subgroup, signal coordination between neighboring intersections is built by a common cycle and offsets. A case study...
This paper presents an optimization model to the application of signal coordination for urban network based on multi-group partition. The proposed algorithm divides a large signalized network into several subgroups with three or more intersections according to real traffic flow. In each subgroup, signal coordination between neighboring intersections is built by a common cycle and offsets. In order...
The paper presents a traffic signal control method using a layer-structured fuzzy neural network (FNN) for learning rules of fuzzy logic control system. The FNN has advantages of both fuzzy expert system (fuzzy reasoning) and artificial neural network (self-study). The system is not needed to build the model of traffic flow for signal control approach at an intersection, it can be successfully trained...
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