Passenger Arrivals Research Articles

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.

Integrated Optimization of Train Diagrams and Rolling Stock Circulation with Full-Length and Short-Turn Routes of Virtual Coupling Trains in Urban Rail Transit

The advancement of virtual coupling technology in urban rail transit has facilitated the online coupling and decoupling of trains, enabling a range of flexible transportation configurations, including various route types and adjustable formations. This study targets the fluctuating passenger demands on urban rail lines, aiming to minimize both passenger travel and operational costs. The model integrates constraints associated with virtual coupling, train operations, rolling stock circulation, and the interaction between virtually coupled trains and passenger arrivals. New decision variables are introduced to depict the train formation state under virtual coupling scenarios. An integrated optimization model for train diagrams and rolling stock circulation under virtual coupling conditions is developed, employing a genetic-simulated annealing algorithm informed by train operation simulations. A case study on an urban rail line during the morning peak examines the optimization of train diagrams for full-length and short-turn routes. Findings confirm that virtual coupling technology effectively adapts to lines with uneven passenger flow distribution, significantly enhancing the match between supply and demand, equalizing spatial and temporal traffic variations, and harmonizing the quality of passenger services with operational efficiency.

Accounting for passenger loss in bus bunching reduction: a robust real-time speed control method

ABSTRACT Bus bunching is a common phenomenon resulting from the inherent instability influenced by factors such as traffic and passenger behaviour. However, the issue of passenger loss caused by extended waiting time, is often overlooked despite being a significant concern for passengers. This paper addresses this problem by modelling passenger arrival and loss as a Markov birth–death process. We present a transit route model that incorporates passenger loss while considering additional factors, such as stochasticity, overtaking, passenger alighting, and bus capacity constraints. The primary objective of our model is to minimise passenger loss. To achieve this, we conduct an analysis about the characteristics of passenger loss. Under mild assumptions, we prove that passenger loss increases as the headways between buses exhibit greater variance. We also develop a robust optimisation (RO) method and corresponding algorithms to minimise passenger loss. To validate the effectiveness of our control method, we conduct numerical experiments using both computer-generated and real-world data. The results demonstrate that our method effectively reduces bus bunching, passenger loss, and waiting time compared to the existing literature.

Combining travel behavior in metro passenger flow prediction: A smart explainable Stacking-Catboost algorithm

Accurately predicting short-term passenger flow is essential to optimize operation resources and improve transportation services in urban metro systems. However, it has become a challenging problem due to spatial-temporal demand fluctuations and heterogeneous passengers’ travel behavior, i.e., the interaction between the departure and arrival passengers. In this paper, we develop an explainable Stacking-Catboost model for passenger flow prediction combining the passenger’s return probability computation. The model explores several basic ensemble learning models and the best stacking strategy. To better characterize the macroscopic spatiotemporal travel patterns other than the micro individual travel behavior, several relevant variables such as train operation characteristics, the nearby bus stations, and points of interest are considered. Ablation studies are conducted to investigate the utility of each component of the proposed model. An explainable analysis is performed to interpret information from “black box” models and quantify the contribution of each feature. We present a real-world case study conducted in the Beijing metro, demonstrating that our proposed method achieves significant improvements over existing techniques in hourly prediction tasks. Specifically, our approach outperforms the CategoricalBoosting (CatBoost) by up to 11.11 % and the Random Forest (RF) by up to 12.90 %, showcasing the effectiveness of incorporating macroscopic spatiotemporal travel patterns and micro individual travel behavior to enhance short-term forecasting accuracy. The global interpretability of models reveals key factors that impact performance, such as returning passenger flow, historical travel demand, and timetable data. Our results offer valuable insights into short-term forecasting challenges and provide a framework for leveraging explainable artificial intelligence to improve transportation services in urban metro systems.

A stochastic programming model for designing bus bridging services under metro disruptions

ABSTRACT With the growing reliance on urban metro networks, any accidental disruption can lead to rapid degradation and significant economic losses. Bus bridging services are common and efficient ways to minimize such adverse impacts. In this study, we investigate the problem of designing bus bridging services in response to unexpected metro disruptions, and propose a routing strategy with multiple bridging routes. In particular, to respond to uncertain factors such as passenger arrivals and bus travel times in the disruption environment, we develop a two-stage stochastic programming model for the collaborative optimization of bus bridging routes, schedules, and passenger assignments. To solve the computational challenges arising with the proposed model, a tailored tabu search algorithm is developed. Finally, several sets of numerical experiments are conducted and experimental results reveal that our proposed routing strategy can effectively improve the service level for the affected passengers during metro disruptions.

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.

Handling of Passenger Arrivals by Sriwijaya Air Cassation Officers at Soetta Airport Tangerang

This study aims to determine the process of handling passengers who will transit or stop and the handling of passengers who require special handling. In this study, the authors used descriptive research methods with a qualitative approach. The results of this study explain that the services provided by Sriwijaya Air officers for arrival services, especially in handling transit or transfer passengers and passengers who require special handling at the destination airport, are by procedures. In handling transit passengers, there are two services: when transit passengers do not change planes and when transit passengers change planes. The Sriwijaya Air officer provides particular passenger handling for each passenger category, so there are no errors in passenger service. It can be concluded that the handling of transit or transfer and handling of special passengers is carried out by procedures that are carried out properly by Sriwijaya Air.

Integration of urban aerial cable cars into public transit: Operational capacity limits due to passenger queuing at stations

As the airspace is increasingly gaining importance as a new frontier to improve urban mobility, aerial cable cars are being discussed and already appropriately implemented worldwide to supplement conventional modes of transport in urban areas. Transit planners and designers should carefully consider the interoperability and integration of cable car services with conventional modes of transport. In particular, excessive delays and overcrowding conditions due to deficits in interoperability should be avoided. This challenge of interoperability arises as conventional modes of transport operate predominantly on a timetable, and most cable car technologies operate in such close headways that they can be considered as almost continuous conveyors. The advantage of having almost always a transport vessel of a cable car ready for boarding ceases when large volumes of passengers arrive in batches, for example from higher-capacity modes of transport or at large events, resulting in long queues. Traditional manuals do not yet reflect these aspects of interoperability adequately. Consequently, this work filled this research gap about the interoperability of cable cars related to handling high volumes of incoming passenger arrivals that transfer in larger batches from feeder modes and often result in queues at cable car stations. The following objectives were targeted: (1) determine passenger capacity limits of conventional modes of transport acting as feeders to cable cars and (2) specify space requirements to be provided due to the potential queues that arise. To answer these, methods of Queuing Theory were used and results were placed in Levels of Services of traditional manuals. Key performance indicators included queue length, waiting time, and corresponding queue space. The results revealed that cable cars can be a useful complement to public transit but are of limited feasibility due to cumulative queues at arrival rates with larger crowds. High-capacity feeder modes (e.g., commuter rails) are limited to 20-minute headways depending on cable car technologies. Further, queuing areas of up to 1000 square meters (around 10,800 ft2) should be considered. Several operational limitations are presented as guidance for practitioners and policymakers.

Innovative Procedures for Equipping Passengers in Railway Passenger Transport in the Conditions of Slovak Railways in the Next Pandemic Period

The global health crisis in 2020 because of the COVID-19 pandemic affected the entire EU and seriously affected passenger rail transport as well. With the declaration of a state of emergency in the Slovak Republic, international rail transport with all neighboring countries was temporarily halted, as was the operation of IC trains. The operation of all customer centers and the reservation office for personal handling or selected sales points was also suspended. The sale of national reservations was suspended, and the queuing of restaurant wagons, sleeping wagons, couchettes and car carrier wagons was also limited. The article focuses on solving the situation of the arrival of many passengers at the same time at the railway stations, when large queues are formed in queue of the personal cash registers in the railway stations. The aim of the contribution is to propose procedures for minimizing the waiting time in personal cash registers and to minimalize the spread of the disease COVID-19. The proposal of procedures for the provision of passengers in the post-pandemic period is applied to Trnava and Košice railway stations.

Comparative Analysis of the Factors Influencing Metro Passenger Arrival Volumes in Wuhan, China, and Lagos, Nigeria: An Application of Association Rule Mining and Neural Network Models

Comparative Analysis of the Factors Influencing Metro Passenger Arrival Volumes in Wuhan, China, and Lagos, Nigeria: An Application of Association Rule Mining and Neural Network Models

Modelling bus bunching along a common line corridor considering passenger arrival time and transfer choice under stochastic travel time

This study examines bus bunching along a common-line corridor, considering crucial factors underexplored in existing literature, such as stochastic travel times, passenger arrival patterns, and passenger transfer behaviours. We first develop a bus motion model that captures the interaction between bus trajectories and passenger movement. Then we formulate a reliability-based passenger arrival time choice and a transfer choice model to characterise passengers’ behaviours. Afterwards, the bus motion model and the passenger choice models are integrated, and a Method of Successive Averages type iterative algorithm is developed to obtain stable passenger arrival patterns and transfer choices. Numerical experiments are carried out on a hypothetical network followed by a case with real-world data. Our findings demonstrate that a high transfer demand could amplify the propagation of bus bunching across lines along the common-line corridor. Meanwhile, a 50% increase in transfer demand leads to a 24%–30% rise in headway fluctuation. Furthermore, our results suggest that non-uniform passenger accumulation patterns can restore headway regularity as a result of coordinated passenger movement and bus motions, thus alleviating the persistent deterioration in bus bunching.

Analysis of Boarding Strategies on an Airbus A320 Using Discrete Event Simulation

Boarding time constitutes a critical element of turnaround time, which is used to measure the efficiency of airline operations. Therefore, to reduce boarding time, it is imperative to reconsider traditional passenger boarding strategies to make them more efficient. In this sense, this study seeks to analyze the impact of different strategies on boarding times using discrete event simulation on an Airbus 320. Seven boarding strategies have been identified and considered in our study, as follows: random, back-to-front, outside-in, reverse pyramid, blocks, Steffen, and modified optimal. The impact of carrying hand luggage and the presence of priority passengers has been considered, as well as the impact of having a continuous arrival of passengers during the boarding process versus having all passengers available at boarding time. In general, simulation results have pointed out that the outside-in and reverse pyramid strategies are the most effective, improving boarding time by up to 15%, when compared to the random strategy. Moreover, the back-to-front strategy, which is generally implemented by airline companies, has been shown to be the most inefficient strategy. Efficient boarding strategies are expected to contribute to the sustainability of air travel by minimizing the turnaround time, improving operational efficiency, and reducing emissions.

Towards a resilience assessment framework for the airport passenger terminal operations

Airport terminals can generate and intensify the undesired phenomena that take place on transportation networks in the wake of disruptive events. The more resilient a terminal is, the more easily it will bounce back to the desired state, causing less nuisance to the users. In this study, we expand the state-of-the-art airport terminal resilience assessment through fast-time simulation and tailored resilience metrics. From the point of view of simulation, we establish delay management strategies based on flight schedule alterations as a response to a disruptive event. In the baseline scenario, no disruption is set, whereas passenger arrival to the airport is blocked for 1 or 2 h in the disrupted scenarios. A previously verified and validated simulation model of a Brazilian domestic airport was used to represent the passenger departing flow, encompassing check-in and security screening. From the resilience perspective, a systematic assessment procedure for new resilience metrics is unveiled, providing means for exploring the metrics’ formulations and their relationship to the operational strategies. Following the simulation runs, resilience metrics for individual passenger and aircraft delays were investigated, something yet scarcely explored in the literature. The results show that the proposed metrics deliver significantly different outcomes from those associated with the current literature. Consequently, we can conclude that a resilience assessment concerning the airport terminal that disregards individual delays may be missing important information. The systematic approach we propose is a contribution in that sense, even though this topic requires more research, considering the vast nature of the possible disruptive events and their subsequent operational impacts.

Passenger Queuing Analysis Method of Security Inspection and Ticket-Checking Area without Archway Metal Detector in Metro Stations

In order to avoid the congestion in front of the entrance gate units, it is necessary to analyse and optimise the queuing situation at the planning and design stage. The security inspection area and the ticket-checking area were jointly considered, and a queuing congestion analysis method was proposed. Firstly, the research problem was stated. Then, the problem of calculating the number of passengers in each subarea at any time was transformed into the problem of calculating the transit time of each passenger in each subarea. The transit time was divided into basic transit time and additional transit time. Based on the velocity-density relationship, a quantisation method for basic transit time was proposed related to passenger arrival time. The additional transit time was determined by the moment when the passengers left the subarea according to the sequence of arrival of passengers, the number of queuing passengers in the subarea and the congestion of the subarea to be entered. Finally, the queuing situation of passengers in each subarea at any moment was obtained through passenger flow recursion. Examples showed that the proposed method can deal with multiple working conditions and avoid the tedious and time-consuming scene construction process of the microsimulation software.

A model for public-private partnership during the COVID-19 pandemic: Lessons from Biolab and public laboratories working in the Hashemite Kingdom of Jordan.

The global COVID-19 pandemic overwhelmed national public health and laboratory capacity in Jordan and globally. In response, Biolab, a private laboratory group with 27 branches across Jordan, assisted with testing. Biolab was equipped to quickly increase molecular testing capacity without compromising quality or turnaround time, allowing them to contribute to national COVID-19 surveillance efforts. Biolab expanded testing in Jordan by operationalizing automated testing platforms at various locations, including 16 branches, 2 drive-through and 2 walk-through centres, and entry points for airports and marine passenger arrivals. Genomic and molecular testing were implemented to track variants. Information technology platforms were introduced for sample management, registration, and commercial sample payments. Data were directly provided to the Ministry of Health through these platforms to support public health decision-making and responses. Biolab prioritized staff well-being by providing mental, financial, and physical health support during the pandemic. Biolab processed more than two million samples, with a turnaround time of ~1.5h. Results were transmitted directly to key stakeholders in near real time. Biolab conducted variant evaluations on >1.4 million samples using molecular variant testing and >1000 samples using whole genome sequencing. Biolab prioritized staff well-being, improving staff satisfaction from 74% to 91%, a remarkable achievement when many laboratory systems experienced staff burnout and dissatisfaction. The collaboration between public and private laboratories during COVID-19 established a model for future joint efforts to prevent outbreaks from becoming pandemics. Biolab's focus on efficiency, quality, and staff well-being enabled consistent, high-quality performance. The introduction of innovative information technology platforms ensured swift information dissemination. Biolab plans to continue investing in these platforms and expand pathogen testing, creating a top-tier testing infrastructure in Jordan with a demonstrated ability to cooperate with the government for public benefit.

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.

COVID’s First and Second Waves in India: Lessons and Policy Implications

In this article, we argue that in a supply-deficient situation, a vaccination strategy that makes use of an analysis of the different stages of the COVID waves and the pattern of their geographical spread can prove to be far more effective than the current non-transparent mechanism of inter-state allocation of the vaccines. We divide the spread of the COVID waves into three stages: (a) seeding stage, (b) explosion stage and (c) subsiding and diffusion stages. In the seeding stage, two critical determinants of seeding the virus into the population are (a) the inter-state pattern of arrival of international passengers and (b) the inter-state pattern of the continuing active COVID-affected population from the previous wave. The explosion stage induces a policy response in the form of lockdowns which is followed by the wave starting to subside. But the lockdown also results in the migration of labour out of the state into the rural and less populated areas, leading to the diffusion stage. We advocate a vaccination strategy targeting universal coverage with strategic sequencing in which prioritisation is given to ‘saturation vaccination’ of high-density and high COVID-intensity urban areas. This would not only minimise the economic costs in terms of duration and spread of lockdowns but also the pressures on the inadequate health infrastructure in terms of availability of hospital beds, ambulances, oxygen and drugs. This strategy may be implemented in curbing the COVID’s subsequent waves which may hit India while its population is still only partially vaccinated.

Optimization of headway and bus stop spacing for low demand bus routes

ABSTRACT We propose a methodology for optimization of service headway and stop spacing along a low-demand bus route that minimizes operator and user costs. This study develops analytical cost models that are representative of low-demand routes by using negative binomial distribution for passenger demand for boarding and alighting pattern to estimate the probability of stopping and both random and planned arrival of passengers are considered to estimate the waiting time. Pareto optimal solutions obtained using multi-objective evolutionary algorithm, NSGA-II indicate that optimal values of headway and stop spacing are underestimated if optimized based on assumptions typical of high-demand routes which is passenger demand for boarding and alighting at bus stops randomly following a Poisson process. With the aid of the study methodology, transit planners will be able to improve the service utilization and passenger accessibility along an under-performing low demand routes by recommending minimal modifications to the existing route and bus schedule.

VISUALISASI DAN PREDIKSI KEDATANGAN PENUMPANG NGURAH RAI MENGGUNAKAN METODE HOLT-WINTERS

I Gusti Ngurah Rai International Airport is one of the airports in Indonesia which is located in Bali which has a large number of passenger arrivals because Bali has many interesting tourist attractions to visit. I Gusti Ngurah Rai international airport data certainly has a large size so that Business Intelligence (BI) technology is needed, namely a dashboard to process this data and gain insight. Forecasting the number of passenger arrivals is also carried out in order to be able to analyze what the number of passenger arrivals would be like in the future. Therefore, in this case the dashboard was made using the prototyping method and data mining with forecasting or forecasting using the Holt-Winters triple exponential smoothing method. The smoothing parameters used are alpha = 0.3, beta = 0.01, and gamma = 0.01 with the MAPE result of 32.04%. With the visualization on the dashboard, it is hoped that it will make it easier for the company that manage the airport to monitor the number of passenger arrivals at I Gusti Ngurah Rai International Airport so that they are able to make decisions in managing the airport.

Effect of Service Quality Lost and Found PT. Gapura Angkasa to Passenger Satisfaction at Bandung Husein Sastranegara International Airport

As far as the author's observations while conducting pre-research at Husein Sastranegara International Airport in Bandung, the authors see that the lost and found service at Husein Sastranegara International Airport Bandung has deficiencies, namely in human resources (HR) there are only two officers per day. As the author observed, on November 9 2021, at the time of arrival of passengers, officers experienced difficulties dealing with passenger baggage problems, there were passengers who complained about problems with their missing luggage and there were also passengers who experienced luggage damage. The purpose of this study was to find out the services provided by Gapura Angkasa passasi officers in serving passengers to achieve passenger satisfaction. This research is expected to provide an overview for companies to determine the Effect of PT. Gapura Angkasa's Lost And Found Service Quality on Passenger Satisfaction at Husein Sastranegara International Airport Bandung. To further optimize and improve the services provided in order to prioritize passenger satisfaction. This study uses quantitative analysis methods to analyze the effect of the lost and found service quality of PT. Gapura Angkasa on passenger satisfaction at Husein Sastranegara Airport, Bandung. The population in this study were all passengers who used PT. Gapura Angkasa's lost and found service at Husein Sastranegara Airport, Bandung. The research sample used purposive sampling. The samples taken in this study were passengers using lost and found services based on age, occupation, and gender. The criteria for the respondents were passengers who used the lost and found service of PT. Gapura Angkasa Bandung Husein Sastranegara International Airport. The samples taken in this study were 50 respondents. Data collection techniques using questionnaires and documentation. The data analysis technique uses an instrument quality test which consists of a validity test, a reliability test. And test the hypothesis using simple linear regression analysis, T test and the coefficient of determination. Based on the data analysis carried out, it was concluded that there is a significant influence between the variables x (Influence of facilities) and Y (Passenger satisfaction). This can be done through SPSS passenger satisfaction has increased 1%, then the variable influence of facilities will also increase by 0.181. The coefficient is positive, so that it can be stated that there is a relationship between the independent variable (Service Effect) and the dependent variable (Passenger Satisfaction) and the T count 10.904 T table 1.353 with a significant value of 0.000 0.05, it can be concluded that the hypothesis (Ho) rejected, and (H1) accepted. the R relationship is 0.879 on the effect of lost and found service quality, which means that 87.9 has an effect on lost and found service quality at Husein Sastranegara Airport, Bandung.