Passenger Traffic Research Articles

EVALUATION OF PUBLIC TRANSPORT FACILITIES AT PORTS, AIRPORTS, STATIONS, AND TERMINALS

Indonesia is known as an archipelagic country because it has thousands of islands spread from Sabang to Merauke. To connect an area or island to another, Indonesia has various modes of public transportation. Facilities on public transportation modes must be adequate to accommodate prospective passengers who will travel to an area. Evaluation of public facilities is very necessary to increase passenger comfort. This research was carried out using direct observation methods and using literature reviews from sources on the internet. In this research, there are several modes of public transportation discussed, namely Ketapang Harbor, Gusti Ngurah Rai Airport, Bandung Station, and Guntur Melati Terminal. Ketapang Port has several shortcomings, namely improving the weather information system, improving the traffic operational system, improving services, and expanding the dock. Then, Gusti Ngurah Rai Airport has a shortage of VVIP parking spaces, so improvements are needed. According to the Regulation of the Minister of Communications No. 63 of 2019 on the Minimum Service Standards for Passenger Traffic by Rail, Bandung Station has not met the standards in terms of safety, security, comfort and convenience. Meanwhile, according to the regulation of PM No. 40 of 2015 on the Standards of Service for the Maintenance of Passenger Terminals of Road Transportation, the Guntur Melati Terminal also still has deficiencies in the safety and conveniency aspects.

Civil Aviation Passenger Traffic Forecasting: Application and Comparative Study of the Seasonal Autoregressive Integrated Moving Average Model and Backpropagation Neural Network

Civil Aviation Passenger Traffic Forecasting: Application and Comparative Study of the Seasonal Autoregressive Integrated Moving Average Model and Backpropagation Neural Network

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.

Transportation sector and Chinese stock volatility forecasting: Evidence from freight and passenger traffic

Based on the GARCH-MIDAS model, this paper explores the forecasting power of four transportation sub-sectors. The empirical results show that the freight volume and passenger traffic of air aviation outperform other models during MCS and DoC tests. This illustrates the important role of air aviation in the operation of a country's economy. The findings of this paper could help the government to formulate infrastructure planning and provide insights into the allocation of different resources to different transportation sectors.

Analysis of the Relationship Between Railway Passenger Traffic and Turnover in China Based on XGBoost Algorithm

Analysis of the Relationship Between Railway Passenger Traffic and Turnover in China Based on XGBoost Algorithm

Passenger Traffic in Bulgaria and North Macedonia – Current State and Prospects for Development

The purpose of this publication is to examine the current state and dynamics of passenger traffic between Bulgaria and North Macedonia and to mark the prospects in the context of tourism. The authors use statistical data, as well as publications of researchers and institutions directly or indirectly related to the problem at hand. Along with this, the text also focuses on cross-border cooperation between the two countries in the field of tourism as a convenient tool for promoting economic development and inclusion to the European idea. Main emphasis in this cooperation can be placed in several directions: joint projects for the implementation of common specialized routes; development in the field of advertising in the digital environment by promoting the destinations by journalists and bloggers; exchange of good practices.

Impact of Subsidizing Air Routes in Russia on Passenger Traffic Dynamics

Impact of Subsidizing Air Routes in Russia on Passenger Traffic Dynamics

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

Multiobjective-Based Decision-Making for the Optimization of an Urban Passenger Traffic System Structure

Urbanization has aggravated the conflict between continuously increasing urban travel demands and limited supply. Moreover, the inability to expand urban roads due to previous land planning and utilization has resulted in significant traffic congestion, traffic safety issues, and environmental problems. To address these problems, this work attempted to develop a multiobjective model to optimize the passenger traffic system while considering carbon emissions, transport costs, and resource utilization. In addition, the ideal point method and entropy weight method were combined to obtain the optimal solution. Based on the operational data on traffic modes and travel data on passengers in Harbin, the northern capital of China, the proposed method was used to solve the case in Harbin. The results show that the proportion of buses increased by 1.05%, that of subways increased by 36.60%, that of taxis decreased by 11.86%, and that of private cars decreased by 25.78% after optimization. Furthermore, the analyses of the results show that the optimized passenger traffic system structure can promote the sustainable development of urban transport and demonstrate the practicality of the proposed method for solving multiobjective optimization problems. Relative to the ideal point method and genetic algorithm, the proposed method is more applicable for optimizing the passenger traffic structure in Harbin. In addition, this study explored the sensitivity of the optimization goals to the four motorized modes. The results show that subways and private cars are the key areas to prioritize in adjusting the urban passenger traffic system structure. Based on the analysis results, recommendations for the development of transportation in Harbin are given. This study provides a reference for decision-makers to formulate policies for the urban sustainable development of Harbin as well as for transportation development in other cities.

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.

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.

Effects of transport-carbon intensity, transportation, and economic complexity on environmental and health expenditures.

Health and the environment are complex components of the countries, influenced by several factors, especially transport, and economics. Thus, this paper assesses the role of transportation and economic complexity in the environment and public health for the Organization for Economic Co-operation Development (OECD) countries from 2001 to 2020. This study also focuses on the relationship between transport and economic complexity with environmental and healthcare expenditures. Precisely, transport and economic activities stimulate healthcare expenditures through multiple channels. The current study employs the STIRPAT model to investigate the association with transportation, economic complexity, transport-carbon intensity, and healthcare expenditure. Besides, the current research confirms the plausible cross-sectional dependency across countries, and it adopts a second-generation technique. Analytical findings suggest that transportation-carbon intensity is positively and significantly associated with healthcare expenditures. Healthcare and transport-household expenditures increase transport-carbon intensity (TCI) by 75% and 45%, respectively, in the long run. In the contrast, TCI and transport-household expenditures have also a remarkable impact on healthcare expenditures and are estimated approximately 95% in the long run. Moreover, economic growth also upsurges TCI and healthcare expenditures through multiple economic activities. Besides, transport-household expenditures (THE) drastically impact transport-carbon intensity and healthcare expenditures (HEX) through passenger traffic (PTF). Diagnostic upshots unveil that the joint effect of THE and PTF increases TCI and HEX by 4 and 3%, respectively. Finally, findings recommend some policy implications and future research directions for the countries based on empirical outcomes. Countries should regulate economic activities to reduce transport carbon emissions.

Sample Survey of Passenger Traffic by Analysing Wi-Fi Data in Moscow Transport Hub. Part 2

In modern, rapidly developing cities of the world, building an urban transport model requires traffic data. The lack of those data does not allow making timely management decisions on distribution of passenger flows, namely within transport flows. Currently, there are various methods and systems for counting passenger flows, such as the manual staff counts, survey and counted ticketed entries methods, and various automated technology-based systems. However, those well-known methods have their drawbacks. For this reason, the task to search for alternative methods and data sources for the study of passenger flows remains relevant. This article is based on the updated results of the study recently conducted by the author during preparation of his master’s thesis. During the study and developing previous author’s papers, data on connections of passengers to Wi-Fi routers were chosen as a data source. Since this phase of the study was conducted on the territory of Moscow transport hub, in metro and on Moscow Central Diameters (MCD), where the cars are equipped with great number of Wi-Fi routers, with free connection and Internet access, it has increased the sample Wi-Fi data array significantly. The objective of the study was to study the possibility of processing Wi-Fi data obtained from Wi-Fi scanners as a passenger flow analysis tool. The study has revealed that, on average, up to 40 % of passengers in metro and MCD cars on the studied lines use the WI-FI module turned on in their mobile devices. The results of the study have confirmed that Wi-Fi data can be used as a tool for passenger traffic analysis, but at the same time revealed the necessity to integrate them with other data sources, as well as the strong dependence of the result of Wi-Fi data processing on the technical features of the Wi-Fi scanner and its location in the vehicle during experiments. This issue contains the second part of the article.

Sample Survey of Passenger Traffic by Analysing Wi-Fi Data in Moscow Transport Hub. Part 1

In modern, rapidly developing cities of the world, building an urban transport model requires traffic data. The lack of those data does not allow making timely management decisions on distribution of passenger flows, namely within transport flows. Currently, there are various methods and systems for counting passenger flows, such as the manual staff counts, survey and counted ticketed entries methods, and various automated technology-based systems. However, those well-known methods have their drawbacks.For this reason, the task to search for alternative methods and data sources for the study of passenger flows remains relevant.This article is based on the updated results of the study recently conducted by the author during preparation of his master’s thesis. During the study and developing previous author’s papers, data on connections of passengers to Wi-Fi routers were chosen as a data source. Since this phase of the study was conducted on the territory of Moscow transport hub, in metro and on Moscow Central Diameters (MCD), where the cars are equipped with great number of Wi-Fi routers, with free connection and Internet access, it has increased the sample Wi-Fi data array significantly.The objective of the study was to study the possibility of processing Wi-Fi data obtained from Wi-Fi scanners as a passenger flow analysis tool.The study has revealed that, on average, up to 40% of passengers in metro and MCD cars on the studied lines use the WI-FI module turned on in their mobile devices.The results of the study have confirmed that Wi-Fi data can be used as a tool for passenger traffic analysis, but at the same time revealed the necessity to integrate them with other data sources, as well as the strong dependence of the result of Wi-Fi data processing on the technical features of the Wi-Fi scanner and its location in the vehicle during experiments. You can find the first part of the article in the issue.

LSTM Network Integrated with Particle Filter for Predicting the Bus Passenger Traffic.

The paper reports a combination of the deep learning technique and bayesian filtering to effectively predict the passenger traffic. The architecture of the model integrates the particle filter with the LSTM network. The time series sequential prediction is best achieved using LSTM network while Markovian behaviour is well extracted using Bayesian (Particle Filter) filters. The temporal and spatial features of the traffic data are analyzed. Three relevant temporal variations viz., morning, noon and post noon patterns are identified after the histogram analysis. These patterns are statistically modelled and the integrated model is used to accurately predict the passenger flow for the next thirty days, facilitating, the bus scheduling for that period. The experimental results proved that the proposed integrated model with coefficient of determination ( ) value of 0.88 is functional in predicting the passenger traffic even when the training data set size is small.

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.

Methods and Algorithms for Detecting Urban Passenger Traffic

The main task of sensors in public transport is to collect information about the transportation of passengers. One of the features of passenger traffic in Russian settlements is the low level of their organization. This is due, among other things, to the lack of professional analysis of traffic flows when designing route networks. The relevance of the work lies in increasing the efficiency of detecting urban passenger traffic using modern methods and algorithms. The article analyzes the current state of passenger traffic in the settlement, describes the role of technical and programs for monitoring passenger traffic, classifies their types and areas of application.

Research on the Issue of Prognosticationing the Volume of Passenger Traffic on Railway Transport in Meanrn Conditions

Research on the Issue of Prognosticationing the Volume of Passenger Traffic on Railway Transport in Meanrn Conditions

Analyzing the Passenger Traffic Demand Patterns of Greek Ferry Terminals.

Greece is a country with more than 200 inhabited islands, in which almost 17% of population live. Sea transport is the only available connection for the vast majority of these islands. The major ferry network of Greece consists of 155 ports, which are handling on an annual basis more than 35 million passengers. This study uses a panel of secondary data for 155 Greek ports, which were collected for the development of the database of ENIRISST (Intelligent Research Infrastructure for Shipping, Supply Chain, Transport and Logistics) project in order to determine demand patterns. At first, STL decomposition is performed for the throughput timeseries of each port. Afterwards, Gini Index Analysis for Seasonality is performed for all ports. Results are useful for planning of decision-makers and constitute an important addition to currently existing traditional econometric models.

Attention mechanism‐based model for short‐term bus traffic passenger volume prediction

AbstractTo explore the relevance between bus stops and make the real‐time prediction of bus passenger flow more accurate, this paper proposes a Traffic Forecast Model based on the Attention mechanism (TFMA). The model combines data preprocessing with bus stops’ information coding to predict short‐term bus passenger flow based on the real‐time relevance of the bus stops. First of all, the paper conducts a statistical analysis of the actual public transportation card data of Suzhou, China, and obtains the characteristics of real‐time relevance of different bus stops. Secondly, bus route and bus stop information, the passenger flow rate of change, weather, date, and other related factors are integrated into the coding information of the bus stops. Then the method relies on the Attention mechanism to calculate the real‐time relevance of the bus stops parallelly; the core algorithm also uses a multi‐headed mechanism to increase the connection of the channel and the residual error, further improving the prediction ability. Finally, this article uses actual data from Suzhou's public transport for verification. The results show that: In terms of accuracy, TFMA outperforms multiple linear regression, GRU, and LightGBM, reaching a very high accuracy of nearly 90%.