Passenger Departure Research Articles

Research on Check-In Baggage Flow Prediction for Airport Departure Passengers Based on Improved PSO-BP Neural Network Combination Model

Accurate forecasting of passenger checked baggage traffic is crucial for efficient and intelligent allocation and optimization of airport service resources. A systematic analysis of the influencing factors and prediction algorithms for the baggage flow is rarely included in existing studies. To accurately capture the trend of baggage flow, a combined PCC-PCA-PSO-BP baggage flow prediction model is proposed. This study applies the model to predict the departing passengers’ checked baggage flow at Chengdu Shuangliu International Airport in China. First, in the preprocessing of the data, multiple interpolation demonstrates a better numerical interpolation effect compared to mean interpolation, regression interpolation, and expectation maximization (EM) interpolation in cases of missing data. Second, in terms of the influencing factors, unlike factors that affect the airport passenger flow, the total retail sales of consumer goods have a weak relationship with the baggage flow. The departure passenger flow and flight takeoff and landing sorties play a dominant role in the baggage flow. The railway passenger flow, highway passenger flow, and months have statistically significant effects on the changes in the baggage flow. Factors such as holidays and weekends also contribute to the baggage flow alternation. Finally, the PCC-PCA-PSO-BP model is proposed for predicting the baggage flow. This model exhibits superior performance in terms of the network convergence speed and prediction accuracy compared to four other models: BP, PCA-BP, PSO-BP, and PCA-PSO-BP. This study provides a novel approach for predicting the flow of checked baggage for airport departure passengers.

Short-term inbound passenger flow prediction at high-speed railway stations considering the departure passenger arrival pattern

Accurate prediction of short-term inbound passenger flow at high-speed railway stations is of great significance for the refined operation of stations, the formulation of emergency plans, and the provision of intelligent services. The arrival of passengers traveling on the same train at the same station shows a similar pattern, which is called the departure passenger arrival pattern (DPAP). The short-term inbound passenger flow at the station is composed of the short-term inbound passenger flow of all waiting trains within the same period. Inspired by this, this paper develops an ensemble prediction model based on the time series decomposition modeling strategy to introduce the DPAP to the short-term inbound passenger flow prediction at stations. Firstly, we propose a new framework for studying the DPAP to calculate the fitted station short-term inbound passenger flow, which is only affected by the DPAP. During this process, we find that 7minutes is the optimal time granularity. Secondly, based on the singular spectrum analysis, we prove that the DPAP is the determining factor affecting the station short-term inbound passenger flow. Finally, we propose an ensemble prediction model that considers the DPAP to achieve short-term inbound passenger flow prediction at stations. The model consists of two parts: the deterministic and stochastic components prediction, where the former is predicted by the fitted station short-term inbound passenger flow, and the latter is achieved by the combination of historical stochastic components and weather type with the help of the Seq2Seq model based on time attention mechanism. Using real inbound passenger flow data, we compare the proposed model with 13 benchmark models and the results show that under different training and prediction steps, our model achieves optimal prediction performance, whether in all-day period and the busiest period of the station. Through further ablation experiments, it has been proven that the introduction of the DPAP effectively improves the prediction accuracy. Our model can provide scientific support for the intelligent operation of stations and the refined management of passenger flow.

Passenger Route and Departure Time Guidance Under Disruptions in Oversaturated Urban Rail Transit Networks

The urban rail transit (URT) system attracts many commuters with its punctuality and convenience. However, it is vulnerable to disruptions caused by factors such as extreme weather and temporary equipment failures, which greatly affect passengers’ journeys and diminish the system’s service quality. In this study, we propose targeted travel guidance for passengers at different space–time locations by devising passenger rescheduling strategies during disruptions. This guidance not only offers insights into route changes but also provides practical recommendations for delaying departure times when required. We present a novel three-feature four-group passenger classification principle, integrating temporal, spatial, and spatiotemporal features to classify passengers in disrupted URT networks. This approach results in the creation of four distinct solution spaces based on passenger groups. A mixed integer programming model is built based on individual level considering the first-in-first-out rule in oversaturated networks. In addition, we present a two-stage solution approach for handling the complex issues in large-scale networks. Experimental results from both small-scale artificial networks and the real-world Beijing URT network validate the efficacy of our proposed passenger rescheduling strategies in mitigating disruptions. Specifically, when compared to scenarios with no travel guidance during disruptions, our strategies achieve a substantial reduction in total passenger travel times by 29.7% and 50.9%, respectively, underscoring the effectiveness in managing unexpected disruptions.

MODEL PERAMALAN PERGERAKAN PENUMPANG DOMESTIK DI BANDAR UDARA YOGYAKARTA INTERNATIONAL AIRPORT

This research is quantitative descriptive research which aims to make predictions using the ARIMA model time series forecasting method. The data obtained was analyzed using the R program. The data used for forecasting is the number of arrivals and departures of domestic passengers at Yogyakarta International Airport for the period January 2022 – December 2023. Based on the results of data analysis, it is known that the ARIMA model time series forecasting method is the best for predicting the number of arrivals. and domestic passenger departures at Yogyakarta International Airport are the AR (1,0,0) model. This method was chosen because the results were significant, met the white noise requirements and had the smallest MAPE value.

The Form of Responsibility of Airlines as Business Entities For Unilateral Flight Cancellations Is Viewed From The Minister of Transportation Regulation Number 89 of 2015 Concerning Handling of Flight Delays In Scheduled Commercial Air Transport Business

An airline is a company that provides services to goods and passengers traveling by air. The definition of aviation based on Law of the Republic of Indonesia Number 1 of 2009 concerning Aviation is a unified system consisting of the use of airspace, aircraft, airports, air transportation, flight navigation, safety and security, environment, supporting facilities and other public facilities. . Every flight activity is the responsibility of the airline company. Specifically responsible for errors in unilateral cancellation of passenger departures made by the airline. As a corporation or legal entity, airlines are obliged to provide compensation to passengers in accordance with the policies contained in aviation law and consumer protection law (UUPK). The research method used in this research is normative research which examines library materials sourced from secondary data, namely primary legal materials, secondary legal materials and tertiary legal materials. This legal material was obtained by taking a statutory approach and a conceptual approach. Then it is analyzed using qualitative methods to obtain a conclusion from the problem formulations that have been discussed. The purpose of this research is to find out the factors that cause departure cancellations by airlines and to find out how airlines are responsible for unilateral flight cancellations in terms of Regulation of the Minister of Transportation of the Republic of Indonesia Number 89 of 2015 concerning Handling Flight Delays in Scheduled Commercial Air Transport Business Entities in Indonesia.

Optimizing the Three-Dimensional Multi-Objective of Feeder Bus Routes Considering the Timetable

To optimize the evacuation process of rail transit passenger flows, the influence of the feeder bus network on bus demand is pivotal. This study first examines the transportation mode preferences of rail transit station passengers and addresses the feeder bus network’s optimization challenge within a three-dimensional framework, incorporating an elastic mechanism. Consequently, a strategic planning model is developed. Subsequently, a multi-objective optimization model is constructed to simultaneously increase passenger numbers and decrease both travel time costs and bus operational expenses. Due to the NP-hard nature of this optimization problem, we introduce an enhanced non-dominated sorting genetic algorithm, INSGA-II. This algorithm integrates innovative encoding and decoding rules, adaptive parameter adjustment strategies, and a combination of crowding distance and distribution entropy mechanisms alongside an external elite archive strategy to enhance population convergence and local search capabilities. The efficacy of the proposed model and algorithm is corroborated through simulations employing standard test functions and instances. The results demonstrate that the INSGA-II algorithm closely approximates the true Pareto front, attaining Pareto optimal solutions that are uniformly distributed. Additionally, an increase in the fleet size correlates with greater passenger volumes and higher operational costs, yet it substantially lowers the average travel cost per customer. An optimal fleet size of 11 vehicles is identified. Moreover, expanding feeder bus routes enhances passenger counts by 18.03%, raises operational costs by 32.33%, and cuts passenger travel time expenses by 21.23%. These findings necessitate revisions to the bus timetable. Therefore, for a bus network with elastic demand, it is essential to holistically optimize the actual passenger flow demand, fleet size, bus schedules, and departure frequencies.

A collective incentive strategy to manage ridership rebound and consumer surplus in mass transit systems

This paper models and analyzes a collective incentive strategy with passenger departure time equilibrium to maximize the total passenger surplus, which could further enhance the sustainability of transit systems in the post-pandemic era. In the proposed collective incentive strategy, passengers with transit passes have dynamic discounts where the discount value depends on the total number of passengers enrolled in the incentive program. A bi-level optimization model is established by considering passengers' demand elasticity and responses to the strategy, which eventually determines the starting time and duration of the collective incentive strategy during the time of the day. We further analyze the properties of the proposed collective incentive model to identify the analytical solutions and performance. Analytical results indicate that there exists a threshold demand elasticity below which the total surplus of commuters with and without transit passes can be improved simultaneously. Properties also show that there could exist multiple local optimal solutions of incentive strategy duration for a given starting time. A case study is conducted with the metro system in Toronto: the results show that the proposed collective incentive strategy could improve both the fare revenues and ridership by 33.31 % and 83.56 %, respectively.

ANALYSIS OF PASSENGER TERMINAL AREA AT PATTIMURA INTERNATIONAL AIRPORT AMBON

Pattimura International Airport in Ambon is located at Jl. Dr. Leimena, Laha, Tlk. Ambon District, Ambon City, Maluku 97236. In this study, the researchers used passenger movement data from Pattimura International Airport spanning for the years 2018 to 2022. Prior to 2018, the airport was undergoing a revitalization process. Given both these revitalization efforts and the impact of the Covid-19 pandemic, it has become imperative to conduct an analysis of the terminal's ability to manage the evolving patterns of passenger arrivals and departures. This requires an assessment of the landside aspect to determine the terminal's maximum capacity and to predict when it will reach that capacity. To measure the degree of saturation, the researchers used calculations based on the Standard Busiest Rate method, using the 40th peak hour in accordance with Federal Aviation Administration (FAA) standards. Keywords : Pattimura International Airport Ambon, ; 40th Peak ; FAA method;Peak Hour.

Accommodating spatio-temporal dependency in airline demand modeling

The objective of the current study is to examine monthly air passenger departures at the airport level considering spatial interactions between airports. In this study, we develop a novel spatial grouped generalized ordered probit (SGGOP) model system of monthly air passenger departures at the airport level. Specifically, we estimate two variants of spatial models including spatial lag model and spatial error model. In the presence of repeated demand measures for the airports, we also consider temporal variations of spatial correlation effects among proximally located airports by employing space and time-based weight matrix. The proposed model is estimated using monthly air passenger departures for five years for 369 airports across the US. The proposed spatial model is implemented using composite marginal likelihood (CML) approach that offers a computationally feasible framework. From the estimation results, it is evident that air passenger departures at the airport level are influenced by different factors including MSA specific demographic characteristics, built environment characteristics, airport specific factors, spatial factors, and temporal factors. Moreover, spatial autocorrelation parameter is found to be significant validating our hypothesis of the presence of common unobserved factors associated with the spatial unit of analysis. In this study, we also perform a validation analysis to examine the predictive performance of the proposed spatial models. The results highlight the superiority of spatial error model compared to spatial lag model and the independent model that ignores the spatial interactions. Finally, we undertake an elasticity analysis to quantify the impact of the independent variables.

Construction and Application of Intelligent Optimization Model of Urban Resource Management for Sustainable Development

Abstract In this paper, we first digitally design the urban management resource base and then construct a dynamic bargaining resource allocation model based on super-marginal analysis with corner point equilibrium and dynamic bargaining. In the context of sustainable development, an intelligent optimization model is used to analyze the problem of water resource allocation and optimize urban traffic infrastructure paths in order to achieve carbon reduction targets. Finally, six recommendations are made for optimizing and managing urban resources in the context of sustainable development. The results of the prioritization coefficients of each subarea are 0.186, 0.164, 0.153, 0.158, 0.153, 0.0769, and 0.111, respectively, and the results of the allocation of the optimal solution for the social objective, the optimal solution for the economic benefit, the optimal solution for the ecological benefit, and the optimal solution for the comprehensive benefit are very close to each other. In addition, the rates of water shortage for agricultural, industrial, and other uses. are 3.29%, 4.85%, 3.94%, 2.92%, and 3.87%, respectively. The variation range of the average passenger load factor and departure frequency under the carbon reduction target is between ±40%, the average vehicle area varies in the range of ±20%, and the variation range of the energy consumption factor and CO2 emission factor is -40%-0%. The intelligent optimization model for urban resource management proposed in this paper has practical significance for urban water resource planning and transportation structure optimization.

Моделирование объемов отправлений городского общественного транспорта

Organizing the effective operation of urban public transport and planning the development of transport infrastructure requires predicting the volume of passenger traffic. In particular, it needs to predict the volume of passenger departures from stopping points, which depends on a large number of different factors, such as the population density near the stopping point, the types of transport departing from it, the number of routes passing through it, etc. In this article, we propose modeling outgoing passenger traffic based on a regression approach. To test the approach and determine the most appropriate specifications for regression equations, data on passenger boarding volumes at bus stops in Ekaterinburg, as well as open data from the database of apartment buildings, were used.

Optimization of Train Timetables in High-Speed Railway Corridors Considering Passenger Departure Time and Price Preferences

Optimization of Train Timetables in High-Speed Railway Corridors Considering Passenger Departure Time and Price Preferences

Study on departure passenger movement during security check process at airport terminal based on an improved cellular automata model

Airport security check is a crucial part of air travel, and the efficiency of this process directly impacts the overall airport experience. This article presents a study on the modelling and simulation of movement of departure passengers during security check at airport terminals, with a particular emphasis on elucidating microscopic properties such as passenger flow patterns, individual movement dynamics and especially queuing phenomena. In order to solve the unreasonable path problem caused by direct application of cellular automata model, we propose an improved cellular automata model in which a new concept of horizontal restrain floor field is innovatively introduced. Considering the factors of spatial distance, number of queuing people and potential queuing people, a checkpoint selection probability model is embedded into the CA model. The results show that the queuing behavior of passengers is influenced by various factors from three aspects: (i) selection strategy of checkpoint, (ii) spatial layout of inspection area, (iii) arrival interval and verification time. The findings of this study can be used by airport operators to have better understanding of departure passengers’ movements at a micro level, improve the efficiency of the security check process and enhance the airport experience for passengers. Some measures can be taken to reduce the queue waiting time, such as personnel guidance, information signage, planning wider entrance and more queue lanes, replacing the manual verification with ticket inspection machines and so on.

The Effect of The Number of Departure Passenger on The Quality of Custumer Service Officer (CSO) at Kualanamu International Airport, Medan

The Covid-19 pandemic, which is getting better accompanied by less stringent rules for flight service users, has also had an impact on the increasing number of passengers. This study aims to determine whether the number of departing passengers has an effect on the quality of Customer Service Officer (CSO) service at Medan's Kualanamu International Airport. The research method used is simple linear regression analysis. Determination of the sample was taken with a total of 10 Customer Service Officers (CSO). The data collection technique uses an assessment score form by the customer service supervisor then the data will be processed using a simple linear regression analysis test using the Statistical Package for the Social Sciences (SPSS) application. Based on the calculation results, a significant number of F count is 0.000 <0.05 and the R Square value is 0.961 which implies that the effect of variable X (number of passengers) on variable Y (quality of service of customer service officers (CSO)) is 96.1%. The results of the simple regression equation are Y = 1,065,632.914 – 10,269.492X, which means that the number of passengers has a negative influence on the quality of Customer Service Officer (CSO) service at Kualanamu International Airport, Medan. Customer Service Officer (CSO) can improve and maintain the quality of service provided to passengers without being affected by the large number of passengers.

Analysis of the Service Level of Wamena Class 1 Airport Terminal Facilities in Jayawijaya Regency

Jayawijaya Regency has one domestic airport, which is in Wamena City, called Wamena Airport. Therefore, the service standards and facilities provided by the airport management must be in accordance with standards that are able to create a comfortable and conducive atmosphere for airport service users. This research aims to find out the service level of Wamena Class 1 Airport Terminal Facilities in Jayawijaya Regency. This research uses data analyzed from Surfey which consists of 1 (one) variable, namely Airport Terminal Facility Services with indicators which include facilities in the passenger departure and arrival process, facilities that provide comfort to passengers and facilities that provide added value. The number of samples used was 123 people. The data analysis technique used is score analysis. The Surfey results show that the respondents' responses from the surfey passenger results were as follows: those who said they were very satisfied with a score of 55, those who were satisfied were 280, less satisfied at 120 and dissatisfied at 4. So the score regarding passenger inspection was 459 and was in the satisfied category.

Analysis of the Relationship Between Foreign Sea Transportation Visits and Gross Domestic Product Regional in the Processing Industry Sector

Sea transportation is also very much needed in the processing industry for ships and passengers and the flow of goods. Suppose the incoming ship can be seen as a sender of goods or the processing industry itself. This is supported very well by the cargo and costs for sea transportation, with much cargo at a relatively low cost. Analyzing the relationship of foreign sea transportation with the regional GDP of the processing industry sector. From ship visits consisting of total and units to passenger visits consisting of passenger arrivals and departure, for variable analysis using correlation and hypothesis testing. The results of overseas sea transportation with the GDP Regional the processing industry are for the correlation results of ship visits from units (0.613) with a strong and total correlation (0.596) with a moderate correlation, for passenger arrivals (0.262) and passenger departures (0.280) both have a low correlation relationship, for the unloading flow of goods (0.607) has a strong correlation. So the visits of foreign sea transportation ships with the GDP Regional of the processing industry sector have a significant relationship of (0.00) from the unit or total ships; for passenger visits, both do not have a substantial relationship for passenger arrivals (0.141) and passenger departures (0.115). The flow of unloading of goods has a significant relationship of (0.00). The greater the number of ship visits and the flow of unloading of goods, the higher the GDP Regional of the Processing Industry Sector, but the increasing number of passenger visits has no relation to the GDP Regional of the Processing Industry Sector.

Analysis of Departure Waiting Room Terminal Capacity on Passenger Volume at Patimura Ambon International Airport, Maluku Province

Departure terminal capacity to passenger volume at Patimura Ambon International Airport, Maluku Province in 2021 has experienced an increase in the number of passengers, won the Airport Service Quality (ASQ) award, in the airport category with a capacity of less than 2 million per year, from this statement the researcher wants to conduct a wide capacity analysis waiting room terminal, seating facilities, and passenger volume, whether the research results need development or not. The data in this study used secondary data, namely data on the number of passengers in the last three years, and primary data, namely data on the number of existing passenger departure terminal seats. Data analysis used descriptive statistical methods based on the formula in SKEP/347/XII/99, Director General of Civil Aviation. The results of the study stated that the area of the departure waiting room was 220 m² based on SKEP 77/VI/2005 Director General of Civil Aviation. The capacity of the departure waiting room at Gate-1 to gate-5, gates 1 and 2 can accommodate passengers, gate-3 = 271 passengers, gate-4 = 267 passengers and gate-5 = 247 passengers. The need for seats based on SKEP 77/VI/2005 Director General of Civil Aviation is 828 seats. The results of the analysis of the capacity of the Departure Waiting Room Terminal for Passenger Volume at Patimura Ambon International Airport, Maluku Province, were declared to be appropriate based on SKEP 77/VI/205 of the Director General of Civil Aviation.

Pengaruh Pelayanan Petugas Ruang Tunggu PT Angkasa Pura I Terhadap Kepuasan Pengguna Jasa di Bandar Udara Internasional Yogyakarta

Yogyakarta International Airport (YIA) is an international airport located in Temon district, Kulon Progo Regency, Special Region of Yogyakarta province. As an aviation service provider company, PT Angkasa Pura I needs to pay attention to the facilities and services in the passenger departure lounge. This research aims to discover the effect of the service of waiting room staff and how much influence it has on passenger satisfaction at Yogyakarta International Airport. This research uses quantitative methods. The data was collected through distributing questionnaires to 100 respondents using the waiting room area of domestic flights on November 1 to December 31, 2022. Data analysis was done by using simple linear regression with the service of the waiting room staff as the independent variable and passanger satisfaction as the dependent variable. The results show that the service of waiting room staff has a significant influence on passanger satisfaction, with a significance value of T = 16.293 > 1.661 with a significance value of 0.05. The service of waiting room staff as the independent variable influences passenger satisfaction by 73%. Meanwhile, the other 27% is influenced by other variables outside of this research. It can be concluded that Ho is rejected and Ha is accepted, which means that there is a significant influence of waiting room staff service on passanger satisfaction at Yogyakarta International Airport.

Joint train line planning and timetabling of intercity high-speed rail with actual time-dependent demand

Intercity high-speed rail (HSR) is a type of mass transit built in densely populated metropolitan areas and has become an essential part of sustainable transportation systems in some countries. One of the most challenging problems is how to jointly optimize the train line plan and timetable with simultaneous consideration of the system efficiency, cost, and level of service. In this paper, we focus on optimizing the train line plan and timetable of intercity HSR given actual time-dependent demand while appropriately considering the operational constraints including overtaking. First, we formulate the optimization problem as a mixed integer nonlinear programming (MINLP). Its objective is to minimize the total system cost comprised of the trains’ fixed and variable operation costs, passengers’ travel time cost, and penalties for passengers’ departure time deviation and passengers’ failing to board a train. We then design a double-layer simulated annealing (SA) algorithm, in which the inner layer algorithm optimizes the stop plan, the departure and arrival times at each station, while the outer layer algorithm optimizes the number of trains, their origin–destination stations, and their departure times at origin stations. Next a case study is conducted on Shanghai-Nanjing intercity HSR using actual time-dependent demand. The numerical results show that the proposed model can help significantly reduce the total number of trains used by increasing the average seat occupancy, while creating little impact on the average passenger travel time and departure time. The study provides valuable guidance for optimizing HSR train line plan and timetable based on real-world time-dependent demand.

Peramalan Banyaknya Penumpang Bandar Udara Internasional Sam Ratulangi Manado Dengan Metode Winter's Exponential Smoothing dan Seasonal ARIMA

The Winter's Exponential Smoothing method is used to overcome seasonal patterns in data. This method is divided into two models, namely additive and multiplicative models. While the Seasonal ARIMA method is an ARIMA method used to solve seasonal time series. The data used is secondary data from PT. Angkasa Pura I (Persero) Sam Ratulangi International Airport Manado for the period January 2015 to December 2022. The purpose of this research is to determine the model for forecasting the number of passengers at PT.Angkasa Pura I (Persero) Sam Ratulangi International Airport Manado, as well as to compare the Winter's method Exponential Smoothing and Seasonal ARIMA based on the smallest MSD value. The results of the comparison of the two methods with the smallest MSD value are the Winter's Exponential Smoothing method with the multiplicative model equation. The results of the analysis on arriving passengers, namely the exponential smoothing of the original data (α) is 0.9, the smoothing of the trend pattern (β) is 0.1, and the smoothing of the seasonal pattern (γ) is 0.1. With the results of the 2023 forecast, namely: January 95,046, February 87,154, March 98,462, April 97,391, May 110,061, June 103,098, July 130,360, August 118,165, September 108,790, October 115,673, November 112,114, and December 136.40. The results of the analysis on domestic passenger departures are α = 0.9, β = 0.1, and γ = 0.2. With the results of forecasting the number of departures in 2023, namely January 108.900, February 88.588, March 100.646, April 98.066, May 111.638, June 112.963, July 126.684, August 111.471, September 111.872, October 116.211, November 111.990, and December 117.431.