Traffic Clustering Algorithm of Urban Data Brain Based on a Hybrid-Augmented Architecture of Quantum Annealing and Brain-Inspired Cognitive Computing  

作　　者：Ning Wang Gege Guo Baonan Wang Chao Wang 

机构地区：[1]Key laboratory of Specialty Fiber Optics and Optical Access Networks,Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication,Shanghai Institute for Advanced Communication and Data Science,Shanghai University,Shanghai 200444,China [2]State Key Laboratory of Cryptology.Beijing 100878,China [3]Center for Quantum Computing,Peng Cheng Laboratory,Shenzhen 518000,China

出　　处：《Tsinghua Science and Technology》2020年第6期813-825,共13页清华大学学报（自然科学版（英文版）

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。

摘　　要：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.

关 键 词：cluster analysis intelligent transportation quantum annealing and brain-inspired clustering algorithm K-means 

分 类 号：U491[交通运输工程—交通运输规划与管理] TP18[交通运输工程—道路与铁道工程]