Traffic Intersection Detection Using Floating Car Data

Hu, Rong; Xia, Ye; Hsu, Chih‐Yu; Chen, Hanlin; Xu, Weihui

doi:10.1109/icbda49040.2020.9101259

Cited by 3 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wang et al [9] used a mean-shift algorithm for the clustering steering angle. Hu et al [10] detected the traffic intersection using floating car data; they used the angle of the direction difference for detecting the traffic intersection, and a density clustering algorithm (DBSCAN) for identifying traffic intersections. Gao et al [11] improved the density peak clustering algorithm.…”

Section: Introductionmentioning

confidence: 99%

Automatic Intersection Extraction Method for Urban Road Networks Based on Trajectory Intersection Points

Gao

Yang

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Automatic intersection identification and extraction are an important foundation for urban road network updates and traffic network analysis and modeling. Existing intersection extraction methods based on steering angles and stopping points suffer from inadequate sampling amounts and threshold settings. To address this problem, we propose a road network intersection automatic extraction method based on vehicle trajectory intersection clustering. First, the continuous trajectory segments are extracted from trajectory data based on the sampling interval. Second, the maximum reconstruction error method is developed to extract straight-line trajectory segments from continuous trajectory segments. The overlapped straight-line trajectory segments belonging to the same direction are merged to reduce the number of segments and enhance road network patterns. To further improve the calculation efficiency of the intersection points of straight-line segments, bounding box filtering and orthogonal filtering are used to filter the straight-line trajectory segments that do not have an intersection relationship. Finally, the obtained straight-line segment intersection points are clustered using a density peak clustering algorithm. The road intersections are automatically extracted using the clustering center. The experimental results on real vehicle trajectories in Lianyungang City show that the proposed method performs well on intersection recognition and calculation efficiency.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic Intersection Extraction Method for Urban Road Networks Based on Trajectory Intersection Points

Gao

Yang

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

show abstract

Automatic Recognition of Intersections Based on Vehicle Trajectory Turning Points Clustering

Gao

Yang

et al. 2021

2021 IEEE 4th International Conference on Computer and Communication Engineering Technology (CCET)

View full text Add to dashboard Cite

Transfer Learning for Transportation Demand Resilience Pattern Prediction Using Floating Car Data

Yang,

Lu,

Lyu

et al. 2024

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Understanding the response of a transportation system to disruptive events is significant for evaluating the resilience of the system. However, data collection during such events is always challenging, and the data volume is insufficient for building a robust model. Transfer learning provides an effective solution to this problem. In this study, we propose a floating car data (FCD) driven transfer learning framework for predicting the resilience of target transportation systems to similar disruptive events to those that have ever occurred in the source systems. The core of the framework is an unsupervised pattern extractor that combines the k-Shape clustering and Bayes inference methods for extracting resilience patterns from the FCD collected in the source systems during the disruption period. The extracted patterns can then be used to assist in the prediction of the resilience of the target systems. We examine the effectiveness of the proposed framework by conducting a case study under the context of the COVID-19 pandemic, in which the source domain cities include Antwerp and Bangkok, and the target domain city is Barcelona. Results show that the extracted resilience patterns can improve the prediction performance of transfer learning neural networks with less pre-event information and limited data volume.

show abstract

Traffic Intersection Detection Using Floating Car Data

Cited by 3 publications

References 6 publications

Automatic Intersection Extraction Method for Urban Road Networks Based on Trajectory Intersection Points

Automatic Intersection Extraction Method for Urban Road Networks Based on Trajectory Intersection Points

Automatic Recognition of Intersections Based on Vehicle Trajectory Turning Points Clustering

Transfer Learning for Transportation Demand Resilience Pattern Prediction Using Floating Car Data

Contact Info

Product

Resources

About