Density Adaptive Approach for Generating Road Network From GPS Trajectories

Zhang, Fu; Fan, Liang; Sun, Yangjie; Tian, Zongshun

doi:10.1109/access.2020.2980174

Cited by 10 publications

(5 citation statements)

References 40 publications

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“…It implements binary thresholds to actualize road binary illustrations within certain regions, thereby discovering central arterial lines of roads through an array of methodologies such as Voronoi segmentation. Researchers such as Fu Z and his colleagues [29] have successfully constructed an efficacious road network, circumventing the necessity for auxiliary parameter amendments, by employing kernel density analysis, Hidden Markov Models, and map matching techniques. Uduwaragoda E and his team [30] utilized non-parametric Kernel Density Estimation (KDE) to scrutinize the probability density distribution of trajectory nodes, subsequently generating geospatial representations containing lane centerlines.…”

Section: Spatio-temporal Trajectory Data Miningmentioning

confidence: 99%

Dynamic Generation Method of Highway ETC Gantry Topology Based on LightGBM

Zou,

Wang,

Cai

et al. 2023

Mathematics

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In Electronic Toll Collection (ETC) systems, accurate gantry topology data are crucial for fair and efficient toll collection. Currently, inaccuracies in the topology data can cause tolls to be based on the shortest route rather than the actual distance travelled, contradicting the ETC system’s purpose. To address this, we adopt a novel Gradient Boosting Decision Tree (GBDT) algorithm, Light Gradient Boosting Machine (LightGBM), to dynamically update ETC gantry topology data on highways. We use ETC gantry and toll booth transaction data from a province in southeast China, where ETC usage is high at 72.8%. From this data, we generate a candidate topology set and extract five key characteristics. We then use Amap API and QGIS map analysis to annotate the candidate set, and, finally, apply LightGBM to train on these features, generating the dynamic topology. Our comparison of LightGBM with 14 other machine learning algorithms showed that LightGBM outperformed the others, achieving an impressive accuracy of 97.6%. This methodology can help transportation departments maintain accurate and up-to-date toll systems, reducing errors and improving efficiency.

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Section: Spatio-temporal Trajectory Data Miningmentioning

confidence: 99%

Dynamic Generation Method of Highway ETC Gantry Topology Based on LightGBM

Zou,

Wang,

Cai

et al. 2023

Mathematics

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“…Crowdsourced vehicle trajectory data is a low-cost, real-time data source that potentially contains rich road information. Tus, many scholars have studied building a navigation map using crowdsourced vehicle trajectories [5][6][7][8]. With the rise of autonomous driving technology, high-defnition navigation maps have become one of the critical capabilities for autonomous driving.…”

Section: Related Workmentioning

confidence: 99%

“…Te likelihood can be understood as the probability of the current trajectory state distribution under the conditions of the known number of lanes and model parameters. It can be expressed as equation (8). In the next section, we will use the log-likelihood of the model to extract the number of lanes.…”

Section: Trajectory Distribution Modeling On the Road Crossmentioning

confidence: 99%

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Lane-Level Road Map Construction considering Vehicle Lane-Changing Behavior

Fan

Zhang

Wan

et al. 2022

Journal of Advanced Transportation

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In recent years, the construction of lane-level road maps has received extensive attention from industry and academia. It has been widely studied because this kind of map provides the foundation for much research, such as high-precision navigation, driving behavior analysis, and traffic analysis. Trajectory-based crowd-mapping is an emerging approach to lane-level map construction. However, the major problem is that existing methods neglect modeling the trajectory distribution in the longitudinal direction of the road, which significantly impacts precision. Thus, this article proposes a two-stage method based on vehicle lane-changing behavior to model the road’s lateral and longitudinal trajectory distributions simultaneously. In the first stage, lane-changing behaviors are extracted from vehicle trajectories. In the second stage, the lane extraction model is established using the weighted constrained Gaussian mixture model and hidden Markov model to estimate lane parameters (e.g., lane counts and lane centerline) on each road cross section. Then accurate and continuous lane centerlines can be constructed accordingly. The proposed method is verified using vehicle trajectory data collected from the crowdsourced platform named Mapillary. The results show that the proposed method can construct lane-level road information satisfactorily.

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“…Benefiting from crowdsourced geospatial data, many scholars had carried out investigations to extract the geometric features of road networks. Numerous map construction methods were proposed, typically using methods such as rasterization-based methods (Davies, Beresford, & Hopper, 2006;Wang et al, 2013), incremental-fusion methods (Cao & Krumm, 2009;Li, Qin, Xie, & Zhao, 2012), density-based methods (Fu, Fan, Sun, & Tian, 2020;Wang, Wang, & Li, 2015;Yang, Ai, & Lu, 2018), clustering-based methods (Mariescu-Istodor & Fränti, 2018Stanojevic, Abbar, Thirumuruganathan, Chawla, & Aleimat, 2018), and machine learning methods (Huang et al, 2018). Recently, Zhang et al (2020) investigated the mining of the changing road patterns and incrementally extracted road networks based on a multi-temporal trajectory partitioning method.…”

Section: Rel Ated Workmentioning

confidence: 99%

Mining crowdsourced trajectory and geo‐tagged data for spatial‐semantic road map construction

Huang

Zhang

Deng

et al. 2021

Transactions in GIS

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The road map is a fundamental part of a spatial data infrastructure (SDI), and is widely applied in navigation, smart transportation, and mobile location services. Recently, with the ubiquity of positioning devices, crowdsourced trajectories have become a significant data resource for road map construction and updating. However, existing trajectory‐based methods mainly place emphasis on extracting road geometry features and may ignore continuous updating of road semantic information. Hence, we propose a divide‐and‐conquer method to construct a spatial‐semantic road map by incorporating multiple data sources (e.g., crowdsourced trajectories and geo‐tagged data). The proposed method divides road map construction into two sub‐tasks, road structure reconstruction and road attributes inference. The road structure reconstruction process starts to partition raw trajectory data into different cliques of roadways and road intersections, and then extracts various targeted road structures by analyzing the turning modes in different trajectory cliques. The road attributes inference process aims to infer three pieces of crucial semantic information about road speeds, turning rules, and road names from crowdsourced trajectories and geo‐tagged data. The case studies in Wuhan were examined to illustrate that the proposed method can construct a routable road map with enhanced geometric structures and rich semantic information, providing a beneficial data solution for car navigation and SDI update.

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Density Adaptive Approach for Generating Road Network From GPS Trajectories

Cited by 10 publications

References 40 publications

Dynamic Generation Method of Highway ETC Gantry Topology Based on LightGBM

Dynamic Generation Method of Highway ETC Gantry Topology Based on LightGBM

Lane-Level Road Map Construction considering Vehicle Lane-Changing Behavior

Mining crowdsourced trajectory and geo‐tagged data for spatial‐semantic road map construction

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