Method Based on Floating Car Data and Gradient-Boosted Decision Tree Classification for the Detection of Auxiliary Through Lanes at Intersections

Abstract

Abstract:The rapid detection of information on continuously changing intersection auxiliary through lane is a major task of lane-level navigation data updates. However, existing lane number detection methods possess long update cycles and high computational costs. Therefore, this study proposes a novel method based on floating car data (FCD) for the detection of auxiliary through lane changes at road intersections. First, roads near intersections are divided into three sections and the spatial distribution characteristics of the FCD of each section are analyzed. Second, the FCD is preprocessed to obtain a standardized FCD dataset by removing redundant data through an improved amplitude-limiting average filtering method. Third, a basic classifier for the number of lanes is constructed. Fourth, the final number of lanes of the road section is determined by combining the basic classifier and the gradient-boosted decision tree model. Finally, the presence of an auxiliary through lane at the intersection is determined in accordance with the change in the number of intersection lanes. The method was tested using data for a road in Wuchang District, Wuhan City. Experimental results show that this method can rapidly obtain auxiliary through lane information from the FCD and is superior to other classification methods.

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

“…To assess the performance of the deep learning approach presented in this study, we conducted a quantitative comparison with several classification methods, including Kernel Density Estimation [7], Naïve Bayesian, Constraint Gaussian Mixture Model [11], Fuzzy Logic [13], Gradient Lifting Decision Tree [14], The Least Square Estimate to Constrain Gaussian Mixture Model [16], and the Weighted Constrained Gaussian Mixture Model and Hidden Markov Model [17]. The results of the lane number identification comparisons are presented in Table 6.…”

“…Affected by passenger flow, the data volume of floating cars covered by different roads on the same day varies, as does the data volume of floating cars covered by the same road on working days and rest days. Based on previous experience [14], to ensure that the distribution density and width of FCD on the road can fully express the information of the number of road lanes, a cleaning experiment was conducted on FCD from 55 roads in the urban area of Wuhan. The data collection cycle was 7 days, and the road types of the 55 selected roads included: two-way two-lane 15 roads, two-way four-lane 22 roads, and two-way six-lane 18 roads (most roads in cities belong to these three types).…”

A Review of Crowdsourcing Update Methods for High-Definition Maps

“…Low-frequency FCD typically originate from public transportation vehicles such as taxis and buses. The sampling frequency for this type of data generally falls within the range of 10 s to 60 s, with positioning accuracy ranging from 10 m to 30 m [24][25][26]. Li et al proposed a method that utilizes FCD data collected from taxis in Wuhan to detect auxiliary lanes at intersections [24].…”

“…The sampling frequency for this type of data generally falls within the range of 10 s to 60 s, with positioning accuracy ranging from 10 m to 30 m [24][25][26]. Li et al proposed a method that utilizes FCD data collected from taxis in Wuhan to detect auxiliary lanes at intersections [24]. Similarly, Kan et al proposed a method to detect traffic congestion based on FCD data collected by taxis, with a sampling frequency of 60 s [25].…”

IoT based Fourth Generation SCADA System for High Voltage Networks Fault Diagnosis based on BSDT-ANN

“…In ensemble classifiers, predictions are performed using multiple classification techniques to achieve higher accuracy and avoid overfitting. Tree-based ensemble techniques are commonly used for classification and regression in many research fields [7], [8]. Two such tree-based ensemble techniques are the Boosted Decision Tree (BSDT) for classification [9] and bagged decision tree for regression problems [10].…”