Multi-Class Freeway Congestion and Emission Based on Robust Dynamic Multi-Objective Optimization

On-ramp control is an effective approach for alleviating traffic congestion on highways. However, there are still lacking on-ramp control approaches applicable to large regional highway networks. Here, we develop a targeted on-ramp control approach applicable to regional highway networks by taking advantage of the vehicle source information, which pinpoints the on-ramps contributing major traffic flow to the highway bottleneck. Furthermore, a combined and tunable controlling index is proposed to enhance the equity of the generated traffic control scheme. The proposed on-ramp control approach is validated on an actual large highway network using actual travel demand data. Results indicate that the proposed approach can well mitigate the traffic congestion of highway bottleneck while at the same time enhance the equity and practicability of the generated traffic control scheme.

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An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering †

Lai

Sato

2021

Algorithms

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Previously, cluster-based multi or many objective function techniques were proposed to reduce the Pareto set. Recently, researchers proposed such techniques to find better solutions in the objective space to solve engineering problems. In this work, we applied a cluster-based approach for solution selection in a multiobjective evolutionary algorithm based on decomposition with bare bones particle swarm optimization for data clustering and investigated its clustering performance. In our previous work, we found that MOEA/D with BBPSO performed the best on 10 datasets. Here, we extend this work applying a cluster-based approach tested on 13 UCI datasets. We compared with six multiobjective evolutionary clustering algorithms from the existing literature and ten from our previous work. The proposed technique was found to perform well on datasets highly overlapping clusters, such as CMC and Sonar. So far, we found only one work that used cluster-based MOEA for clustering data, the hierarchical topology multiobjective clustering algorithm. All other cluster-based MOEA found were used to solve other problems that are not data clustering problems. By clustering Pareto solutions and evaluating new candidates against the found cluster representatives, local search is introduced in the solution selection process within the objective space, which can be effective on datasets with highly overlapping clusters. This is an added layer of search control in the objective space. The results are found to be promising, prompting different areas of future research which are discussed, including the study of its effects with an increasing number of clusters as well as with other objective functions.

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Multi-Class Freeway Congestion and Emission Based on Robust Dynamic Multi-Objective Optimization

Cited by 3 publications

References 22 publications

Traffic assignment in urban transportation network problem with emission constraints in China: a cooperative game theory

Traffic assignment in urban transportation network problem with emission constraints in China: a cooperative game theory

A new targeted on-ramp control approach considering both efficiency and equity

An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering †

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