Road network generation from low frequency GPS trajectory data

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.

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An Automatic Road Network Construction Method Using Massive GPS Trajectory Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi6120400 ◽

2017 ◽

Vol 6 (12) ◽

pp. 400 ◽

Cited By ~ 9

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

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Generating urban road intersection models from low-frequency GPS trajectory data

International Journal of Geographical Information Science ◽

10.1080/13658816.2018.1510124 ◽

2018 ◽

Vol 32 (12) ◽

pp. 2337-2361 ◽

Cited By ~ 10

Author(s):

Min Deng ◽

Jincai Huang ◽

Yunfei Zhang ◽

Huimin Liu ◽

Luliang Tang ◽

...

Keyword(s):

Low Frequency ◽

Trajectory Data ◽

Urban Road ◽

Gps Trajectory Data ◽

Gps Trajectory

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

Sensors ◽

10.3390/s21217341 ◽

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.

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Automatically Tracking Road Centerlines from Low-Frequency GPS Trajectory Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030122 ◽

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.

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