Passenger Traffic Volume Research Articles

Investigating spatial patterns and determinants of tourist attractions utilizing POI data: A case study of Hubei Province, China

Exploring the spatial distribution characteristics of tourist attractions and the influencing factors is of significant importance for destination development, yet little relevant research has been conducted. This study explores the spatial patterns and determinants of tourist attractions using Hubei Province of China as a case based on the POI (Points of Interest) data, combined with standard deviation ellipse, GeoDetector method and so on. The results show that: (1) The distribution of tourist attractions in Hubei Province is concentrated in Wuhan and Huanggang. (2) The overall spatial patterns of tourist attractions in Hubei Province show a trend of “overall dispersion, partial concentration”, with the direction of northwest-southeast. (3) The permanent population, passenger traffic volume, per capita GDP, and the added value of the tertiary industry are the primary factors influencing the spatial distribution of tourist attractions in Hubei Province. Additionally, topography and river systems factors also impact their distribution. This study provides critical information for theory and practice in terms of tourism resources optimization.

Spatial effects of township health centers' health resource allocation efficiency in China.

China is a large agricultural nation with the majority of the population residing in rural areas. The allocation of health resources in rural areas significantly affects the basic rights to life and health for rural residents. Despite the progress made by the Chinese government in improving rural healthcare, there is still room for improvement. This study aims to assess the spatial spillover effects of rural health resource allocation efficiency in China, particularly focusing on township health centers (THCs), and examine the factors influencing this efficiency to provide recommendations to optimize the allocation of health resources in rural China. This study analyzed health resource allocation efficiency in Chinese rural areas from 2012 to 2021 by using the super-efficiency SBM model and the global Malmquist model. Additionally, the spatial auto-correlation of THC health resource allocation efficiency was verified through Moran test, and three spatial econometric models were constructed to further analyze the factors influencing efficiency. The key findings are: firstly, the average efficiency of health resource allocation in THCs was 0.676, suggesting a generally inefficient allocation of health resources over the decade. Secondly, the average Malmquist productivity index of THCs was 0.968, indicating a downward trend in efficiency with both non-scale and non-technical efficient features. Thirdly, Moran's Index analysis revealed that efficiency has a significant spatial auto-correlation and most provinces' values are located in the spatial agglomeration quadrant. Fourthly, the SDM model identified several factors that impact THC health resource allocation efficiency to varying degrees, including the efficiency of total health resource allocation, population density, PGDP, urban unemployment rate, per capita disposable income, per capita healthcare expenditure ratio, public health budget, and passenger traffic volume. To enhance the efficiency of THC healthcare resource allocation in China, the government should not only manage the investment of health resources to align with the actual demand for health services but also make use of the spatial spillover effect of efficiency. This involves focusing on factors such as total healthcare resource allocation efficiency, population density, etc. to effectively enhance the efficiency of health resource allocation and ensure the health of rural residents.

Decoupling tourism growth from carbon emissions: Evidence from Chengdu, China.

The fast growth increases in energy use and greenhouse gas emissions in China's tourism industry. This article assessed the carbon emissions of tourist Chengdu's traffic patterns and how they are expected to change between 2005 and 2021 and explains why tourism carbon decoupling is important for sustainable tourism development. In order to investigate how carbon emissions from tourism may expand without necessarily increasing in tandem with GDP and the variables that influence it, the Tapir model and the LMDI (logarithmic mean Divisia index) method were used. In our study, we looked at six important variables, including (1) the number of visitors, (2) the level of tourism spending per capita, (3) the contribution rate of the tourism sector with according to the country's total national income, (4) the amount of passenger traffic relative to GDP, and (5) the energy consumption relative to the volume of passenger traffic. There have been five stages in the correlation: positively charged decoupling, weak decoupling, negative decoupling, strong coupling, and strong decoupling. All of these describe the relationship between the expansion of China's tourist industry and the country's carbon emissions. Findings highlighted the importance of large numbers of visitors, high levels of per-person tourism spending, and low passenger traffic volume per unit of energy consumption as key positive factors. The rise of carbon emissions may also be slowed by increasing the number of passengers transported per unit of GDP. Carbon emissions from tourists in Chengdu are fluctuating, and this influences the city's economy. The findings have significant theoretical and practical implications for Chengdu's transition to a low-carbon economy and for the formulation of policies to reduce emissions. During the research period, most Chinese provinces exhibited ideal decoupling situations. This research has the potential to be used as a scientific resource for guiding the long-term growth as a result of China's tourist sector.

The effect of airport efficiency on air traffic, using DEA and multilateral resistance terms gravity models

This study evaluates the bias-corrected airport efficiency by employing a slacks-based measurement-data envelopment analysis model with a bootstrapping technique. The impacts of airport efficiency on bilateral passenger traffic and freight volume are then assessed by developing a novel robust gravity model incorporating multilateral resistance (MR) terms and the Poisson pseudo maximum likelihood (PPML) method to address heteroscedasticity. We collected air route data using Incheon airport with a sample of 50 airports in 28 countries for 2006–2015. The results from all the applied models revealed a significant and positive effect of airport efficiency on passenger traffic and freight volume.

ПРОБЛЕМЫ ПОВЫШЕНИЯ КАЧЕСТВА В СФЕРЕ ПЕРЕВОЗОК ПАССАЖИРОВ

The article analyzes the existing methods for determining the quality indicators of passenger transportation. A decrease in passenger traffic volumes in the Russian Federation and a decrease in demand for public transport services were revealed. The regulatory and legal documents of this service sector are considered. There are restrictions that do not allow to effectively assess the quality, and, consequently, to effectively organize the work of passenger transport in the subjects of the Russian Federation.

Predicting Model for Air Transport Demand under Uncertainties Based on Particle Filter

The outbreak of the COVID-19 has brought about huge economic loss and civil aviation industries all over the world have suffered severe damage. An effective method is urgently needed to accurately predict air-transport demand under the influences of such accidental factors. This paper proposes a novel predicting framework for the air-transport demand considering the uncertainties caused by accidental factors including regional wars, climatic anomalies, and virus outbreaks. By employing a seasonal autoregressive integrated moving average (sARIMA) model as the basic model, a particle filter (PF)-based sARIMA-pf model is proposed. The applicability of adapting the high-order sARIMA model as the state transition model in a PF framework is shown and proven to be effective. The proposed method has the advantage of coping with short-term prediction with known uncertainties. By conducting case studies on the prediction of air passenger traffic volume in China, the sARIMA-pf model showed better performance than the sARIMA model and improved the accuracy by 49.29% and 44.96% under the conventional and pandemic scenarios, respectively, when using the root mean square error (RMSE) as the indicator.

О прогнозировании пассажиропотоков для высокоскоростных магистралей с учетом конкуренции на рынке пассажирских перевозок

Purpose: The study of the problem of passenger traffic forecasting for high-speed railways given peculiarities of regional development and competition in passenger transportation market in Russian Federation conditions. The influence of a large number of different factors on passenger traffic volume formation is the main reason for the difficulty of obtaining reliable forecasts of passenger traffic in the long term. Based on the analysis of Russian and foreign scientific papers, devoted to the problem under study, the most significant factors were identified from ones’ variety: population, per capita income, gross domestic and regional products. The analysis showed that scientists ambiguously assess the impact of gross regional product on passenger traffic volume. The authors investigated the relationship between the volumes of passenger traffic and gross regional product on directions of being projected high–speed railways Moscow - St. Petersburg and Moscow — Kazan. As a result, the sections of gross regional product with the greatest degree of influence on passenger traffic were identified. Also, in modern conditions, concurrence in passenger transportation market has significantly increased which mainly determines the distribution of passenger traffic by transport kinds. Methods: System analysis, correlation and regression analysis, time series analysis. Results: The obtained results showed that in market conditions, because of the dynamism and uncertainty of population’s demand for transport services which is caused by a passenger’s choice of traffic method depending on transportation travel time, cost and comfort; the complexity of forecasting passenger traffic has increased. Two variants of passenger traffic forecasting methodology for high-speed highways are proposed in view of competition in the passenger transportation market. Practical significance: The application of a mathematical model for forecasting passenger traffic, taking into account the peculiarities of Russian Federation regions development and the competition between different kinds of passenger transport, will allow to obtain a reliable estimate for promising passenger traffic volumes that will provide for the reduction of investment risks in the construction and operation of capital-intensive high-speed railways.

Forecasting the volume of passenger transportation by inland water transport

The article discusses the currently used methods of forecasting passenger traffic volumes. The authors considered both statistical forecasting methods based on the construction of trend lines, and methods that take into account, in addition to the trend values of traffic volume indicators, other factors that affect the forecast values of passenger transport. On the example of water transport, the forecast values of traffic volumes were calculated by various methods, and the analysis of the calculation results was carried out.

Assessment of the internet impact on road transportation industry operational efficiency

The Internet of Things has been driving the development of China's transportation industry. This paper used a dynamic direct distance function Data Envelopment Analysis model with internet access as the input index to more comprehensively assess the efficiency and the improvements needed in the application of internet technology in the road transportation industries. It was found that (1) the average total efficiency was higher when the internet influences were considered than when they are not considered. (2) The commercial passenger vehicle, freight vehicle, passenger traffic, and freight volume efficiencies in most provinces were positively affected by the internet.

Assessment of sustainability of the Lviv city transport system according to the indicator of accessibility

Transport accessibility of the territory determines the possibility of getting a certain area using a certain transport mode of the existing transport network. The paper describes the concept of accessibility of the territory as a factor of sustainability of the urban transport system and methods of its assessment for urban conditions. It is proposed to use the indicator of the number of non-stop public transport routes between transport zones as a criterion for the "cost" of travel. Non-stop travel increases the comfort of public transport and reduces the financial costs of the passenger. The route network of the city of Lviv and interconnections of transport zones by public transport routes (bus, tram, and trolleybus) are analyzed. Modelling of passenger traffic volumes and their distribution by modes (private transport, public transport, and pedestrian traffic) was done in the PTV Visum software based on the matrix of a duration of movement and the matrix of the number of non-stop routes. It was found that at commensurate distances between transport zones, the number of non-stop routes increases the share of public transport users. The relationship between the number of non-stop routes and the share of public transport users is described by the logarithmic dependence. Comparing the simulated passenger flows on the public transport routes with the results of field researches, it was found that taking into account the number of non-stop routes between transport zones during the simulation allows increasing the accuracy of the results. Further research may focus on analyzing the impact of other factors that characterize the accessibility of the territory on the distribution of transport users between modes, and on the development of recommendations to the responsible city authorities to improve the city's passenger route network.

A Time-Varying Coupling Analysis of Expressway Traffic Volume and Manufacturing PMI

This study investigates the time-varying coupling relationship between expressway traffic volume and manufacturing purchasing manager index (PMI). First, for the traffic volume and manufacturing PMI time-series data, unit root stability test and Johansen cointegration test are applied to determine the stability of single sequence and the long-term stable correlation between variables, respectively. Then, a time-varying vector autoregressive model (TVP-VAR) is developed to quantify the time-varying correlation between variables. The time-varying parameters of TVP-VAR are estimated using the Markov chain Monte Carlo (MCMC) theory. Finally, the model is validated using examples from China. In the numeric example, three variables, i.e., expressway car traffic volume, expressway truck traffic volume, and manufacturing PMI, are selected for analysis. Results show that there is a positive interaction between expressway traffic volume (both car and truck) and manufacturing PMI. Express traffic volume slowly promotes the development of manufacturing industry. However, with the reform policy of road freight structure in China, the promotion effect of truck traffic on manufacturing PMI in the past two years has decreased significantly. Moreover, as affected by the China demand-led economic development model in recent years, the stimulus effect of manufacturing PMI on expressway passenger traffic volume has increased year by year. And, while the expressway freight structure remains stable, truck traffic volume is hardly affected by fluctuations in manufacturing PMI. These research results are helpful for policy makers to understand the time-varying coupling relationship between expressway traffic volume and manufacturing development and finally to improve the expressway management level.

Study on Traffic Status and Carbon Emission of Urban Transport in Beijing

At present, most of the literatures on transportation carbon emissions in China are to calculate the total amount of transportation carbon emissions. There are few literatures on carbon emission of specific transportation mode. There are few literatures on the calculation and analysis of urban traffic carbon emissions in Beijing. The historical data in these documents are very early, which can not effectively evaluate the current situation of urban transport in Beijing. Firstly, this paper analyzes the travel situation of Beijing residents in recent years. Then, based on the average daily passenger traffic volume and travel share rate of Beijing residents, a statistical model of urban transport vehicle energy consumption is established. Finally, the carbon emission of Beijing passenger transport is estimated.

الحركةُ الْجويّةُ الداخليّةُ لركّاب الخطوطِ الجويةِ العربيّةِ السِّعوديةِ: دراسةُ حالةِ مطار الملك عبد العزيز بجدة

تتناول الدارسةُ الحركةَ الجويةَ الداخليةَ للركّاب في مطارِ الملك عبد العزيز بجدة، الذي يعد من أهم مطارات المملكة العربية السعودية؛ فهو البوابة الجوية للحرمين الشريفين، ويخدم سكان محافظات المنطقة، وذلك من خلال شركة الخطوط الجوية العربية السعودية، التي تُعدُّ من أقدم شركات الطيران في الوطن العربي. تهدف الدراسة إلى تحليل حجم الحركة الجوية الداخلية للركاب في منطقة الدراسة، وتحديد اتجاهات هذه الحركة، من خلال تحديد المسارات الأكثر حركة، لما في ذلك من أهمية كبرى لإدارة المطارات وشركات الطيران؛ فحجم الحركة من أهم المعايير التي تحدد أهمية الخطوط الجوية واتجاهاتها على مستوى المراكز العمرانية في الأقاليم المختلفة. ولتحقيق أهداف الدراسة اُعتُمِدَ على المنهج الوصفي التحليلي لوصف الظاهرة المدروسة والعوامل المؤثرة فيها، كما اُعتُمِدَ على المنهج الكمي لجدولة بيانات الدراسة وتصنيفها. فيما اُستعين بالأشكال والرسوم البيانية والأسلوب الخرائطي لعرض النتائج والاستنتاجات. خُتمت الدراسة بعرض أهم النتائج التي تُوصل إليها، وهي أن هناك تباينًا فِي حجم حركة القادمين والمغادرين حسب الأشهر، وذلك لعدة أسباب منها الإجازات الرسمية في المملكة ودور مناخ المنطقة الذي يؤثر سلبًا وإيجابًا حسب أشهر السنة، فضلًا عن تأثير شهر رمضان المبارك الذي يعد من أهم المتغيرات الموسمية التي تُؤثّر على منطقة الدراسة. كما أن هناك تباينًا في أعداد الركاب الداخليين بين مختلف المسارات، ويعزى سبب ذلك إلى اختلاف وظائف مدن المطارات وعدد سكانها، فضلًا عن الظروف الطبيعية والأنشطة الاقتصادية التي تميزت بها بعض المدن، فضلاً عن تأثير وسائل النقل الأخرى على حجم حركة النقل الجوي، وتنافس المطارات في المنطقة الواحدة، ولا سيما تلك المطارات التي تقع في مدن ذات ثقل سكاني كبير. وفيما يخص المسارات الأكثر حركة في منطقة الدراسة، فقد تركزت حركة الركاب في خمسة مسارات جوية، وبنسبة (77.57%) من إجمالي حجم حركة الركاب على جميع المسارات.

Linear Regression Model of Railway Passenger Traffic Volume and Its Influencing Factors

The railway is one of the most crucial transportation modes in China and plenty of people choose to travel by railway. Finding the influencing factors of passenger traffic volume of the railway can help us to develop the railway construction. Substantial research has probed factors influencing the railway passenger volume and has identified several influencing factors. In this study, we try to find a long-term relationship between passenger traffic volume of the railway and six influencing factors, which are total population, employed persons, number of national railway passenger coaches owned, GDP, and household consumption expenditure. In order to find long term relationship models, we collected the data of r passenger traffic volume of the railway and other six influencing factors from 1978 to 2019. By developing several multiple linear regression models, we can see that all of these six factors can impact the passenger traffic volume of the railway and the model of six factors can describe the relationship well. After simplifying the model with six factors, we find two more models that can describe the relationship with less influencing factors, which are y = 1.059 × 105 - 1.773 × 10-2 x 1 + 2.729 × 10-1 x 5 + ε and log(y) = 11.5-3.309 × 10-8x1+1.435 × 10-6x5+ε. Using the diagnostic plots to analysis these two models, we find that log(y) = 11.5-3.309 × 10-8x1+1.435×10-6x5+ε is better. According to the model, GDP and traffic volume are two main influencing factors on the railway passenger volume. We use this model to predict the railway passengers in 2019 and compare the predict value with the data. The error rate i 5.4688% and we believe that this log(y) model can also be used to predict the railway passenger volume.

АНАЛИЗ ВРЕМЕННОГО РАСПРЕДЕЛЕНИЯ ПАССАЖИРОПОТОКА НА АВТОМОБИЛЬНОМ ТРАНСПОРТЕ НА ОСНОВЕ ДАННЫХ РАЙДШЕРИНГА

The paper studies indicators of passenger movement volumes on road transport for regional transport network that are gained on the basis of ride-sharing on-line services. For the processing of data arrays of common rides’ services web-scraping is used. The paper also investigates time distribution of passenger traffic that shows the filling of cars by passengers on various sections of a route. Peculiarities and dependencies of day fluctuations of passenger traffic for the Sverdlovsk region transport network are analysed. The author has considered a task of modeling of seasonal and cyclical fluctuations of passenger traffic volumes. She has used all necessary statistical procedures required for identification and assessment of model parameters and verification of its adequacy and accuracy. The time series model with polynomial smoothing is used for short-term forecasting that allows analysing the dynamics of passenger traffic, decreasing costs on monitoring of information on passenger traffic and allows using results for solving tasks of technological organization of public transport.

Analysis of passenger traffic in Bohai Strait Tunnel

China envisages building a new undersea tunnel called Bohai Strait Tunnel to connect Dalian to Yantai, for the purpose of solving the detour problem in transportation. This paper analyses the passenger traffic in Bohai Strait Tunnel. A modified negative exponent network flow assignment model and an increasing rate method are used to predict the network flow assignment and passenger traffic volume after the completion of Bohai Strait Tunnel in 2050. Two formulas of economic benefits are employed to evaluate the effect of undersea tunnel on passenger traffic. The result indicates that the new undersea tunnel can shorten the distance, reduce the transport cost, save travel time, and produce good economic benefits. Tunnel transportation will attract a large number of passengers and become main transportation way. The analysis result can provide technical storage and references for the future undersea tunnel construction.

Fire Evacuation in Metro Stations: Modeling Research on the Effects of Two Key Parameters

Metro lines have undergone a rapid development in China and a large number of metro stations have also been built. The passenger traffic volume has reached or exceeded the designed transport capacity in some big cities such as Beijing and Shanghai. The safety evacuation problem within metro stations under emergency has become a worldwide concern. In this study, BuildingEXODUS was employed as the simulation platform and a metro station in Shanghai was selected for model development. Based on field survey data, the evacuation process in different fire cases was simulated, so as to evaluate the effects of two parameters (i.e., escalators and automatic ticket checkers) on evacuation performance. The research found that the use of two stopped escalators (normal metro stations have two) as fixed evacuation passages is effective and essential for safety evacuation. However, it surprisingly decreases the evacuation efficiency if using only one stopped escalator as the fixed evacuating passage. The evacuation efficiency can be improved by opening the automatic ticket checkers compared with maintaining normal status. Removing the automatic ticket checkers does not pose any difference in improving evacuation efficiency.

Analysis of Geographically Anomalous 2019 Novel Coronavirus Transmission in China

Approximately in the late-December of 2019, the coronavirus disease outbreak took place in China. COVID-19 (Coronavirus Disease 2019) is contagious and detrimental to the human body, which can even lead to death. As a result, the understanding of COVID-19 has become especially important. This paper studies four cases of anomalous disease-spreading in China (Guangdong Province, Heilongjiang province, Tianjin municipality, and Guizhou province) and analyzes four influencing factors of the transmission (temperature, transportation and passenger traffic volume, household size and distribution, and awareness). Major conclusions in this paper are as follows. Transportation and passenger traffic volume and the number of larger households are positively related to the extent of disease-spreading; the degree of awareness is negatively associated with the extent of disease-spreading. Provinces, municipalities, and autonomous regions with a more urbanized distribution of households are prone to experience a greater extent of disease transmission. Although the novel coronavirus prefers colder environment, temperature appears to be a secondary influencing factor, as regions with negative temperatures have fewer diagnoses. Disease transmission in Guangdong province is caused by a high volume of passenger traffic, large and urbanized households, and low awareness. Heilongjiang province is mainly a result of high passenger traffic volume, long travelling trips, and low public awareness. Guizhou province is benefited from high awareness, limited passenger volume, and scattered households. Tianjin municipality is protected from the severe disease-spreading owe to its beneficial temperature, low land transportation volume, and high public and government awareness.

Patterns of passenger traffic formation in urban public transport

The purpose of the study is to perform a detailed statistical analysis of passenger traffic flows at stops of various bus routes in the city of Krasnoyarsk. The authors used the following methods: spectral analysis, factor analysis, correlation and cross-correlation analyses. During the study, indicators of passenger traffic irregularity in time and direction in Krasnoyarsk were obtained. The authors established a rather inertial nature of passenger traffic, with a positive correlation of up to two weeks, indicating stable patterns that can be predicted. The spectral analysis revealed three peaks in passenger traffic seasonality. The correlation and cross-correlation analyses showed strong relationships between inter-stop passenger traffic flows. The factor analysis made it possible to determine that only one principal component can provide 65% of information about the entire passenger traffic volume on urban public transport routes.

Equilibrium between Road Traffic Congestion and Low-Carbon Economy: A Case Study from Beijing, China

China has allocated low-carbon targets into all regions and trades, and road traffic also has its own emission reduction targets. Congestion may increase carbon emissions from road traffic. It is worthwhile to study whether it is possible to achieve the goal of road traffic reduction by controlling congestion; that is, to achieve the equilibrium between traffic congestion and a low-carbon economy. The innovation of this paper is mainly reflected in the innovative topic selection, the introduction of a traffic index, and the establishment of the first traffic congestion and low-carbon economic equilibrium model. First, the relevant calculation method of the traffic index is introduced, and the traffic index is used to quantify the traffic congestion degree. Using the traffic index, GDP, and road passenger traffic volume, a nonlinear regression model of road traffic carbon emissions is constructed. Then, the calculation method of the carbon emission intensity of road traffic in the region is proposed. The equilibrium model of traffic congestion and a low-carbon economy is constructed to look for the degree of road traffic congestion that may occur under the permitted carbon emission intensity. Taking Beijing, where electric vehicles account for less than 3% of the total vehicles, as an example, it is difficult to achieve the equilibrium target between road traffic congestion and a low-carbon economy by alleviating traffic congestion in 2020. If the target of traffic carbon emission reduction in 2020 is adjusted from 40%–45% to 19.7% based on 2005, the equilibrium will be achieved. A negative correlation between road traffic carbon emissions and the reciprocal of the traffic index (1/TI) is found after eliminating the effects of GDP and PTV (road passenger traffic volume). As the traffic index decreases by units, the carbon emission reduction accelerates. The results show that carbon reduction targets cannot be simply allocated to various industries. The results of the research on the degree of the impact of traffic congestion on carbon emissions can be used as a basis for carbon reduction decisions of the traffic sector. The research method of this paper can provide a reference for the study of the equilibrium of traffic congestion and a low-carbon economy in other regions.