Passenger Flow Prediction Research Articles

A semi-supervised co-training model for predicting passenger flow change in expanding subways

Subways in many big cities are experiencing rapid development with new lines being planned, built and put into operations. Understanding passenger flow change in expanding subways is of significant importance, because it is a crucial piece of information for the new networks’ planning and operation. Here, we employ large-scale smart card data to simulate the change of section passenger flow in an actual expanding subway. We find that the geographic properties of new subway lines can influence the passenger flows of existing sections. Based on the empirical findings, we develop a semi-supervised co-training (S-MLR-XGBoost) model to solve the problem of insufficient training samples and predict passenger flow change in the expanding subway. The proposed S-MLR-XGBoost model captures the relationship between the geographic properties of new subway lines and the passenger flow change in existing lines and achieves higher prediction accuracy comparing with several benchmark models. Finally, sensitivity analysis of the proposed model and possible reasons for passenger flow change in expanding subways are discussed.

Multi-point short-term prediction of station passenger flow based on temporal multi-graph convolutional network

Prediction of passenger flow distribution in urban rail transit stations can provide important data support for passenger flow organization and passenger travel guidance. However, complex station space structure and simulation-based passenger flow data bring challenges to accurate analysis and prediction of the passenger flow inside the station. This paper proposes a temporal graph attention convolutional neural network model (TGACN) to predict the passenger flow volume and density in key areas of the station. Firstly, considering the topological structure of key areas and the characteristics of passenger flow and flow trend in the station, a multi-graph generation method for continuous space in stations is designed, including geographic neighborhood graph and semantic neighborhood graph, to represent the static and dynamic correlation between nodes. Secondly, a new method of spatio-temporal feature fusion is proposed, which takes multi-graph as input to optimize the extraction and expression of spatial and temporal correlation. Finally, the TGACN is verified by passenger flow data set, which is constructed based on real-time video monitoring data of a transit station in Guangzhou. Experiments demonstrate that the TGACN can obtain the spatio-temporal correlation from passenger flow data, and the prediction results are better than the existing baseline models.

Short-Term Forecast of OD Passenger Flow Based on Ensemble Empirical Mode Decomposition

The development of metro systems can be a good solution to many problems in urban transport and promote sustainable urban development. A metro system plays an important role in urban public transit, and the passenger-flow forecasting is fundamental to assisting operators in establishing an intelligent transport system (ITS). In order to accurately predict the passenger flow of urban metros in different periods and provide a scientific basis for schedule planning, a short-term metro passenger-flow prediction model is constructed by integrating ensemble empirical mode decomposition (EEMD) and long short-term memory neural network (LSTM) to solve the problem that the existing empirical mode decomposition (EMD) is prone to modal aliasing. According to the processed metro-card data, the time series of historical OD data of metro passenger flow is obtained. After EEMD modal decomposition, several intrinsic mode functions sub-items and residual items are obtained. Then, an LSTM network is constructed for prediction. The time step of the network is decided according to the partial autocorrelation functions. The prediction results of intrinsic mode function (IMF) and residual items are integrated to obtain prediction results. The station is classified according to the land types around the station, and the model is tested by using the metro automatic fare collection (AFC) data. In order to test the actual prediction, a different number of training set samples are selected to predict. The measured data of the next day is continuously added to the original training set to compare the prediction accuracy. The results show that the mean absolute percentage error (MAPE) and root mean square error (RMSE) of the EEMD-LSTM model are better than the EMD-LSTM in predicting the OD value of commercial–residential stations and scenic–residential stations. Compared with the EMD-LSTM model, the EEMD-LSTM model showed an average reduction by 3.112% in MAPE values and 15.889 in RMSE, indicating that the EEMD-LSTM has higher prediction accuracy, and EEMD-LSTM model has higher accuracy in short-term metro passenger-flow prediction. The average MAPE for the 35-to-42-day historical data sample decreased from 13.02% to 10.39% with a decreasing trend. It shows that the prediction accuracy keeps improving as the training set samples increase.

A Comprehensive Spatio-Temporal Model for Subway Passenger Flow Prediction

Accurate subway passenger flow prediction is crucial to operation management and line scheduling. It can also promote the construction of intelligent transportation systems (ITS). Due to the complex spatial features and time-varying traffic patterns of subway networks, the prediction task is still challenging. Thus, a hybrid neural network model, GCTN (graph convolutional and comprehensive temporal neural network), is proposed. The model combines the Transformer network and long short-term memory (LSTM) network to capture the global and local temporal dependency. Besides, it uses a graph convolutional network (GCN) to capture the spatial features of the subway network. For the sake of the stability and accuracy for long-term passenger flow prediction, we enhance the influence of the station itself and the global station and combine the convolutional neural networks (CNN) and Transformer. The model is verified by the passenger flow data of the Shanghai Subway. Compared with some typical data-driven methods, the results show that the proposed model improves the prediction accuracy in different time intervals and exhibits superiority in prediction stability and robustness. Besides, the model has a better performance in the peak value and the period when passenger flow changes quickly.

An adaptive Grey-Markov model based on parameters Self-optimization with application to passenger flow volume prediction

It has been demonstrated that local prediction approaches show better prediction performance compared with global ones. The paper proposes a novel local prediction model named as the adaptive grey-Markov prediction model for incomplete and complex dynamic systems. This model displays favorable adaptability, flexibility and universality because of four advantages. Firstly, because of the randomness and non-repeatability of time series, the paper proposes adaptive exponential accumulated generating operators to describe different contribution degrees of historical information to future change trends of system characteristics. Secondly, this paper proposes a new paradigm of adaptive grey background value, which can automatically adjust parameters based on historical information to optimize this model and reduce prediction errors. Thirdly, the optimization model based on average absolute percentage error is constructed, and the corresponding adaptive parameters are searched by Ant Lion Optimizer algorithm. Fourthly, this model consists of a quadratic polynomial function describing nonlinearity and a periodic function generated by a Fourier series characterizing noise and period. Thereupon, the adaptive odd-period grey model is proposed. Its residual modified model, named as adaptive grey-Markov modified model, is given based on Markov chain (MC) and virtual linguistic trust degree (VLTD) to further improve prediction accuracy. The modified values of absolute errors are determined by the score functions produced from trust degrees of absolute errors to states on MC calculated by VLTD method. This modified way retains the influence degrees of absolute errors on the modified values and improves the prediction accuracy. Finally, the paper further demonstrates performance and practicability of the proposed prediction model through predicting passenger flow of Chengdu Metro Line1 and comparative analysis.

Intelligent Tourism Information Service Model considering Tourist Experience in the Environment of Internet of Things.

Due to the narrow range of route search and the single choice of the optimal route during the peak period of tourists' tourism, resulting in a long walking time and relatively high route cost, an intelligent tourism information service model considering tourists' experience is proposed in the environment of Internet of things. Firstly, the overall architecture of the Internet of things environment is designed to obtain the characteristic data of the carrying capacity of scenic spots, including tourism resources, psychology, ecology, and economic carrying capacity, based on which the tourist perception experience model is constructed; different service functions such as in-depth mining of Web content, personalized information push, and data information conversion under the environment of Internet of things and the construction of intelligent tourism information service model are realized. The experimental results show that the passenger flow prediction error of the designed model is small and has a good prediction effect of tourist attraction selection demand.

Multi-featured spatial-temporal and dynamic multi-graph convolutional network for metro passenger flow prediction

Metro passenger flow prediction is an essential part of crowd flow forecasting and intelligent transportation management systems. However, two challenges still need to be addressed to achieve a more accurate prediction: (1) accounting for featural dependence instead of considering only the temporal connection and spatial relations; (2) utilising graph structures to address non-European relationships of spatial and featural dependence. To address these challenges, we developed a novel model called the multi-featured spatial-temporal (MFST) and dynamic multi-graph convolutional network (DMGCN) model. Temporal connections are learned from both the local and global information in a time-series sequence using the combination of a time-trend feature mapping block and a gated recurrent unit block. Spatial relation and featural dependence are separately captured by two DMGCN blocks. Each DMGCN block encodes various relationships by constructing multiple graphs consisting of predefined and non-defined topologies. The results of evaluations conducted of the MFST tensor and the DMGCN on the real-world Beijing subway dataset indicate that the prediction performance of the proposed model is superior to that of the existing baselines. The proposed model thus contributes significantly to the improvement of public safety by providing early warnings of large passenger flow and enabling the smart scheduling of resources.

MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction

MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction

Application of Improved Algorithm Based on Four-Dimensional ResNet in Rural Tourism Passenger Flow Prediction

In rural tourism, precise visitor flow forecasts may aid management in making better decisions. It aids in the reduction of visitor crowds and trash. It also has the potential to improve visitor security. As a result, it is critical to continue to encourage tourism’s long-term growth. However, regional tourist flow of rural tourism has the characteristics of high volatility, complex nonlinearity, and susceptibility to seasonal influences. Moreover, a single neural network model cannot learn both temporal and spatial correlation. Therefore, by examining the variables impacting regional tourist flow and integrating residual networks with fully linked networks, this research offers an enhanced Quad-ResNet model for forecasting regional tourist flow of rural tourism. To be specific, this model learns spatial correlation through deep convolution; combines four residual networks to learn temporal proximity, similarity, periodicity and tendency; and uses one fully connected network to learn seasonal effects. Furthermore, this study compares the Quad-ResNet model with LSTM, CNN, and ST-ResNet models on the same dataset for regional tourist flow prediction experiments. The findings reveal that the Quad-ResNet model has less error and is substantially simpler to train and predict than the LSTM model, making it more suited for predicting regional visitor flows in rural tourism. For relevant stakeholders, the generated model may serve as a useful decision-making tool.

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.

Tourism Management Strategies under the Intelligent Tourism IoT Service Platform

This paper conducts an in-depth study and analysis of the tourism management strategy of the intelligent tourism IoT service platform. The intelligent tourism system is designed from the overall system architecture to the design of each functional module of the system, and the database design is completed with the help of the database E-R diagram. At the same time, this paper uses a weighted regression mathematical method to analyze the passenger flow at different stages of the scenic spot by establishing a scenic spot passenger flow prediction model and giving the results of passenger flow prediction for the scenic spot in the next seven days. In addition, this paper conducts a research based on the ant colony algorithm to establish an itinerary planning and recommendation model to recommend an itinerary that meets the needs of scenic tourists and provides reliable tour guidance based on their expected touring time and touring expenses in the scenic area. The method simplifies the solution of service combination problems in the IoT environment by transforming them into service instance preferences within abstract services. The effectiveness of the method is demonstrated through theoretical derivation. On this basis, the residual energy and energy consumption of smart terminal devices abstracted as cumulative aggregation QoS attributes, and an energy consumption balancing service combination method based on cumulative aggregation QoS is proposed. The countermeasures and suggestions are proposed to increase tourism publicity and give full play to the advantages of tourism resources; strengthen tourism environment monitoring and improve the legal and regulatory system; raise tourism construction funds and strengthen infrastructure construction; improve residents' participation and promote healthy tourism development, as well as to make long-term planning and innovate the management mode; establish a coordinating body and improve the supervision system; establish government macro-control and formulate regulations; and strengthen the management function and the management optimization strategy of cultivating grassroots organizations. Through experimental comparison with other methods, the results show that the method ensures the lifecycle and stability of the entire service portfolio workflow while providing low-energy portfolio services.

Analyzing detour behavior of metro passengers based on mobile phone data

ABSTRACT In this paper, a method of metro-passenger path extraction based on mobile phone data is presented. Paths that differ from theoretical optimal paths derived from the shortest-path model are identified as detours. According to transfer times or the effect on passenger flow of the metro network, two classification methods for detours are proposed. Based on analysis of one-month mobile phone data in Shanghai, China, 10.45% of trips are identified as detours. Without considering detours, the accuracy of the theoretical model in predicting passenger flow by segment is only 69.43%. Detours with as equal transfers as on theoretically optimal paths (ETs) occur mainly at the stations far away from the city center. Detours with more transfers (MTs) occur in the center city. Detours with fewer transfers (FTs) occur in stations on the loop line. To alleviate congestion on the metro network, FTs should be encouraged and others should be discouraged.

Short-term prediction of metro passenger flow with multi-source data: A neural network model fusing spatial and temporal features

Accurate short-term prediction of metro passenger flow can ensure the efficient operation of metro system and comfort and safety of passengers. Most existing studies use a single data source such as smart card data to predict metro passenger flow, which only consider the internal passenger flows of the metro system and fails to consider the potential passenger flows outside metro stations. However, the potential passenger flow outside metro stations actually has an impact on metro passenger flow in the short term. Using multiple sources of data including smart card data, mobile phone data and metro network data, this paper presents a neural network (NN) model for short-term prediction of metro passenger flow. In the proposed NN model, various information are taken into account by extracting spatial and temporal features inside and outside the metro system. The proposed model structure includes long short-term memory layers to extract historical pattern of the extracted features and fully connected layers to grasp potential connection and interaction among the features. The advantages of the proposed model are demonstrated by using multi-source data from Suzhou, China. The results show that the proposed NN model outperforms various baseline models in terms of accuracy and stability. It can also be found that the inclusion of mobile phone data improves the accuracy of prediction.

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.

Multi-layer perceptron based transfer passenger flow prediction in Istanbul transportation system

Estimating passenger movement in transportation networks is a critical aspect of public transportation systems. It allows for a greater understanding of traffic patterns, as well as efficient system evaluation and monitoring. It could also help with precise timing to emergencies or important events, as well as the improvement of urban transport system weaknesses and service quality. The number of transfer passengers demand in Istanbul, Turkey's biggest and most developed metropolis, was used to construct a real-world forecasting model in this study. The number of transfer passengers has been forecasted using popular machine learning methods such as kNN (k-Nearest Neighbours), LR (Linear Regression), RF (Random Forest), SVM (Support Vector Machine), XGBoost and MLP. The dataset utilized is made up of hourly passenger transfer counts gathered at two public transportation transfer stations in Istanbul in January 2020. Using MSE, RMSE, MAE and R2 parameters, each model's experimental data have been thoroughly evaluated. MLP has more successfully other machine learning algorithms in the majority of transportation lines, according to the experimental results.

Passenger Flow Prediction of Scenic Spot Using a GCN–RNN Model

The prediction and control of passenger flow in scenic spots is very important to the traffic management and safety of scenic spots. This study aims to predict the passenger flow of a scenic spot based on the passenger flow of the bus and subway stations around the scenic spots. We propose a passenger flow prediction model based on graph convolutional network–recurrent neural network (GCN–RNN). First, a “graph” is constructed according to the geographical relationship between the scenic spot and the surrounding bus and subway stations. Then, characteristics of surrounding areas of bus and subway stations are constructed based on the crowd behavior analysis, and these are then used as the node-information of the “graph”. Last, the GCN–RNN model is used to extract the temporal and spatial characteristics of the passenger flow data of the scenic spot to realize the prediction. The experimental results show that the proposed model is effective in passenger flow prediction in scenic spots.

Improving Synchronization in High-Speed Railway and Air Intermodality: Integrated Train Timetable Rescheduling and Passenger Flow Forecasting

With the aim of synchronizing high-speed railway (HSR) and aviation services to adapt intermodality traffic needs, this paper is concerned with HSR-Air timetable coordination (HATC) problem. This problem is solved by rescheduling the HSR timetable with the goal of attracting the maximum HSR-Air passenger flow, and at the same time, considering the minimum adjustments to the initial HSR timetable. The HATC problem is an integration of train timetable rescheduling and HSR-air passenger flow predicting problem. There is a trade-off increasing the HSR-Air passenger flow and the adjustments to the initial train timetable. In order to capture the complex feature interactions of passenger flow impact features, a novel HSR-Air passenger flow prediction model is proposed by using factorization machine and deep neural networks in this paper. Moreover, this passenger flow prediction model then is integrated into the train timetable rescheduling model to calculate the passenger flow under different HSR-Air service network. An approach based on a genetic algorithm is developed to solve the integrated model of deep learning and integer programming. The model and approach are tested in a real-world HSR-Air case in China with 15 HSR stations, 200 trains and 82 flights. The results show that the proposed model can obtain the satisfactory predictions and effectively enhance the HSR-Air passenger flow within an acceptable level of deviations to initial train timetable.

Metro passenger flow forecasting though multi-source time-series fusion: An ensemble deep learning approach

The increasing free access of the Internet provides us with favorable circumstances to investigate search engine index reflecting more and more personal behavior information. Part of valuable travel search information can assist us to achieve more robust and reliable prediction of metro passenger flow. Inspired by this, the paper develops a new multi-source time series fusion and direct interval prediction approach to grasp the dynamic law of metro passenger flow effectively. Multi-source index regarding metro travel from three major search engines (Baidu, Sogou and 360) in China are screened out and fused into the powerful predictors. By integrating an optimized multivariate mode decomposition strategy and long short-term memory model, lower and upper bounds of prediction interval are estimated directly by a multi-objective framework that combines the advantages of both the deep learning models long short-term memory and the ensemble learning approach. Especially, two sets of real experiment data of Beijing and Shanghai metro systems are employed to test our approach. Findings show that fusion of multi-source index information promotes the predictability of metro passenger flow, contributing to improving operation management and service quality.

Short-Term Subway Inbound Passenger Flow Prediction Based on AFC Data and PSO-LSTM Optimized Model

Making accurate predictions of subway passenger flow is conducive to optimizing operation plans. This study aims to analyze the regularity of subway passenger flow and combine the modeling skills of deep learning with transportation knowledge to predict the short-term subway passenger flow in the scenarios of workdays and holidays. The processed data were collected from two months of Automated Fare Collection (AFC) data from Xizhimen station of Beijing metro. The data were first cleaned by the established cleansing rules to delete malformed and abnormal logic data. The cleaned data were used to analyze the spatial characteristics in passenger flow. Second, a short-term subway passenger flow prediction model was built on the basis of long short-term memory (LSTM). Determining that the error will be relatively high in peak hours, we proposed gradual optimizations from data input by dividing one whole day into different time periods, and then used particle swarm optimization (PSO) to search for the optimal hyperparameters setting. Finally, inbound passenger flow of Beijing Xizhimen subway station in 2018 was selected for numerical experiments. Predictions of the LSTM-based model had higher accuracy than the traditional machine learning support vector regression (SVR) model, with mean absolute percentage error (MAPE) of 21.97% and 4.80% in the scenarios of workdays and holidays, respectively, which are both lower than those of the SVR model. The optimized PSO-LSTM model has been verified for its effectiveness and accurateness by the AFC data.

Passenger flow prediction in bus transportation system using deep learning.

The forecasting of bus passenger flow is important to the bus transit system’s operation. Because of the complicated structure of the bus operation system, it’s difficult to explain how passengers travel along different routes. Due to the huge number of passengers at the bus stop, bus delays, and irregularity, people are experiencing difficulties of using buses nowadays. It is important to determine the passenger flow in each station, and the transportation department may utilize this information to schedule buses for each region. In Our proposed system we are using an approach called the deep learning method with long short-term memory, recurrent neural network, and greedy layer-wise algorithm are used to predict the Karnataka State Road Transport Corporation (KSRTC) passenger flow. In the dataset, some of the parameters are considered for prediction are bus id, bus type, source, destination, passenger count, slot number, and revenue These parameters are processed in a greedy layer-wise algorithm to make it has cluster data into regions after cluster data move to the long short-term memory model to remove redundant data in the obtained data and recurrent neural network it gives the prediction result based on the iteration factors of the data. These algorithms are more accurate in predicting bus passengers. This technique handles the problem of passenger flow forecasting in Karnataka State Road Transport Corporation Bus Rapid Transit (KSRTCBRT) transportation, and the framework provides resource planning and revenue estimation predictions for the KSRTCBRT.