Passenger Flow Forecast Research Articles

Passenger Flow Forecast for Low Carbon Urban Transport Based on Bi-Level Programming Model

<p>In the context of low-carbon city development, this paper further implements a rail transit passenger flow forecasting method to optimize energy consumption by combining the MMA allocation model with a two-tier planning model for carbon emission control. Through this approach, this paper not only fills the gap of rail transportation planning theories and methods compatible with low-carbon city development, but also emphasizes the importance of energy consumption in transportation planning. Based on a two-tier planning model, this paper considers the Starkberg game between multi-modal and multi-type passenger flow forecasting of rail transit and CO2 emissions of integrated transportation systems. By optimizing the allocation of users in the transportation network from the perspective of both users and planners, while optimizing the CO2 emissions of the integrated transportation system, the dual optimization of energy consumption and environmental benefits is achieved. The method will also be tested in Shanghai, and this paper will comparatively study three different carbon emission control schemes. By assigning passenger flows to the entire transportation system network in Shanghai based on information from the Fourth Integrated Transport Survey, including passenger flows on each road in the road network, passenger flows on each rail line, and characteristic indicators, this paper provides a reliable data base. This study provides a solid foundation for planning the layout of rail transit in a low-carbon mode and makes a positive contribution to sustainable urban development by optimizing energy consumption.</p> <p> </p>

Short-term urban rail transit passenger flow forecasting based on fusion model methods using univariate time series

Global urbanization has made the urban rail transit system an essential service for a growing population. To help urban rail transit stations design optimal operational plans, previous studies have devoted extensive efforts to passenger flow forecasting, especially short-term predictions. By considering the complex pattern of passenger flow, previous research investigated the feasibility of machine learning (ML) methods on different data features and found the limited application of a single ML method. Based on the dynamic historical passenger flow data at an urban rail station, this study proposes an ML-fusion strategy to enhance prediction accuracy, including data aggregation, time series forecasting model selection, and fusion model strategy. First, this study aggregates the data into working days, weekends, and hourly time series for single model development. Based on the predictive performance of single model development, this study selects XGBoost, AdaBoost, and LightGBM from the widely used ML-method pool. To overcome prediction errors caused by the discrepancy between characteristics of passenger flow and single prediction models, the proposed ML-fusion model combines single forecasting models with dynamically predicted passenger flow to enhance the accuracy and efficiency of the prediction. Based on the experimental results, the mean absolute error is 1.54, and the regression coefficient is 0.99, which is in close agreement with unity, which validates that the proposed ML-fusion method has displayed superiority over all other single models tested both in accuracy and stability.

Adequacy of the Gravity Model of Railway Passenger Flows

The most accurate modelling of spatial distribution of passenger flows is a prerequisite for successful planning of development of the transport system. It is the basis for calculation of a predictive trip matrix. An approach based on the gravity model is among main modelling methods.The work investigates the issue of the adequacy of the gravity model with a double constraint and an exponential-power function of gravitation. It is this specification of the model and its particular cases with exponential and power functions of gravitation that are most often used to estimate spatial distribution of passenger flows both in theoretical and applied research.Calibration and validation of the specified model is shown on the observed (actual) matrix of railway passenger origin­destination matrix. It was built with the help of the data of Express [railway ticketing] ADB ACS: the number of tickets sold for long-distance trains for all the pairs of directly linked stations.Since calibration of the gravity model can be carried out by different methods (depending on how the model incorporates stochasticity, which is responsible for differences between the modelled and observed data), after a detailed analysis of the most common methods for calibrating the gravity model, the approach was chosen based on the maximum likelihood estimation. The work also analyses the gravity model validation tools used to estimate the proximity between the observed and modelled trip matrices.Comparison of the modelled and observed trip matrices resulted in the conclusion that the gravity model under consideration predicts several aggregate indicators with a high degree of accuracy: total passenger turnover, average travel distance, and travel distance distribution. At the same time, it is shown that the error in the forecast of passenger flow for most individual origin-destination trips is quite large. This circumstance significantly reduces the possibility of practical application of the gravity model or the analysis and modelling of passenger flows in long-distance railway passenger traffic.

Deep Learning for Metro Short-Term Origin-Destination Passenger Flow Forecasting Considering Section Capacity Utilization Ratio

Origin-destination (OD) short-term passenger flow forecasting (OD STPFF) in urban rail transit (URT) is essential for developing timely network measures. The capacity utilization ratios of critical sections are key factors in developing these measures. The OD pairs passing through critical sections require a higher prediction accuracy than others; however, most studies have raised equal concerns on the prediction accuracy of each OD pair, namely, prediction at the network level. To address this problem, we raise heterogeneous time-variant concerns on OD pairs and employ an operation-oriented deep-learning architecture called the spatiotemporal convolutional neural network (STCNN) for realizing short-term OD passenger flow prediction. The architecture contains OD pair importance calculation, lagged spatiotemporal relationship construction, lagged spatiotemporal learning, real-time information learning, and sequential-temporal learning blocks. To this end, critical OD pairs are ascertained first, and the topological lagged spatiotemporal relationship among critical OD pairs are constructed and then normalized into grid-shaped data. The third block utilizes a convolutional neural network (CNN) to learn the grid-shaped lagged spatiotemporal feature and real-time information in parallel. A temporal convolutional neural network (TCN) is utilized for learning the sequential-temporal feature at last. Further, we design a time-varying weighted masked loss function to jointly reinforce the concerns on critical OD pairs during model training. Finally, we test the proposed STCNN and its components on a field dataset from Chengdu Metro. Although the proposed STCNN performs only slightly better at the network level than the other models, it outperforms state-of-the-art methods with significant superiority on critical OD pairs.

Short-term Bus Passenger Flow Forecast Based on CNN-BiLSTM

Effective prediction of urban bus passenger flow is critical for improving urban bus operation efficiency and optimizing the bus network. However, there are some issues with predicting urban bus passenger flow at the moment, such as lack of single eigenvalue consideration and insufficient research depth. In order to improve the short-term prediction accuracy of urban bus passenger flow, this paper proposed a deep learning prediction model that is based on CNN-BiLSTM. Based on historical data of urban bus passenger flow, this paper analyzes the dependence of bus credit card data, clusters the travel feature of different groups of people, and analyzes the dependence of bus passengers. Simultaneously, external factors of passenger flow, such as rainfall, weather condition, traffic flow state, and date, are introduced to build the bus passenger flow prediction feature matrix, and the correlation analysis of the characteristic matrix structure is performed to optimize the matrix structure. Finally, the optimized passenger flow characteristic matrix is fed into the CNN-BiLSTM deep learning model for prediction, and the results are compared to the LSTM, CNN and CNN-LSTM models. The results shown that the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the the CNN-BiLSTM deep learning model are lower than those of other models, and the prediction accuracy is the highest. Meanwhile, this method has a good generalization effect and can improve deep learning prediction accuracy.

Short-Term Subway Passenger Flow Prediction Based on Time Series Adaptive Decomposition and Multi-Model Combination (IVMD-SE-MSSA)

The accurate forecasting of short-term subway passenger flow is beneficial for promoting operational efficiency and passenger satisfaction. However, the nonlinearity and nonstationarity of passenger flow time series bring challenges to short-term passenger flow prediction. To solve this challenge, a prediction model based on improved variational mode decomposition (IVMD) and multi-model combination is proposed. Firstly, the mixed-strategy improved sparrow search algorithm (MSSA) is used to adaptively determine the parameters of the VMD with envelope entropy as the fitness value. Then, IVMD is applied to decompose the original passenger flow time series into several sub-series adaptively. Meanwhile, the sample entropy is utilized to divide the sub-series into high-frequency and low-frequency components, and different models are established to predict the sub-series with different frequencies. Finally, the MSSA is employed to determine the weight coefficients of each sub-series to combine the prediction results of the sub-series and get the final passenger flow prediction results. To verify the prediction performance of the established model, passenger flow datasets from four different types of Nanning Metro stations were taken as examples for carrying out experiments. The experimental results showed that: (a) The proposed hybrid model for short-term passenger flow prediction is superior to several baseline models in terms of both prediction accuracy and versatility. (b) The proposed hybrid model is excellent in multi-step prediction. Taking station 1 as an example, the MAEs of the proposed model are 3.677, 5.7697, and 8.1881, respectively, which can provide technical support for subway operations management.

Multivariate Transfer Passenger Flow Forecasting with Data Imputation by Joint Deep Learning and Matrix Factorization

Accurate forecasting of the future transfer passenger flow from historical data is essential for helping travelers to adjust their trips, optimal resource allocation and alleviating traffic congestion. However, current studies have mainly emphasized predicting traffic parameters for a single type of transport, while lacking research into transfer passenger flow influenced by multiple factors across different transport modes. Additionally, efficient traffic prediction relies on high-quality traffic data, yet data loss issues are inevitable but often ignored. To fill these gaps, we present for the first time a reliable joint long short-term memory with matrix factorization deep learning model (i.e., Joint-IF) for accurate imputation and forecasting of transfer passenger flow between metro and bus. This hybrid Joint-IF model uses a repair-before-prediction strategy to deliver the final high-quality outputs. In particular, we simulate a variety of missing combinations under the natural conditions and apply a low-rank matrix factorization to infer those lost values. In addition, we investigate the effects of crucial parameters and spatiotemporal features on transfer flow prediction. To validate the effectiveness of Joint-IF, a large series of experiments are carried out for models’ comparison and validation on the real-world transfer passenger flow dataset of the Shenzhen public transport system, and the results show that the proposed Joint-IF performs better for both imputation and forecasting of transfer passenger flow relative to the baseline models in terms of accuracy and stability.

FORECASTING OF AIRLINE PASSENGERS BASED ON MACHINE LEARNING

The management of airlines depends on the forecasting of air passenger flow, but standard forecasting techniques cannot guarantee the accuracy of the forecast. When they encounter large-scale, multidimensional, nonlinear, and non-normal distributing time series data, they have the ability to generalize. In this paper the SVM regression is implemented to help with forecasting air passenger flow. We discover that the SVM regression algorithm's outcome exhibits the least inaccuracy when compared to the other two forecasting techniques by carefully choosing the parameters and kernel function. Concerns about demand splits among service providers have been given to every sector by the nearperfect competition scenario. This is especially important in the airline business, where high service standards are the norm. Demand is driven by the player who achieves the greatest mappings between every one of his the airline's offerings and the set of consumer preferences. The airline business has grown exponentially as a result of the economic reforms of 1991, which were promptly followed by the privatization of Indian skies, creating nearly ideal competition. More specifically, cross-border activities have started in the international sectors, where previously only domestic carriers operated. Keywords – Airline Passengers, Support Vector Machine, Forecasting, Machine Learning

A multiple spatio‐temporal features fusion approach for short‐term passenger flow forecasting in urban rail transit

AbstractTimely and precise short‐term passenger flow forecasting (STPFF) is a key to intelligent urban rail transit (URT) operation. The uncertainty of passenger travel behaviors leads to passenger flow data showing high nonlinearities and complex spatio‐temporal correlation. Therefore, the ability to capture such correlations determines the prediction performance of the model. However, current methods either consider only the physical topology of URT network or lack the ability to model global spatial dynamics, making it difficult to obtain satisfactory predictions. Therefore, a Multiple Spatio‐Temporal Features Fusion (MSTFF) network is proposed for STPFF in URT networks. Specifically, the connectivity and passenger flow similarity relationships in URT networks are encoded as physical adjacency graph and virtual functional similarity graph, respectively, and then these two complementary graphs are input into two designed Residual Graph Convolutional Gated Recurrent Unit (RGCGRU) modules to extract their spatio‐temporal features. Second, an Attention‐based Gated Recurrent Unit (AGRU) module is designed to capture global dynamic evolutionary trends in both spatial and temporal dimensions. Finally, a fully connected neural network is used to fuse these features. Experiments on two real passenger flow datasets of URT show that MSTFF performs well on various prediction tasks (15, 30, 45, and 60 min).

IG-Net: An Interaction Graph Network Model for Metro Passenger Flow Forecasting

The urban metro system accommodates significant travel demand and alleviates traffic congestion. Improving metro operational efficiency can increase the metro operator revenue and promote the development of robust urban transportation. To achieve this goal, passenger flow forecasting is a crucial and well-recognized task in metro operation. However, passenger flow forecasting is a challenging task as there exist many unquantifiable factors in resident travel. To address this problem, we propose an innovative model named Interaction Graph Network (IG-Net) to perform passenger flow forecasting at the station level, capable of capturing the non-Euclidean relationships between stations. Three kinds of inter-station interaction graphs are developed to model these inter-station interactions: connectivity, similarity, and temporal correlation graphs. Moreover, we apply multiple channels of graph convolutional neural networks to capture interaction representations and develop a multi-task learning architecture across multiple stations. The proposed IG-Net achieved better performance than the benchmark models when forecasting passenger flow over multiple stations, based on experiments with the Suzhou metro. Finally, we identify the significant effects of interaction graph combinations and multi-task loss functions via further experimentation.

A deep neural network model with GCN and 3D convolutional network for short‐term metro passenger flow forecasting

AbstractRail transit has many advantages, such as large passenger capacity, convenience, safety, and environmental protection, making it the preferred travel mode for most passengers. Deep learning has become an effective method for short‐term rail passenger flow prediction. A deep learning model which combines a graph convolutional network and a three‐dimensional Convolutional Neural Network improved by a residual module and an attention mechanism (ARConv‐CGN) is proposed. First, historical passenger inflow and outflow data are aggregated into three patterns: recent pattern, daily pattern, and weekly pattern separately. The GCN is applied to capture the spatiotemporal and topological information of passenger flows in each pattern. Second, a three‐dimensional convolutional neural network is used to deeply integrate three patterns of passenger flow information. Additionally, a residual module is used to increase the number of neural network layers and prevent the gradient of the deep neural network from disappearing. Finally, an attention mechanism is also introduced to adjust the importance of passenger flow in different pattern, so that improving the performance of the model. Training with passenger flow data from automatic fare collection on weekdays in Beijing and Xiamen provides a good demonstration of the superiority of ARConv‐CGN in metro passenger flow forecasting.

Forecast of Short-Term Passenger Flow in Multi-Level Rail Transit Network Based on a Multi-Task Learning Model

With the refinement of the urban transportation network, more and more passengers choose the combined mode. To provide better inter-trip services, it is necessary to integrate and forecast the passenger flow of multi-level rail transit network to improve the connectivity of different transport modes. The difficulty of multi-level rail transit passenger flow prediction lies in the complexity of the spatiotemporal characteristics of the data, the different characteristics of passenger flow composition, and the difficulty of research. At present, most of the research focuses on one mode of transportation or the passenger flow within the city, while the comprehensive analysis of passenger flow under various modes of transportation is less. This study takes the key nodes of the multi-level rail transit railway hub as the research object, establishes a multi-task learning model, and forecasts the short-term passenger flow of rail transit by combining the trunk railway, intercity rail transit and subway. Different from the existing research, the model introduces convolution layer and multi-head attention mechanism to improve and optimize the Transformer multi-task learning framework, trains and processes the data of trunk railway, intercity railway, and subway as different tasks, and considers the correlation of passenger flow of trunk railway, intercity railway, and subway in the prediction. At the same time, a new residual network structure is introduced to solve the problems of over-fitting, gradient disappearance, and gradient explosion in the training process. Taking the large comprehensive transportation hub in Guangzhou metropolitan area as an example, the proposed multi-task learning model is evaluated. The improved Transformer has the highest prediction accuracy (Average prediction accuracy of passenger flow of three traffic modes) 88.569%, and others methods HA, FC-LSTM and STGCN are 81.579%, 82.230% and 81.761%, respectively. The results show that the proposed multi-task learning model has better prediction performance than the existing models.

Network-Scale Passenger Flow Forecasting Methods in URT Based on Similarity Measurement

Accurate passenger flow forecasting in urban rail transit (URT) could provide a vital reference for operators’ timely operation management. However, due to the enormous scale of the metro network, it is unwise to forecast passenger flows at the station scale individually. In this paper, a forecasting framework is proposed for network-scale forecasting tasks considering both accuracy and efficiency. There are mainly three stages in the forecasting framework. Firstly, three kinds of similarity measurements are presented regarding the adjacent similarity, geographic location similarity, and trend similarity. Secondly, three similarity graphs are formed by combining the three kinds of similarity measurements and flow time series. Thirdly, the multigraph network is applied to perform passenger flow forecasting. The experimental results indicated that the proposed method performs relatively accurately for the network-scale prediction with economic time costs. Specifically, the proposed model could acquire the feasible forecasts compared with the best disaggregate model using less than half of calculation costs, and above 10% reduction in root-mean squared error (RMSE) compared with the best aggregate model in the benchmark trials. Extensive contrast experiments were conducted to investigate the sensitivity and interpretability of the proposed model.

A Deep Spatiotemporal Fuzzy Neural Network for Subway Passenger Flow Prediction With COVID-19 Search Engine Data

Passenger flow prediction is of great significance in the operation and management of subways, especially in reducing energy consumption and improving service quality. Due to the impact of COVID-19, subway passenger flow fluctuates a lot, which makes passenger flow estimation or forecasting a very challenging task. This article mainly carries out two aspects of work to solve the task of subway passenger flow prediction under pandemic. First, this article introduces search engine data as a new data source and provides a systematic method to extract valid quires and search volumes that are closely associated with subway passenger flow under pandemic. Second, this article combines the fuzzy theory and neural network to propose a deep learning architecture called “deep spatiotemporal fuzzy neural network” to deal with the complex spatiotemporal features and uncertain external data of subway passenger flow prediction. Experiments on the actual dataset of the Beijing subway prove the superiority of the model and the effectiveness of search engine data in subway passenger flow forecasting.

LSTM-Based Transformer for Transfer Passenger Flow Forecasting between Transportation Integrated Hubs in Urban Agglomeration

A crucial component of multimodal transportation networks and long-distance travel chains is the forecasting of transfer passenger flow between integrated hubs in urban agglomerations, particularly during periods of high passenger flow or unusual weather. Deep learning is better suited to managing massive amounts of traffic data and predicting extended time series. In order to solve the problem of gradient explosion or gradient disappearance that recurrent neural networks are prone to when dealing with long time sequences, this study used a transformer prediction model to estimate short-term transfer passenger flow between two integrated hubs in an urban agglomeration and a long short-term memory network to incorporate previous historical data. The experimental analysis uses two sets of transfer passenger data from the Beijing-Tianjin-Hebei urban agglomeration, collected every 30 min in May 2021 on the transfer corridors between an airport and a high-speed railway station. The findings demonstrate the high adaptability and good performance of the suggested model in passenger flow forecasting. The suggested model and forecasting outcomes assist management in making capacity adjustments in time to correspond with changes, enhance the effectiveness of multimodal transportation systems in urban agglomerations and significantly enhance the service of long-distance multimodal passenger travel.

A hybrid ensemble forecasting model of passenger flow based on improved variational mode decomposition and boosting.

An accurate passenger flow forecast can provide key information for intelligent transportation and smart cities, and help promote the development of smart cities. In this paper, a mixed passenger flow forecasting model based on the golden jackal optimization algorithm (GJO), variational mode decomposition (VMD) and boosting algorithm was proposed. First, the data characteristics of the original passenger flow sequence were extended. Second, an improved variational modal decomposition method based on the Sobol sequence improved GJO algorithm was proposed. Next, according to the sample entropy of each intrinsic mode function (IMF), IMF with similar complexity is combined into a new subsequence. Finally, according to the determination rules of the sub-sequence prediction model, the boosting modeling and prediction of different sub-sequences were carried out, and the final passenger flow prediction result was obtained. Based on the experimental results of three scenic spots, the mean absolute percentage error (MAPE) of the mixed set model is 0.0797, 0.0424 and 0.0849, respectively. The fitting degree reached 95.33%, 95.63% and 95.97% simultaneously. The results show that the hybrid model proposed in this study has high prediction accuracy and can provide reliable information sources for relevant departments, scenic spot managers and tourists.

Railway Passenger Flow Forecasting by Integrating Passenger Flow Relationship and Spatiotemporal Similarity

Railway Passenger Flow Forecasting by Integrating Passenger Flow Relationship and Spatiotemporal Similarity

Efficient Long-Term Dependencies Learning for Passenger Flow Prediction With Selective Feedback Mechanism

With the rapid growth of worldwide urbanization, the increasing demand for public transportation is indispensable. To improve the service quality, predicting the flow of passengers is important for the transport operators. Information on density of passengers can be used as early warnings of overcrowding and to determine if additional fleet is required. However, passenger flow forecasting is a challenging task, as it is affected by many complex factors such as spatial dependencies, temporal dependencies, and external influences. Furthermore, the ability to learn the long-term dependency of the data is also crucial, as the distant past flow information contributes to the flow over time. Most of the existing studies struggle to solve this issue, especially to learn the long-term dependency of the data, as they rely heavily on the raw handcrafted features and require high memory bandwidth to compute. To address these issues, we propose a Selective Feedback Transformer (SFT) capable of learning long-term dependency efficiently, where the selective feedback mechanism only computes the important feedback from the dominant query-key pairs in the memory. Experimental results demonstrate that the proposed model outperforms all the benchmarked methods by 27% - 37% in terms of RMSE and 36% - 50% in terms of MAE. Additionally, when the proposed model is tested with shallowed model (less number of decoding layer), it exhibits a substantial improvement of 14% - 57% in the training time and 15% - 46% in the inference time, with minimal impact on the accuracies.

Research of Passenger Flow Forecast of Urban Rail Transit Based on Data Mining

With the development of China's economy and society, urban construction is constantly improving, urban rail transit is becoming more mature, and people's demand for travel quality is getting higher and higher. However, the imperfect operation and management leads to the contradiction between supply and demand of urban rail transit. Passenger flow data of rail transit is the basis of operation scheduling, and accurate prediction can effectively improve the utilization rate of operating energy. In this paper, through data mining of passenger flow data, the law of passenger flow in time dimension is analyzed, and three different forecasting models are established for rail transit passenger flow data. Finally, the forecasting effects of each model are compared. The characteristics of passenger flow are analyzed in the time dimension, which shows the different changing rules of passenger flow on working days and rest days. In the discussion of the three forecasting methods, firstly, the time series forecasting method is realized by SPSS software, and the final model parameters are determined by unit root test, autocorrelation analysis, partial autocorrelation analysis and Bayesian information criterion. After that, the regression prediction model of support vector machine and BP neural network model are established by MATLAB. The former maps nonlinear passenger flow data into high-dimensional space to find linear relationship for prediction, while the latter realizes passenger flow prediction by establishing neural network model. Finally, by comparing the three prediction models, the results show that the average absolute error of BP neural network prediction method is 13%, which is 44% and 10% lower than that of time series method and support vector machine method, respectively, with high accuracy.

Multi-Model Attention Fusion Multilayer Perceptron Prediction Method for Subway OD Passenger Flow under COVID-19

At present, machine learning has been successfully applied in many fields and has achieved amazing results. Meanwhile, over the past few years, the pandemic has transformed the transportation industry. The two hot issues prompt us to rethink the traditional problem of passenger flow forecasting. As a special structure embedded in the machine learning model, the attention mechanism is used to automatically learn and calculate the contribution degree of input data to output data. Therefore, this paper uses the attention mechanism to find the best model to predict OD passenger flow under COVID-19. Holiday characteristics, minimum temperature, COVID-19 factors, and past origin-destination (OD) passenger flow were used as input characteristics. In the first stage, the attention mechanism was used to capture the advantages of the trained random forest, extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), and Adaboost models, and then the MLP was trained. Afterward, the weight distribution of the two models is carried out by using the historical passenger flow. The multi-model attention+ MLP model was used to evaluate the OD passenger flow prediction of Dalian Metro Line 1 under COVID-19. All the possible choices in this process were taken as a comparison experiment. The results show that only the fusion model combining the attention mechanism of random forest and XGBoost with MLP has the highest prediction accuracy.