Metro Passenger Flow Research Articles

Metro Passenger Flow Forecast with a Novel Markov-Grey Model

Accurate forecasts of passenger flow entering and leaving metro stations is an important work for Metro operation management, such as for the automatic adjustment of train operation diagrams or station passenger crowd regulation planning measures. In this study, Grey theory is introduced to develop a time series GM (1, 1) model for total passenger forecasting. Two modification factors determined by two minimum mean square error principles are proposed to decrease the discreteness of input data and thus improve the forecast accuracy. Moreover, the Markov chain approach is further used to optimize the residual error series. Passenger flow data entering and leaving the Xiaozhai station of Xi'an Metro Line 2 from September 1-30, 2015, were utilized to verify the effectiveness of the proposed method; the forecast results show that this novel Markov-Grey model performs well in terms of forecast accuracy with smaller SMSE and MAPE values. To this effect, the proposed method is especially well-suited to smooth passenger flow forecasting compared to other forecast techniques.

Data-driven model for passenger route choice in urban metro network

Passenger flow distribution in the metro system is fundamental for many applications such as network planning and design, passenger flow forecasting, individual travel activity modeling and emergency response management. However, in most metro systems the smart card automated fare collection (AFC) equipment in Beijing only record when and where a passenger enters and leaves the metro network. Therefore, how to accurately determine passenger flow distribution in unknown travel routes remains a challenging task for the managers. This paper presents a methodology for reconstructing metro passenger flow distribution from large-scale smart card data. A clustering method was first applied to group the travel time of passengers between origin–destination (OD) station pairs into different clusters. Then an approach was proposed that considered both uncertain walking time and transfer time, to estimate the theoretical travel time of all possible routes between the OD pair. An approach to measure the similarity was further employed to match each travel time cluster to a most-likely travel route, and finally obtained the passengers’ flow of every route. Compared with two classical methods, the proposed approach was more accurate and efficient.

Исследование модели пассажиропотока станции метро в среде имитационного моделирования AnyLogic при введении дополнительных услуг и с поиском «узких мест».

Исследование модели пассажиропотока станции метро в среде имитационного моделирования AnyLogic при введении дополнительных услуг и с поиском «узких мест».

DeepPF: A deep learning based architecture for metro passenger flow prediction

This study aims to combine the modeling skills of deep learning and the domain knowledge in transportation into prediction of metro passenger flow. We present an end-to-end deep learning architecture, termed as Deep Passenger Flow (DeepPF), to forecast the metro inbound/outbound passenger flow. The architecture of the model is highly flexible and extendable; thus, enabling the integration and modeling of external environmental factors, temporal dependencies, spatial characteristics, and metro operational properties in short-term metro passenger flow prediction. Furthermore, the proposed framework achieves a high prediction accuracy due to the ease of integrating multi-source data. Numerical experiments demonstrate that the proposed DeepPF model can be extended to general conditions to fit the diverse constraints that exist in the transportation domain.

Impact of a New Metro Line: Analysis of Metro Passenger Flow and Travel Time Based on Smart Card Data

Over the past few decades, massive volumes of smart card data from metro systems have been used to investigate passengers’ mobility patterns and assess the performance of metro network. With the rapid development of urban rail transit in densely populated areas, new metro lines are constantly designed and operated in recent years. The appearance of new metro lines may significantly affect passenger flow and travel time in the metro network. In this study, smart card data of metro system from Nanjing, China, are used to study the changes of metro passenger flow and travel time due to the operation of a new metro line (i.e., Line 4, opened on 18 January 2017). The impact of the new metro line on passenger flow distribution and travel time in the metro network is first analysed. As commuting is one of the major purposes of metro trips, the impact of the new metro line on commuters’ trips is then explicitly investigated. The results show that the new metro line influences passenger flow, travel time, and travel time reliability in the metro network and has different impacts on different categories of commuters.

Short-Term Origin-Destination Based Metro Flow Prediction with Probabilistic Model Selection Approach

Reliable prediction of short-term passenger flow could greatly support metro authorities’ decision processes, help passengers to adjust their travel schedule, or, in extreme cases, assist emergency management. The inflow and outflow of the metro station are strongly associated with the travel demand within metro networks. The purpose of this paper is to obtain such prediction. We first collect the origin-destination information from the smart-card data and explore the passenger flow patterns in a metro system. We then propose a data driven framework for short-term metro passenger flow prediction with the ability to utilize both spatial and temporal related information. The approach adopts two forecasts as basic models and then uses a probabilistic model selection method, random forest classification, to combine the two outputs to achieve a better forecast. In the experiments, we compare the proposed model with four other prediction models, i.e., autoregressive-moving-average, neural networks, support vector regression, and averaging ensemble model, as well as the basic models. The results indicate that the proposed approach outperforms the others in most cases. The origin-destination flows extracted from smart-card data can be successfully exploited to describe different metro travel patterns. And the framework proposed here, especially the probabilistic combination method, can improve the performance of short-term transportation prediction.

Capacity optimization and allocation of an urban rail transit network based on multi-source data

This study establishes a multi-objective optimization model for the capacity allocation of an urban rail transit network based on multi-source data on the Beijing metro passenger flow. The model considers the operating costs of trains and the expenses related to the waiting time of transferring passengers. The model constraints include the distribution characteristics of passenger flow, headway, load factor, and available trains. The capacity allocation scheme for 16 railway lines was obtained by adopting a model of the Beijing rail transit network and its passenger flow. We also analyzed the frequency at which Line 4 is reduced from 55 to 45 and the frequency at which Line 10 is reduced from 60 to 52 if a short turn is adopted. In addition, when the upper limit of the load factor increased from 60 to 70%, the operational costs were reduced by 4.6%, while the total passenger waiting time increased by 1%. The transfer costs changed the capacity optimization and allocation scheme, and the proportion of the transfer cost among the total costs increased when the time value increased.

Short-to-medium Term Passenger Flow Forecasting for Metro Stations using a Hybrid Model

Metro passenger flow forecasting is an essential component of intelligent transportation system. To enhance the forecasting accuracy and explainable of traditional models, a hybrid model combining symbolic regression and Autoregressive Integrated Moving Average Model (ARIMA) was proposed in this paper. It can take unique strength of each single model to capture the complexity patterns beneath data structure. Using the real data from Xi’an metro line 1, the performance of the hybrid model was compared with the ARIMA model and Back Propagation (BP) neural networks. The results show that the hybrid model outperforms other two models. Mean Absolute Percentage Error (MAPE) of hybrid models have an extra 54.24%, 58.98% increase over the BP neural networks and an extra 64.44%, 68.27% increase over the ARIMA models for entrance and exit respectively. In addition, the t-test of MAPE during workday and holiday reflects the hybrid model possesses comparable forecasting ability under different conditions. Moreover, with the increase of the prediction steps, the superiority of the proposed model is more significant.

Metro passenger flow control with station-to-station cooperation based on stop-skipping and boarding limiting

Metro passenger flow control problem is studied under given total inbound demand in this work, which considers passenger demand control and train capacity supply. Relevant connotations are analyzed and a mathematical model is developed. The decision variables are boarding limiting and stop-skipping strategies and the objective is the maximal passenger profit. And a passenger original station choice model based on utility theory is built to modify the inbound passenger distribution among stations. Algorithm of metro passenger flow control scheme is designed, where two key technologies of stopping-station choice and headway adjustment are given and boarding limiting and train stopping-station scheme are optimized. Finally, a real case of Beijing metro is taken for example to verify validity. The results show that in the three scenarios with different ratios of normal trains to stop-skipping trains, the total limited passenger volume is the smallest and the systematic profit is the largest in scenario 3.

Generating route choice sets with operation information on metro networks

In recent years, the metro system has advanced into an efficient transport system and become the mainstay of urban passenger transport in many mega-cities. Passenger flow is the foundation of making and coordinating operation plans for the metro system, and therefore, a variety of studies were conducted on transit assignment models. Nevertheless route choice sets of passengers also play a paramount role in flow estimation and demand prediction. This paper first discusses the main route constraints of which the train schedule is the most important, that distinguish rail networks from road networks. Then, a two-step approach to generate route choice set in a metro network is proposed. Particularly, the improved approach introduces a route filtering with train operational information based on the conventional method. An initial numerical test shows that the proposed approach gives more reasonable route choice sets for scheduled metro networks, and, consequently, obtains more accurate results from passenger flow assignment. Recommendations for possible opportunities to apply this approach to metro operations are also provided, including its integration into a metro passenger flow assignment and simulation system in practice to help metro authorities provide more precise guidance information for passengers to travel.

The Rules of Beijing Metro Passenger Flow Based on AFC Data

With the development of rail transportation in many cities, how to build efficient, safe and comfortable operation network of urban development has become a major problem to be solved in rail transportation. Beijing subway network has been relatively mature, and the Beijing subway passenger traffic inherent rules has an important significance in other cities in the development of rail transit.Based on the Beijing subway AFC data, this paper is around the Beijing subway passenger laws and researches on the systematic work, aiming at finding its inherent laws with the adoption of data mining. This paper analyses the results in order to ensure the accuracy of early access to a large number of relevant documents, and makes adequate preparations on data processing

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.

The transactions

This study aims to combine the modeling skills of deep learning and the domain knowledge in transportation into prediction of metro passenger flow. We present an end-to-end deep learning architecture, termed as Deep Passenger Flow (DeepPF), to forecast the metro inbound/outbound passenger flow. The architecture of the model is highly flexible and extendable; thus, enabling the integration and modeling of external environmental factors, temporal dependencies, spatial characteristics, and metro operational properties in short-term metro passenger flow prediction. Furthermore, the proposed framework achieves a high prediction accuracy due to the ease of integrating multi-source data. Numerical experiments demonstrate that the proposed DeepPF model can be extended to general conditions to fit the diverse constraints that exist in the transportation domain.