Mitigating location and speed errors in floating car data using context-based accuracy estimation

AbstractThe purpose of the study was to develop a deep learning network for estimating and constructing highly accurate 3D bone models directly from actual X-ray images and to verify its accuracy. The data used were 173 computed tomography (CT) images and 105 actual X-ray images of a healthy wrist joint. To compensate for the small size of the dataset, digitally reconstructed radiography (DRR) images generated from CT were used as training data instead of actual X-ray images. The DRR-like images were generated from actual X-ray images in the test and adapted to the network, and high-accuracy estimation of a 3D bone model from a small data set was possible. The 3D shape of the radius and ulna were estimated from actual X-ray images with accuracies of 1.05 ± 0.36 and 1.45 ± 0.41 mm, respectively.

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The Analysis of Urban Road Traffic State Based on Kinds Floating Car Data

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.694.80 ◽

2014 ◽

Vol 694 ◽

pp. 80-84

Author(s):

Xiao Tong Yin ◽

Chao Qun Ma ◽

Liang Peng Qu

Keyword(s):

Road Traffic ◽

Velocity Estimation ◽

Least Square ◽

Single Type ◽

Estimation Model ◽

Average Speed ◽

Urban Road ◽

Traffic State ◽

Floating Car Data ◽

Different Types

The analysis of the unban road traffic state based on kinds of floating car data, is based on the model and algorithm of floating car data preprocessing and map matching, etc. Firstly, according to the characteristics of the different types of urban road, the urban road section division has been carried on the elaboration and optimization. And this paper introduces the method of calculating the section average speed with single floating car data, also applies the dynamic consolidation of sections to estimate the section average velocity.Then the minimum sample size of floating car data is studied, and section average velocity estimation model based on single type of floating car data in the different case of floating car data sample sizes has been built. Finally, the section average speed of floating car in different types is fitted to the section average car speed by the least square method, using section average speed as the judgment standard, the grade division standard of urban road traffic state is established to obtain the information of road traffic state.

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Mining Vehicle Trajectories to Discover Individual Significant Places: Case Study using Floating Car Data in the Paris Region

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198121995500 ◽

2021 ◽

pp. 036119812199550

Author(s):

Danyang Sun ◽

Fabien Leurent ◽

Xiaoyan Xie

Keyword(s):

Large Scale ◽

City Planning ◽

Human Mobility ◽

Work Place ◽

Trajectory Data ◽

Floating Car Data ◽

Vehicle Trajectories ◽

Activity Types ◽

Paris Region

In this study we discovered significant places in individual mobility by exploring vehicle trajectories from floating car data. The objective was to detect the geo-locations of significant places and further identify their functional types. Vehicle trajectories were first segmented into meaningful trips to recover corresponding stay points. A customized density-based clustering approach was implemented to cluster stay points into places and determine the significant ones for each individual vehicle. Next, a two-level hierarchy method was developed to identify the place types, which firstly identified the activity types by mixture model clustering on stay characteristics, and secondly discovered the place types by assessing their profiles of activity composition and frequentation. An applicational case study was conducted in the Paris region. As a result, five types of significant places were identified, including home place, work place, and three other types of secondary places. The results of the proposed method were compared with those from a commonly used rule-based identification, and showed a highly consistent matching on place recognition for the same vehicles. Overall, this study provides a large-scale instance of the study of human mobility anchors by mining passive trajectory data without prior knowledge. Such mined information can further help to understand human mobility regularities and facilitate city planning.

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