Passenger Flow Prediction of Exhibition Based on ARMA

2014 ◽  
Vol 667 ◽  
pp. 11-15 ◽  
Author(s):  
Ling Ling Chen ◽  
Pei Hua He ◽  
Lei Cao ◽  
Shu Guang Liu ◽  
Dan Ping Liu ◽  
...  
Keyword(s):  
Arma Model ◽  
High Accuracy ◽  
Prediction Methods ◽  
Time Interval ◽  
Passenger Flow ◽  
Flow Prediction ◽  
The Mean ◽  
Object Of Study ◽  
Accuracy Of Prediction ◽  
Short Time

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.

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

2020 ◽  
Vol 10 (11) ◽  
pp. 3788 ◽  
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.

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

2012 ◽  
Vol 605-607 ◽  
pp. 2366-2369 ◽  
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.

scholarly journals Passenger Flow Prediction of Integrated Passenger Terminal Based on K-Means–GRNN

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yifan Tan ◽  
Haixu Liu ◽  
Yun Pu ◽  
Xuemei Wu ◽  
Yubo Jiao
Keyword(s):  
Prediction Model ◽  
Cluster Algorithm ◽  
Flow Distribution ◽  
High Accuracy ◽  
Distribution Center ◽  
Passenger Transport ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Passenger Travel ◽  
Clustering Effect

As the passenger flow distribution center cooperating with various modes of transportation, the comprehensive passenger transport hub brings convenience to passengers. With the diversification of passenger travel modes, the passenger flow scale gradually increases, which brings significant challenges to the integrated passenger hub. Therefore, it is urgent to solve the problem of early warning and response to the future passenger flow to avoid congestion accidents. In this paper, the passenger flow GRNN prediction model is proposed, based on the K-means cluster algorithm, and an improved index named BWPs (Between-Within Proportion-Similarity) is proposed to improve the clustering effect of K-means so that the clustering effect of the new index is verified. In addition, the passenger flow data are studied and trained by combining with the GRNN neural network model based on parameter optimization (GA); the passenger flow prediction model is obtained. Finally, the passenger flow of Chengdu East Railway Station has been taken as an example, which is divided into 16 models, and each type of passenger flow is predicted, respectively. Compared with the traditional method, the results show that the model can predict the passenger flow with high accuracy.

Abstract TP39: High Accuracy of Magnetic Resonance T2*- Weighted Sequences for Detection of Intracranial Arterial Thrombus

Stroke ◽  
2016 ◽  
Vol 47 (suppl_1) ◽  
Author(s):  
Afonso P Liberato ◽  
Santosh Shah ◽  
Noor Maza ◽  
Isabelle Barnaure ◽  
Ramon G Gonzalez ◽  
...  
Keyword(s):  
Ct Angiography ◽  
Imaging Modality ◽  
Arterial Occlusion ◽  
High Accuracy ◽  
Time Interval ◽  
Vessel Occlusion ◽  
Predictive Values ◽  
Arterial Thrombus ◽  
The Mean ◽  
Mean Time

Introduction: Rapid detection and location of vessel occlusion are pivotal in the intra-arterial management of patients with acute stroke in the emergency room. MRI has demonstrated to detect intravascular thrombus but its accuracy compared to CT angiography has not been well established. Hypothesis: Our purpose is to determine the accuracy of 1.5 T MRI T2*-weighted (W) sequences compared to immediate CT angiography as the standard reference imaging modality, for detection of intra-arterial thrombus in patients with suspected acute MCA infarction. Methods: Consecutive patients with suspected middle cerebral artery (MCA) territory stroke were selected from 2008 to 2009. The inclusion criteria for the study subjects: CTA, T2*W sequences included on MRI protocol and restricted diffusion in MCA territory on DWI within 12 hrs of clinical onset. Two investigators reviewed DWI and T2*W sequences for the presence of infarction and thrombus. Intracranial internal carotid artery (ICA), M1 and M2 segments of the MCA were accessed. Consensus was reached with a third reviewer for data analyses. Accuracy, sensitivity, specificity, positive and negative predictive values (PPV/NPV) were calculated. Results: Fifty-one patients were included in the study, of which 40 patients had confirmed arterial thrombus and 11 patients had normal studies on CTA. Of the subjects with arterial occlusion on CTA, the mean time interval from stroke onset to CTA was 4.2 h +/- 2.3 h (range, 0.4-12h). The mean time interval from CTA to MRI was 29.5 min +/- 11.1 min. Twenty-six cases showed M1 thrombus on CTA, of these, 22 cases had corresponding thrombus and 4 cases had no abnormality in T2*W sequences on MRI. Nevertheless, 25 patients demonstrated no M1 thrombus, either on CTA or MRI. After statistical analyses, we observed an accuracy of 92%, sensitivity of 85%, specificity of 100%, PPV 100% and NPV of 86% for M1 occlusion. The Kappa obtained was 0.79. Conclusion: In conclusion, T2*W sequences demonstrated overall high accuracy and specificity for detection of arterial thrombus in the M1 segment of the MCA in patients with suspected acute MCA ischemic stroke.

scholarly journals Efficacy of two inducing agents PG and DoM+SGnRH in the induced breeding of the major carp, kalibaus (Labeo calbasu)

2013 ◽  
Vol 31 ◽  
pp. 23-26
Author(s):  
Jesmin Ahktar ◽  
Abdus Salam Bhuiyan
Keyword(s):  
Body Weight ◽  
Single Dose ◽  
Wheat Bran ◽  
The Other ◽  
Time Interval ◽  
Breeding Performance ◽  
Endangered Fish ◽  
Nursery Pond ◽  
The Mean ◽  
Short Time

An experiment on the induced breeding of the endangered fish, Labeo calbasu (Hamilton-Buchanam) was conducted in the Fish Seed Multiplication Farm,Rajshahi to know the efficacy of two inducing agents (PG and DoM+SGnRH). Three breeding trials of each inducing agent were performed. A total of 24 females weighing from 1.5 kg to 2 kg were given an initial and a resolving dose of 1.5 mg and 6 mg PG extract per kg body weight respectively as treatment-1. On the other hand, a total of 24 females weighing from 1.5 kg to 2 kg were given a single dose of 12 mg DoM + SGnRH/kg as treatment-2. In case of treatment-1, 12 males weighing from 1.5 kg to 1.95 kg were administered a single dose of 1.5 mg PG/kg body weight during resolving dose of female. In treatment-2, 12 males weighing from 1.5 kg to 1.8 kg were administered 3 mg DoM+SGnRH /kg body weight during initial dose of females. In treatment-1, the time interval between initial and resolving dose was 5 hours and ovulation occurred in all the injected females within 6 hours after resolving dose. Ovulation occurred within 6 to 8 hours after the injection of inducing agents for treatment-2. The mean rates of ovulation, fertilization and hatching were 100%, 77.36% and 74.5% respectively in treatment-1. On the contrary, the mean rates of ovulation, fertilization and hatching were 83.33%, 63.83% and 59.66% in treatment-2. Hatchery produced fry were reared in nursery pond for 40 days. In nursery pond. Flour, oil cake and wheat bran were applied as nursery feeds. Both the inducing agents were effective in respect of overall breeding performance. But the best results were obtained with PG although in case of DoM+SGnRH complete breeding takes place within short time with less labour and cost than that of PG.DOI: http://dx.doi.org/10.3329/ujzru.v31i0.15376Univ. j. zool. Rajshahi Univ. Vol. 31, 2012 pp. 23-26

scholarly journals Combination of lidocaine/prilocaine with tramadol for short time anaesthesia-analgesia in chelonians: 18 cases

2015 ◽  
Vol 84 (1) ◽  
pp. 71-75 ◽  
Author(s):  
Filippo Spadola ◽  
Manuel Morici ◽  
Zdeněk Knotek
Keyword(s):  
Surgical Treatment ◽  
Induction Time ◽  
Recovery Time ◽  
Hind Limb ◽  
Time Interval ◽  
Withdrawal Reflex ◽  
The Mean ◽  
Lidocaine Prilocaine ◽  
Short Time ◽  
Pain Stimuli

The aim of this study was the evaluation of the practical use of lidocaine/prilocaine cream as the local anaesthetic in combination with tramadol anaesthesia for the surgical treatment of prolapsed penis in chelonians. Eighteen animals were included in this study. After administration of tramadol, prolapsed penis was cleaned and disinfected. Lidocaine/prilocaine cream at a dose of 1g/10 cm2 was applied on the penile mucosa. The time interval from lidocaine/prilocaine application to a loss of hind limb withdrawal reflex and the response to penile mucosa pinching was recorded as the induction time for surgical anaesthesia. The time interval from lidocaine/prilocaine application to full restoration of tail and hind limb withdrawal reflex was recorded as the recovery time. In two male chelonians the response to pain stimuli persisted for more than 20 min after lidocaine/prilocaine cream application, and the anaesthetic cream had to be re-administered. After this second application of lidocaine/prilocaine cream, surgical anaesthesia was reached within 28 and 32 min. The mean induction time for surgical anaesthesia was 19.22 ± 4.36 min. The mean recovery time was 40.83 ± 7.68 min. In all 18 male chelonians the surgical treatment of prolapsed penile was uneventful. Topical application of lidocaine/prilocaine cream can be used as a feasible form of local anaesthesia in combination with tramadol analgesia management for minor surgical procedures in chelonians.

scholarly journals Proposal of Measurement Occasions for Unbiased Evaluation of Psychotherapy

2021 ◽  
pp. 1-6
Author(s):  
Edgar Geissner ◽  
Petra Maria Ivert ◽  
Manfred Schmitt
Keyword(s):  
Single Case ◽  
Treatment Evaluation ◽  
Time Interval ◽  
Short Time Interval ◽  
Symptom Scores ◽  
The Mean ◽  
Therapy Success ◽  
Short Time ◽  
Simplified Calculation

Studies complementing the assessment of symptoms right before (t1), right after therapy (t2), and at follow-up (t3) with an assessment of symptoms preceding the waiting period without intervention (t0) have revealed substantial t0–t1 changes. We discuss this phenomenon based on our own data and address the following questions: does it make sense to compare symptoms at the beginning of therapy (t1) with symptoms at the end of therapy (t2) or at follow-up (t3)? Or does it make more sense to use t0 instead of t1? We argue for the latter alternative based on the following reasons. (1) Symptom descriptions at t0 are realistic. (2) Expecting therapy success mitigates symptom descriptions at t1. (3) Security signals emitted from the therapy context also mitigate symptoms, especially anxiety, at t1. (4) Regression toward the mean reduces the validity of single occasion assessments. Controlling for regression requires two occasions of measurement with a short time interval at t0 (t01 and t02). It follows from this reasoning that therapy success should be evaluated using the t02–t2 and t02–t3 intervals. Single case evaluations require reliable critical differences. This will be illustrated using a concrete example. The validity of treatment evaluation can be increased via the elimination of non-pathological symptom scores. A simplified calculation of cut-off scores can facilitate applied treatment evaluation. Unspecific t0–t1 changes do not challenge therapy effects according to t1–t2 changes. Rather, they are part of the whole therapy process.

scholarly journals An Intelligent Passenger Flow Prediction Method for Pricing Strategy and Hotel Operations

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Tianyang Wang
Keyword(s):  
Prediction Model ◽  
Linear Prediction ◽  
Short Term Memory ◽  
Risk Level ◽  
Support Vector ◽  
Prediction Methods ◽  
Nonlinear Prediction ◽  
Prediction Ability ◽  
Passenger Flow ◽  
Flow Prediction

Hospitality industry plays a crucial role in the development of tourism. Predicting the future demand of a hotel is a key step in the process of hotel revenue management. Hotel passenger flow prediction plays an important role in guiding the formulation of hotel pricing and operating strategies. On the one hand, hotel passenger flow prediction can provide decision support for hotel managers and effectively avoid the waste of hotel resources and loss of revenue caused by the loss of customers. On the other hand, it is the guarantee of the priority occupation of business opportunities by hotel enterprises, which can help hotel enterprises adjust their operation strategies reasonably to better adapt to the market situation. In addition, hotel passenger flow prediction is helpful to judge the overall operating condition of the hotel industry and assess the risk level of the hotel project to be built. Hotel passenger flow is affected by many factors, such as weather, environment, season, holidays, economy, and emergencies, and has the characteristics of complex nonlinear fluctuation. The existing demand predicting methods include linear methods and nonlinear methods. The linear prediction methods rely on the stability of environment and time series, so they cannot completely simulate the complex nonlinear fluctuations characteristics of hotel passenger flow. Traditional nonlinear prediction methods need to improve the prediction accuracy, and they are difficult to deal with the increasing data of hotel passenger flow. Based on the above analysis, this paper constructs a deep learning prediction model based on Long Short-Term Memory (LSTM) to predict the number of actual monthly arrival bookings. The number of actual monthly arrival bookings can reflect the actual monthly passenger flow of a hotel. The prediction model can effectively reduce the loss caused by cancellation or nonarrival of bookings due to various reasons and improve the hotel revenue. The experimental part of this paper is based on the booking demand dataset of a resort hotel in Portugal from July 1, 2015, to August 31, 2017. Artificial neural network (ANN) and support vector regression (SVR) are built as benchmark models to predict the number of actual monthly arrival bookings of this hotel. The experimental results show that, compared with the benchmark models, the LSTM model can effectively improve the prediction ability and provide necessary reference for the hotel's future pricing decision and operation mode arrangement.

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