Deep Learning Short-Time Interval Passenger Flow Prediction Based on Isomap Algorithm

In this paper,the means of WiFi was used to access to mobile phone MAC address to get passenger flow data and existing prediction methods were compared. Then N6 of Chongqing International Expo Exhibition Center was taken as the object of study and 5min was taken as the time interval to count N6 hall passenger flow from 9:00 am to 17:00 pm of 5 open days to obtain time series. At last, ARMA model was established to predict passenger flow of short time. The results show that the mean we use in this paper has high accuracy of prediction, MAE is 2.8771,and the means can be used for the passenger flow prediction of exhibition well.

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An LSTM-Based Method Considering History and Real-Time Data for Passenger Flow Prediction

Applied Sciences ◽

10.3390/app10113788 ◽

2020 ◽

Vol 10 (11) ◽

pp. 3788 ◽

Cited By ~ 1

Author(s):

Qi Ouyang ◽

Yongbo Lv ◽

Jihui Ma ◽

Jing Li

Keyword(s):

Feature Extraction ◽

Real Time ◽

Short Term Memory ◽

Historical Data ◽

Time Interval ◽

Information Coding ◽

Time Data ◽

Passenger Flow ◽

Flow Prediction ◽

Real Time Data

With the development of big data and deep learning, bus passenger flow prediction considering real-time data becomes possible. Real-time traffic flow prediction helps to grasp real-time passenger flow dynamics, provide early warning for a sudden passenger flow and data support for real-time bus plan changes, and improve the stability of urban transportation systems. To solve the problem of passenger flow prediction considering real-time data, this paper proposes a novel passenger flow prediction network model based on long short-term memory (LSTM) networks. The model includes four parts: feature extraction based on Xgboost model, information coding based on historical data, information coding based on real-time data, and decoding based on a multi-layer neural network. In the feature extraction part, the data dimension is increased by fusing bus data and points of interest to improve the number of parameters and model accuracy. In the historical information coding part, we use the date as the index in the LSTM structure to encode historical data and provide relevant information for prediction; in the real-time data coding part, the daily half-hour time interval is used as the index to encode real-time data and provide real-time prediction information; in the decoding part, the passenger flow data for the next two 30 min interval outputs by decoding all the information. To our best knowledge, it is the first time to real-time information has been taken into consideration in passenger flow prediction based on LSTM. The proposed model can achieve better accuracy compared to the LSTM and other baseline methods.

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Relation between Verbal Estimation and Reproduction of a Short Time Interval: Preliminary Study

Perceptual and Motor Skills ◽

10.2466/pms.1963.17.1.45 ◽

1963 ◽

Vol 17 (1) ◽

pp. 45-46 ◽

Cited By ~ 4

Author(s):

P. D. Du Preez

Keyword(s):

Time Interval ◽

Short Time Interval ◽

Verbal Estimation ◽

Preliminary Study ◽

Short Time

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Estimation of Time-Dependent Turning Fractions at Signalized Intersections

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1644-15 ◽

1998 ◽

Vol 1644 (1) ◽

pp. 142-149 ◽

Cited By ~ 6

Author(s):

Gang-Len Chang ◽

Xianding Tao

Keyword(s):

Signalized Intersections ◽

Efficient Estimation ◽

Estimation Accuracy ◽

Time Interval ◽

Short Time Interval ◽

Simulation Experiments ◽

Proposed Model ◽

Short Time ◽

Estimation Of Time ◽

Additional Constraints

An effective method for estimating time-varying turning fractions at signalized intersections is described. With the inclusion of approximate intersection delay, the proposed model can account for the impacts of signal setting on the dynamic distribution of intersection flows. To improve the estimation accuracy, the use of preestimated turning fractions from a relatively longer time interval has been proposed to serve as additional constraints for the same estimation but over a short time interval. The results of extensive simulation experiments indicated that the proposed method can yield sufficiently accurate as well as efficient estimation of dynamic turning fractions for signalized intersections.

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Micro-Expression Recognition using 3D - CNN

10.54216/fpa.010101 ◽

2020 ◽

pp. 5-13

Author(s):

Vishal Dubey ◽

◽

Bhavya Takkar ◽

...

Keyword(s):

Emotional Response ◽

High Speed ◽

Actual State ◽

Time Interval ◽

Short Time Interval ◽

Expression Recognition ◽

Specific Emotion ◽

3D Cnn ◽

Short Time ◽

Micro Expression

Micro-expression comes under nonverbal communication, and for a matter of fact, it appears for minute fractions of a second. One cannot control micro-expression as it tells about our actual state emotionally, even if we try to hide or conceal our genuine emotions. As we know that micro-expressions are very rapid due to which it becomes challenging for any human being to detect it with bare eyes. This subtle-expression is spontaneous, and involuntary gives the emotional response. It happens when a person wants to conceal the specific emotion, but the brain is reacting appropriately to what that person is feeling then. Due to which the person displays their true feelings very briefly and later tries to make a false emotional response. Human emotions tend to last about 0.5 - 4.0 seconds, whereas micro-expression can last less than 1/2 of a second. On comparing micro-expression with regular facial expressions, it is found that for micro-expression, it is complicated to hide responses of a particular situation. Micro-expressions cannot be controlled because of the short time interval, but with a high-speed camera, we can capture one's expressions and replay them at a slow speed. Over the last ten years, researchers from all over the globe are researching automatic micro-expression recognition in the fields of computer science, security, psychology, and many more. The objective of this paper is to provide insight regarding micro-expression analysis using 3D CNN. A lot of datasets of micro-expression have been released in the last decade, we have performed this experiment on SMIC micro-expression dataset and compared the results after applying two different activation functions.

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Inter-rater and inter-device reliability of mechanical thresholds measurement with the Electronic von Frey Anaesthesiometer and the SMALGO in healthy cats

Journal of Feline Medicine and Surgery ◽

10.1177/1098612x18813426 ◽

2018 ◽

Vol 21 (10) ◽

pp. 979-984 ◽

Cited By ~ 2

Author(s):

Chiara Adami ◽

Elena Lardone ◽

Paolo Monticelli

Keyword(s):

Small Animal ◽

Time Interval ◽

Short Time Interval ◽

Sensory Thresholds ◽

Additional Tool ◽

Bland Altman Method ◽

Device Reliability ◽

Short Time ◽

Mechanical Thresholds ◽

Level Of Agreement

Objectives The aim of this study was to compare the Electronic von Frey Anaesthesiometer (EVF) and the Small Animal ALGOmeter (SMALGO), used to measure sensory thresholds in 13 healthy cats at both the stifle and the lumbosacral joint, in terms of inter-rater and inter-device reliability. Methods Two independent observers carried out the sets of measurements in a randomised order, with a 45 min interval between them, in each cat. The inter-rater and inter-device reliability were evaluated by calculating the inter-rater correlation coefficient (ICC) for each pair of measurements. The Bland–Altman method was used as an additional tool to assess the level of agreement between the two algometers. Results The mean ± SD sensory thresholds measured with the EVF were 311 ± 116 g and 378 ± 178 g for the stifle and for the lumbosacral junction, respectively, whereas those measured with the SMALGO were 391 ±172 g and 476 ± 172 g. The inter-rater reliability was fair (ICC >0.4) for each pair of measurements except those taken at the level of the stifle with the SMALGO, for which the level of agreement between observers A and B was poor (ICC = 0.01). The inter-device reliability was good (ICC = 0.73; P = 0.001). The repetition of the measurements affected reliability, as the thresholds obtained after the 45 min break were consistently lower than those measured during the first part of the trial ( P = 0.02). Conclusions and relevance The EVF and the SMALGO may be used interchangeably in cats, especially when the area to be tested is the lumbosacral joint. However, when the thresholds are measured at the stifle, the inter-observer reliability is better with the EVF than with the SMALGO. The reliability decreases when the measurements are repeated within a short time interval, suggesting a limited clinical applicability of quantitative sensory testing with both algometers in cats.

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Approximate exit probabilities for a Brownian bridge on a short time interval, and applications

Advances in Applied Probability ◽

10.2307/1427195 ◽

1989 ◽

Vol 21 (1) ◽

pp. 1-19 ◽

Cited By ~ 7

Author(s):

H. R. Lerche ◽

D. Siegmund

Keyword(s):

Virial Coefficient ◽

Large Deviation ◽

Brownian Bridge ◽

Exit Time ◽

Second Virial Coefficient ◽

Dimensional Euclidean Space ◽

Time Interval ◽

Short Time Interval ◽

First Exit Time ◽

Short Time

Let T be the first exit time of Brownian motion W(t) from a region ℛ in d-dimensional Euclidean space having a smooth boundary. Given points ξ0 and ξ1 in ℛ, ordinary and large-deviation approximations are given for Pr{T < ε |W(0) = ξ0, W(ε) = ξ 1} as ε → 0. Applications are given to hearing the shape of a drum and approximating the second virial coefficient.

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The Research of Railway Passenger Flow Prediction Model Based on BP Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.605-607.2366 ◽

2012 ◽

Vol 605-607 ◽

pp. 2366-2369 ◽

Cited By ~ 2

Author(s):

Yao Wang ◽

Dan Zheng ◽

Shi Min Luo ◽

Dong Ming Zhan ◽

Peng Nie

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Forecast Model ◽

Good Effect ◽

Passenger Flow ◽

Model Based ◽

Flow Prediction ◽

Transport Organization ◽

Railway Passenger ◽

Short Time

Based on analyzing the principle of BP neural network and time sequence characteristics of railway passenger flow, the forecast model of railway short-term passenger flow based on BP neural network was established. This paper mainly researches on fluctuation characteristics and short-time forecast of holiday passenger flow. Through analysis of passenger flow and then be used in passenger flow forecasting in order to guide the transport organization program especially the train plan of extra passenger train. And the result shows the forecast model based on BP neural network has a good effect on railway passenger flow prediction.

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The physicochemical properties of five vegetable oils exposed at high temperature for a short-time-interval

Journal of Food Composition and Analysis ◽

10.1016/j.jfca.2021.104305 ◽

2021 ◽

pp. 104305

Author(s):

Laura Mitrea ◽

Bernadette-Emoke Teleky ◽

Loredana-Florina Leopold ◽

Silvia-Amalia Nemes ◽

Diana Plamada ◽

...

Keyword(s):

High Temperature ◽

Physicochemical Properties ◽

Vegetable Oils ◽

Time Interval ◽

Short Time Interval ◽

Short Time

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Active Optimum Control of Orthotropic Plates Using Piezoelectric Stiffeners

14th Biennial Conference on Mechanical Vibration and Noise: Intelligent Structures, Materials, and Vibrations ◽

10.1115/detc1993-0157 ◽

1993 ◽

Author(s):

Victor Birman ◽

Sarp Adali

Keyword(s):

Time Interval ◽

Short Time Interval ◽

Bending Moments ◽

Optimum Control ◽

Orthotropic Plates ◽

Time Intervals ◽

Control Forces ◽

Short Time ◽

Alternating Voltage ◽

Over Time

Abstract Active control of orthotropic plates subjected to an impulse loading is considered. The dynamic response is minimized using in-plane forces or bending moments induced by piezoelectric stiffeners bonded to the opposite surfaces of the plate and placed symmetrically with respect to the middle plane. The control forces and moments are activated by a piece-wise constant alternating voltage with varying switch-over time intervals. The magnitude of voltage is bounded while the switch-over time intervals are constantly adjusted to achieve an optimum control. Numerical examples presented in the paper demonstrate the effectiveness of the method and the possibility of reducing the vibrations to very small amplitudes within a short time interval which is in the order of a second.

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