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 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 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 Estimating Urban Road GPS Environment Friendliness with Bus Trajectories: A City-Scale Approach

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1580
Author(s):  
Liantao Ma ◽  
Chaohe Zhang ◽  
Yasha Wang ◽  
Guangju Peng ◽  
Chao Chen ◽  
...  
Keyword(s):  
Urban Areas ◽  
Negative Impact ◽  
Field Tests ◽  
Location Based Services ◽  
Trajectory Data ◽  
Gps Positioning ◽  
Urban Road ◽  
Gps Trajectory Data ◽  
Gps Trajectory ◽  
Road Segments

GPS is taken as the most prevalent positioning system in practice. However, in urban areas, as the GPS satellite signal could be blocked by buildings, the GPS positioning is not accurate due to multi-path errors. Estimating the negative impact of urban environments on GPS accuracy, that is the GPS environment friendliness (GEF) in this paper, will help to predict the GPS errors in different road segments. It enhances user experiences of location-based services and helps to determine where to deploy auxiliary assistant positioning devices. In this paper, we propose a method of processing and analysing massive historical bus GPS trajectory data to estimate the urban road GEF integrated with the contextual information of roads. First, our approach takes full advantage of the particular feature that bus routes are fixed to improve the performance of map matching. In order to estimate the GEF of all roads fairly and reasonably, the method estimates the GPS positioning error of each bus on the roads that are not covered by its route, by taking POIinformation, tag information of roads, and building layout information into account. Finally, we utilize a weighted estimation strategy to calculate the GEF of each road based on the GPS positioning performance of all buses. Based on one month of GPS trajectory data of 4835 buses within the second ring road in Chengdu, China, we estimate the GEF of 8831 different road segments and verify the rationality of the results by satellite maps, street views, and field tests.

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

2018 ◽  
Vol 32 (12) ◽  
pp. 2337-2361 ◽  
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

scholarly journals Identifying the Key Nodes and Sections of Urban Roadway Network Based on GPS Trajectory Data

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Jing Wang ◽  
Chunjiao Dong ◽  
Chunfu Shao ◽  
Shichen Huang ◽  
Shuang Wang
Keyword(s):  
Network Model ◽  
Evaluation System ◽  
Large Scale ◽  
Urban Traffic ◽  
Identification Algorithm ◽  
Trajectory Data ◽  
Dynamic Traffic ◽  
Gps Trajectory Data ◽  
Gps Trajectory ◽  
Key Nodes

This paper proposes a novel approach to identify the key nodes and sections of the roadway network. The taxi-GPS trajectory data are regarded as mobile sensor to probe a large scale of urban traffic flows in real time. First, the urban primary roadway network model and dual roadway network model are developed, respectively, based on the weighted complex network. Second, an evaluation system of the key nodes and sections is developed from the aspects of dynamic traffic attributes and static topology. At the end, the taxi-GPS data collected in Xicheng District of Beijing, China, are analyzed. A comprehensive analysis of the spatial-temporal changes of the key nodes and sections is performed. Moreover, the repetition rate is used to evaluate the performance of the identification algorithm of key nodes and sections. The results show that the proposed method realizes the expression of topological structure and dynamic traffic attributes of the roadway network simultaneously, which is more practicable and effective in a large scale.

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