Passenger Flow Forecast Research Articles

Impacts of Weather on Short-Term Metro Passenger Flow Forecasting Using a Deep LSTM Neural Network

Metro systems play a key role in meeting urban transport demands in large cities. The close relationship between historical weather conditions and the corresponding passenger flow has been widely analyzed by researchers. However, few studies have explored the issue of how to use historical weather data to make passenger flow forecasting more accurate. To this end, an hourly metro passenger flow forecasting model using a deep long short-term memory neural network (LSTM_NN) was developed. The optimized traditional input variables, including the different temporal data and historical passenger flow data, were combined with weather variables for data modeling. A comprehensive analysis of the weather impacts on short-term metro passenger flow forecasting is discussed in this paper. The experimental results confirm that weather variables have a significant effect on passenger flow forecasting. It is interesting to find out that the previous variables of one-hour temperature and wind speed are the two most important weather variables to obtain more accurate forecasting results on rainy days at Taipei Main Station, which is a primary interchange station in Taipei. Compared to the four widely used algorithms, the deep LSTM_NN is an extremely powerful method, which has the capability of making more accurate forecasts when suitable weather variables are included.

Quantile Dependence in Tourism Demand Time Series: Evidence in the Southern Italy Market

Travel and tourism is an important economic activity in most countries around the world. In 2018, international tourist arrivals grew 5% to reach the 1.4 billion mark and at the same time export earnings generated by tourism have grown to USD 1.7 trillion. The rapid growth of the tourism industry has globally attracted the interest of researchers for a long time. The literature has tried to model tourism demand to analyze the effects of different factors and predict the future behavior of the demand. Forecasting of tourism demand is crucial not only for academia but for tourism industries too, especially in line with the principles of sustainable tourism. The hospitality branch is an important part of the tourism industry and accurate passenger flow forecasting is a key link in the governance of the resources of a destination or in revenue management systems. In this context, the paper studies the interdependence of tourism demand in one of the main Italian tourist destinations, the Campania region, using a quantile-on-quantile approach between overall and specific tourism demand. Data are represented by monthly arrivals and nights spent by residents and non-residents in hotels and complementary accommodations from January 2008 to December 2018. The results of the analysis show that the hotel-accommodation component of the tourism demand appears to be more vulnerable than extra-hotel accommodation component to the fluctuations of the overall tourism demand and this feature is more evident for the arrivals than for nights spent. Moreover, the dependence on high quantiles suggests strategy of diversification or market segmentation to avoid overtourism phenomena and/or carrying capacity problems. Conversely, dependence on low quantiles suggests the use of push strategies to stimulate tourism demand. Finally, the results suggest that it could be very useful if the stakeholders of the tourism sector in Campania focused their attention on the collaboration theory.

A Novel Hierarchical Hybrid Model for Short-Term Bus Passenger Flow Forecasting

For the increasing travel demands and public transport problems, dynamically adjusting timetable or bus scheduling is necessary based on accurate real-time passenger flow forecasting. In order to get more accurate passenger flow in future, this paper proposes a novel hierarchical hybrid model based on time series model, deep belief networks (DBNs), and improved incremental extreme learning machine (Im-ELM) to forecast short-term passenger flow. The proposed model is named HTSDBNE with two modelling steps. First, referring the idea of parallelization, the hybrid model, constructed by time series model, DBN, and Im-ELM, is used to forecast short-term passenger flow in different time scales hierarchically and parallel. Second, Im-ELM is utilized to analyse the relationship of forecasting results from the first step, and the weighted outputs of Im-ELM are as the final forecasting results. Comparing with single forecasting models and typical hybrid forecasting models, the testing results indicate that HTSDBNE has better performances. The mean absolute percent error of prediction results is around 10% and fully meets the application requirements of bus operation enterprise.

Research on passenger flow forecast of Urban Rail Transit

Urban rail transit has the characteristics of large traffic volume, fast and convenient, so it has become the urban passenger transport mode which is given priority in the planning and design of each city. The prediction and analysis of rail transit passenger flow is a very important part in the planning and design stage. And it also provides important basic data for urban rail planning, construction and operation. When the rail transit network needs to be adjusted, the passenger flow forecast is also an essential step. In this paper, Taking Dongguan urban rail transit line 2 as an example, based on the full consideration of the uniqueness of Dongguan urban rail transit and the prediction of various social and economic indicators of Dongguan City, the “four step method” is adopted to forecast passenger flow. Through the investigation of resident’s travel survey, we can master the current travel distribution of all modes. Based on the analysis of land use change, traffic development policy and related influencing factors of Dongguan City, the trip distribution of each characteristic year is predicted. Then, the OD matrix of public transport (including subway and conventional public transport) is obtained through the study of traffic mode division model, and the predicted rail passenger flow is obtained through the allocation model of cooperation and competition. In the stage of passenger flow assignment, the influence of various factors on travel route selection is comprehensively considered in the public transport allocation model. Finally, the prediction index and sensitivity of rail passenger flow are analyzed.

基于Logit模型的京津城际铁路客流分担率预测

基于Logit模型的京津城际铁路客流分担率预测

Research on Sample Selection of Urban Rail Transit Passenger Flow Forecasting Based on SCBP Algorithm

Due to the wide applications of deep learning in the field of urban rail transit passenger flow forecasting, the selection problem of training samples has become increasingly more worthy of researchers' attention, as it is closely related to urban rail transit passenger flow time series. Therefore, it is necessary to study the distribution characteristics of the contribution degree of the training sample to guide sample selection in the deep learning training process. In this study, based on the prediction accuracy and the sample contribution degree, the optimal sample contribution combination algorithm (GWO-SCBP) was ultimately generated by the grey wolf optimizer (GWO) and error back propagation (EBP) algorithms. The contribution of training samples for each station of the Xi'an metro network was calculated and analyzed. The results show that the sample contribution is not only related to the distance between the sample and predicted value, but is also closely related to the station flow characteristics. By classifying the network stations and fitting the contribution degree of the central station of each type of station, linear equations of sample contribution degree were obtained, and the R 2 values attained at least 0.65, indicating a good fitting effect.

Passenger Flow Forecast of Rail Station Based on Multi-Source Data and Long Short Term Memory Network

The existing rail station passenger flow prediction models are inefficient, due to that most of them use single-source data to predict. In this paper, a novel method is proposed based on multi-layer LSTM, which integrates multi-source traffic data and multi-techniques (including feature selection based on Spearman correlation and time feature clustering), to improve the performance of predicting passenger flow. The experimental results show that the multi-source data and the techniques integrated in the model are helpful, and the proposed method obtains a higher prediction accuracy which outperforms other methods (e.g. SARIMA, SVR and BP network) greatly.

Urban Rail Transit Passenger Flow Forecasting Method Based on the Coupling of Artificial Fish Swarm and Improved Particle Swarm Optimization Algorithms

Urban rail transit passenger flow forecasting is an important basis for station design, passenger flow organization, and train operation plan optimization. In this work, we combined the artificial fish swarm and improved particle swarm optimization (AFSA-PSO) algorithms. Taking the Window of the World station of the Shenzhen Metro Line 1 as an example, subway passenger flow prediction research was carried out. The AFSA-PSO algorithm successfully preserved the fast convergence and strong traceability of the original algorithm through particle self-adjustment and dynamic weights, and it effectively overcame its shortcomings, such as the tendency to fall into local optimum and lower convergence speed. In addition to accurately predicting normal passenger flow, the algorithm can also effectively identify and predict the large-scale tourist attractions passenger flow as it has strong applicability and robustness. Compared with single PSO or AFSA algorithms, the new algorithm has better prediction effects, such as faster convergence, lower average absolute percentage error, and a higher correlation coefficient with real values.

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.

Analysis of Prediction Accuracy under the Selection of Optimum Time Granularity in Different Metro Stations

The improvement of accuracy of short-term passenger flow prediction plays a key role in the efficient and sustainable development of metro operation. The primary objective of this study is to explore the factors that influence prediction accuracy from time granularity and station class. An important aim of the study was also in presenting the proposition of change in a forecasting method. Passenger flow data from 87 Metro stations in Xi’an was collected and analyzed. A framework of short-term passenger flow based on the Empirical Mode Decomposition-Support Vector Regression (EMD-SVR) was proposed to predict passenger flow for different types of stations. Also, the relationship between the generation of passenger flow prediction error and passenger flow data was investigated. First, the metro network was classified into four categories by using eight clustering factors based on the characteristics of inbound passenger flow. Second, Pearson correlation coefficient was utilized to explore the time interval and time granularity for short-term passenger flow prediction. Third, the EMD-SVR was used to predict the passenger flow in the optimal time interval for each station. Results showed that the proposed approach has a significant improvement compared to the traditional passenger flow forecast approach. Lookback Volatility (LVB) was applied to reflect the fluctuation difference of passenger flow data, and the linear fitting of prediction error was conducted. The goodness-of-fit (R2) was found to be 0.768, indicating a good fitting of the data. Furthermore, it revealed that there are obvious differences in the prediction error of the four kinds of stations.

Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow.

Short-term metro passenger flow forecasting is an essential component of intelligent transportation systems (ITS) and can be applied to optimize the passenger flow organization of a station and offer data support for metro passenger flow early warning and system management. LSTM neural networks have recently achieved remarkable recent in the field of natural language processing (NLP) because they are well suited for learning from experience to predict time series. For this purpose, we propose an empirical mode decomposition (EMD)-based long short-term memory (LSTM) neural network model for predicting short-term metro inbound passenger flow. The EMD algorithm decomposes the original sequential passenger flow into several intrinsic mode functions (IMFs) and a residual. Selected IMFs that are strongly correlated with the original data can be obtained via feature selection. The selected IMFs and the original data are integrated into inputs for LSTM neural networks, and a single LSTM prediction model and an EMD-LSTM hybrid forecasting model are developed. Finally, historical real automatic fare collection (AFC) data from metro passengers are collected from Chengdu Metro to verify the validity of the proposed EMD-LSTM prediction model. The results indicate that the proposed EMD-LSTM hybrid forecasting model outperforms the LSTM, ARIMA and BPN models.

Passenger flow forecast for customized bus based on time series fuzzy clustering algorithm

Abstract Customized bus services are conducive to improving urban traffic and environment, and have attracted widespread attention. However, the problems encountered in the new customized bus mode include the large difference between the basis of customized bus passenger flow data analysis and the basis of the traditional bus passenger flow data analysis, and the difficulty in different vehicle scheduling caused by the combination of traditional and customized bus modes. We propose a customized bus passenger flow analysis algorithm and multi-destination customized bus line capacity scheduling algorithm, and display them in an intuitive way. The experimental results show that the algorithm model established in this paper can basically meet the data requirements of operation and management, and can provide decision support for customized bus line planning.

Urban rail transit passenger flow forecast based on LSTM with enhanced long‐term features

Outbreak passenger flow is the main cause of rail transit congestion. In this regard, the accurate forecast of passenger flow in advance will facilitate the traffic control department to redeploy infrastructures. Traffic sequence is a typical time series with long temporal dependence. For instance, an emergency may cause traffic congestion for the next several hours. Only a few studies focused on the way to capture long temporal dependence of passenger flow in the rail transit system. Here, an improved model enhanced long-term features based on long-short-term memory (ELF-LSTM) neural network is proposed. It takes full advantages of LSTM Neural Network (LSTM NN) models in processing time series and overcomes its limitations in insufficient learning of long temporal dependency due to time lag. The proposed network strengthens the long temporal dependency features embedded in passenger flow data and incorporates the short-term features to predict the origin destination (OD) flow in the next hour. The experiment results show that ELF-LSTM outperforms other state-of-the-art methods in terms of forecasting.

Research on forecast method of railway passenger flow demand in pre-sale period

The railway demand scientific forecasting is a rapid response mechanism for the railway sector, rational layout train plan for achieving definitive dynamic adjustment, and the basis for carrying out revenue management. In the railway industry, with changes of the seasons, holidays, emergencies and strategic competitor (such as airline routes for a certain period of reduced discount), passenger flow demand changes. Based on actual demand, this paper proposes an adaptive and flexible method of the railway passenger flow forecasting, which can be adapted to the above factors.

Short‐term passenger flow forecast of urban rail transit based on GPR and KRR

Short-term passenger flow forecasting can help the operation management department to adjust the related work. At the same time, it can also guide the traveller to choose a reasonable travel time and route, which plays an important role in promoting the development and construction of the city. In this study, the authors propose a hybrid prediction model based on kernel ridge regression (KRR) and Gaussian process regression (GPR) to predict the short-term passenger flow of urban rail transit, and verify it on the Automatic Fare Collection System (AFC) dataset. Firstly, they utilise the stability feature selection algorithm to control the error of finite samples and use a GPR algorithm to obtain the original result. Then, they introduce stacked auto-encoder network to construct a feature extraction model, and apply k -means method to divide the stations into different types, defining as a site feature. Furthermore, they choose KRR algorithm with the combination of GPR prediction result and the holiday information, the station category information mentioned above, achieving the final prediction. The algorithm proposed in this study effectively improves the prediction accuracy and ensures time efficiency, and all the indicators are better than the existing algorithms.

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.

Effective passenger flow forecasting using STL and ESN based on two improvement strategies

Accurate passenger flow prediction is fairly challenging because of chaotic nature of transportation system and influence mechanism originated from multiple factors. It has been found that passenger flow has a nonlinear characteristic and a remarkable seasonal tendency. In this study, two novel hybrid approaches combining seasonal-trend decomposition procedures based on loess(STL) with echo state network(ESN) improved by grasshopper optimization algorithm(GOA) and adaptive boosting(Adaboost) framework respectively are proposed to forecast monthly passenger flow in China. According to the proposed methods(STL-GESN, STL-AESN), the original passenger flow data are firstly decomposed into seasonal, trend and remainder components via STL. Then the improved ESN is adopted to forecast the trend and the remainder components, and the seasonal-naive method is utilized to forecast the seasonal component. Finally, the forecasting results of the three components are summed to obtain the final forecasting of monthly passenger flow. Two passenger flow forecasting applications based on air data and railway data respectively are conducted to verify the effectiveness and scalability of the proposed approaches. The experimental results show that STL-GESN and STL-AESN obtain higher prediction accuracy compared with other forecasting approaches. Application studies also demonstrate that the proposed approaches are practical choice for passenger flow forecasting.

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.

Анализ динамики пассажиропотока поездов «Сапсан» и авиационного транспорта на линии «Москва–Санкт-Петербург» до 2025 г.

Objective: To analyze passenger flow trends of “Sapsan” trains and air transport on “Moscow–Saint Petersburg” direction; to forecast passenger flow of the given types of transport taking into account economic activity and tourism in Moscow and Saint Petersburg for the period of 2010 through to 2025; to calculate the predicted values of passenger flow at the given direction. Methods: Correlation and multiple regression analysis were applied. Results: The trends of passenger flows of “Sapsan” trains were analyzed for the period in question and of air transport for the period of 2010 through to 2018 on the route Moscow–Saint Petersburg. Special attention is paid to the design of passenger flow forecast mathematical model with allowance for its dependence on social and economic factors in the operating domain under study. Statistical data on the occupancy of hotels in Moscow and Saint Petersburg was selected as the factors in question; the former characterize mainly tourist passenger flow section as well as gross regional product which reflect the economic development and economic activity of the cities. Passenger flow forecast of the described types of transport was carried out for the period up to 2025. Practical importance: The conclusion was made on feasibility of studies in the sphere of passenger traffic forecast, especially under the conditions of changing tourist and economic activity on the given route, as well as strong competition among the studing types of transport in question.

Subway Passenger Flow Forecasting With Multi-Station and External Factors

With the rapid development of urban rail transit, more and more people choose to travel by subway. Therefore, accurate passenger flow forecasting is of great significance for passengers and municipal construction and contributes to smart city services. In this paper, we propose a multi-type attention-based network to forecast the subway passenger flow with multi-station and external factors. The proposed network has different types of attention mechanisms to adaptively extract relevant features, including multi-station, external factors, and historical data. In addition, the hierarchical attention mechanism is used to model the hierarchical relationship between subway lines and stations. In addition, the embedding method is applied to better combine the different kinds of data. The experiments on real subway passenger flow data in a city in China demonstrate that our method outperforms five baseline methods. Moreover, our method can visualize the impact of different stations and other factors on traffic, which plays an important role in passenger travel and subway dispatch.