Movement-aware map construction

ABSTRACT

Map construction algorithms attempt to derive a spatial graph representing a road network from GPS-sampled movement trajectories. Existing methods commonly use trajectories without considering the specific sampling methodology. Hence, the movement information is not preserved in the map construction results. The proposed map-construction method considers the particularities of the sampling process and how they affect the trajectory data to improve the overall result quality. Specifically, our proposed algorithm constructs nodes by clustering turn points. We use an adaptive clustering approach that considers when a turn point was sampled in relation to the ‘true’ node location based on the trajectory geometry. As nodes are the aggregates of turn points, edges are constructed by conflating trajectories that either connect turn points or are in close proximity to inferred nodes. Experiments using trajectory datasets at different spatial scales, data complexities, and data sources in combination with several assessment methods show that the proposed movement-aware map construction method produces maps of greater accuracy than those from the existing approaches.

KEYWORDS:

• VGI trajectories
• map construction
• movement-aware
• node inference

Data and codes availability statement

The data and source code that supports the findings of this study are available at the following link https://doi.org/10.6084/m9.figshare.12752546. The results are also published at http://www.mapconstruction.org.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [Grant No. 41631175]; Nanjing University of Posts and Telecommunications [Grant No. NY218137]; and the National Science Foundation [Grant No. CCF-1637541].

Notes on contributors

Haiyang Lyu

Haiyang Lyu received his Ph.D. in Geographic Information Science from Nanjing Normal University, China, and has been working as an Assistant Professor at Nanjing University of Posts and Telecommunications since 2017. His research interests are crowd-sourced trajectory data mining and map construction.

Dieter Pfoser

Dieter Pfoser is Professor and Chair of the Department of Geography and Geoinformation Science at George Mason University. His research interests are data mining for spatial and spatiotemporal data, user-generated content, e.g., map-matching and map construction algorithms, and urban analytics.

Yehua Sheng

Yehua Sheng is a Professor at Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, Jiangsu, China. His research interests are spatiotemporal data organization and modeling for geographic scenes.