Analysis of the relationship among road navigation attributes based on multi-model features of road networks and crowd-sourced trajectories

doi:10.13474/j.cnki.11-2246.2025.0420

Abstract

Abstract: Geometrical and topological information about the road network is certainly important,but navigation attribute information such as road classes,number of lanes,and speed limits is also essential for the implementation of core road network applications,and route planning,vehicle navigation,and location services are typical cases. This research explores the hierarchical relationship among these three attributes and proposes potential multi-modal progressive classification methods considering upstream and downstream information for predicting road classes,number of lanes,and speed limits of road sections. First,we preprocessed trajectories and road networks and realized the connection between track points and road sections; then,we took a road section as the analysis unit and mine the multivariate and multi-order features of road networks and crowd-sourced trajectories; finally,potential methods are summarized and analyzed,and these methods are based on random forest algorithm that integrate these complementary features of current and adjacent road sections and considers hierarchical information to identify road classes,number of lanes,and speed limits based on the voting method. Experimental results in Wuhan and Xian show that our exploration has a certain reference value.

Key words: intelligent transportation, crowd-sourced trajectories, multi-mode feature fusion, road navigation attributes, hierarchical information, road adjacency information

CLC Number:

P208

ZHANG Caili, XIANG Longgang, LI Yali, ZHOU Yushi, LIU Zhen, LU Chunyang. Analysis of the relationship among road navigation attributes based on multi-model features of road networks and crowd-sourced trajectories[J]. Bulletin of Surveying and Mapping, 2025, 0(4): 120-126.

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