Passenger Traffic Flow Research Articles

Study on Urban Rail Transit Passenger Flow Analysis and Economic Improvement Strategy

This study takes the analysis of traffic passenger flow from the perspective of commuting as the research object, and selects Suzhou rail transit as a thematic research case. Based on the analysis of the "Annual Commuter Monitoring Report of China's Major Cities 2022" and the research of Suzhou rail transit, the following conclusions are drawn: First, rail construction should take the service of commuter passenger flow as the core, emphasize the fit between line direction and commuting demand, so as to improve economy. Second, Improving the leading role of rail transit and the efficiency of the whole process is the key, and attention should be paid to the improvement of the transportation mode structure and the efficiency of "door-to-door" service. Third, rail transit should guide urban development, pay attention to function matching, in particular, attention should be paid to the balance between employment and housing of rail lines and guaranteed housing for youth, so as to promote the economic development and community stability of the city. Finally, the paper puts forward some measures to improve rail commuter coverage, including the establishment of an interactive monitoring platform between rail construction and urban development, so as to support the improvement of rail commuter coverage.

Multivariate long-time series traffic passenger flow prediction using causal convolutional sparse self-attention MTS-Informer

Multivariate long-time series traffic passenger flow prediction using causal convolutional sparse self-attention MTS-Informer

A COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR THE PURPOSE OF PREDICTING NORWEGIAN AIR PASSENGER TRAFFIC

This analysis aims to provide an overview of potential machine learning algorithms that may aid the aviation industry in predicting future air passenger traffic flow, which can help increase stakeholder value as well as improve customer experiences. A review and discussion of the aviation industry’s past, current, and future challenges is provided, as well as an overview of machine learning algorithms, neural networks, and learning methods. Further, an overview and discussion of the architecture of the Long Short-Term Memory (LSTM) network, Support Vector Regression Machine (SVRM), and Random Forest (RF) algorithms is provided. The comparative analysis provides an overview and comparison of the performance of the LSTM, SVRM, and RF models based on Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The dataset used includes the hourly number of passengers from scheduled flights at Oslo Airport Gardermoen for the period of January 1, 2009, to December 31, 2019, including the datetime features such as Time (hour), day, month, and year, as well as the weather features of air temperature and mean wind speed, with a total of 96185 samples. The Long Short-Term Memory model exhibited the highest generalization ability, with a performance evaluation on the testing dataset of 0.00445/0.06667 MSE/RMSE. Additionally, the performance of the SVRM and RF models on the testing dataset is 0.00511/0.07147 and 0.00543/0.07368 MSE/RMSE respectively. In addition to the performance, each of the models’ complexity, stability, and ability to predict the hourly and daily fluctuations of passengers are discussed.

Clustering Analysis of Multilayer Complex Network of Nanjing Metro Based on Traffic Line and Passenger Flow Big Data

Complex networks in reality are not just single-layer networks. The connection of nodes in an urban metro network includes two kinds of connections: line and passenger flow. In fact, it is a multilayer network. The line network constructed by the Space L model based on a complex network reflects the geographical proximity of stations, which is an undirected and weightless network. The passenger flow network constructed with smart card big data reflects the passenger flow relationship between stations, which is a directed weighted network. The construction of a line-flow multilayer network can reflect the actual situation of metro traffic passenger flow, and the node clustering coefficient can measure the passenger flow clustering effect of the station on adjacent stations. Combined with the situation of subway lines in Nanjing and card-swiping big data, this research constructs the line network with the Space L model and the passenger flow network with smart card big data, and uses these two networks to construct the multilayer network of line flow. This research improves the calculation method of the clustering coefficient of weighted networks, proposes the concept of node group, distinguishes the inflow and outflow, and successively calculates the clustering coefficient of nodes and the whole network in the multilayer network. The degree of passenger flow activity in the network thermal diagram is used to represent the passenger flow activity of the line-flow network. This method can be used to evaluate the clustering effect of metro stations and identify the business districts in the metro network, so as to improve the level of intelligent transportation management and provide a theoretical basis for transportation construction and business planning.

A Hybrid Deep Learning Approach for Real-Time Estimation of Passenger Traffic Flow in Urban Railway Systems

This research introduces a hybrid deep learning approach to perform real-time forecasting of passenger traffic flow for the metro railway system (MRS). By integrating long short-term memory (LSTM) and the graph convolutional network (GCN), a hybrid deep learning neural network named the graph convolutional memory network (GCMN) was constructed and trained for accurate real-time prediction of passenger traffic flow for the MRS. Data collected of the traffic flow in Delhi’s metro rail network system in the period from October 2012 to May 2017 were utilized to demonstrate the effectiveness of the developed model. The results indicate that (1) the developed method provides accurate predictions of the traffic flow with an average coefficient of determination (R2) of 0.920, RMSE of 368.364, and MAE of 549.527, and (2) the GCMN model outperforms state-of-the-art methods, including LSTM and the light gradient boosting machine (LightGBM). This study contributes to the state of practice in proposing a novel framework that provides reliable estimations of passenger traffic flow. The developed model can also be used as a benchmark for planning and upgrading works of the MRS by metro owners and architects.

Rail Track for Light Rail Transit Systems

The analysis of the well-known designs and technologies for manufacture of rail tracks for light rail transit (LRT) allows to assess trends in development of this type of transportation, as well as to reveal an underestimation of public rail transport referring to the existing «tendency to eliminate tram traffic» in several Russian cities, despite the problems of urban ground passenger transport associated with a limited resource of urban space.The prospects for development of urban ground passenger transport systems of LRT type were considered regarding adoption of new types of rolling stock, infrastructure development, reduction of costs and time of renewal of existing and construction of new tracks.The objective of this article is to present an option of a rail track design for LRT considering its structural and technological features, as well as comparative assessment of its technical and economic indicators. The research method is based on the analysis of the current state, prospects, and trends in development of LRT in Russia, which made it possible to propose a solution to the existing problem of urban passenger transport. Research was reflected in relevant patents, in pending applications for alleged inventions, as well as in experience of manufacturing full-scale samples.Comparative assessment referred to the known designs of sleeper and sleeperless, ballast and ballastless track used for light rail transit.A prefabricated, two-level structure of a ballastless rail track for light rail transit is suggested. The design consists of a pile foundation with superposed longitudinal sleeper track connected by transverse braces. The design and technological features, as well as advantages of the proposed design in terms of essential indicators are shown in comparison with the known rail track designs for light rail transit. It is shown that the proposed track design makes it possible to develop a self-sufficient, self-organising logistics system and to quickly proceed with permanent construction, processing passenger traffic and cargo flows. Since the concept of «from infrastructure to facility» is replaced by the concept of «from facility to infrastructure», it allows development of the facility to outpace development of transport infrastructure, as a costly system with a distant payback.

Spatial–Temporal Data Imputation Model of Traffic Passenger Flow Based on Grid Division

Traffic flows (e.g., the traffic of vehicles, passengers, and bikes) aim to reveal traffic flow phenomena generated by traffic participants in traffic activities. Various studies of traffic flows rely heavily on high-quality traffic data. The taxi GPS trajectory data are location data that include latitude, longitude, and time. These data are critical for traffic flow analysis, planning, infrastructure layout, and recommendations for urban residents. A city map can be divided into multiple grids according to the latitude and longitude coordinates, and traffic passenger flows data derived from taxi trajectory data can be extracted. However, random missing data occur due to weather and equipment failure. Therefore, the effective imputation of missing traffic flow data is a hot topic. This study proposes the spatio-temporal generative adversarial imputation net (ST-GAIN) model to solve the traffic passenger flows imputation. An adversarial game with multiple generators and one discriminator is established. The generator observes some components of the time-domain and regional traffic data vector extracted from the grid. It effectively imputes the missing values of the spatio-temporal traffic passenger flow data. The experimental data are accurate Kunming taxi trajectory data, and experimental results show that the proposed method outperforms five baseline methods regarding the imputation accuracy. It is significant and suggests the possibility of effectively applying the model to predict the passenger flows in some areas where traffic data cannot be collected for some reason or traffic data are randomly missing.

Выбор и оценка ARIMA-модели для прогнозирования пассажиропотока

Short-term forecasting of transport traffic flow is one of the main areas of research in the field of intelligent transport systems. In recent years, a large number of studies on traffic forecasting have been conducted. However, it is difficult to pick the most appropriate traffic forecasting method for a particular application. Naive methods are widely used due to their low computational costs and simplicity of implementation. But the accuracy of these methods is usually quite low. Clustering methods average out the traffic variables within a certain group of days based on similar traffic models. Macroscopic simulation models take into account only such global variables of the road network as traffic density, average speeds and flows. The advantage of nonparametric models lies in the fact that they enable us to model complex, dynamic and nonlinear processes occurring in road traffic. One of the forecasting options for passenger traffic is the forecasting on the basis of time series processing, which allows obtaining rather reliable results. Currently, considerable attention is given to the spatiotemporal forecasting. ARIMA processes are flexible linear models which can accurately describe a wide range of various time series behavior, including medium-term peaks and troughs. Despite the fact that these basic models are quite simple to describe, their evaluation requires extensive computer calculations. This paper investigates the problem of seasonality modeling when studying the time distribution of passenger traffic flow in road transport. The basic information for predicting passenger traffic flow is the data obtained during the daily parsing of the online service for searching traveling car companions (edem.rf (едем.рф)) for 12 months (February 2022 - January 2023).

Calculation of service devices in the projected traffic-transfer hub

The object of the work is the projected traffic-transfer hub (TTH) in the area of station B. Based on the passenger flows between all modes of transport present in the transport hub, the parameters of service devices are determined by simulation modeling. The obtained data will allow organizing the necessary capacity of the hub during "peak hours", at the same time during the period of decreasing passenger traffic flow there will be no significant idle time of the passenger devices. Based on the data obtained, technical and economic comparison of options for equipping the transport hub with service devices was made, as a outcome of which the most optimal number of devices was identified.

GEOSPATIAL TECHNOLOGY-BASED STUDY ON TOURIST ACCOMMODATION IN BUCHAREST METROPOLITAN AREA–IN RELATION TO PASSENGER TRAFFIC IN MAJOR TRANSPORTATION HUBS

The driving role of technology in building a sustainable and resilient future has been long acknowledged. As one of the most resilient industries, the hospitality sector is the focus of this research, particularlythe hotel lodging industry in the Bucharest metropolitan area (Romania’s capital city) as it relates to passenger traffic at two important international transportation hubs: the international airport and railway station. The site selection, or selection of an appropriate location for lodging, is astrategic choice. The distinction between success and failure is also determined by other external factors as well as internal company factors. There is a wealth of literature on business location, and the main approaches are highlighted in the section onliterature reviews. Future research will need to address the issue of business location in relation to transportation hubs that can attract potential customers. There are two specific questions to reach the research objective: to find out if the traffic from the transit terminals influences the number of accommodation facilities and if there is a specific pattern for choosing the location of tourist accommodation facilities (hotels) in the studied area. The investigation methodology consists of both secondary sources and primary research, respectively: national statistics as well as international databases such as Google Maps and OpenStreetMap, extensively use the geographic information system (GIS) database and software tools (QGIS 3.22 and Google Earth Pro 7.3.4.8573). The intent is to find whether there is a relationship between the two databases studied. It can be concluded that the number of tourist accommodation facilities and its locations are related to the main passenger traffic flows from/to the Bucharest “Henry Coanda” International Airport OTP and Bucharest North Railway station. The results, which are presented in both tabular and dynamic graphical formats, are beneficial for scholars and mostly for practitioners and businesspeople. Besides, the findings from this study is valuable for policymakers and strategists at a macroeconomic level, as well as for tourism and hospitality entrepreneurs and managers. The implications are highlighted, and recommendations are presented in this paper.

Blockchain‐enabled handover skipping for high mobility train passengers

Due to the cell size decrements and limited sojourn time on a high speed train mobility, unnecessary handovers (HOs) occur, which can lead to higher network communication costs, and affect passengers quality of service (QoS). This paper proposes a novel blockchain-enabled privacy preserving HO skipping framework by using train mobility dataset from the city of London. Using a complex dataset parameters, passenger traffic flows are modelled by averaging various train lines and station's footfall numbers utilising blockchain to maintain privacy. The framework stores pseudonym addresses in order to track the path of users. The proposed framework allows for a better trade-off in terms of 2% (approx.) gain in average throughput, over 100% gain in the last-hop signal quality, and a 50% reduction in HO costs, while also addressing the needs for resources to operate the blockchain.

Recurrent Neural Networks for Time Series Forecasting. Choosing the best Architecture for Passenger Traffic Data

Accurately predicting the urban traffic passenger flow is of great importance for transportation resource scheduling, planning, public safety, and risk assessment. Traditional statistical approaches for forecasting time series are not effective in practice. They often require either strict or weak data stationarity, which is almost impossible to obtain with real data. An alternative method is time series forecasting using neural networks. By their nature, neural networks are non-linear and learn based on input and output data. With this approach, increasing the efficiency of the network is reduced to increasing the amount of data of the initial sample. Today, the class of recurrent neural networks is mainly used for forecasting time series. Another important stage is the choice of neural network architecture. In this article the use of long short term memory and gated recurrent units architecture is considered and also is compared their performance for passenger flow forecasting.

Методика оценки целесообразности назначения мультимодальной пассажирской перевозки

Purpose: To study the impact of multimodal transportations on long-distance passenger transportations at the landfill of “FPC” Ltd. since in modern realities, train traffic planning without taking into account passenger demand can reduce railway competitive advantages in the market of transport mixed kind transportation and in the future, can lead to passenger dissatisfaction. To show the importance of making traffic schedule in accordance with passenger demand. To consider the matter on necessity of the supplement of existing approaches to multimodal route selection and on possibility to apply a regression model to forecast passenger flows. To pursue the testing of the developed methodology on the example of particular multimodal route. Methods: Comparison of existing approaches to multimodal transportation organization and their complement. Methods of mathematical statistics for passenger traffic flow forecasting. Results: The influence of multimodal transportations on long-distance passenger transportations at “FPC” Ltd landfill is considered. The criteria of multimodal transportation are defined. The methodology for feasibility assessment of multimodal route organization is proposed that allows estimating expected profit from transportation organization as well as predicting transportation impact on transportation process indicators. Practical importance: The recommendations and approaches proposed in the developed by the author methodology can be used for multimodal route development. The developed algorithm makes it possible to assess the prospects for passenger traffic change and allows yet at the preliminary selection stage for multimodal transportation organization options to perform a preliminary analysis of this organization economic feasibility.

Intelligent Bus Scheduling Control Based on On-Board Bus Controller and Simulated Annealing Genetic Algorithm

The stable and fast service of a bus network is one of the important indicators of the service quality and management level of urban public transport. With the continuous expansion of cities, the bus network complexity has been increasing accordingly. The application of new technologies such as self-driving buses has made the bus network more complex and its vulnerability more obvious. Therefore, how to collect information on passenger flow, traffic flow, and transport distribution using intelligent means, and how to establish an effective intelligent bus scheduling control method have been important questions surrounding the improvement of the level of urban bus operation. To address this challenge, this paper proposes the design method of a bus controller based on data collection and the edge computing requirements of autonomous driving buses; and installs them widely on buses. In addition, an intelligent bus control scheduling method based on the simulated annealing genetic algorithm was developed according to the current scheduling requirements. The proposed method combines the strong local search ability of the simulated annealing algorithm, which prevents the search process from falling into a local optimum, and the strong search ability of the genetic algorithm in the overall search process, leading an intelligent bus control scheduling method based on the simulated annealing genetic algorithm. The proposed method was verified by experiments on the optimal scheduling of multi-destination public transport as an example, we verified the research method, and finally, simulated it using historical data. There is good model prediction of the experimental results. Therefore, the intelligent traffic control can be realized through efficient bus scheduling, thus improving the robustness of the bus network operation.

ИССЛЕДОВАНИЕ И ОЦЕНКА ВЛИЯНИЯ ПАНДЕМИИ СOVID – 19 НА ПАССАЖИРОПОТОК В АВИАЦИОННОЙ ОТРАСЛИ

This article presents the impact of the Covid-19 pandemic on passenger traffic in the aviation industry at the global, Russian and regional levels. International data on airline traffic and passenger traffic at airports are presented. Data on passenger traffic at Pulkovo Airport from 2019 to the present is presented. On the basis of the collected data, graphs of air traffic decline are presented A detailed analysis of passenger traffic flows to domestic and international destinations is performed, and the main development trends are identified. Based on Pulkovo Airport passenger traffic analysis, the main directions are highlighted, and points for passenger traffic increase and recovery of air transport industry in North-West Russia are identified. The presented analytical data is the basis for analysis of the existing route aviation networks, creating the basis for solving the tasks of forecasting development and making decisions on effective expansion of destinations. Despite international restrictions, the expansion of domestic destinations is one of the ways to increase passenger traffic and achieve pre-crisis levels. The analytical bottom line presented is compiled from available airline and airport monitoring information systems.

Methodical Approach to Organisation of Transportation on Electric Bus Routes

When developing projects for introduction of electric buses into operation and determining the required infrastructure, it is necessary to justify the concept and methodological approaches to choosing of routes, to estimate the number of charging stations, depending on their type, and to conduct economic assessment of the proposed solutions in specific operating conditions.The article discusses the main issues of organising operation of buses with electric traction (electric buses) on the routes of public urban surface transport. A primary analysis of both current and prospective electric bus routes was carried out regarding the city of Moscow.Adaptive calculation methods are proposed based on classical methods for determining the required number of vehicles for a route regarding a given transit capacity, passenger traffic flow, and a given traffic interval.The current and prospective schemes for location of charging stations are considered, a method for determining their required number is proposed.A methodological approach of determining direct costs associated with transportation has been adapted to operation of electric buses on the route.

The Role and Influence of Industry 4.0. in Airport Operations in the Context of COVID-19

Due to the COVID-19 pandemic, aviation at the global level is currently facing one of the biggest challenges in its sustainability. The entire philosophy related to airport operations and passenger processing changes due to the fast-spreading nature of this disease. The “new normal” in the aviation sector, primarily at the airport, will provide the passenger a different quality level of service in comparison to what we used to have until now. Following World Health Organization measures, especially those related to social distances, the future focus will be on the implementation of fully automated self-service passenger traffic flows that will change the airport passenger traffic flows’ processing structure. This paper shows current technologies and processes and how this “new normal” will change philosophy related to Industry 4.0 at the airport passenger operations, functionality, and human resource (HR) planning and optimizations.

A Case Study of Bus Line Passenger Volumes of Bakrajo Bus Lines in Sulaimani City

Transit agencies constantly need information about system operations and passengers to support their regular scheduling and operation planning processes. The lack of these processes and cultural motivations to use public transportations contributes enormously to the reliance on the private cars rather than public transportation, resulting in traffic congestions. The traffic congestions occur mainly during peak hours and the accidents happening as a result of road accidents and construction works. This study investigates the effects of weekday and weekend travel variability on peak hours of the passenger flow distribution on bus lines, which can effectively reflect the degree of traffic congestion. A study of passenger traffic flow patterns during these times can impact planning decisions on transportation engineers. It can be viewed as a building block for generating a reliable schedule for a given bus route within the context of an optimization process. Collecting data sets of a two-directional bus line in Sulaimani city, connecting a residential district to the city center, for three days (Wednesday, Thursday, and Friday), and this study analyzed the flow and distribution of weekday and weekend passenger movements along with the bus stops of each direction on-peak and off-peak periods. The results indicate that Thursday passenger volume is more similar to Friday passenger volume than on Wednesday passenger volume. The passenger volume of direction D1 (from the residential district to the city center) is double that of direction D2 (from the city center to the residential district). The maximum morning peak time is on Wednesday with 442 passengers/hr, starting from 7:30 to 9:00, and the maximum evening peak hour is on Thursday with 420 passengers/hr, beginning from 14:30 to 16:30.

Forecasting air passenger traffic flow based on the two-phase learning model

The future airports will head toward a highly intelligent direction, like the unmanned check-in services, while the scale and resources allocation of the ground service are tightly related to the air passenger flow. Therefore, forecasting passenger flow accurately will affect the development of future airports and the optimization of service of civil airlines significantly. As a kind of time series, air passenger flow is influenced by multiple factors, particularly, the stochastic part of seasonality, trend and volatility. These will ultimately affect the accuracy of the prediction. Therefore, this paper introduces a prediction model based on a two-phase learning framework. In phase one, various predictors cope with different features of time series in parallel and the prediction results are integrated in phase two. Furthermore, this paper has compared principal error indicators with actual data and results show that the two-phase learning model performs better than current fusion models and owns stable performance.

Affirmation of Passenger Traffic Flows on the Danube Corridor – Perspective of River Cruise Tourism

Cruise tourism as a propulsive branch of tourism is being increasingly affirmed in the field of river transport. Accordingly, the main research problem in this paper is the analysis of the possibility of further affirmation and growth of passenger flows on river cruises on the Danube as the backbone of river traffic in the European Union. In the context of the defined research problem, the paper analyzes: relevant geo–traffic and socio–economic characteristics of the Danube, relevant indicators of passenger flows on cruises on the Danube – intensity, structure, dynamics and distribution of passenger traffic flows. Based on the aforementioned content, the further dynamics of passenger flows on cruises is concluded and forecasted, and the guidelines and factors of valorization of cruise tourism along the Danube Corridor are highlighted.