Road network generalization considering traffic flow patterns. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Yu, Wenhao; Zhang, Yifan; Ai, Tinghua; Guan, Qingfeng; Chen, Zhanlong; Li, Haixia
• Is Part Of: International journal of geographical information science. Volume 34:Issue 1(2020)
• Page Start: 119
• Page End: 149
• Publication Date: 2020-01-02
• Subjects: Map generalization -- stroke -- traffic flow -- cartography -- road network; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2019.1650936
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: As one of the major concerns in cartographic generalization, road network generalization aims at maintaining the patterns of road networks across map scales. Previous methods define the pattern of road networks mainly from the perspectives of geometry and topology. However, for navigation purposes, traffic flow information is also important to generalize road networks. More specifically, road segments that have a proximity relationship in the traffic flow system should be retained together on small-scale maps to preserve the completeness of the driving route. In this regard, this study proposes an improved method for road network generalization that considers network geometry, topology, and traffic flow patterns. First, strokes are constructed from the road network data based on the 'every best fit' geometric principle. Then, the relationships among strokes are developed on the basis of traffic flow patterns, which are extracted from taxi trajectory data. The strokes are then selected in sequence based on the indicators of geometry, topology, and traffic flow. Our experimental results demonstrate that the proposed method can preserve both the 'Good Continuity' principle and the transport function relationship of roads after generalization.