Characteristics Of Passenger Flow Research Articles

Analysis of the Influence and Propagation Law of Urban Rail Transit Disruptions: A Case Study of Beijing Rail Transit

In the context of the network operation of urban rail transit systems, disruptions caused by signal interruptions influence not only the operation of the service at a single station but also the level of service of the whole network. Moreover, it is even possible to induce the cascading failure of the urban rail transit network. Therefore, it is essential to maintain the real-time dynamic monitoring of abnormal stations in urban rail transit systems for security reasons. Based on the large amounts of automated fare collection (AFC) data, a real-time calculation method to estimate the influence intensity of the passenger flow is presented, the spatiotemporal distribution of the influence characteristics is analyzed, and the propagation law of disruptions in the urban rail transit network is explored. First, the fluctuation threshold of passenger flow in a normal situation for all stations was calculated. Accordingly, abnormal stations influenced by the disruption were identified. Then, an evaluation method for calculating the influence intensity of the passenger flow was proposed. Finally, a real-world case study based on the Beijing rail transit system was conducted. All abnormal stations were identified dynamically and displayed in real time, and the distribution and propagation law of abnormal stations were constructed by spatiotemporal diagrams. The influence intensity of passenger flow was analyzed in detail from the perspective of the whole network and representative stations. The results revealed that transfer stations were more vulnerable to the effects of disruption, and the duration for which these stations were affected was longer than that of ordinary stations. Moreover, short-distance travelers were less affected by the disruption than long-distance travelers. The method proposed in this paper can provide a theoretical basis for rail management departments to grasp the characteristics of passenger flow in real time, formulate disposal measures dynamically, and provide more accurate information services for passengers.

Artificial Intelligence Assisted Intelligent Adjustment Method for Urban Rail Transit Train Operation

<p>The operation of intercity rail transit has greatly relieved the pressure of urban traffic. In order to improve the operation quality and passenger carrying capacity, the scheduling strategy of urban rail needs to be timely adjusted according to the passenger flow and other disturbing factors, especially the traffic control problems brought by the outbreak of the epidemic. In this paper, according to the epidemic situation and the characteristics of peak passenger flow in the morning and evening, an optimization model is designed to minimize the travel cost of passengers and the daily cost of the urban rail operation company. The optimal solution of the model is found through the reinforcement learning algorithm. Finally, based on the parameters of Shijiazhuang Metro, the optimal train scheduling scheme is obtained through simulation, which verifies the effectiveness of the research method in this paper.</p> <p> </p>

Classification and determinants of high-speed rail stations using multi-source data: A case study in Jiangsu Province, China

High-speed rail (HSR) stations play a vital role in the HSR system. HSR stations not only facilitate the accessibility of interregional transportation but also stimulate population movements across various cities in China. HSR stations in different cities can vary greatly, and an efficient HSR system requires an in-depth understanding of the interrelations between the related influential factors and spatiotemporal passenger flow patterns of different HSR stations. This study adopts a new scheme for clustering HSR stations based on passengers' arrival and departure time series using mobile phone data in Jiangsu, China. To this end, 71 HSR stations are clustered into 3 classes and the spatiotemporal characteristics of passenger flow at different stations are compared. Finally, a geographically weighted multinomial logit model (GWMNL) is built to explore the influence of the built environment, socioeconomic indicators, and HSR station attributes on the classification results of HSR stations related to the time-varying characteristics of passenger flow. The model results show that the number of entertainment POIs, population, population density, area, GDP and building area are significantly associated with the classification results of HSR stations. Additionally, for HSR stations under the same classification result, these variables also have different effects on them in the geographical dimension. According to these findings, quantitative analysis of the linkages between the passenger flow patterns at different HSR stations and the impacting factors would offer implications for planners and policymakers in HSR station planning and associated urban development.

Statistical Analysis of Train Operation and Passenger Distribution Based on Real Records: A Case Study of Wuhan-Guangzhou HSR

This paper summarizes the results of an effort aimed at improving train operation schedules on Wuhan-Guangzhou high-speed railway (WG-HSR). The real-record train operation and passenger tickets-booking records of WG-HSR are used for statistical analysis on the train service quality and passenger distribution. More specifically, the train service frequency and interval time at each station are analyzed. Based on this, the temporal and spatial distribution of capacity utilization in each section are investigated. In order to get a holistic view of passenger flow characteristics, the passenger volume during different time periods and between several origin and destination (OD) pairs are investigated to characterize travellers’ spatial-temporal preferences. The passenger distributions on some long-distance trains are shown to get the number and proportion of cross-line passengers travelling on the WG-HSR. Moreover, for a better understanding of the seat capacity utilization of trains, the load rates of trains in various sections and time periods are investigated. Specifically, the relationship between the average load rate of trains and trains’ running distance is explored, finding that the longer the non-cross-line train travels is, the higher the average load rate is. This study provides insightful findings that help understanding HSR operation and conducting further research.

A general metro timetable rescheduling approach for the minimisation of the capacity loss after random line disruption

This study proposes a generic metro timetable rescheduling method for the minimization of the capacity loss that integrates spatial and temporal information on random line disruptions and time-varying characteristics of passenger flows. The proposed emergency operating rules can be immediately deployed after a random disruption using one crossover track. The spatiotemporal information of a disruption and the current state of the line are integrated into a metro disruption management (MDM) model, which considers deviated from the original schedule and the number of stranded passengers as optimization objectives. An iterative meta-heuristic for the general metro rescheduling (IMH-GMR) algorithm is developed to flexibly classify an accident and determine rescheduling solutions for the MDM model within an effective running time (e.g., 15-60 sec). Test results show that the line capacity loss is significantly reduced (94.95%) compared with the total loss caused by the accident disposal in the test scenario.

Based on AFC Data Calculation of Walking Time in Metro Stations Considering the Impact of Passenger Flows

The walking time in metro stations is influenced by passenger flow with large fluctuation. Therefore, this paper proposes a method of station walking time calculation considering the influence of passenger flow: firstly, the time, entry, and exit direction and volume distribution characteristics of station passenger flow are analyzed, and the threshold of passenger flow that affects walking time is defined; then, an entry and exit walking time calculation model is established by five independent travel time chains and three station walking time constraints, and the transfer walking time is calculated based on the entry and exit walking time results; finally, the accuracy and validity of the walking time projection model are verified by using the calculation results. The results show that the calculation results are accurate and the validity of the model reaches 91.5%, and the constraint effect on walking time at station time is most obvious when the passenger flow reaches 3000 passengers per 30 min.

A Learning Based Intelligent Train Regulation Method With Dynamic Prediction for the Metro Passenger Flow

With the acceleration of urbanization, the dynamic passenger flow has an ever-growing impact on the actual train operation. In this paper, we propose a learning based intelligent train regulation method with dynamic passenger flow prediction. To capture the characteristics of the dynamic metro passenger flow, a convolutional neural network is established to predict the real-time passenger flow from two dimensions including space and time. As the prediction accuracy is restricted by the insufficiency of the practical passenger flow data, a deep convolutional generative adversarial network is constructed to generate data that have the same distribution as the original passenger flow dataset. Then, by considering the effects of the dynamic passenger flow on the train operation and the train capacity constraints, the dynamic train regulation is formulated as a multi-stage optimal control problem with the objective function of minimizing the train traction energy consumption and the total traveling time of passengers. To efficiently obtain the optimal regulation strategy at each decision step, a deep Q-network algorithm is proposed to solve the formulated problem such the dimensionality curse caused by the excessive state space is avoided. The numerical experiments demonstrate the high efficiency and effectiveness of our proposed algorithm and model.

Optimization for Metro Operation Scheme of Suburban Lines: A New Method for Dealing with the Imbalanced Passenger Flow

The development of some central cities tends to be saturated, so some mega cities try to adopt the express and local mode of the metro. Suburban lines with imbalanced passenger flow between stations will lead to extremely crowded passenger flow in some stations, while scarce passenger flow in other stations. To balance the train’s carrying capacity with the imbalanced passenger flow, this study explores the collaborative optimization of train operation under the new mode of express and local. First, a time-stamped data cropping strategy based on fine-grained time zones as the grid index is developed, which is used to deeply explore the OD spatiotemporal representation of passenger flow correlation mapping mechanism based on multisource data; then, the calculation model of the proportion of trains matching with the OD temporal and spatial characteristics of suburban passenger flow are constructed, and an efficient solution algorithm is developed to solve the problem of interest. Finally, a set of numerical experiments with operation data from Shanghai Metro Line 16 are conducted to verify the performance and effectiveness of the proposed model and algorithm. The experimental results show that the proposed approach can effectively realize the collaborative optimization of passenger OD prediction, train proportion, stop scheme, and travel time, so as to provide decision-making support and method guidance for the optimization of metro organizations in megacities.

Integrated optimization of multi‐vehicle‐type timetabling and scheduling to accommodate periodic passenger flow

AbstractA method was developed for integrating the process of multitype bus timetable generation and chains of trips formed by considering multiperiod passenger flow characteristics. First, the cumulative passenger demand curve was fitted and combined with the vehicle trajectory. Second, according to the results, the combined departure interval for multiple vehicle types was determined, and a one‐way possible timetable set was established. Third, considering the departure time window, the upward and downward possible timetable sets were connected. A bi‐objective mixed integer nonlinear programming model based on a spatiotemporal network was developed through two‐way matching. The objective of the model was to minimize the generalized fleet cost (a problem solved by the continuous deficit function of multiple vehicle types) and passenger waiting time. For two‐way matching, the computational complexity was reduced according to the postroot jump traversal rule, and the 𝑘‐shortest path algorithm was used to solve the bi‐objective Pareto‐optimal solution set. Finally, the Harbin No. 96 bus line was used as an example to validate the proposed model and algorithm. The optimization reduced the bus purchase cost by 51.28%, energy loss cost by 31.78%, and passenger waiting time by 37.07%, indicating that the proposed model can significantly reduce the costs for bus companies and passengers.

Application of Agent-Based Modelling for Evaluating Level of Service in Airports

Airports worldwide evaluate Level of service (LOS) to assess performance of the terminal for the current scenario and to compare it to a projected scenario. The International Air Transport Association (IATA) global standard is the widely accepted framework that evaluate airport LOS based on space and time characteristics. The application of simulation tools can be used in this regard to depict the airport LOS scenarios more interactively and in detail. Moreover, this approach also enables modelling future scenarios of technological or infrastructure modifications to cater terminal capacity constraints. The use of Agent-Based Modelling (ABM) in evaluating airport LOS is discussed in this paper. The study utilized field study at the check-in hall of a large-sized airport, the Chennai International airport, India to capture the passenger flow characteristics such as passenger entry rate, walking speed, group size, passenger flow sequence and flight movement frequency. These are used as inputs to a microscopic pedestrian modelling tool, AnyLogic. Three scenarios of airport LOS are modelled to evaluate the passenger density and the dwell times. The results show that the variation in number of service points and flow rate significantly affect the passenger dwell time, queuing length, queuing time and passenger density at the check-in zone. Visual and quantitative representations of the scenarios are also presented, analyzed and compared with the existing standard.

DEASeq2Seq: An attention based sequence to sequence model for short-term metro passenger flow prediction within decomposition-ensemble strategy

Short-term passenger flow prediction has practical significance for metro management and operation. However, the complex nonlinear and non-stationary characteristics make it challenging to detect evolution characteristics of passenger flow. To address this problem, a hybrid short-term metro passenger flow prediction model named decomposition ensemble attention sequence to sequence (DEASeq2Seq) is proposed in this paper. The proposed DEASeq2Seq includes three phases: decomposition, ensemble, and prediction. First, complete empirical mode decomposition with adaptive noise (CEEMDAN) is utilized to decompose the original passenger flow data into several intrinsic mode functions (IMFs) and a residue. Second, recurrence quantification analysis (RQA) is performed to reconstruct the decomposed modes into a stochastic part, a deterministic part, and a trend part via determinism evaluation. Third, a Seq2Seq model with the attention mechanism is proposed to execute multistep prediction for short-term passenger flow and explore the influence mechanism of the reconstructed components on the prediction targets. The real dataset from Chengdu metro, China, is used to verify the proposed model. The experiment results show that the proposed DEASeq2Seq model outperforms the benchmark models. Further model interpretations are conducted to analyze the impacts of decomposition strategy, ensemble strategy, and attention mechanism.

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.

Analysis of Passenger Flow Characteristics of Urban Rail Transit Based on Spatial Data Dynamic Analysis Technology

Based on spatial data dynamic analysis technology, the characteristics of urban rail transit passenger flow are analyzed. This study analyzes the passenger flow characteristics of urban rail transit from the perspective of operation and planning by using the dynamic analysis technology of spatial data, combined with social and economic development. Based on the analysis of research data from the study of passenger traffic and passenger traffic data measurement, the characteristics of passenger traffic from urban railways are derived from the time distribution, spatial distribution, and transfer of passengers waiting on the platform. The final length of line 3 is 39.7 m, the passenger volume is 102, with 11 people/day, the passenger flow density is 1.8/2.3, and the high section in peak hours is 2.4/3.2. By considering the various influencing factors, efforts should be made to develop urban rail transit projects to promote the growth of social and economic benefits.

Analysis of the Low-Carbon, Environmental-Friendly, Energy-Saving, and Emission-Reduction Evaluation Model of Urban Rail Transit Based on the Spatiotemporal Distribution of Passenger Flow

The development of the urban economy and the effect of linkage radiation are inseparable from the urban transportation system’s efficient operation. In the context of the new era, environmental pollution caused by economic development has gradually become an invisible killer that endangers human health and the atmospheric environment. It is a pillar industry of economic development, a key part of urban infrastructure construction, and a necessary guarantee for urban residents to travel and live, and it is important to develop a low-carbon, environmental-friendly, energy-saving, and emission-reduction potential for urban transportation systems. On the basis of a large number of literature research, this paper attempts to establish the role of an urban rail transit system in energy conservation and emission reduction in three aspects: residents’ travel behavior, ground transportation operation, and low carbon, energy conservation, and reduced emission under the influence of the urban rail transit system. Based on the temporal and spatial distribution characteristics of urban rail transit passenger flow, a relatively complete energy-saving and emission-reduction evaluation model is established. Through case analysis, it is verified that the model can effectively evaluate the effect of energy saving and emission reduction under different rail transit settings and its spatial and temporal distribution characteristics, and provides ideas and technical guidance for multidimensional quantitative analysis of urban rail transit carbon environmental protection, energy conservation, and emission reduction.

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

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

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

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

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.

Time Differential Pricing Model of Urban Rail Transit Considering Passenger Exchange Coefficient

Passenger exchange coefficient is a significant factor which has great impact on the pricing model of urban rail transit. This paper introduces passenger exchange coefficient into a bi-level programming model with time differential pricing for urban rail transit by analysing variation regularity of passenger flow characteristics. Meanwhile, exchange cost coefficient is also considered as a restrictive factor in the pricing model. The improved particle swarm optimisation algorithm (IPSO) was ap-plied to solve the model, and simulation results show that the proposed improved pricing model can effectively re-alise stratification of fares for different time periods with different routes. Taking Line 2 and Line 8 of the Beijing rail transit network as an example, the simulation result shows that passenger flows of Line 2 and Line 8 in peak hours decreased by 9.94% and 19.48% and therefore increased by 32.23% and 44.96% in off-peak hours, re-spectively. The case study reveals that dispersing pas-senger flows by means of fare adjustment can effectively drop peak load and increase off-peak load. The time dif-ferential pricing model of urban rail transit proposed in this paper has great influences on dispersing passenger flow and ensures safety operation of urban rail transit. It is also a valuable reference for other metropolitan rail transit operating companies.

Investigating Evaluation Indicators of Intelligent Vehicle Sharing Based on Operation Efficiency: A Case Study in Xiong’an New Area, China

Under the background of implementing green travel in response to the national energy conservation and emission reduction policy, the concept of shared travel as a new transportation mode has been promoted. This paper aims at providing a more scientific and quantitative method to explore the shared travel traffic mode and evaluating efficiency with data mining technology. Based on the analysis of the evaluation index system of the existing intelligent shared mobility, this paper firstly points out some issues in reflecting the operation efficiency of a specific shared mobility service. In order to find out the characteristics of passenger flow and propose recommend indicators, this study presents a detail analysis using data in Xiong’an new area from September 2020 to September 2021. Then, this paper proposes a time-series algorithm to identify ridesharing behavior of demand responsive (DR) bus and to recommend indicators on operation efficiency considering capacity, turnover, and time. Moreover, the definition of indicators and calculation of case study are carried out. Results show that the utilization rate of vehicle seat for DR bus was increased by 1.6–2.5 times, and the turnover efficiency was increased by about 2 times compared to private cars and taxi. In general, this paper quantitatively describes the improvement of operation efficiency brought by bus sharing, which shows that this kind of shared mobility has the attributes of public transport in a certain sense. Also, this paper shows that the above indicators are quantifiable and comparable, which is a useful supplement to the existing evaluation index system.

A prediction model to forecast passenger flow based on flight arrangement in airport terminals

Passenger flow plays an important role in the indoor environment and energy consumption of airport terminals. In this paper, field investigations were carried out in four typical airport terminals with different scales and operation states to reveal the characteristics of passenger flow. A prediction model is established to forecast passengers’ distribution in the main areas of an airport terminal based on its flight arrangement. The results indicate the dislocation peaks of passenger numbers in these areas, due to the airport's departure process. The peak time interval is about 30 min between the check-in hall and the security check area, and 60–80 min between the check-in hall and the departure hall. RD value (i.e., the ratio of the actual passenger number in a certain area to the design value) is used to describe this peak shifting feature. When the annual passenger throughput of an airport terminal reaches or even exceeds its design value, the total peak RD value is normally 0.6–0.8. For the airport affected by COVID-19, the peak RD is only 0.2, which reflects the decline in terminal passenger numbers during the pandemic. This research provides useful insight into the characteristics of passenger flow in airport terminals, and is beneficial for their design and operation.