scholarly journals Analysis on the Travel Characteristics of Sharing Bikes Connected with Different Types of Public Transportation Stations

2021 ◽  
Author(s):  
Xiaohua Yu ◽  
Xiaohui Wang ◽  
Yanna Zhao
Keyword(s):  
Feature Extraction ◽  
Public Transport ◽  
Public Transportation ◽  
Extraction Method ◽  
Supply And Demand ◽  
Feature Extraction Method ◽  
The Public ◽  
Different Types ◽  
Bike Sharing ◽  
Land Types

In order to better solve the problem of unbalanced supply and demand of connected shared bikes, this paper takes shared bikes as the research object, analyzes the usage characteristics of connected bikes in different types of public transport stations, and puts forward a data-based feature extraction method of shared bikes. Firstly, the usage data of shared bikes were collected, and the starting and finishing points were decoded. The public transport stations were divided into five typical types according to the decoded longitude, latitude and surrounding land types. Secondly, the connectivity activity, connectivity distance and user loyalty are put forward as the characteristic indicators of bike-sharing travel. Finally, taking the bicycle data of Chaoyang District of Beijing as an example, the travel characteristic indexes of shared bikes are analyzed. The results show that, as the “last kilometer” travel connecting tool of public transport, the peak of the use of shared bikes connecting residential stations is 6:30 to 9:30, and that of other stations is 7:30 to 9:30. The connecting distance of shared bikes is generally less than 2km, but the connecting distance of office sites can reach 3km, and this site has the highest user loyalty.

A Sum-of-Squares Polynomial Approach for Road Anomaly Detection Using Vehicle Sensor Measurements

2017 ◽  
Author(s):  
Dule Shu ◽  
Constantino Lagoa ◽  
Timothy Cleary
Keyword(s):  
Feature Extraction ◽  
Anomaly Detection ◽  
Extraction Method ◽  
Sum Of Squares ◽  
New Method ◽  
Feature Points ◽  
Feature Extraction Method ◽  
Different Types ◽  
Simulation Results ◽  
Vehicle Sensor

This paper presents a new method for road anomaly detection. The existence of road anomalies is determined by the behaviors of vehicles. A special polynomial named Sum-of-Squares (SOS) polynomial is used as a metric to evaluate the normality of vehicle behaviors. The method can process multiple types of sensor measurements. A feature extraction method is used to obtain concise representations of the sensor measurements. These representations, called feature points, are used to calculate the value of the SOS polynomial. Simulation results have been shown to demonstrate that the proposed method can effectively detect different types of road anomalies.

scholarly journals Fault Feature Extraction for Anchor Bolt Loosening of Escalator Based on EWT and Bispectrum Analysis

CONVERTER ◽  
2021 ◽  
pp. 681-695
Author(s):  
Zheng Yan
Keyword(s):  
Feature Extraction ◽  
Public Transportation ◽  
Extraction Method ◽  
Large Scale ◽  
Anchor Bolt ◽  
Feature Extraction Method ◽  
Feature Vectors ◽  
Empirical Wavelet Transform ◽  
Bolt Loosening ◽  
Fault Feature Extraction

Escalator is an essential large-scale public transportation equipment. Once the failure occurs, it will inevitably affect the operation and even cause safety accidents.  As an important part of the structure of escalator, the loosening of the anchor bolt will lead to abnormal operation of escalator.  Aiming at the current difficultyin extracting the fault features of anchor bolt loosening, a fault feature extraction method of escalator anchor loosening is constructed based on empirical wavelet transform (EWT) and bispectrum analysis. First, perform EWT decomposition of the original footing vibration acceleration signal to obtain a series of empirical mode functions(EMFs).Then, for each empirical mode function, the bispectrum was calculated by using bispectrum analysis method, and six texture features of the bispectrum were extracted as fault feature vectors by means of gray-gradient co-occurrence matrix.  Finally, the extracted multi-scale fault feature vectors and bi-directional longshort-term memory (BI-LSTM) were used to classify and identify the four types of fault signals with different degrees of foot loosening, and the fault types of foot loosening were determined. The results show that the feature extraction method based on empirical wavelet decomposition and bispectrum analysis can more effectively identify the loosening level of anchor bolts.

scholarly journals A Grid-Based Spatial Analysis for Detecting Supply–Demand Gaps of Public Transports: A Case Study of the Bangkok Metropolitan Region

2020 ◽  
Vol 12 (24) ◽  
pp. 10382 ◽  
Author(s):  
Apantri Peungnumsai ◽  
Hiroyuki Miyazaki ◽  
Apichon Witayangkurn ◽  
Sohee Minsun Kim
Keyword(s):  
Low Income ◽  
Public Transport ◽  
Public Transportation ◽  
Traffic Congestion ◽  
Supply And Demand ◽  
Metropolitan Region ◽  
Transport Service ◽  
Lorenz Curves ◽  
The Public ◽  
Public Transport Service

Public transport service has been promoted to reduce the problems of traffic congestion and environmental impacts due to car dependency. Several public transportation modes are available in Bangkok Metropolitan Region (BMR) such as buses, heavy rails, vans, boats, taxis, and trains while in some areas have fewer modes of public transport available. The disparity of public transport service negatively impacts social equity. This study aims to identify the gaps between public transport supply and demand and to demonstrate introduced indicators to assess the public transport performance incorporating transport capacity and equilibrium access aspects. Supply index was used to evaluate the level of service, and the demand index was applied to estimate travel needs. Furthermore, the Lorenz curves and the Gini coefficients were used to measure the equity of public transport. The results highlight that more than half of the BMR population is living in low-supply high-demand areas for public transportation. Moreover, the equitable access analysis has identified that the high-income population has better access to public transport than the low-income population. The results suggest that public transport gaps and equity indicate the inclusiveness of public transportation, as well as to the areas where to improve the public transport service. Thus, the methodology used in this study can be applied to another city or region similar to BMR.

A Novel Prediction of Quaternary Structural Type of Proteins with Gene Ontology

2020 ◽  
Vol 27 (4) ◽  
pp. 313-320 ◽  
Author(s):  
Xuan Xiao ◽  
Wei-Jie Chen ◽  
Wang-Ren Qiu
Keyword(s):  
Gene Ontology ◽  
Feature Extraction ◽  
Extraction Method ◽  
Quaternary Structure ◽  
Structural Type ◽  
Sequence Information ◽  
Prediction System ◽  
Data Set ◽  
Feature Extraction Method ◽  
Prediction Rate

Background: The information of quaternary structure attributes of proteins is very important because it is closely related to the biological functions of proteins. With the rapid development of new generation sequencing technology, we are facing a challenge: how to automatically identify the four-level attributes of new polypeptide chains according to their sequence information (i.e., whether they are formed as just as a monomer, or as a hetero-oligomer, or a homo-oligomer). Objective: In this article, our goal is to find a new way to represent protein sequences, thereby improving the prediction rate of protein quaternary structure. Methods: In this article, we developed a prediction system for protein quaternary structural type in which a protein sequence was expressed by combining the Pfam functional-domain and gene ontology. turn protein features into digital sequences, and complete the prediction of quaternary structure through specific machine learning algorithms and verification algorithm. Results: Our data set contains 5495 protein samples. Through the method provided in this paper, we classify proteins into monomer, or as a hetero-oligomer, or a homo-oligomer, and the prediction rate is 74.38%, which is 3.24% higher than that of previous studies. Through this new feature extraction method, we can further classify the four-level structure of proteins, and the results are also correspondingly improved. Conclusion: After the applying the new prediction system, compared with the previous results, we have successfully improved the prediction rate. We have reason to believe that the feature extraction method in this paper has better practicability and can be used as a reference for other protein classification problems.

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