Passenger Flow Forecast Research Articles

A study of short-term passenger flow forecasting for public transportation based on the time series method

Abstract. In order to solve the conflicts between the supply and demand of urban transport, improve the living standard of urban residents, raise the level of urbanization, and promote the sustainable socio-economic development of the whole city, it is necessary to increase the pace of urban transport construction, planning and management in China. And at the same time, its necessary to take various measures to regulate the structure of urban transport and guide urban transport to develop in the direction of taking public transport as the mainstay. The experience of the development of urban transportation in China and abroad also proves that prioritizing the development of public transportation is the fundamental way to solve the problem of urban transportation. Public transport passenger flow data is the basis for optimizing public transport operations and scheduling. Accurate passenger flow forecasts on public transport routes can effectively guide public transport operation decisions, formulate operation scheduling plans and effectively improve the operational efficiency of the public transport system. The following are some of the key factors that can be taken into consideration when making passenger flow forecasts. The main contribution of this article is to propose the use of data mining technology, use bus IC card data and bus GPS data to integrate and organize them, conduct correlation analysis on the data, and on this basis, obtain the main distribution characteristics of bus passenger flow, and provide short-term Passenger flow forecast provides relevant ideas.

A CNN-LSTM Model for Short-Term Passenger Flow Forecast Considering the Built Environment in Urban Rail Transit Stations

A CNN-LSTM Model for Short-Term Passenger Flow Forecast Considering the Built Environment in Urban Rail Transit Stations

Short-Term Prediction of Origin–Destination Passenger Flow in Urban Rail Transit Systems with Multi-Source Data: A Deep Learning Method Fusing High-Dimensional Features

Short-term origin–destination (OD) passenger flow forecasting is crucial for urban rail transit enterprises aiming to optimise transportation products and increase operating income. As there are large-scale OD pairs in an urban rail transit system, OD passenger flow cannot be obtained in real time (temporal hysteresis). Additionally, the distribution characteristics are also complex. Previous studies mainly focus on passenger flow prediction at metro stations, while few methods solve the OD passenger flow prediction problems of an urban rail transit system. In view of this, we propose a novel deep learning method fusing high-dimensional features (HDF-DL) with multi-source data. The HDF-DL method is combined with three modules. The temporal module incorporates the time-varying, trend, and cyclic characteristics of OD passenger flow, while the latest OD passenger flow time sequence (within 1 h) is excluded from the time-varying characteristics. In the spatial module, the K-means and K-shape algorithms are used to classify OD pairs from multiple perspectives and capture the spatial features, reducing the difficulty of OD passenger flow predictions with large-scale and complex characteristics. Weather factors are considered in the external feature module. The HDF-DL method is tested on a large-scale metro system in China, in which eight baseline models are designed. The results show that the HDF-DL method achieves high prediction accuracy across multiple time granularities, with a mean absolute percentage error of about 10%. OD passenger flow in every departure time interval can be predicted with high and stable accuracy, effectively capturing temporal characteristics. The modular design of HDF-DL, which fuses high-dimensional features and employs appropriate neural networks for different data types, significantly reduces prediction errors and outperforms baseline models.

Short-term subway passenger flow forecasting approach based on multi-source data fusion

Accurate short-term subway passenger flow forecasting is essential in improving the efficiency of daily operation and the management of subway system. Existing research often overlooks the impacts of randomness and unevenness in passenger flows caused by population migrations, also predominantly emphasizes feature selection while neglecting effective feature fusion. To address this gap, we propose a novel hybrid subway passenger flow forecasting approach adopting multi-source data fusion technique. Firstly, the dual processing module consisting of Grey Relation Analysis and SHapley Additive exPlanations is used to gather influencing features. Then, the multivariate empirical mode decomposition decomposes the selected features into multidimensional intrinsic mode functions. To discern and extract the effective components from the decomposed intrinsic mode functions, secondary feature selection module based on Spearman Correlation Coefficient is performed. Finally, the determined components are categorized into three classes by combining recurrence plot and picture information entropy, each class is assigned suitable predictor to ensure accurate forecasting. Experimental results based on three real subway passenger flow datasets from Guangzhou, Beijing, and Chengdu verifies the effectiveness and robustness of the proposed approach. Therefore, the introduction of population migration index and the effective fusion of information are significant to improve subway passenger flow forecasting.

Decision-making Support Platform and Security Design for Rural Leisure Tour Industry based on SEA Method and SVR Model

An accurate short-term passenger flow forecast of rural tourism can avoid accidents as much as possible. However, the short-term passenger flow of rural leisure tourism shows nonlinear, seasonal, random, and other complex characteristics. Meanwhile, the traditional forecasting methods are often difficult to achieve accurate forecasting. Therefore, this study now used the nonlinear mapping function in the support vector regression to convert the passenger traffic training sample into a high-dimensional feature space and established a linear decision function. Then the influence of periodicity on the prediction effect through seasonal index adjustment was reduced. Finally, event triggering combined with core embedding technology for tamper-proof detection was adopted to improve the security of the platform. The results showed that the minimum absolute error of prediction with improved SVM model was 0.27% compared with traditional model and autoregressive integrated moving average model. After the introduction of the Internet search factor, the traffic prediction result was more accurate, which was 0.0425 smaller than that without the introduction of the Internet search factor. When the concurrency was less than 100 times/s, the average response time difference before and after adding the core embedded program was small, indicating that the security of page tampering technology was high. This research method can effectively predict the passenger flow of rural leisure tourism industry and ensure the safety of the platform.

Optimizing Metro Passenger Flow Prediction: Integrating Machine Learning and Time-Series Analysis with Multimodal Data Fusion

Accurate passenger flow forecasting is crucial in urban areas with growing transit demand. In this paper, we propose a method that combines advanced machine learning with rigorous time series analysis to improve prediction accuracy by integrating different datasets, providing a prescriptive example for passenger flow prediction in urban rail transit systems. The study employs advanced machine learning algorithms and proposes a novel prediction model that combines two-stage decomposition (seasonal and trend decomposition using LOESS–ensemble empirical mode decomposition (STL-EEMD)) and gated recurrent units. First, the STL decomposition algorithm is applied to break down the preprocessed data into trend terms, periodic terms, and irregular fluctuation terms. Then, the EEMD decomposition algorithm is employed to further decompose the irregular fluctuation terms, yielding multiple IMF components and residual residuals. Subsequently, the decomposed data from STL and EEMD are partitioned into training and test sets and normalized. The training set is utilized to train the model for optimal performance in predicting subway short-time passenger flow. The synthesis of these sophisticated methodologies serves to substantially enhance both the predictive precision and the broad applicability of the forecasting models. The efficacy of the proposed approach is rigorously evaluated through its application to empirical metro passenger flow datasets from diverse urban centers, demonstrating marked superiority in predictive performance over traditional forecasting methods. The insights gleaned from this study bear significant ramifications for the strategic planning and administration of public transportation infrastructures, potentially leading to more strategic resource allocation and an enhanced commuter experience.

Passenger Flow Enhancement Strategies for Metro Operations

This paper introduces a comprehensive framework for passenger flow enhancement for metro operations. The proposed framework takes into account the state of urban transportation and the forecasting of passenger flow, based on which the internal and external influencing factors on passenger flow are analyzed. By utilizing the Suzhou Metro as a case study, specific strategies for enhancing passenger flow are suggested. This research contributes valuable insights and actionable strategies for metro systems, offering a holistic approach to address the challenges associated with passenger flow enhancement in metro system.

A Metro Passenger Flow Forecasting Model Based On Time-series Evolving Interaction Graph Network

Passenger flow forecasting is an important task in metro operation management. In order to achieve more accurate metro passenger flow forecasting, this paper proposes a metro passenger flow forecasting model based on time-series evolution interaction graph. First, by introducing two kinds of inter-station interaction graphs, namely connectivity graph and temporal correlation graph, to capture the potential interaction relationship among metro passenger flow stations. Then, by using the time-series evolving graph, the weights of the graph convolutional neural network are dynamically evolved in time series. Finally, taking Suzhou Metro as an example, the short-term passenger flow of the metro is forecasted. The experimental results show that the Root Mean Squared Error (RMSE) of this model is 34.17, the Mean Absolute Error (MAE) is 16.35, the R-Squared is 0.94, the Mean Absolute Percent Error (MAPE) is 0.21. All evaluation metrics are better than the baseline models, thus verifying the effectiveness and applicability of the metro passenger flow forecasting model based on the time series evolution interaction graph.

Improved Long-Term Forecasting of Passenger Flow at Rail Transit Stations Based on an Artificial Neural Network

When new rail stations or lines are planned, long-term planning for decades to come is required. The short-term passenger flow prediction is no longer of practical significance, as it only takes a few factors that affect passenger flow into consideration. To overcome this problem, we propose several long-term factors affecting the passenger flow of rail transit in this paper. We also create a visual analysis of these factors using ArcGIS and construct a long-term passenger flow prediction model for rail transit based on a class neural network using an SPSS Modeler. After optimizing relevant parameters, the prediction accuracy reaches 94.6%. We compare the results with other models and find that the neural network model has a good performance in predicting long-term rail transit passenger flow. Finally, the factors affecting passenger flow are ranked in terms of importance. It is found that among these factors, bicycles available for connection have the biggest influence on the passenger flow of rail stations.

Unleashing the Potential of Boosting Techniques to Optimize Station-Pairs Passenger Flow Forecasting

Station-pair passenger flow forecast modeling is crucial for public transportation to address emerging needs. The accurate prediction and estimation will provide backbone support for various features of transport, viz. (a) Demand forecasting for resource allocation during peak travel periods; (b) Service planning to determine transportation frequency on specific routes; (c) Resource allocation for vehicles and capacity adjustments, including planning for new fleet size; (d) Infrastructure planning to identify areas requiring additional development; (e) Enhancing passenger experience with ample seating and amenities; and (f) Emergency planning for crowd control and passenger safety. Recent advancements in transit information station-pair estimation methods can be implemented with Data Science. Over the years, there has been significant progress in developing innovative modeling approaches to estimate travel behaviour, taking into account various factors such as timeslots, holidays, and other relevant features. In this study, we have synthesized ticket data from Thane Municipal Transport (TMT), collected from e-ticket machines, spanning from January 1 to June 23, 2023, covering all routes and comprising more than 2.80 crore tickets. This paper presents a comprehensive station-pair short-term passenger flow forecast by utilizing three boosting models: extreme Gradient Boosting (XGBoost), Light Gradient Boosting model (LightGBM), and Categorical Boosting model (CatBoost). Their implementation is well-suited based on statistical parameters. The study concludes by comparing the results of the models, considering computation time and statistical parameters, to validate their superiority. By looking into day to day requirements, the LightGBM model is most appropriate for implementation with an accurate result. The research findings contribute to providing valuable data support and a robust scientific foundation for managing passenger flow through route planning and dispatching in urban transit systems.

Optimization of Short-Term Passenger Flow Forecast Based on Improved Sparrow Search Algorithm in LSTM Network

Optimization of Short-Term Passenger Flow Forecast Based on Improved Sparrow Search Algorithm in LSTM Network

TMFO-AGGRU: A Graph Convolutional Gated Recurrent Network for Metro Passenger Flow Forecasting

Accurately exploiting the spatial correlation of passenger flow related to the metro network is important for improving metro passenger flow prediction. In this paper, a novel metro passenger flow forecasting model termed TMFO-AGGRU is proposed based on parameter estimation optimization for a graph convolutional gated recurrent neural network. First, we model a graph convolutional gated recurrent neural network, which is capable of real-time spatial and temporal information training on the passenger flow at a target station. Second, we design graph convolution operations to replace the linear operations of gated recurrent neural networks. To fully utilize the weighted distribution of output data based on different traffic characteristics, we convert the adjacent static matrices into dynamic characteristic matrices using feature learning and construct the attention mechanism module. Last, an adaptive moth-flame optimization method based on T-variance is proposed in this paper to achieve dynamic optimization of structural parameters and to avoid affecting the accuracy of prediction due to improper parameter selection. Experimental results show that the proposed TMFO-AGGRU effectively improves the prediction accuracy and convergence speed, which outperforms traditional metro passenger flow prediction methods.

Passenger Flow Prediction and Risk Early Warning for Metro Cross-section based on Transportation Big Data

With the increasing pace of urbanization and the diversification of urban transportation modes, the demand for urban transportation in people's life is also increasing. For the purpose of relieving urban traffic, cities have made great efforts to develop public transport, especially metro transport. This paper will use the subway swipe card data of Changsha city from March 1-15, 2023, and optimize the LSTM model based on particle swarm algorithm, to predict the metro cross-sectional passenger volume. Based on the data and methods, this paper investigates the short-time cross-sectional passenger flow forecasting of the metro using the variables of initial passenger flow, inbound passenger volume and outbound passenger volume as references. It is found that the combined short-time passenger flow prediction model (PSO-LSTM) has good prediction effect. Based on the standard LSTM model, the PSO algorithm optimizes the learning rate, the number of iterations and the number of hidden neurons in the LSTM parameters to construct the PSO-LSTM model. The video recognition technology more accurately reflects the traffic congestion and makes early warning. The short-time cross-sectional passenger flow prediction of subway will help to improve people's living standard and save transportation time cost, which is of practical significance to urban rail operating companies.

Analysis of Urban Rail Transit Short-term Passenger Flow Forecast and Its Optimization

With the growth of urban population and the increase of car ownership, the number of cars continues to increase, leading to traffic congestion becoming a major problem in many cities around the world. Traffic congestion not only wastes time, but also increases air pollution and energy consumption. To address these challenges, governments and transportation authorities around the world have begun to adopt advanced technical models and big data based approaches. By using the power of intelligent data analysis, this paper enables people to understand rail transit more deeply, establish an ARIMA model for short-term prediction, optimize traffic flow, and improve the overall performance of the system. Through the final experimental results, it is found that the trend of rail transit passenger volume in the next few days is increasing, but its growth trend is not as high as that of the previous few days. The ARIMA model infers that the peak period in the next few days may not be as high as that of the previous few days.

Short-term Passenger Flow Prediction of Urban Rail Transit based on ARIMA Model

Urban rail transit is very important in modern urban transportation, which meets the needs of People's Daily travel. This paper aims to introduce the ARIMA model into the short-term passenger flow forecast of urban rail transit, analyze its principle and application method, and build ARIMA model combined with AFC data to forecast short-term passenger flow of urban rail transit. Through the research and improvement of ARIMA model, it is anticipated that it would give urban rail transport operators more precise and trustworthy short-term passenger flow forecasting capabilities, so as to realize more intelligent and efficient operation of urban rail transit system. In this study, the ARIMA model was established, and rigorous scientific methods such as residual test, autocorrelation function (ACF) and partial autocorrelation function (PACF) test, and comparison of predicted value and real value were adopted to conduct tests step by step according to the experimental process. Finally, it was confirmed that the excellent prediction accuracy of the ARIMA prediction model used in this paper, can provide accurate prediction results, and can be used in practical scenarios.

Short-time subway passenger flow forecast based on the ARIMA-LSTM

In recent years, with the rapid development of Chinas economy and the continuous increase in population, the urbanization process has intensified. This travel issue caused by peak traffic congestion affects a significant portion of urban populations. In order to conduct a thorough analysis, this article primarily focuses on the urban rail transit industrys subway system. In this study, three models - LSTM, ARIMA and ARIMA-LSTM were employed to analyze and predict short-term inbound flow data for the Hangzhou subway in January 2019. While the LSTM model used for forecasting, it was found to be ineffective in predicting data with extreme peaks. The ARIMA model is subsequently employed for data prediction, revealing its inadequacy in accurately forecasting unstable and non-patterned data. To overcome this limitation, a combined ARIMA-LSTM model is proposed to mitigate the shortcomings of individual models and achieve superior performance. The implementation of the ARIMA-LSTM model effectively mitigates subway congestion issues, thereby facilitating informed decision-making by subway operators. Moreover, it has a certain degree of robustness, even if the other data are not regular enough. The model can be applied in the real subway passenger flow prediction, which is conducive to the choice of people traveling to work and the management decisions of subway operators.

Passenger flow forecast of tourist attraction based on MACBL in LBS big data environment

Abstract The existing scenic spot passenger flow prediction models have poor prediction accuracy and inadequate feature extraction ability. To address these issues, a multi-attentional convolutional bidirectional long short-term memory (MACBL)-based method for predicting tourist flow in tourist scenic locations in a location-based services big data environment is proposed in this study. First, a convolutional neural network is employed to identify local features and reduce the dimension of the input data. Then, a bidirectional long short-term memory network is utilized to extract time-series information. Second, the multi-head attention mechanism is employed to parallelize the input data and assign weights to the feature data, which deepens the extraction of important feature information. Next, the dropout layer is used to avoid the overfitting of the model. Finally, three layers of the above network are stacked to form a deep conformity network and output the passenger flow prediction sequence. In contrast to the state-of-the-art models, the MACBL model has enhanced the root mean square error index by at least 2.049, 2.926, and 1.338 for prediction steps of 24, 32, and 60 h, respectively. Moreover, it has also enhanced the mean absolute error index by at least 1.352, 1.489, and 0.938, and the mean absolute percentage error index by at least 0.0447, 0.0345, and 0.0379% for the same prediction steps. The experimental results indicate that the MACBL is better than the existing models in evaluating indexes of different granularities, and it is effective in enhancing the forecasting precision of tourist attractions.

Study on Induced Passenger Flow Forecast for Intercity High-Speed Rail

To forecast the induced passenger flow of intercity high-speed rail, two related multinomial logit models are applied to describe travelers’ travel choice behavior under changes in the travel environment. The first model describes different choices of travel mode by travelers between origin–destination pairs, while the second model describes the choice of travel frequency by different travelers. By introducing the consumption surplus variable of travelers, the two selected models are correlated and the model is calibrated using data from a behavioral survey and an intention survey. The results show that the polynomial logit model is applicable for forecasting induced passenger flow after changes in the travel environment. The consumption surplus of travelers is the key factor influencing changes in travel frequency, and the social characteristics of travelers and the economic development features of origin and destination cities have significant impacts on travel frequency. In-transit time is the key factor affecting the demand elasticity of travel frequency. The range of demand elasticity for short-distance business and non-business travel is between −0.61 and −0.79. The results of the demand elasticity analysis were applied to predict that the travel frequency of people from Nanjing to Huai’an, Suqian, Lianyungang, and Yancheng will increase by about 45% after the opening of the Nanjing-Huai’an intercity high-speed railway.

How spatial features affect urban rail transit prediction accuracy: a deep learning based passenger flow prediction method

Urban rail transit is an integral part of public transit, and has been extensive built in China. Previous studies have proved that the spatial features are closely related to rail transit ridership, considering a fundamental role of short-term passenger flow forecast in the urban rail operation, it is meaningful to explore how these factors affect the prediction accuracy. This study aims to find a way to improve prediction accuracy by considering spatial features of stations based on deep learning. Therefore, a CNN-LSTM model capturing the spatial and temporal features was applied and Suzhou (China) was choosing as a case study to explore the influence of three spatial features, namely relative position, location, and land use, on the prediction accuracy. The predict model used can extract spatiotemporal features and accurately predict the citywide stations, and the results show that, for the relative position, the inbound and outbound flow prediction errors of transfer stations and middle stations are the lowest, respectively. As for locational features, the more distant the station is from the city center, the more accurate the results are. For stations where land use is dominated by work and living services, the predictions are more accurate. The error rate is higher for stations whose services are mainly tourism, transportation, and leisure services. This study’s results can help operators predict the short-term passenger flow of target stations based on different demands and optimize their services on this basis.

Research on Short-Term Passenger Flow Prediction of LSTM Rail Transit Based on Wavelet Denoising

Urban rail transit offers advantages such as high safety, energy efficiency, and environmental friendliness. With cities rapidly expanding, travelers are increasingly using rail systems, heightening demands for passenger capacity and efficiency while also pressuring these networks. Passenger flow forecasting is an essential part of transportation systems. Short-term passenger flow forecasting for rail transit can estimate future station volumes, providing valuable data to guide operations management and mitigate congestion. This paper investigates short-term forecasting for Suzhou’s Shantang Street station. Shantang Street’s high commercial presence and distinct weekday versus weekend ridership patterns make it an interesting test case, making it a representative subway station. Wavelet denoising and Long Short Term Memory (LSTM) were combined to predict short-term flows, comparing the results to those of standalone LSTM, Support Vector Regression (SVR), Artificial Neural Network (ANN), and Autoregressive Integrated Moving Average Model (ARIMA). This study illustrates that the algorithms adopted exhibit good performance for passenger prediction. The LSTM model with wavelet denoising proved most accurate, demonstrating applicability for short-term rail transit forecasting and practical significance. The research findings can provide fundamental recommendations for implementing appropriate passenger flow control measures at stations and offer effective references for predicting passenger flow and mitigating traffic pressure in various cities.