scholarly journals A METHOD OF URBAN ROAD NETWORK EXTRACTION BASED ON FLOATING CAR TRAJECTORY DATA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1579-1585
Author(s):  
C. Mi ◽  
F. Lu
Keyword(s):  
Road Network ◽  
Network Data ◽  
Trajectory Data ◽  
Network Information ◽  
Urban Road ◽  
The Road ◽  
Road Network Extraction ◽  
Geometric Precision ◽  
Road Width ◽  
Adaptive Radius

<p><strong>Abstract.</strong> With the gradual opening of floating car trajectory data, it is possible to extract road network information from it. Currently, most road network extraction algorithms use unified thresholds to ignore the density difference of trajectory data, and only consider the trajectory shape without considering the direction of the trajectory, which seriously affects the geometric precision and topological accuracy of their results. Therefore, an adaptive radius centroid drift clustering method is proposed in this paper, which can automatically adjust clustering parameters according to the track density and the road width, using trajectory direction to complete the topological connection of roads. The algorithm is verified by the floating car trajectory data of a day in Futian District, Shenzhen. The experimental results are qualitatively and quantitatively analyzed with ones of the other two methods. It indicates that the road network data extracted by this algorithm has a significant improvement in geometric precision and topological accuracy, and which is suitable for big data processing.</p>

scholarly journals An Automatic Method for Detection and Update of Additive Changes in Road Network with GPS Trajectory Data

2019 ◽  
Vol 8 (9) ◽  
pp. 411 ◽  
Author(s):  
Tang ◽  
Deng ◽  
Huang ◽  
Liu ◽  
Chen
Keyword(s):  
Road Network ◽  
Sampling Rate ◽  
Quality Data ◽  
Automatic Method ◽  
Trajectory Data ◽  
The Road ◽  
High Sampling Rate ◽  
Map Generation ◽  
Gps Trajectory Data ◽  
Gps Trajectory

Ubiquitous trajectory data provides new opportunities for production and update of the road network. A number of methods have been proposed for road network construction and update based on trajectory data. However, existing methods were mainly focused on reconstruction of the existing road network, and the update of newly added roads was not given much attention. Besides, most of existing methods were designed for high sampling rate trajectory data, while the commonly available GPS trajectory data are usually low-quality data with noise, low sampling rates, and uneven spatial distributions. In this paper, we present an automatic method for detection and update of newly added roads based on the common low-quality trajectory data. First, additive changes (i.e., newly added roads) are detected using a point-to-segment matching algorithm. Then, the geometric structures of new roads are constructed based on a newly developed decomposition-combination map generation algorithm. Finally, the detected new roads are refined and combined with the original road network. Seven trajectory data were used to test the proposed method. Experiments show that the proposed method can successfully detect the additive changes and generate a road network which updates efficiently.

Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

2022 ◽  
Vol 13 (2) ◽  
pp. 1-25
Author(s):  
Bin Lu ◽  
Xiaoying Gan ◽  
Haiming Jin ◽  
Luoyi Fu ◽  
Xinbing Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Large Scale ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Propagation Mechanism ◽  
Urban Road ◽  
The Road ◽  
Traffic Flow Forecasting ◽  
Gating Mechanism

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

scholarly journals GIS-Based Urban Road Network Accessibility Modeling Using MLR, ANN and ANFIS Methods

2021 ◽  
Vol 22 (1) ◽  
pp. 15-28
Author(s):  
K. Sai Sahitya ◽  
Csrk Prasad
Keyword(s):  
Network Structure ◽  
Road Network ◽  
Structural Parameters ◽  
Transportation Network ◽  
Sustainable Transportation ◽  
Geographical Information ◽  
Urban Road Network ◽  
Inference System ◽  
Urban Road ◽  
The Road

Abstract A sustainable transportation system is possible only through an efficient evaluation of transportation network performance. The efficiency of the transport network structure is analyzed in terms of its connectivity, accessibility, network development, and spatial pattern. This study primarily aims to propose a methodology for modeling the accessibility based on the structural parameters of the urban road network. Accessibility depends on the arrangement of the urban road network structure. The influence of the structural parameters on the accessibility is modeled using Multiple Linear Regression (MLR) analysis. The study attempts to introduce two methods of Artificial Intelligence (AI) namely Artificial Neural Networks (ANN) and Adaptive network-based neuro-fuzzy inference system (ANFIS) in modeling the urban road network accessibility. The study also focuses on comparing the results obtained from MLR, ANN and ANFIS modeling techniques in predicting the accessibility. The results of the study present that the structural parameters of the road network have a considerable impact on accessibility. ANFIS method has shown the best performance in modeling the road network accessibility with a MAPE value of 0.287%. The present study adopted Geographical Information Systems (GIS) to quantify, extract and analyze different features of the urban transportation network structure. The combination of GIS, ANN, and ANFIS help in improved decision-making. The results of the study may be used by transportation planning authorities to implement better planning practices in order to improve accessibility.

scholarly journals Impact of Road Network Topology on Public Transportation Development

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Minzhi Chen ◽  
Fan Wu ◽  
Min Yin ◽  
Jiangang Xu
Keyword(s):  
Public Transportation ◽  
Road Network ◽  
Road Networks ◽  
Urban Road Network ◽  
Urban Road ◽  
The Road ◽  
Layout Structure ◽  
Per Capita ◽  
Transportation Development ◽  
The Impact

Planning of road networks is fundamental for public transportation. The impact of road network density on public transportation has been extensively studied, but few studies in this regard involved evaluation indicators for connectivity and layout of road networks. With 29 cities in China as the study cases, this paper quantifies the layout structure of the road network based on the network’s betweenness centralization and establishes a multivariate linear regression model to perform regression of the logarithm of the frequency of per capita public transportation on betweenness centralization. It is found in the present work that there is a significant correlation between the layout structure of an urban road network and the residents’ utilization degree of public transportation. A greater betweenness centralization of the urban road network, namely a more centralized road network, means a higher frequency of per capita public transportation of urban residents and a higher degree of the residents’ utilization of public transportation. In the development of public transportation, centralized and axial-shaped layout structures of road networks can be promoted to improve the utilization of public transportation.

Spatial Analysis Based Urban Road Network Partition Method for Macroscopic Evaluation

2011 ◽  
Vol 97-98 ◽  
pp. 512-517
Author(s):  
Wen Jie Zou ◽  
Jian Cheng Weng ◽  
Jian Rong ◽  
Wei Zhou
Keyword(s):  
Spatial Analysis ◽  
Road Network ◽  
Urban Road Network ◽  
Network Partition ◽  
Urban Road ◽  
The Road ◽  
Network Operation ◽  
Partition Method ◽  
Operation Status

In order to improve the reliability of urban road network operation evaluation, the road network regional Partition methods were launched in this paper. The geographic grid was introduced first, and a 4-level road network model was defined. Then, the spatial analysis based urban road network division method was proposed by analyzing the characteristics of road network operation. This method can reflect the influence between adjacent regional units, and improve the reliability of urban road network division. Finally, this research took a certain area in Beijing as a case study, and divided the road network as several regional units. Macroscopic evaluation result shows that it is effective for scientifically describing the road network operation status.

Pedestrian network information extraction based on VGI

GEOMATICA ◽  
2018 ◽  
Vol 72 (3) ◽  
pp. 85-99 ◽  
Author(s):  
Xuejing Xie ◽  
Guojian Ou
Keyword(s):  
Information Extraction ◽  
Road Network ◽  
New Method ◽  
Location Based Service ◽  
Network Information ◽  
The Road ◽  
Pedestrian Network ◽  
Gps Trajectories ◽  
Data Extracting

Pedestrian network information plays an important role in pedestrian location based service (LBS), and its completeness determines the quality of a pedestrian LBS. This study used volunteered data and BaiduMap to research how to extract pedestrian network information on the basis of pedestrian GPS trajectories. The method extracts human road information by three steps: cleaning track data, extracting the road network, and detecting and analysing the recognised pedestrian road facilities. Once the road network information is extracted, the information regarding road facilities can be obtained, e.g., pedestrian crossings, overpasses, and underground passages. This paper describes a new method for incrementally updating electronic maps.

scholarly journals Learning to Generate Maps from Trajectories

2020 ◽  
Vol 34 (01) ◽  
pp. 890-897 ◽  
Author(s):  
Sijie Ruan ◽  
Cheng Long ◽  
Jie Bao ◽  
Chunyang Li ◽  
Zisheng Yu ◽  
...  
Keyword(s):  
Road Network ◽  
Traditional Approach ◽  
Trajectory Data ◽  
Embedded Devices ◽  
The Road ◽  
Mobile Objects ◽  
Map Generation ◽  
Spatial View ◽  
Topological Connectivity ◽  
Wide Usage

Accurate and updated road network data is vital in many urban applications, such as car-sharing, and logistics. The traditional approach to identifying the road network, i.e., field survey, requires a significant amount of time and effort. With the wide usage of GPS embedded devices, a huge amount of trajectory data has been generated by different types of mobile objects, which provides a new opportunity to extract the underlying road network. However, the existing trajectory-based map recovery approaches require many empirical parameters and do not utilize the prior knowledge in existing maps, which over-simplifies or over-complicates the reconstructed road network. To this end, we propose a deep learning-based map generation framework, i.e., DeepMG, which learns the structure of the existing road network to overcome the noisy GPS positions. More specifically, DeepMG extracts features from trajectories in both spatial view and transition view and uses a convolutional deep neural network T2RNet to infer road centerlines. After that, a trajectory-based post-processing algorithm is proposed to refine the topological connectivity of the recovered map. Extensive experiments on two real-world trajectory datasets confirm that DeepMG significantly outperforms the state-of-the-art methods.

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