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

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.

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

Sustainability ◽

10.3390/su11195281 ◽

2019 ◽

Vol 11 (19) ◽

pp. 5281 ◽

Cited By ~ 1

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.

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LSTM Based Architecture for Short-Term Metro Passenger Flow Prediction

CICTP 2020 ◽

10.1061/9780784483053.082 ◽

2020 ◽

Author(s):

Yunshi Long ◽

Liang Zou

Keyword(s):

Short Term ◽

Passenger Flow ◽

Flow Prediction

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Short-term Passenger Flow Prediction on Bus Stop Based on Hybrid Model

Proceedings of the 2017 2nd International Conference on Electrical, Control and Automation Engineering (ECAE 2017) ◽

10.2991/ecae-17.2018.74 ◽

2018 ◽

Author(s):

Zhijian Wang ◽

Chunlei Yang ◽

Chao Zang

Keyword(s):

Hybrid Model ◽

Short Term ◽

Bus Stop ◽

Passenger Flow ◽

Flow Prediction

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An AutoEncoder and LSTM-Based Traffic Flow Prediction Method

Sensors ◽

10.3390/s19132946 ◽

2019 ◽

Vol 19 (13) ◽

pp. 2946 ◽

Cited By ~ 19

Author(s):

Wangyang Wei ◽

Honghai Wu ◽

Huadong Ma

Keyword(s):

Traffic Flow ◽

Prediction Accuracy ◽

Short Term Memory ◽

Smart Cities ◽

Prediction Method ◽

Short Term ◽

Term Memory ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Long Short Term Memory

Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.

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Short-Term Abnormal Passenger Flow Prediction Based on the Fusion of SVR and LSTM

IEEE Access ◽

10.1109/access.2019.2907739 ◽

2019 ◽

Vol 7 ◽

pp. 42946-42955 ◽

Cited By ~ 21

Author(s):

Jianyuan Guo ◽

Zhen Xie ◽

Yong Qin ◽

Limin Jia ◽

Yaguan Wang

Keyword(s):

Short Term ◽

Passenger Flow ◽

Flow Prediction

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Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.5819 ◽

2020 ◽

Vol 34 (04) ◽

pp. 4020-4027

Author(s):

Yongshun Gong ◽

Zhibin Li ◽

Jian Zhang ◽

Wei Liu ◽

Jinfeng Yi

Keyword(s):

Real World ◽

Intelligent Transportation Systems ◽

Domain Knowledge ◽

Urban Transportation ◽

Transportation Systems ◽

Metro Station ◽

Practical Applications ◽

Passenger Flow ◽

Flow Prediction ◽

Transportation Development

Recently, practical applications for passenger flow prediction have brought many benefits to urban transportation development. With the development of urbanization, a real-world demand from transportation managers is to construct a new metro station in one city area that never planned before. Authorities are interested in the picture of the future volume of commuters before constructing a new station, and estimate how would it affect other areas. In this paper, this specific problem is termed as potential passenger flow (PPF) prediction, which is a novel and important study connected with urban computing and intelligent transportation systems. For example, an accurate PPF predictor can provide invaluable knowledge to designers, such as the advice of station scales and influences on other areas, etc. To address this problem, we propose a multi-view localized correlation learning method. The core idea of our strategy is to learn the passenger flow correlations between the target areas and their localized areas with adaptive-weight. To improve the prediction accuracy, other domain knowledge is involved via a multi-view learning process. We conduct intensive experiments to evaluate the effectiveness of our method with real-world official transportation datasets. The results demonstrate that our method can achieve excellent performance compared with other available baselines. Besides, our method can provide an effective solution to the cold-start problem in the recommender system as well, which proved by its outperformed experimental results.

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

Journal of Advanced Transportation ◽

10.1155/2018/5942763 ◽

2018 ◽

Vol 2018 ◽

pp. 1-15 ◽

Cited By ~ 7

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.

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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 ◽

10.3846/16484142.2011.555472 ◽

2011 ◽

Vol 26 (1) ◽

pp. 5-10 ◽

Cited By ~ 16

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.

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