Short-term Passenger Flow Research Articles

Machine learning based method for forecasting short-term passenger flow in urban rail stations

Urban rail transit features much strength, such as large capacity, safety and environment-friendliness, and it becomes a preferred choice for most passengers. It also plays a prominent part in solving urban traffic problems. In order to improve the operation efficiency of the urban rail transit system and achieve the goal of smart operations, this paper applies machine learning algorithms and completes the feature engineering of urban rail transit passenger flow data in terms of time, space and external factors. Based on passenger flow characteristics as collected, we build the short-term passenger flow forecast models, which include light gradient boosting machine (LightGBM) model, long short-term memory (LSTM) model, and LightGBM-LSTM fusion model. Besides, we construct the autoregressive integrated moving average (ARIMA) model and extreme gradient boosting (XGBoost) model for experiment comparison. Finally, we conduct the passenger flow forecasting experiments on the Hangzhou subway dataset based on the five prediction-models mentioned above. Then, five types of subway stations are selected (residential type, occupation type, residential-occupation type, business type, and transportation hubs type) and three accuracy evaluation indicators are chosen (mean absolute error, root mean square error and mean absolute percentage error) to evaluate the prediction accuracy of the five prediction models. The experimental results show that, the multi-feature machine learning model can effectively forecast urban rail transit short-term passenger flow which is difficult for the traditional time series model. However, the single model has poor adaptability to different types of subway stations. Compared with a single model, LightGBM-LSTM model equipped with merits of multiple models, fulfills a better function in forecasting and a better adaptability to different types of urban rail transit stations.

GMM Clustering Based on WOA Optimization and Space-Time Coupled Urban Rail Traffic Flow Prediction by CEEMD-SE-BiGRU-AM

Accurately predicting the short-term passenger flow of urban subways is very important for urban subway stations to formulate passenger flow organization and evacuation plans effectively and rationally plan passenger travel routes. This paper establishes a novel framework to predict the hourly inbound and outbound passenger flow of subway stations based on WOA-GMM station classifiers, CEEMD-SE noise reduction, and BiGRU optimized by attention. Firstly, this paper classifies subway stations using the improved Gaussian mixture model (GMM) with Whale Optimization Algorithm (WOA) to realize the feature extraction of different types of subway stations. Secondly, this paper uses the Complementary Ensemble Empirical Mode Decomposition (CEEMD) to decompose the noise reduction of each station’s hourly inbound and outbound passenger flow. It combines the empirical modal components by calculating the sample entropy (SE), which makes the time series stable and reduces the time cost of forecasting. Finally, a Bidirectional Gated Recurrent Unit (BiGRU) model improved by attention mechanism (AM) is established for each station’s inbound and outbound passenger flow. The prediction model established in this paper is verified by subway passenger flow data in Shanghai, China, within 24 days. Finally, it is concluded that the model can predict the passenger flow of subway stations. Compared with the traditional Backpropagation Neural Network (BP), the Long Short-Term Memory (LSTM), and the normal BiGRU model, the model proposed in this paper has an average reduction of 65.90%, 64.54%, and 49.06% in Mean Absolute Percentage Error (MAPE) in the prediction of the hourly inbound and outbound passenger flow of each type of station, respectively.

Short-Term Passenger Flow Prediction Using a Bus Network Graph Convolutional Long Short-Term Memory Neural Network Model

Short-term passenger flow prediction is critical to managing real-time bus networks, responding to emergencies quickly, making crowdedness-aware route recommendations, and adjusting service schedules over time. Some recent studies have attempted to predict passenger flow using deep learning models. The complexity of transportation networks, coupled with emerging real-time data collection and information dissemination systems, has increased the popularity of these approaches. There has also been a growing interest in using a new deep learning approach, the graph neural network that captures graph dependence by passing messages between its nodes. Researchers in various transportation domains have used such tools for modeling and predicting transportation networks, as many of these networks consist of nodes and links and can be naturally categorized as graphs. This paper develops a bus network graph convolutional long short-term memory (BNG-ConvLSTM) neural network model to forecast short-term passenger flows in bus networks. Validating the proposed model is done using real-world data collected from the Laval bus network in Canada. Based on a set of comparisons between the proposed model and some other popular deep learning approaches, it clearly indicates that the BNG-ConvLSTM model is more scalable and robust than other baselines in making network-wide predictions for short-term passenger flows.

Short-Term Passenger Flow Prediction of Urban Rail Transit Based on a Combined Deep Learning Model

It is difficult for a single model to simultaneously capture the nonlinear, correlation, and periodicity of data series in the passenger flow prediction of urban rail transit (URT). To better predict the short-term passenger flow of URT, based on the long short-term memory network (LSTM) model, a deep learning model prediction method combining the time convolution network (TCN) and the long short-term memory network (LSTM) based on machine learning is proposed. The model couples the external factors such as date attributes, weather conditions, and air quality, to improve the overall prediction performance and solve the difficulty of accurate prediction due to the large fluctuation and randomness of short-term passenger flow in rail transit. Using the swiping data and related weather information of some stations of Chongqing Rail Transit Line 3, the TCN-LSTM model is verified by an example, and the prediction results of the single LSTM model are given for comparison. The results show that the TCN-LSTM model can better predict the passenger flow characteristics of different stations at different times. Compared with the single LSTM model, the TCN-LSTM model has better prediction accuracy and data generalization ability.

JP-DAP: An Intelligent Data Analytics Platform for Metro Rail Transport Systems

This paper deals with an intelligent data analytics platform - Jaison-Paul Data Analytics Platform (JP-DAP) - for metro rail transport systems. JP-DAP is intended to ensure smooth functioning, improved customer experience, ridership forecasting, and efficient administration of metro rail transportation systems by integrating and analysing its many data sources. It consists of a middleware which is built on the top of a Hadoop Distributed File System (HDFS) and Spark framework, along with a set of open-source software tools like Apache Hive, Pandas, Google TensorFlow and Spark ML-lib for real-time and legacy data processing. The benchmarking of JP-DAP was conducted using TestDFSIO and have found that it performs well according to industry standards. The specific use case for this project is Kochi Metro Rail Limited (KMRL). The analysis of Automated Fare Collection data from KMRL on JP-DAP framework have produced descriptive statistics visualisation of inflow and outflow analysis, travel patterns during weekdays and weekends, origin-destination matrix, etc.. Moreover JP-DAP framework is capable of producing short term passenger flow predictions using SVR machine learning algorithm with linear, radial basis function and polynomial kernels. Our experiments have shown that SVR linear kernel gives the most accurate results with the least errors in predicting the next day’s passenger count using the previous five weekdays data. The station usage (one-to-all) prediction using Long Short-Term Memory (LSTM) is also integrated to this framework. The visualisation as well as analytical outcomes of JP-DAP framework have also been made available to the external world using a rich set of REST APIs and are projected on to a web-dashboard.

Naïve Bayes-Based Transition Model for Short-Term Metro Passenger Flow Prediction under Planned Events

Short-term passenger flow prediction under planned events is important to reduce passenger delay and ensure operational safety in metro systems. However, most studies make predictions under normal conditions. The study proposes a naïve Bayes transition model for short-term passenger flow prediction under planned events. The target prediction scenario identification is modeled as a binary classification problem using naïve Bayes. The sub-models are developed using gradient boosting decision tree (GBDT) and deep learning (DL) models for normal and planned event scenarios with predictor variables tailored to different passenger demand patterns. The sub-predictor from GBDT or DL is selected based on the inferred prediction scenario. The case study uses automatic fare collection (AFC) data of Shanghai and Hong Kong metro systems. The results show that the proposed model outperforms other representative individual and fusion models. The results also highlight the effectiveness of the predictive transition mechanism between the normal and planned events and also the event information representation.

Learn traffic as a signal: Using ensemble empirical mode decomposition to enhance short-term passenger flow prediction in metro systems

Due to the complex temporal dependency and various external factors, it is challenging to capture its nonlinear and unsteady trends accurately. In addition, there are several inevitable errors in the traffic sensor record, including bias and noise. However, most recent works regard the record data as exact input ignoring the effect of unknown errors. In this research, a novel framework that integrates Ensemble Empirical Mode Decomposition (EEMD) method and the Bidirectional Gate Recurrent Units (BiGRU) model was proposed to eliminate noise and enhance short-term prediction. The proposed model is mainly divided into three stages. Firstly, the EEMD algorithm adaptively decomposes the nonlinear and non-steady passenger flow signal into several sub-signals, which share more straightforward fluctuation trends and higher correlation coefficients in the preprocessing stage. Secondly, in the feature recognition and extraction stage, knowledge of the transportation field and statistical theories are applied to analyze and extract the critical decomposed components. Finally, in the prediction stage, the stacked BiGRU can learn and extract information from the input features in both directions and use a multi-step prediction to output the final prediction result. A real dataset of the Chengdu metro system is included in our experiments. The experimental results reveal that the proposed EEMD-BiGRU model's prediction performance exceeds all benchmark models. The Root Mean Square Error (RMSE) of the proposed model is reduced by up to 28.29% compared to a single GRU model without EEMD preprocessing. Also, experiments show the effectiveness and robustness of the proposed method for predicting short-term passenger flow in metro systems.

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

Short‐term passenger flow forecasting using CEEMDAN meshed CNN‐LSTM‐attention model under wireless sensor network

For a long time, the accurate prediction of passenger flow can provide early warning information for various industries such as the public service industry, tourism industry, and industrial business, thus opportunely arranging passengers and providing homologous services to relieve the overloading of places and the accidents caused by overcrowding of people. In recent years, by using the wireless sensor network to sense the passenger data in advance, the technique of machine learning and neural networks has been utilized to assist the short-term passenger flow prediction. In this study, building on convolutional neural network (CNN) and long short-term memory network (LSTM), a complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) and attention-based CNN-LSTM network to extract both temporal and spatial characteristics of passenger flow data, is proposed. Moreover, the problem of the inaccuracy of the noise part is properly solved by adding the CEEMDAN algorithm to the input layer. With the proposed network structure, the CNN-LSTM network is replaced with the Conv-LSTM network to reduce the information loss and get a further performance improvement. The result shows that 39% performance improvement can be achieved than the case with a single LSTM network, and 28% performance improvement can be achieved than the CNN-LSTM network.

A Bus Passenger Flow Prediction Model Fused with Point-of-Interest Data Based on Extreme Gradient Boosting

Bus operation scheduling is closely related to passenger flow. Accurate bus passenger flow prediction can help improve urban bus planning and service quality and reduce the cost of bus operation. Using machine learning algorithms to find the rules of urban bus passenger flow has become one of the research hotspots in the field of public transportation, especially with the rise of big data technology. Bus IC card data are an important data resource and are more valuable to passenger flow prediction in comparison with manual survey data. Aiming at the balance between efficiency and accuracy of passenger flow prediction for multiple lines, we propose a novel passenger flow prediction model based on the point-of-interest (POI) data and extreme gradient boosting (XGBoost), called PFP-XPOI. Firstly, we collected POI data around bus stops based on the Amap Web service application interface. Secondly, three dimensions were considered for building the model. Finally, the XGBoost algorithm was chosen to train the model for each bus line. Results show that the model has higher prediction accuracy through comparison with other models, and thus this method can be used for short-term passenger flow forecasting using bus IC cards. It plays a very important role in providing decision basis for more refined bus operation management.

Deaseq2seq: An Attention Based Sequence to Sequence Model for Short-Term Metro Passenger Flow Prediction within Decomposition-Ensemble Strategy

Deaseq2seq: An Attention Based Sequence to Sequence Model for Short-Term Metro Passenger Flow Prediction within Decomposition-Ensemble Strategy

Multi-Point Short-Term Passenger Flow Prediction in the Station Based on Temporal Multi-Graph Convolutional Network

Multi-Point Short-Term Passenger Flow Prediction in the Station Based on Temporal Multi-Graph Convolutional Network

A decomposition-based forecasting method with transfer learning for railway short-term passenger flow in holidays

The forecasting of the railway short-term volume plays an essential role in the railway company, which is the basement of the ticket assignment, the line planning, and the passenger station management, and so on. However, the short-term flow forecasting tends to yield poor performance in holidays in China and leads to a low service-of-level for the passengers. In order to improve the accuracy of the short-term passenger flow volume forecasting of high-speed railway, and overcome the challenges in holidays, transfer learning with a time series decomposition is managed. The proposed decomposition-based forecasting method with transfer learning is based on a boosting method which is an instance-based transfer. Firstly, the time series is decomposed into linear time series and nonlinear time series. Using the SARIMA model to predict linear time series, the nonlinear time series is acquired and transformed as feature-label samples with a feature selection to the transfer learning. To limit the negative transfer, two sample filtering methods are proposed by the computing of similarity of time series. Finally, the samples in the source domain are learned by a TrAdaboost model which is embedded with Random Forest adjusting the weights of training samples to reduce the negative transfer. The 24 stations of the Jinghu High-speed railway are tested in our experiments. Our transfer learning model outperforms the baselines. The results also show the effectiveness of feature selection and sample filtering methods. It is proved that this method can be applied to the short-term passenger flow prediction of high-speed railway efficiently, and it is beneficial to improve the efficiency of resource allocation and the service level of high-speed railway transportation.

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

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.

Short-term Passenger Flow Forecast of Urban Subway Transportation Based on Deep Learning Methods

With the acceleration of urban construction, many urban rail transits has entered the stage of network development. Facing the rail passenger flow with larger flow and more complex characteristics, how to forecast the rail passenger flow in real time and accurately has become an important topic in the field of transportation planning. Rail passenger flow is closely related to the travel patterns of urban residents, and is affected by factors such as weather conditions and station development Based on the heterogeneous data such as rail passenger flow data, POI data, meteorological data, air quality data and road network distribution data, this article analyzes the spatial and temporal distribution characteristics of rail passenger flow, conducts functional clustering analysis of stations, and constructs hybrid neural network using deep learning method to realize high-precision prediction of short-term passenger flow at rail stations.

Forecasting the All-Weather Short-Term Metro Passenger Flow Based on Seasonal and Nonlinear LSSVM

Accurate metro ridership prediction can guide passengers in efficiently selecting their departure time and simultaneously help traffic operators develop a passenger organization strategy. However, short-term passenger flow prediction needs to consider many factors, and the results of the existing models for short-term subway passenger flow forecasting are often unsatisfactory. Along this line, we propose a parallel architecture, called the seasonal and nonlinear least squares support vector machine (SN-LSSVM), to extract the periodicity and nonlinearity characteristics of passenger flow. Various forecasting models, including auto-regressive integrated moving average, long short-term memory network, and support vector machine, are employed for evaluating the performance of the proposed architecture. Moreover, we first applied the method to the Tiyu Xilu station which is the most crowded station in the Guangzhou metro. The results indicate that the proposed model can effectively make all-weather and year-round passenger flow predictions, thus contributing to the management of the station.

Short-Term Subway Passenger Flow Prediction Based on GCN-BiLSTM

Aiming at the problem that the accuracy of passenger flow prediction is not high, this paper presents a short-term passenger flow forecasting model based on Graph Convolutional Neural Network (GCN) and Bidirectional Long-term Memory Network (BiLSTM). Firstly, the historical traffic time series is divided into three time modes: recent period, daily period and weekly period; Secondly, we construct three models based on GCN and BiLSTM to capture the spatial and temporal dependence of the three patterns; Finally, the parameter matrix is used to fuse the output of the three time modes to obtain the final prediction result. By testing the data set of subway passenger flow in a city in January 2019, the experimental results show that the root mean square error of the model is reduced by 8.515% and the average absolute error is reduced by 4.239% compared with the single BiLSTM model, it has a high fitting degree with the real passenger flow value and has certain application value for the reasonable allocation of subway capacity.

Understanding and Predicting the Short-Term Passenger Flow of Station-Free Shared Bikes: A Spatiotemporal Deep Learning Approach

Accurate passenger flow prediction of shared bikes provides useful information for operators to optimize the repository of bikes in different regions. However, it is very challenging as the usage of shared bikes is affected by many complex factors. In this article, we propose an end-to-end deep learning architecture, termed spatial-temporal fusion network (STFNet), to forecast short-term passenger flow in the new station-free bike-sharing system. The architecture utilizes the different neural network structures jointly to capture the complex nonlinear relationships of spatiotemporal dependencies and external factors from multiple data sources. Furthermore, the attention mechanism is introduced to improve the model’s interpretability and prediction ability. Based on two real-world data sets collected from Chinese cities, Beijing and Shenzhen, detailed spatiotemporal usage patterns of shared bikes are analyzed. Ablation studies are performed to test the effectiveness of different components on the whole framework. Experiment results show that the proposed STFNet can effectively capture the spatiotemporal correlations, and the predictions outperform state-of-art baselines in different predicting horizons.

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

Short-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.

An Ensemble Learning Model for Short-Term Passenger Flow Prediction

In recent years, with the continuous improvement of urban public transportation capacity, citizens’ travel has become more and more convenient, but there are still some potential problems, such as morning and evening peak congestion, imbalance between the supply and demand of vehicles and passenger flow, emergencies, and social local passenger flow surged due to special circumstances such as activities and inclement weather. If you want to properly guide the local passenger flow and make a reasonable deployment of operating buses, it is necessary to grasp the changing law of public transportation short-term passenger flow. This paper builds a short-term passenger flow prediction model for urban public transportation based on the idea of integrated learning. The goal is to use the integrated model to accurately predict the short-term passenger flow of urban public transportation, using Multivariable Linear Regression (MLR), K-Nearest Neighbor (KNN), eXtreme Gradient Boosting (XGBoost), and Gated Recurrent Unit (GRU) as the four seed models, and then use regression algorithm to integrate the model and predict the passenger flow, station boarding and landing, and cross-sectional passenger flow data of the typical representative line 428 in the “Huitian Area” of Beijing from January 1, 2020, to May 31, 2020. Finally, the prediction results of the submodels are compared with those of the integrated model to verify the superiority of the integrated model. The research results of this paper can enrich the short-term passenger flow forecasting system of urban public transportation and provide effective data support and scientific basis for the passenger flow, vehicle management, and dispatch of urban public transportation.