The NURBS book
Mining GPS data to augment road models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Trajectories Mining for Traffic Condition Renewing
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
From GPS traces to a routable road map
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
IMM-based lane-change prediction in highways with low-cost GPS/INS
IEEE Transactions on Intelligent Transportation Systems
Using self-adapting navigation data for intelligent, personalized vehicle guidance
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Probabilistic modeling of traffic lanes from GPS traces
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Detecting road intersections from GPS traces
GIScience'10 Proceedings of the 6th international conference on Geographic information science
Integration of GPS traces with road map
Proceedings of the Second International Workshop on Computational Transportation Science
Frequent trajectory mining on GPS data
Proceedings of the 3rd International Workshop on Location and the Web
Road extraction using smart phones GPS
Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications
EasyTracker: automatic transit tracking, mapping, and arrival time prediction using smartphones
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
Mining large-scale, sparse GPS traces for map inference: comparison of approaches
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Trajectory mining from anonymous binary motion sensors in Smart Environment
Knowledge-Based Systems
Map inference in the face of noise and disparity
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
On vehicle tracking data-based road network generation
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Matching GPS traces to (possibly) incomplete map data: bridging map building and map matching
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
CrowdAtlas: self-updating maps for cloud and personal use
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
From taxi GPS traces to social and community dynamics: A survey
ACM Computing Surveys (CSUR)
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Despite the increasing popularity of route guidance systems, current digital maps are still inadequate for many advanced applications in automotive safety and convenience. Among the drawbacks are the insufficient accuracy of road geometry and the lack of fine-grained information, such as lane positions and intersection structure. In this paper, we present an approach to induce high-precision maps from traces of vehicles equipped with differential GPS receivers. Since the cost of these systems is rapidly decreasing and wireless technology is advancing to provide the communication infrastructure, we expect that in the next few years large amounts of car data will be available inexpensively. Our approach consists of successive processing steps: individual vehicle trajectories are divided into road segments and intersections; a road centerline is derived for each segment; lane positions are determined by clustering the perpendicular offsets from it; and the transitions of traces between segments are utilized in the generation of intersection models. This paper describes an approach to this complex data-mining task in a contiguous manner. Among the new contributions are a spatial clustering algorithm for inferring the connectivity structure, more powerful lane finding algorithms that are able to handle lane splits and merges, and an approach to inferring detailed intersection models.