scholarly journals Analysis of Prediction Accuracy under the Selection of Optimum Time Granularity in Different Metro Stations

2019 ◽  
Vol 11 (19) ◽  
pp. 5281 ◽  
Author(s):  
Peikun Li ◽  
Chaoqun Ma ◽  
Jing Ning ◽  
Yun Wang ◽  
Caihua Zhu
Keyword(s):  
Prediction Error ◽  
Prediction Accuracy ◽  
Goodness Of Fit ◽  
Pearson Correlation ◽  
Support Vector ◽  
Time Interval ◽  
Flow Data ◽  
Short Term ◽  
Passenger Flow ◽  
Flow Prediction

The improvement of accuracy of short-term passenger flow prediction plays a key role in the efficient and sustainable development of metro operation. The primary objective of this study is to explore the factors that influence prediction accuracy from time granularity and station class. An important aim of the study was also in presenting the proposition of change in a forecasting method. Passenger flow data from 87 Metro stations in Xi’an was collected and analyzed. A framework of short-term passenger flow based on the Empirical Mode Decomposition-Support Vector Regression (EMD-SVR) was proposed to predict passenger flow for different types of stations. Also, the relationship between the generation of passenger flow prediction error and passenger flow data was investigated. First, the metro network was classified into four categories by using eight clustering factors based on the characteristics of inbound passenger flow. Second, Pearson correlation coefficient was utilized to explore the time interval and time granularity for short-term passenger flow prediction. Third, the EMD-SVR was used to predict the passenger flow in the optimal time interval for each station. Results showed that the proposed approach has a significant improvement compared to the traditional passenger flow forecast approach. Lookback Volatility (LVB) was applied to reflect the fluctuation difference of passenger flow data, and the linear fitting of prediction error was conducted. The goodness-of-fit (R2) was found to be 0.768, indicating a good fitting of the data. Furthermore, it revealed that there are obvious differences in the prediction error of the four kinds of stations.

scholarly journals Short-Term Origin-Destination Based Metro Flow Prediction with Probabilistic Model Selection Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xiaoqing Dai ◽  
Lijun Sun ◽  
Yanyan Xu
Keyword(s):  
Model Selection ◽  
Smart Card ◽  
Probabilistic Model ◽  
Travel Demand ◽  
Combination Method ◽  
Support Vector ◽  
Short Term ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Smart Card Data

Reliable prediction of short-term passenger flow could greatly support metro authorities’ decision processes, help passengers to adjust their travel schedule, or, in extreme cases, assist emergency management. The inflow and outflow of the metro station are strongly associated with the travel demand within metro networks. The purpose of this paper is to obtain such prediction. We first collect the origin-destination information from the smart-card data and explore the passenger flow patterns in a metro system. We then propose a data driven framework for short-term metro passenger flow prediction with the ability to utilize both spatial and temporal related information. The approach adopts two forecasts as basic models and then uses a probabilistic model selection method, random forest classification, to combine the two outputs to achieve a better forecast. In the experiments, we compare the proposed model with four other prediction models, i.e., autoregressive-moving-average, neural networks, support vector regression, and averaging ensemble model, as well as the basic models. The results indicate that the proposed approach outperforms the others in most cases. The origin-destination flows extracted from smart-card data can be successfully exploited to describe different metro travel patterns. And the framework proposed here, especially the probabilistic combination method, can improve the performance of short-term transportation prediction.

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 Short-term passenger flow forecast for urban rail transit based on multi-source data

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wei Li ◽  
Liying Sui ◽  
Min Zhou ◽  
Hairong Dong
Keyword(s):  
Traffic Flow ◽  
Urban Traffic ◽  
Urban Rail Transit ◽  
Support Vector ◽  
Rail Transit ◽  
Short Term ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Urban Rail ◽  
Svm Model

AbstractShort-term passenger flow prediction in urban rail transit plays an important role because it in-forms decision-making on operation scheduling. However, passenger flow prediction is affected by many factors. This study uses the seasonal autoregressive integrated moving average model (SARIMA) and support vector machines (SVM) to establish a traffic flow prediction model. The model is built using intelligent data provided by a large-scale urban traffic flow warning system, such as accurate passenger flow data, collected using the Internet of things and sensor networks. The model proposed in this paper can adapt to the complexity, nonlinearity, and periodicity of passenger flow in urban rail transit. Test results on a Beijing traffic dataset show that the SARI-MA–SVM model can improve accuracy and reduce errors in traffic prediction. The obtained pre-diction fits well with the measured data. Therefore, the SARIMA–SVM model can fully charac-terize traffic variations and is suitable for passenger flow prediction.

scholarly journals An Incremental Learning Based Convolutional Neural Network Model for Large-Scale and Short-Term Traffic Flow

2021 ◽  
Vol 11 (2) ◽  
pp. 143-151
Author(s):  
Feng Yu ◽  
◽  
Jinglong Fang ◽  
Bin Chen ◽  
Yanli Shao
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Neural Network Model ◽  
Incremental Learning ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Flow Data ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction

Traffic flow prediction is very important for smooth road conditions in cities and convenient travel for residents. With the explosive growth of traffic flow data size, traditional machine learning algorithms cannot fit large-scale training data effectively and the deep learning algorithms do not work well because of the huge training and update costs, and the prediction accuracy may need to be further improved when an emergency affecting traffic occurs. In this study, an incremental learning based convolutional neural network model, TF-net, is proposed to achieve the efficient and accurate prediction of large-scale and short-term traffic flow. The key idea is to introduce the uncertainty features into the model without increasing the training cost to improve the prediction accuracy. Meanwhile, based on the idea of combining incremental learning with active learning, a certain percentage of typical samples in historical traffic flow data are sampled to fine-tune the prediction model, so as to further improve the prediction accuracy for special situations and ensure the real-time requirement. The experimental results show that the proposed traffic flow prediction model has better performance than the existing methods.

Novel Three-Stage Framework for Prioritizing and Selecting Feature Variables for Short-Term Metro Passenger Flow Prediction

2020 ◽  
Vol 2674 (8) ◽  
pp. 192-205
Author(s):  
Yangyang Zhao ◽  
Lu Ren ◽  
Zhenliang Ma ◽  
Xinguo Jiang
Keyword(s):  
Prediction Accuracy ◽  
Clustering Algorithm ◽  
Predictor Variables ◽  
Short Term ◽  
Metro Station ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Testing Dataset ◽  
Metro Systems ◽  
Selection Of

Short-term metro passenger flow prediction is vital for the operation and management of metro systems. Most studies focus on the higher prediction accuracy with statistical and machine learning methods, but little attention has been paid to the prioritization and selection of feature variables, especially for different metro station types. This study aims to analyze the effect of feature variables on the prediction results, and then select appropriate predictor variables accordingly. A novel three-stage framework is proposed to prioritize feature variables for short-term metro passenger flow prediction, including station clustering, feature extraction, and variable prioritization. A hierarchical clustering algorithm (AHC) is developed for station clustering, the results of which are verified by the K-means and Davies-Bouldin (DB) statistical index. We then extract the temporal, spatial, and external features. Finally, the association between the variables and the prediction results is explored using tree-based models. The proposed framework is demonstrated and validated with data collected from Shanghai Metro Automatic Fare Collection (AFC) system. The results highlight that the importance of feature variables for developing models varies between stations, whereas only a few variables are found to explain most of the variation in the testing dataset; different feature variables lead to distinct differences in prediction accuracy, and simply adding more predictor variables does not necessarily lead to higher prediction accuracy. In addition, the station type and prediction type (i.e., tap-in and tap-out) have little influence on the selection of feature variables.

Support Vector Machine for Short-Term Traffic Flow Prediction and Improvement of Its Model Training using Nearest Neighbor Approach

2020 ◽  
pp. 036119812098043
Author(s):  
Trinh Dinh Toan ◽  
Viet-Hung Truong
Keyword(s):  
Support Vector Machine ◽  
Traffic Flow ◽  
Prediction Accuracy ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Rolling Horizon ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction

Short-term prediction of traffic flow is essential for the deployment of intelligent transportation systems. In this paper we present an efficient method for short-term traffic flow prediction using a Support Vector Machine (SVM) in comparison with baseline methods, including the historical average, the Current Time Based, and the Double Exponential Smoothing predictors. To demonstrate the efficiency and accuracy of the SVM method, we used one-month time-series traffic flow data on a segment of the Pan Island Expressway in Singapore for training and testing the model. The results show that the SVM method significantly outperforms the baseline methods for most prediction intervals, and under various traffic conditions, for the rolling horizon of 30 min. In investigating the effect of the input-data dimension on prediction accuracy, we found that the rolling horizon has a clear effect on the SVM’s prediction accuracy: for the rolling horizon of 30–60 min, the longer the rolling horizon, the more accurate the SVM prediction is. To look for a solution for improvement of the SVM’s training performance, we investigate the application of k-Nearest Neighbor method for SVM training using both actual data and simulated incident data. The results show that the k- Nearest Neighbor method facilitates a substantial reduction of SVM training size to accelerate the training without compromising predictive performance.

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.

scholarly journals Prediction of Agricultural Commodities Futures Prices: A DQN-LSTM Method

2021 ◽  
Author(s):  
Hanxiao Xu ◽  
Jie Liang ◽  
Wenchaun Zang
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Prediction Accuracy ◽  
Future Application ◽  
Support Vector ◽  
Structural Parameter ◽  
Futures Prices ◽  
Agricultural Commodities ◽  
Short Term ◽  
Time Duration

Abstract This paper combines deep Q network (DQN) with long and short-term memory (LSTM) and proposes a novel hybrid deep learning method called DQN-LSTM framework. The proposed method aims to address the prediction of five Chinese agricultural commodities futures prices over different time duration. The DQN-LSTM applies the strategy enhancement of deep reinforcement learning to the structural parameter optimization of deep recurrent networks, and achieves the organic integration of two types of deep learning algorithms. The new framework has the capacity of self-optimization and learning of parameters, thus improving the performance of prediction by its own iteration, which shows great prospects for future application in financial prediction and other directions. The performance of the proposed method is evaluated by comparing the effectiveness of the DQN-LSTM method with that of traditional predicting methods such as auto-regressive integrated moving average (ARIMA), support vector machine (SVR) and LSTM. The results show that the DQN-LSTM method can effectively optimize the traditional LSTM structural parameters through policy iteration of the deep reinforcement learning algorithm, which contributes to a better long and short-term prediction accuracy. In particular, the longer the prediction period, the more obvious the advantage of prediction accuracy of a DQN-LSTM method.

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