Mobility pattern recognition based prediction for the subway station related bike-sharing trips

2021 ◽  
Vol 133 ◽  
pp. 103404
Author(s):  
Ying Lv ◽  
Danyue Zhi ◽  
Huijun Sun ◽  
Geqi Qi
Keyword(s):  
Pattern Recognition ◽  
Subway Station ◽  
Mobility Pattern ◽  
Bike Sharing

scholarly journals Data-Driven Approach for Passenger Mobility Pattern Recognition Using Spatiotemporal Embedding

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Chao Yu ◽  
Haiying Li ◽  
Xinyue Xu ◽  
Jun Liu ◽  
Jianrui Miao ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Clustering Algorithm ◽  
Data Driven ◽  
Mobility Pattern ◽  
Mobility Patterns ◽  
Start Time ◽  
Traffic Demand ◽  
Spatiotemporal Information ◽  
Data Driven Approach ◽  
Low Dimensional

Urban mobility pattern recognition has great potential in revealing human travel mechanism, discovering passenger travel purpose, and predicting and managing traffic demand. This paper aims to propose a data-driven method to identify metro passenger mobility patterns based on Automatic Fare Collection (AFC) data and geo-based data. First, Point of Information (POI) data within 500 meters of the metro stations are captured to characterize the spatial attributes of the stations. Especially, a fusion method of multisource geo-based data is proposed to convert raw POI data into weighted POI data considering service capabilities. Second, an unsupervised learning framework based on stacked auto-encoder (SAE) is designed to embed the spatiotemporal information of trips into low-dimensional dense trip vectors. In detail, the embedded spatiotemporal information includes spatial features (POI categories around the origin station and that around the destination station) and temporal features (start time, day of the week, and travel time). Third, a density-based clustering algorithm is introduced to identify passenger mobility patterns based on the embedded dense trip vectors. Finally, a case of Beijing metro network is used to verify the feasibility of the above methodology. The results show that the proposed method performs well in recognizing mobility patterns and outperforms the existing methods.

Impacts of Bike Sharing Program on Subway Ridership in New York City

2021 ◽  
pp. 036119812110049
Author(s):  
Md Tanvir Ashraf ◽  
Md Amdad Hossen ◽  
Kakan Dey ◽  
Sarah El-Dabaja ◽  
Moathe Aljeri ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Subway Station ◽  
Transit System ◽  
Last Mile ◽  
Bike Sharing ◽  
Transit Services ◽  
Transportation Agencies ◽  
Citi Bike

Bike sharing programs have become increasingly popular in many cities. These services allow users to rent bikes for utilitarian and recreational trips in the urban area. Bike sharing has been considered a suitable mode to support the first- and last-mile connectivity problems of fixed-route transit services. Bike sharing has also emerged as a convenient mode for short-distance trips that previously would not have been possible without using public transit or personal bikes. This study investigated the impacts of Citi Bike—a bike sharing program—on the subway ridership in New York City (NYC), using Poisson-Gamma models. Bike sharing trips with destinations within a quarter-mile radius of a subway station were associated with subway ridership increase. A 10% increase in the number of bike trips increased the average daily subway ridership by 2.3%. However, a higher number of bike stations around a subway station decreased the subway ridership in instances where more bike trips originated (as opposed to ended) in the subway station’s service area. The presence of dedicated bike lanes and bike racks attracted more bike users and increased subway ridership. Findings from this study indicate that the development of bike-friendly infrastructure such as activities outlined in the recent NYC Department of Transport (DOT) “Green Wave” program can increase both bike sharing and subway ridership. In addition, policies and initiatives by transportation agencies to better integrate bike sharing programs with the transit system have the potential to increase the attractiveness of bike sharing programs and maximize the subway ridership.

SmartHO: mobility pattern recognition assisted intelligent handoff in wireless overlay networks

2015 ◽  
Vol 20 (10) ◽  
pp. 4121-4130 ◽  
Author(s):  
Ming Tao ◽  
Huaqiang Yuan ◽  
Xiaoyu Hong ◽  
Jie Zhang
Keyword(s):  
Pattern Recognition ◽  
Overlay Networks ◽  
Mobility Pattern

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Bayesian removal of noise for increased sensitivity in vector pattern recognition lattice imaging of interfaces

1993 ◽  
Vol 51 ◽  
pp. 994-995
Author(s):  
L. Fei ◽  
P. Fraundorf
Keyword(s):  
Pattern Recognition ◽  
Perfect Crystal ◽  
Unit Cell ◽  
Ion Milling ◽  
Beam Radiation ◽  
Major Interest ◽  
Unit Cells ◽  
Mapping Process ◽  
Increased Sensitivity ◽  
Electron Microscopes

Interface structure is of major interest in microscopy. With high resolution transmission electron microscopes (TEMs) and scanning probe microscopes, it is possible to reveal structure of interfaces in unit cells, in some cases with atomic resolution. A. Ourmazd et al. proposed quantifying such observations by using vector pattern recognition to map chemical composition changes across the interface in TEM images with unit cell resolution. The sensitivity of the mapping process, however, is limited by the repeatability of unit cell images of perfect crystal, and hence by the amount of delocalized noise, e.g. due to ion milling or beam radiation damage. Bayesian removal of noise, based on statistical inference, can be used to reduce the amount of non-periodic noise in images after acquisition. The basic principle of Bayesian phase-model background subtraction, according to our previous study, is that the optimum (rms error minimizing strategy) Fourier phases of the noise can be obtained provided the amplitudes of the noise is given, while the noise amplitude can often be estimated from the image itself.

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