Relationship Between Urban Road Traffic Characteristics and Road Grade Based on a Time Series Clustering Model： A Case Study in Nanjing, China

作     者：WANG Jiechen WU Jiayi NI Jianhua CHEN Jie XI Changbai 

作者机构：Department of Geographic Information Science Nanjing University Nanjing 210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Nanjing 210023 China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023 China 

出 版 物：《Chinese Geographical Science》 (中国地理科学（英文版）)

年 卷 期：2018年第28卷第6期

页      面：1048-1060页

核心收录：

学科分类：0303[法学-社会学] 08[工学] 0708[理学-地球物理学] 0705[理学-地理学] 0813[工学-建筑学] 0704[理学-天文学] 082302[工学-交通信息工程及控制] 0833[工学-城乡规划学] 0823[工学-交通运输工程] 

基　　金：Under the auspices of National Natural Science Foundation of China(No.41571377) 

主　　题：道路等级 道路交通 聚类模型 特征时间 特征和 城市 南京 案例 

摘      要：With the increasing number of vehicles in large-and medium-sized cities challenges in urban traffic management, control, and road planning are being faced. Taxi GPS trajectory data is a novel data source that can be used to study the potential dynamic traffic characteristics of urban roads, and thus identify locations that show a notable lack of road planning. Considering that road traffic characteristics on their own are insufficient for a comprehensive understanding of urban traffic, we develop a road traffic characteristic time series clustering model to analyze the relationship between urban road traffic characteristics and road grade based on existing taxi trajectory data. We select the main urban area of Nanjing as our study area and use the taxi trajectory data of a single month for evaluating our method. The experiments show that the clustering model exhibit good performance and can be successfully used for road traffic characteristic classification. Moreover, we analyze the correlation between traffic characteristics and road grade to identify road segments with planning designs that do not match the actual traffic demands.