On-Line Map-Matching Algorithm for Long Time Interval Floating Car Data

ICCTP 2011 ◽  
2011 ◽  
Author(s):  
XI-wei She ◽  
Zhao-cheng He ◽  
Wen-chen Yang ◽  
Wei-liang Zeng ◽  
Zhi-ren Sha
Keyword(s):  
Time Interval ◽  
Map Matching ◽  
Matching Algorithm ◽  
Floating Car Data ◽  
Long Time ◽  
On Line

scholarly journals Local Path Searching Based Map Matching Algorithm for Floating Car Data

2011 ◽  
Vol 10 ◽  
pp. 576-582 ◽  
Author(s):  
Feng Chen ◽  
Mingyu Shen ◽  
Yongning Tang
Keyword(s):  
Map Matching ◽  
Matching Algorithm ◽  
Floating Car Data ◽  
Local Path

On-line map-matching framework for floating car data with low sampling rate in urban road networks

2013 ◽  
Vol 7 (4) ◽  
pp. 404-414 ◽  
Author(s):  
Zhao-cheng He ◽  
She Xi-wei ◽  
Pei-lin Nie ◽  
Li-jian Zhuang
Keyword(s):  
Sampling Rate ◽  
Road Networks ◽  
Map Matching ◽  
Urban Road ◽  
Floating Car Data ◽  
On Line

Map-matching algorithm for large-scale low-frequency floating car data

2013 ◽  
Vol 28 (1) ◽  
pp. 22-38 ◽  
Author(s):  
Bi Yu Chen ◽  
Hui Yuan ◽  
Qingquan Li ◽  
William H.K. Lam ◽  
Shih-Lung Shaw ◽  
...  
Keyword(s):  
Large Scale ◽  
Low Frequency ◽  
Map Matching ◽  
Matching Algorithm ◽  
Floating Car Data

A Simplified Map-Matching Algorithm for Floating Car Data

2021 ◽  
pp. 711-720
Author(s):  
Federico Karagulian ◽  
Gabriella Messina ◽  
Gaetano Valenti ◽  
Carlo Liberto ◽  
Fabio Carapellucci
Keyword(s):  
Map Matching ◽  
Matching Algorithm ◽  
Floating Car Data

scholarly journals A Data Correction Algorithm for Low-Frequency Floating Car Data

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3639 ◽  
Author(s):  
Bijun Li ◽  
Yuan Guo ◽  
Jian Zhou ◽  
Yi Cai
Keyword(s):  
Low Cost ◽  
Low Frequency ◽  
Map Matching ◽  
Correction Algorithm ◽  
Trajectory Data ◽  
Matching Algorithm ◽  
Data Correction ◽  
Floating Car Data ◽  
Attraction Model ◽  
Physical Attraction

The data collected by floating cars is an important source for lane-level map production. Compared with other data sources, this method is a low-cost but challenging way to generate high-accuracy maps. In this paper, we propose a data correction algorithm for low-frequency floating car data. First, we preprocess the trajectory data by an adaptive density optimizing method to remove the noise points with large mistakes. Then, we match the trajectory data with OpenStreetMap (OSM) using an efficient hierarchical map matching algorithm. Lastly, we correct the floating car data by an OSM-based physical attraction model. Experiments are conducted exploiting the data collected by thousands of taxies over one week in Wuhan City, China. The results show that the accuracy of the data is improved and the proposed algorithm is demonstrated to be practical and effective.

On-line image readout in CTEM

1994 ◽  
Vol 52 ◽  
pp. 400-401
Author(s):  
K.-H. Herrmann ◽  
W. D. Rau ◽  
R. Sikeler
Keyword(s):  
Primary Electron ◽  
Digital Information ◽  
Electron Hole ◽  
Detection Technology ◽  
Long Time ◽  
On Line ◽  
Electron Image ◽  
Optical Transfer ◽  
Detection Quantum Efficiency ◽  
Technical Solutions

Quantitative recording of electron patterns and their rapid conversion into digital information is an outstanding goal which the photoplate fails to solve satisfactorily. For a long time, LLL-TV cameras have been used for EM adjustment but due to their inferior pixel number they were never a real alternative to the photoplate. This situation has changed with the availability of scientific grade slow-scan charged coupled devices (CCD) with pixel numbers exceeding 106, photometric accuracy and, by Peltier cooling, both excellent storage and noise figures previously inaccessible in image detection technology. Again the electron image is converted into a photon image fed to the CCD by some light optical transfer link. Subsequently, some technical solutions are discussed using the detection quantum efficiency (DQE), resolution, pixel number and exposure range as figures of merit.A key quantity is the number of electron-hole pairs released in the CCD sensor by a single primary electron (PE) which can be estimated from the energy deposit ΔE in the scintillator,

An Adaptive and Learning System for Feedback-Controlled Evoked Response Studies

1981 ◽  
Vol 20 (03) ◽  
pp. 169-173
Author(s):  
J. Wagner ◽  
G. Pfurtscheixer
Keyword(s):  
Brain Activity ◽  
Evoked Response ◽  
Learning System ◽  
Time Interval ◽  
Electrical Brain Activity ◽  
Long Time ◽  
The Subject ◽  
Eeg Power ◽  
Stimulation System ◽  
Background Eeg

The shape, latency and amplitude of changes in electrical brain activity related to a stimulus (Evoked Potential) depend both on the stimulus parameters and on the background EEG at the time of stimulation. An adaptive, learnable stimulation system is introduced, whereby the subject is stimulated (e.g. with light), whenever the EEG power is subthreshold and minimal. Additionally, the system is conceived in such a way that a certain number of stimuli could be given within a particular time interval. Related to this time criterion, the threshold specific for each subject is calculated at the beginning of the experiment (preprocessing) and adapted to the EEG power during the processing mode because of long-time fluctuations and trends in the EEG. The process of adaptation is directed by a table which contains the necessary correction numbers for the threshold. Experiences of the stimulation system are reflected in an automatic correction of this table. Because the corrected and improved table is stored after each experiment and is used as the starting table for the next experiment, the system >learns<. The system introduced here can be used both for evoked response studies and for alpha-feedback experiments.

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