Short-term Passenger Flow Forecasting Research Articles

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

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.

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.

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.

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.

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.

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).

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.

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.

Multi‐graph convolutional network for short‐term passenger flow forecasting in urban rail transit

Short-term passenger flow forecasting is a crucial task for urban rail transit operations. Emerging deep-learning technologies have become effective methods used to overcome this problem. In this study, the authors propose a deep-learning architecture called Conv-GCN that combines a graph convolutional network (GCN) and a three-dimensional (3D) convolutional neural network (3D CNN). First, they introduce a multi-graph GCN to deal with three inflow and outflow patterns (recent, daily, and weekly) separately. Multi-graph GCN networks can capture spatiotemporal correlations and topological information within the entire network. A 3D CNN is then applied to deeply integrate the inflow and outflow information. High-level spatiotemporal features between different inflow and outflow patterns and between stations that are nearby and far away can be extracted by 3D CNN. Finally, a fully connected layer is used to output results. The Conv-GCN model is evaluated on smart card data of the Beijing subway under the time interval of 10, 15, and 30 min. Results show that this model yields the best performance compared with seven other models. In terms of the root-mean-square errors, the performances under three time intervals have been improved by 9.402, 7.756, and 9.256%, respectively. This study can provide critical insights for subway operators to optimise urban rail transit operations.

Short-Term Passenger Flow Forecast of Rail Transit Station Based on MIC Feature Selection and ST-LightGBM considering Transfer Passenger Flow

To solve the problems of current short-term forecasting methods for metro passenger flow, such as unclear influencing factors, low accuracy, and high time-space complexity, a method for metro passenger flow based on ST-LightGBM after considering transfer passenger flow is proposed. Firstly, using historical data as the training set to transform the problem into a data-driven multi-input single-output regression prediction problem, the problem of the short-term prediction of metro passenger flow is formalized and the difficulties of the problem are identified. Secondly, we extract the candidate temporal and spatial features that may affect passenger flow at a metro station from passenger travel data based on the spatial transfer and spatial similarity of passenger flow. Thirdly, we use a maximal information coefficient (MIC) feature selection algorithm to select the significant impact features as the input. Finally, a short-term forecasting model for metro passenger flow based on the light gradient boosting machine (LightGBM) model is established. Taking transfer passenger flow into account, this method has a low space-time cost and high accuracy. The experimental results on the dataset of Lianban metro station in Xiamen city show that the proposed method obtains higher prediction accuracy than SARIMA, SVR, and BP network.

Deep Learning Architecture for Short-Term Passenger Flow Forecasting in Urban Rail Transit

Short-term passenger flow forecasting is an essential component in urban rail transit operation. Emerging deep learning models provide good insight into improving prediction precision. Therefore, we propose a deep learning architecture combining the residual network (ResNet), graph convolutional network (GCN), and long short-term memory (LSTM) (called “ResLSTM”) to forecast short-term passenger flow in urban rail transit on a network scale. First, improved methodologies of the ResNet, GCN, and attention LSTM models are presented. Then, the model architecture is proposed, wherein ResNet is used to capture deep abstract spatial correlations between subway stations, GCN is applied to extract network topology information, and attention LSTM is used to extract temporal correlations. The model architecture includes four branches for inflow, outflow, graph-network topology, as well as weather conditions and air quality. To the best of our knowledge, this is the first time that air-quality indicators have been taken into account, and their influences on prediction precision quantified. Finally, ResLSTM is applied to the Beijing subway using three time granularities (10, 15, and 30 min) to conduct short-term passenger flow forecasting. A comparison of the prediction performance of ResLSTM with those of many state-of-the-art models illustrates the advantages and robustness of ResLSTM. Moreover, a comparison of the prediction precisions obtained for time granularities of 10, 15, and 30 min indicates that prediction precision increases with increasing time granularity. This study can provide subway operators with insight into short-term passenger flow forecasting by leveraging deep learning models.

Forecasting Short-Term Passenger Flow: An Empirical Study on Shenzhen Metro

Forecasting short-term traffic flow has been a critical topic in transportation research for decades, which aims to facilitate dynamic traffic control proactively by monitoring the present traffic and foreseeing its immediate future. In this paper, we focus on forecasting short-term passenger flow at subway stations by utilizing the data collected through an automatic fare collection (AFC) system along with various external factors, where passenger flow refers to the volume of arrivals at stations during a given period of time. Along this line, we propose a data-driven three-stage framework for short-term passenger flow forecasting, consisting of traffic data profiling, feature extraction, and predictive modeling. We investigate the effect of temporal and spatial features as well as external weather influence on passenger flow forecasting. Various forecasting models, including the time series model auto-regressive integrated moving average, linear regression, and support vector regression, are employed for evaluating the performance of the proposed framework. Moreover, using a real data set collected from the Shenzhen AFC system, we conduct extensive experiments for methods validation, feature evaluation, and data resolution demonstration.

Cluster-Based LSTM Network for Short-Term Passenger Flow Forecasting in Urban Rail Transit

Short-term passenger flow forecasting is an essential component for the operation of urban rail transit (URT). Therefore, it is necessary to obtain a higher prediction precision with the development of URT. As artificial intelligence becomes increasingly prevalent, many prediction methods including the long short-term memory network (LSTM) in the deep learning field have been applied in road transportation systems, which can give critical insights for URT. First, we propose a novel two-step K-Means clustering model to capture not only the passenger flow variation trends but also the ridership volume characteristics. Then, a predictability assessment model is developed to recommend a reasonable time granularity interval to aggregate passenger flows. Based on the clustering results and the recommended time granularity interval, the LSTM model, which is called CB-LSTM model, is proposed to conduct short-term passenger flow forecasting. Results show that the prediction based on subway station clusters can not only avoid the complication of developing numerous models for each of the hundreds of stations, but also improve the prediction performance, which make it possible to predict short-term passenger flow on a network scale using limited dataset. The results provide critical insights for subway operators and transportation policymakers.

A multi-pattern deep fusion model for short-term bus passenger flow forecasting

Short-term passenger flow forecasting is one of the crucial components in transportation systems with data support for transportation planning and management. For forecasting bus passenger flow, this paper proposes a multi-pattern deep fusion (MPDF) approach that is constructed by fusing deep belief networks (DBNs) corresponding to multiple patterns. The dataset of the short-term bus passenger flow is first segmented into different clusters by an affinity propagation algorithm. The passenger flow distribution of these clusters is subsequently analyzed for identifying different patterns. In each pattern, a DBN is developed as a deep representation for the passenger flow. The outputs of the DBNs are finally fused by chronological order rearrangement. Taking a bus line in Guangzhou city of China as an example, the present MPDF approach is modeled. Five approaches, non-parametric and parametric models, are applied to the same case for comparison. The results show that, the proposed model overwhelms all the peer methods in terms of mean absolute percentage error, root-mean-square error, and determination coefficient criteria. In addition, there exists significant difference between the addressed model and the comparison models. It is recommended from the present study that the deep learning technique incorporating the pattern analysis is promising in forecasting the short-term passenger flow.

Forecasting the short-term passenger flow on high-speed railway with neural networks.

Short-term passenger flow forecasting is an important component of transportation systems. The forecasting result can be applied to support transportation system operation and management such as operation planning and revenue management. In this paper, a divide-and-conquer method based on neural network and origin-destination (OD) matrix estimation is developed to forecast the short-term passenger flow in high-speed railway system. There are three steps in the forecasting method. Firstly, the numbers of passengers who arrive at each station or depart from each station are obtained from historical passenger flow data, which are OD matrices in this paper. Secondly, short-term passenger flow forecasting of the numbers of passengers who arrive at each station or depart from each station based on neural network is realized. At last, the OD matrices in short-term time are obtained with an OD matrix estimation method. The experimental results indicate that the proposed divide-and-conquer method performs well in forecasting the short-term passenger flow on high-speed railway.

Wavelet Neural Network-based Short-Term Passenger Flow Forecasting on Urban Rail Transit

Accurate forecasting of short-term passenger flow has been one of the most important issues in urban rail transit planning and operation. Considering the shortcomings of traditional forecasting methods, and in order to improve forecasting accuracy of passenger flow, this paper presents a wavelet neural network (WNN) for short-term passenger flow forecasting. One real urban rail transit station with large and significantly changed passenger flow is chosen to be the example. The proposed method and BP neural network have been compared with the results, which show that the WNN model has more advantages. The WNN model features higher learning speed and drastically less convergence time, showing that it is meaningful in practical application. Furthermore, the calculated relative errors using BP neural network are nearly in the range [-0.4, 0.3] and the calculated relative errors are in the range [-0.25, 0.1] using the WNN, which demonstrate the superior accuracy of using this approach. DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.3096 Full Text: PDF

Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks

Short-term passenger flow forecasting is a vital component of transportation systems. The forecasting results can be applied to support transportation system management such as operation planning, and station passenger crowd regulation planning. In this paper, a hybrid EMD–BPN forecasting approach which combines empirical mode decomposition (EMD) and back-propagation neural networks (BPN) is developed to predict the short-term passenger flow in metro systems. There are three stages in the EMD–BPN forecasting approach. The first stage (EMD Stage) decomposes the short-term passenger flow series data into a number of intrinsic mode function (IMF) components. The second stage (Component Identification Stage) identifies the meaningful IMFs as inputs for BPN. The third stage (BPN Stage) applies BPN to perform the passenger flow forecasting. The historical passenger flow data, the extracted EMD components and temporal factors (i.e., the day of the week, the time period of the day, and weekday or weekend) are taken as inputs in the third stage. The experimental results indicate that the proposed hybrid EMD–BPN approach performs well and stably in forecasting the short-term metro passenger flow.