In recent years,the urbanization process has brought modernity while also causing key issues,such as traffic congestion and parking conflicts.Therefore,cities need a more intelligent"brain"to form more intel...In recent years,the urbanization process has brought modernity while also causing key issues,such as traffic congestion and parking conflicts.Therefore,cities need a more intelligent"brain"to form more intelligent and efficient transportation systems.At present,as a type of machine learning,the traditional clustering algorithm 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 robustness.Therefore,based on the hybrid architecture of Quantum Annealing(QA)and brain-inspired cognitive computing,this study proposes QA and Brain-Inspired Clustering Algorithm(QABICA)to solve the problem of urban taxi-stand locations.Based 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 compared.We 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 robustness.QA 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 direction.Thus,we will further consider applying our algorithm to analyze urban traffic flow,and solve traffic congestion and other key problems in intelligent transportation.展开更多
基金the Special Zone Project of National Defense Innovation,the National Natural Science Foundation of China(Nos.61572304 and 61272096)the Key Program of the National Natural Science Foundation of China(No.61332019)Open Research Fund of State Key Laboratory of Cryptology。
文摘In recent years,the urbanization process has brought modernity while also causing key issues,such as traffic congestion and parking conflicts.Therefore,cities need a more intelligent"brain"to form more intelligent and efficient transportation systems.At present,as a type of machine learning,the traditional clustering algorithm 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 robustness.Therefore,based on the hybrid architecture of Quantum Annealing(QA)and brain-inspired cognitive computing,this study proposes QA and Brain-Inspired Clustering Algorithm(QABICA)to solve the problem of urban taxi-stand locations.Based 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 compared.We 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 robustness.QA 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 direction.Thus,we will further consider applying our algorithm to analyze urban traffic flow,and solve traffic congestion and other key problems in intelligent transportation.
