In recent years,the urbanization process has brought modernity while also causing key issues,such as traffic congestion and parking ***,cities need a more intelligent"brain"to form more intelligent and effic...
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In recent years,the urbanization process has brought modernity while also causing key issues,such as traffic congestion and parking ***,cities need a more intelligent"brain"to form more intelligent and efficient transportation *** present,as a type of machine learning,the traditional clusteringalgorithm still has limitations.K-means algorithm is widely used to solve traffic clustering problems,but it has limitations,such as sensitivity to initial points and poor ***,based on the hybrid architecture of quantumannealing(QA)and brain-inspired cognitive computing,this study proposes QA and brain-inspiredclusteringalgorithm(QABICA)to solve the problem of urban taxi-stand *** on the traffic trajectory data of Xi’an and Chengdu provided by Didi Chuxing,the clustering results of our algorithm and K-means algorithm are *** find that the average taxi-stand location bias of the final result based on QABICA is smaller than that based on K-means,and the bias of our algorithm can effectively reduce the tradition K-means bias by approximately 42%,up to approximately 83%,with higher *** algorithm is able to jump out of the local suboptimal solutions and approach the global optimum,and brain-inspired cognitive computing provides search feedback and ***,we will further consider applying our algorithm to analyze urban traffic flow,and solve traffic congestion and other key problems in intelligent transportation.
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