Inferring road type in crowdsourced map services

Ye Ding, Jiangchuan Zheng, Haoyu Tan, Wuman Luo, Lionel M. Ni

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Citations (Scopus)


In crowdsourced map services, digital maps are created and updated manually by volunteered users. Existing service providers usually provide users with a feature-rich map editor to add, drop, and modify roads. To make the map data more useful for widely-used applications such as navigation systems and travel planning services, it is important to provide not only the topology of the road network and the shapes of the roads, but also the types of each road segment (e.g., highway, regular road, secondary way, etc.). To reduce the cost of manual map editing, it is desirable to generate proper recommendations for users to choose from or conduct further modifications. There are several recent works aimed at generating road shapes from large number of historical trajectories; while to the best of our knowledge, none of the existing works have addressed the problem of inferring road types from historical trajectories. In this paper, we propose a model-based approach to infer road types from taxis trajectories. We use a combined inference method based on stacked generalization, taking into account both the topology of the road network and the historical trajectories. The experiment results show that our approach can generate quality recommendations of road types for users to choose from.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - 19th International Conference, DASFAA 2014, Proceedings
PublisherSpringer Verlag
Number of pages15
EditionPART 2
ISBN (Print)9783319058122
Publication statusPublished - 2014
Externally publishedYes
Event19th International Conference on Database Systems for Advanced Applications, DASFAA 2014 - Bali, Indonesia
Duration: 21 Apr 201424 Apr 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8422 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th International Conference on Database Systems for Advanced Applications, DASFAA 2014


Dive into the research topics of 'Inferring road type in crowdsourced map services'. Together they form a unique fingerprint.

Cite this