Urban Road Lane Number Mining from Low-Frequency Floating Car Data Based on Deep Learning

Abstract

Lane-level road information is especially crucial now that high-precision navigation maps are in more demand. Road information may be obtained rapidly and affordably by mining floating vehicle data (FCD). A method is proposed to extract the number of lanes on urban roads by combining deep learning and low-frequency FCD. Initially, the FCD is cleaned using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering technique. Then, the FCD is split into three categories based on the typical urban road types: one-way one-lane, one-way two-lane, and one-way three-lane, and the deep learning sample data is created using segmentation, rotation, and gridding. Lastly, the number of urban road lanes is obtained by training and predicting the sample data using the LeNet-5 model. The number of urban road lanes was effectively identified from the low-frequency FCD with a detection accuracy of 92.7% through the cleaning and classification of Wuhan FCD. Urban roads can be efficiently covered by the FCD on a regular basis, and lane information can be efficiently collected using deep learning techniques. This method can be used to generate and update lane number information for high-precision navigation maps.

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“…As the cost of positioning devices decreases, most vehicles such as buses and taxis are now equipped with GNSS devices capable of recording the time, speed, and position information of vehicles. These data are commonly referred to as floating car data (FCD), offering advantages of broad coverage and real-time capabilities [19]. Leveraging abundant FCD not only facilitates the acquisition of lane information but also allows for inferring changes in road usage, such as traffic congestion, road closures, and temporary restrictions.…”