Next Article in Journal
Weather Conditions, Weather Information and Car Crashes
Previous Article in Journal
An Improved PDR/Magnetometer/Floor Map Integration Algorithm for Ubiquitous Positioning Using the Adaptive Unscented Kalman Filter
Article

Lane-Level Road Information Mining from Vehicle GPS Trajectories Based on Naïve Bayesian Classification

1
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Shenzhen Key Laboratory of Spatial Smart Sensing and Services, College of Civil Engineering, Shenzhen University, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2015, 4(4), 2660-2680; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4042660
Received: 16 June 2015 / Revised: 11 November 2015 / Accepted: 17 November 2015 / Published: 26 November 2015
In this paper, we propose a novel approach for mining lane-level road network information from low-precision vehicle GPS trajectories (MLIT), which includes the number and turn rules of traffic lanes based on naïve Bayesian classification. First, the proposed method (MLIT) uses an adaptive density optimization method to remove outliers from the raw GPS trajectories based on their space-time distribution and density clustering. Second, MLIT acquires the number of lanes in two steps. The first step establishes a naïve Bayesian classifier according to the trace features of the road plane and road profiles and the real number of lanes, as found in the training samples. The second step confirms the number of lanes using test samples in reference to the naïve Bayesian classifier using the known trace features of test sample. Third, MLIT infers the turn rules of each lane through tracking GPS trajectories. Experiments were conducted using the GPS trajectories of taxis in Wuhan, China. Compared with human-interpreted results, the automatically generated lane-level road network information was demonstrated to be of higher quality in terms of displaying detailed road networks with the number of lanes and turn rules of each lane. View Full-Text
Keywords: GPS trajectories; adaptive density optimization method; naïve Bayesian classifier; lane-level information; big data GPS trajectories; adaptive density optimization method; naïve Bayesian classifier; lane-level information; big data
Show Figures

Figure 1

MDPI and ACS Style

Tang, L.; Yang, X.; Kan, Z.; Li, Q. Lane-Level Road Information Mining from Vehicle GPS Trajectories Based on Naïve Bayesian Classification. ISPRS Int. J. Geo-Inf. 2015, 4, 2660-2680. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4042660

AMA Style

Tang L, Yang X, Kan Z, Li Q. Lane-Level Road Information Mining from Vehicle GPS Trajectories Based on Naïve Bayesian Classification. ISPRS International Journal of Geo-Information. 2015; 4(4):2660-2680. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4042660

Chicago/Turabian Style

Tang, Luliang, Xue Yang, Zihan Kan, and Qingquan Li. 2015. "Lane-Level Road Information Mining from Vehicle GPS Trajectories Based on Naïve Bayesian Classification" ISPRS International Journal of Geo-Information 4, no. 4: 2660-2680. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4042660

Find Other Styles

Article Access Map by Country/Region

1
Back to TopTop