Lane-Level Road Information Mining from Vehicle GPS Trajectories Based on Naïve Bayesian Classification
Abstract
:1. Introduction
- (1)
- We propose a new method, the adaptive density optimization method, for vehicle GPS trajectory optimization based on the density clustering method and the spatial distribution of tracking points. Outliers mixed in the raw data are removed automatically using adaptive density optimization method.
- (2)
- We explore a novel way to infer lane-level information from low-precision spatiotemporal vehicle GPS trajectories (MLIT).
- (3)
- We detect turn rules of each lane by tracking vehicle trajectories in relation to the rate of reckless driving.
2. Related Work
3. Lane-Level Road Network Information Extraction from Vehicle GPS Trajectories
3.1. Vehicle GPS Trajectory Optimization
3.1.1. Adaptive Density Optimization Method
3.1.2. Optimization
3.2. Lane Number Extraction Based on Naïve Bayesian Classification
3.2.1. The Basic Method
3.2.2. Naïve Bayesian Classifier
/*Initialization*/ |
Coordinate origin: n1; |
horizontal axis: the direction of the current TSS; |
longitudinal axis: UYi = 0; DYi = 0; |
Sliding window: length = l; width = w; proportion = 0; |
/*Assignment*/ |
for each TSSi, do |
repeat |
Moving the sliding window along the positive direction and negative direction of the longitudinal axis and accumulating the Proportion (Proportion = current points number in sliding window/all points in the current TSS) |
until proportion = 100% |
set Dwi = ∑ (maximum |UYj| + | maximum |DYj|)/(h/l); j = 1,2,…, (h/l). |
set Coordinate origin changed to ni+1; UYi+1 = 0;DYi+1 = 0; i = 1,2,…,m. |
end for |
3.3. The Detection of Turn Rules of Each Lane
4. Experiments and Results
4.1. Trajectory Optimization
4.2. The Construction of Naïve Bayesian Classifier
4.3. Lane Information Extraction
Training Sample (ID) | Trace Feature: x(1)/m | Trace Feature: x(2) | Category Label Set: y |
---|---|---|---|
1 | 7.9–12.2 | 2 | 2 |
2 | 7.9–12.2 | 3 | 2 |
… | … | … | … |
2,780 | 7.9–12.2 | 2 | 2 |
2,781 | 10.2–19.8 | 3 | 3 |
2,782 | 10.2–19.8 | 3 | 3 |
… | … | … | … |
4,980 | 10.2–19.8 | 4 | 3 |
4,981 | 13.2–20.8 | 4 | 4 |
4,982 | 13.2–20.8 | 4 | 4 |
… | … | … | … |
6,870 | 13.2–20.8 | 3 | 4 |
6,871 | 17.6–25.8 | 4 | 5 |
6,872 | 17.6–25.8 | 5 | 5 |
… | … | … | … |
7,650 | 17.6–25.8 | 5 | 5 |
TS | TSS | x(1)/m | x(2) | The Number of Lanes (Detections) | The Number of Lanes (True Value) | Driving Direction (Detections) | Driving Direction (True Value) |
---|---|---|---|---|---|---|---|
TS001 | TSS001 | 10.1 | 2 | 2 | 2 | ||
TSS002 | 9.9 | 2 | 2 | 2 | |||
… | … | … | … | … | … | … | |
TS002 | TSS001 | 14.1 | 4 | 4 | 3 | ||
TSS002 | 14.2 | 4 | 4 | 3 | |||
… | … | … | … | … | … | … | |
TSS016 | 15.4 | 3 | 3 | 3 | |||
… | … | … | … | … | … | … | |
TS003 | TSS001 | 19.2 | 4 | 4 | 4 | ||
TSS002 | 20.3 | 4 | 4 | 4 | |||
TSS003 | 20.3 | 3 | 3 | 4 | |||
… | … | … | … | … | … | … | |
TSS042 | 20.3 | 5 | 5 | 3 |
4.4. Quantitative Evaluation
4.4.1. Quantitative Evaluation for Number of Lane Identification
4.4.2. Quantitative Evaluation for Turn Rules Detection
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
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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://doi.org/10.3390/ijgi4042660
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://doi.org/10.3390/ijgi4042660
Chicago/Turabian StyleTang, 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://doi.org/10.3390/ijgi4042660