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.

THE USE OF LS-SVM FOR SHORT-TERM PASSENGER FLOW PREDICTION / MAŽIAUSIŲ KVADRATŲ ATRAMINIŲ VEKTORIŲ METODO TAIKYMAS TRUMPALAIKIAM KELEIVIŲ SRAUTUI PROGNOZUOTI / ПРИМЕНЕНИЕ МЕТОДА ОПОРНЫХ ВЕКТОРОВ С КВАДРАТИЧНОЙ ФУНКЦИЕЙ ПОТЕРЬ ДЛЯ ПРОГНОЗИРОВАНИЯ КРАТКОСРОЧНЫХ ПОТОКОВ ПАССАЖИРОВ

Transport ◽  
2011 ◽  
Vol 26 (1) ◽  
pp. 5-10 ◽  
Author(s):  
Qian Chen ◽  
Wenquan Li ◽  
Jinhuan Zhao
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Support Vector ◽  
Short Term ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Transit Flow ◽  
Kernel Parameter

Transit flow is the basement of transit planning and scheduling. The paper presents a new transit flow prediction model based on Least Squares Support Vector Machine (LS-SVM). With reference to the theory of Support Vector Machine and Genetic Algorithm, a new short-term passenger flow prediction model is built employing LSSVM, and a new evaluation indicator is used for presenting training permanence. An improved genetic algorithm is designed by enhancing crossover and variation in the use of optimizing the penalty parameter γ and kernel parameter s in LS-SVM. By using this method, passenger flow in a certain bus route is predicted in Changchun. The obtained result shows that there is little difference between actual value and prediction, and the majority of the equal coefficients of a training set are larger than 0.90, which shows the validity of the approach. Santrauka Tranzito srautas yra tranzito planavimo ir eismo tvarkaraščių sudarymo pagrindas. Straipsnis pateikia naują tranzitinio srauto prognozavimo modelį, grindžiamą mažiausių kvadratų atraminių vektorių metodu (Least Squares Support Vector machine, LS-SVm). Remiantis atraminių vektorių metodu (Support Vector machine) ir genetiniu algoritmu (Genetic Algorithm), sudarytas naujas trumpalaikis keleivių srauto prognozavimo modelis, pasitelkiant LS-SVM ir pristatomas naujas vertinimo rodiklis. Taikant naują metodą prognozuojamas keleivių srautas konkrečiame autobuso maršrute Čangčuno mieste Kinijoje. Gautos prognozės rezultatai lyginami su faktiniais. Резюме Транзитный поток – основной фактор при планировании транзита и составлении расписаний движения. В статье представлена новая модель прогноз*а транзитного потока, основанная на методе опорных векторов с квадратичной функцией потерь (Least Squares Support Vector machine – LS-SVm). Представленный новый метод используется для прогноза потока пассажиров на конкретном автобусном маршруте города Чаньчуня (Китай). Результаты прогноза сравниваются с фактическими результатами.

scholarly journals Subway Sudden Passenger Flow Prediction Method Based on Two Factors: Case Study of the Dongsishitiao Station in Beijing

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Chengguang Xie ◽  
Xiaofeng Li ◽  
Bingfa Chen ◽  
Feng Lin ◽  
Yushun Lin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Prediction Method ◽  
Sudden Increase ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Two Factors ◽  
Daily Task ◽  
Subway System ◽  
Passenger Flows ◽  
Factor Data

A sudden increase in passenger flow can primitively lead to continuous congestion of a subway network and thus have a profound impact on the subway system. To prevent the risk caused by sudden overcrowding, the prediction of passenger flow is a daily task of the rail transit management. Most current short-term passenger flow forecasts rely only on inbound passenger flow, which cannot accurately characterize the total impact of sudden passenger flow. To enhance the prediction accuracy, we propose a sudden passenger flow prediction model with two factors, the outbound and inbound passenger flows. The wavelet neural network (WNN) model was used to detect the sudden passenger flow, and subsequently, it is optimized by the genetic algorithm (GA), according to two-factor data characteristics. Sudden passenger flow events from 2014 to 2016 in the Beijing Dongsishitiao Station (DS) were used to train and verify the reliability of the prediction model. The optimized WNN results proved better than the conventional WNN, and the error of models based on two factors was significantly smaller than the models with a single-factor.

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.

Study on the Choice Model of Passenger Travel in Inter-City Transportation Corridor

2013 ◽  
Vol 361-363 ◽  
pp. 2321-2325
Author(s):  
Ai Yun Wang ◽  
Su Li Zhang ◽  
Xin Chun Bo
Keyword(s):  
Choice Behavior ◽  
Influence Factor ◽  
Choice Model ◽  
Flow Distribution ◽  
Likelihood Method ◽  
Passenger Flow ◽  
Transportation Corridor ◽  
Passenger Travel ◽  
Travel Mode Choice ◽  
Generalized Cost

This paper analyzed the influence factor of passenger travel selection in regional transportation corridor. From the point of inter-city traffic integration and the entire travel chain, introduced generalized cost function, established improved Logit model based on differences of travel mode choice behavior of passenger grouped by the income separately and calibrated the parameters of the model using maximum likelihood method. Finally, a case was made combined with Huizhou-Heyuan inter-city transportation corridor, analyzed and verified feasibility and practicality of the passenger flow distribution ratios model and predicted the changes of distribution ratios after the completion of inter-city rail transit in the future.

scholarly journals Passenger Flow Prediction of Subway Transfer Stations Based on Nonparametric Regression Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yujuan Sun ◽  
Guanghou Zhang ◽  
Huanhuan Yin
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Nonparametric Regression ◽  
Support Vector ◽  
Nonparametric Regression Model ◽  
Filter Model ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Transfer Station ◽  
Transfer Stations

Passenger flow is increasing dramatically with accomplishment of subway network system in big cities of China. As convergence nodes of subway lines, transfer stations need to assume more passengers due to amount transfer demand among different lines. Then, transfer facilities have to face great pressure such as pedestrian congestion or other abnormal situations. In order to avoid pedestrian congestion or warn the management before it occurs, it is very necessary to predict the transfer passenger flow to forecast pedestrian congestions. Thus, based on nonparametric regression theory, a transfer passenger flow prediction model was proposed. In order to test and illustrate the prediction model, data of transfer passenger flow for one month in XIDAN transfer station were used to calibrate and validate the model. By comparing with Kalman filter model and support vector machine regression model, the results show that the nonparametric regression model has the advantages of high accuracy and strong transplant ability and could predict transfer passenger flow accurately for different intervals.

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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