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.

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.

scholarly journals Extended Classification Course Improves Road Intersection Detection from Low-Frequency GPS Trajectory Data

2020 ◽  
Vol 9 (3) ◽  
pp. 181
Author(s):  
Banqiao Chen ◽  
Chibiao Ding ◽  
Wenjuan Ren ◽  
Guangluan Xu
Keyword(s):  
Clustering Algorithm ◽  
Recall Rate ◽  
Location Based Services ◽  
Trajectory Data ◽  
Intersection Detection ◽  
Map Generation ◽  
Gps Trajectory Data ◽  
Road Intersections ◽  
Road Maps ◽  
Gps Trajectory

The requirements of location-based services have generated an increasing need for up-to-date digital road maps. However, traditional methods are expensive and time-consuming, requiring many skilled operators. The feasibility of using massive GPS trajectory data provides a cheap and quick means for generating and updating road maps. The detection of road intersections, being the critical component of a road map, is a key problem in map generation. Unfortunately, low sampling rates and high disparities are ubiquitous among floating car data (FCD), making road intersection detection from such GPS trajectories very challenging. In this paper, we extend a point clustering-based road intersection detection framework to include a post-classification course, which utilizes the geometric features of road intersections. First, we propose a novel turn-point position compensation algorithm, in order to improve the concentration of selected turn-points under low sampling rates. The initial detection results given by the clustering algorithm are recall-focused. Then, we rule out false detections in an extended classification course based on an image thinning algorithm. The detection results of the proposed method are quantitatively evaluated by matching with intersections from OpenStreetMap using a variety of distance thresholds. Compared with other methods, our approach can achieve a much higher recall rate and better overall performance, thereby better supporting map generation and other similar applications.

scholarly journals An Automatic Road Network Construction Method Using Massive GPS Trajectory Data

2017 ◽  
Vol 6 (12) ◽  
pp. 400 ◽  
Author(s):  
Yongchuan Zhang ◽  
Jiping Liu ◽  
Xinlin Qian ◽  
Agen Qiu ◽  
Fuhao Zhang
Keyword(s):  
Road Network ◽  
Construction Method ◽  
Trajectory Data ◽  
Network Construction ◽  
Gps Trajectory Data ◽  
Gps Trajectory

scholarly journals Dynamic Traffic Assignment Model Based on GPS Data and Point of Interest (POI) in Shanghai

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7341
Author(s):  
Xueying Song ◽  
Zheng Yang ◽  
Tao Wang ◽  
Chaoyang Li ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Traffic Assignment ◽  
Trajectory Data ◽  
Dynamic Traffic ◽  
Urban Road ◽  
Assignment Model ◽  
Flow Assignment ◽  
Gps Trajectory Data ◽  
Gps Trajectory

Dynamic traffic flow, which can facilitate the efficient operation of traffic road networks, is an important prerequisite for the application of reasonable assignment of traffic demands in an urban road network. In order to improve the accuracy of dynamic traffic flow assignment, this paper proposes a dynamic traffic flow assignment model based on GPS trajectory data and the influence of POI. First, this paper explores the impact patterns of POI on regional road network congestion during peak hours through qualitative and quantitative analysis. Then, based on the user equilibrium theory, a dynamic traffic flow assignment model, in which the effect of POI on links is reflected using the link-node impedance function, is proposed. Finally, the accuracy of the model is validated by the GPS trajectory data and origin–destination (OD) traffic data of motor vehicles in Xuhui District, Shanghai, China. The results show that the model can be used to coordinate and optimize the traffic assignment of the regional road network under the influence of POI during peak hours and alleviate the congestion of the road network. The findings can provide a powerful reference for developing scientific and rational traffic assignment decisions and management strategies for urban road network traffic.

scholarly journals Automatically Tracking Road Centerlines from Low-Frequency GPS Trajectory Data

2021 ◽  
Vol 10 (3) ◽  
pp. 122
Author(s):  
Banqiao Chen ◽  
Chibiao Ding ◽  
Wenjuan Ren ◽  
Guangluan Xu
Keyword(s):  
Active Contour ◽  
Low Frequency ◽  
Tracking Algorithm ◽  
Location Based Services ◽  
Trajectory Data ◽  
The Road ◽  
Gps Trajectory Data ◽  
Road Maps ◽  
Gps Trajectory ◽  
Refinement Algorithm

High-quality digital road maps are essential prerequisites of location-based services and smart city applications. The massive and accessible GPS trajectory data generated by mobile GPS devices provide a new means through which to generate maps. However, due to the low sampling rate and multi-level disparity problems, automatically generating road maps is challenging and the generated maps cannot yet meet commercial requirements. In this paper, we present a GPS trajectory data-based road tracking algorithm, including an active contour-based road centerline refinement algorithm as the necessary post-processing. First, the low-frequency trajectory data were transferred into a density estimation map representing the roads through a kernel density estimator, for a seeding algorithm to automatically generate the initial points of the road-tracking algorithm. Then, we present a template-matching-based road-direction extraction algorithm for the road trackers to conduct simple correction, based on local density information. Last, we present an active contour-based road centerline refinement algorithm, considering both the geometric information of roads and density information. The generated road map was quantitatively evaluated using maps offered by the OpenStreetMap. Compared to other methods, our approach could produce a higher quality map with fewer zig-zag roads, and therefore more accurately represents reality.

A Heading Maintaining Oriented Compression Algorithm for GPS Trajectory Data

Informatica ◽  
2019 ◽  
Vol 30 (1) ◽  
pp. 33-52 ◽  
Author(s):  
Pengfei HAO ◽  
Chunlong YAO ◽  
Qingbin MENG ◽  
Xiaoqiang YU ◽  
Xu LI
Keyword(s):  
Compression Algorithm ◽  
Trajectory Data ◽  
Gps Trajectory Data ◽  
Gps Trajectory

Enabling Smart Urban Services with GPS Trajectory Data

2021 ◽  
Author(s):  
Chao Chen ◽  
Daqing Zhang ◽  
Yasha Wang ◽  
Hongyu Huang
Keyword(s):  
Trajectory Data ◽  
Urban Services ◽  
Gps Trajectory Data ◽  
Gps Trajectory
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