Passenger Data Research Articles

Emergence of the B.1.214.2 SARS-CoV-2 lineage with an Omicron-like spike insertion and a unique upper airway immune signature

We investigate the emergence, mutation profile, and dissemination of SARS-CoV-2 lineage B.1.214.2, first identified in Belgium in January 2021. This variant, featuring a 3-amino acid insertion in the spike protein similar to the Omicron variant, was speculated to enhance transmissibility or immune evasion. Initially detected in international travelers, it substantially transmitted in Central Africa, Belgium, Switzerland, and France, peaking in April 2021. Our travel-aware phylogeographic analysis, incorporating travel history, estimated the origin to the Republic of the Congo, with primary European entry through France and Belgium, and multiple smaller introductions during the epidemic. We correlate its spread with human travel patterns and air passenger data. Further, upon reviewing national reports of SARS-CoV-2 outbreaks in Belgian nursing homes, we found this strain caused moderately severe outcomes (8.7% case fatality ratio). A distinct nasopharyngeal immune response was observed in elderly patients, characterized by 80% unique signatures, higher B- and T-cell activation, increased type I IFN signaling, and reduced NK, Th17, and complement system activation, compared to similar outbreaks. This unique immune response may explain the variant's epidemiological behavior and underscores the need for nasal vaccine strategies against emerging variants.

Assessing exit screening of SARS-CoV-2 in Japan: an analysis of the airport screening data of passengers from the United Kingdom, 2020–2022

BackgroundJapan implemented strict border control measures and all incoming passengers were subject to entry screening with reverse transcription–polymerase chain reaction or antigen testing. From late 2020, exit screening within 72 h of departure to Japan also became mandatory. In this study, we evaluated the effectiveness of the exit screening policy in Japan by analyzing airport screening data from October 2020 to April 2022.MethodsIn addition to assessing entry screening data over time of passengers from the United Kingdom, we examined the prevalence of coronavirus disease 2019 (COVID-19) in the United Kingdom based on the Office of National Statistics infection survey. We constructed a statistical model that described entry screening positivity over time using Office of National Statistics prevalence data as the explanatory variable. Ideally, the time-dependent patterns of entry screening and Office of National Statistics prevalence data should resemble each other; however, we found that, sometimes, they were different and regarded the difference to statistically partly reflect the effectiveness of exit screening.ResultsThe average proportion positive in one month before mandatory exit screening was implemented among Japanese passengers was 0.67% (95% confidence interval [CI]: 0.45, 0.98), whereas the proportion positive decreased to 0.49% (95% CI: 0.21, 1.15) in the first month of exit screening. Adjusting for time-dependent prevalence at the origin, we concluded that exit screening contributed to reducing passenger positivity by 59.3% (95% CI: 19.6, 81.3). The overall positivity values among passengers during the Delta and Omicron variant periods were 3.46 times and 1.46 times that during the pre-Delta variant period, respectively.ConclusionsWe used a simplistic statistical model and empirical data from passengers arriving in Japan from the United Kingdom to support that exit screening helped to reduce the proportion positive by 59%. Although the proportion positive later increased considerably and precluded preventing the introduction of imported cases, submitting a certificate for a negative test result contributed to reducing the positivity among travelers.

Air travel during times of crisis: The role of social preferences and uncertainty

This paper investigates the role of social preferences and uncertainty for air travel demand, by linking experimentally validated measures of social preferences and uncertainty index to passenger data for 36 European countries from 2019 to 2022. The empirical analysis separates the effects of government restrictions from that of social preferences, while considering the impact of uncertainty surrounding the pandemic event. The results highlight the importance of accounting for the individuals’ social preferences when implementing aviation-related policy measures. The findings also reveal that uncertainty can trigger unexpected behaviour from the passengers, resulting in the ineffectiveness of public policies during times of crisis.

Airports in Turkey: efficiency limits and the role of technological change

PurposeThis paper aims to present an example of the measurement of airport efficiency, a topic of great interest in civil aviation today. The methodology used is data envelopment analysis (DEA) and the Malmquist Index. The calculation of airport efficiency with up-to-date data for each period is of great importance in the context of sustainability. The study selected ten airports with high air traffic in Turkey as the sample set. The objective of this study is to evaluate the current state of airport efficiency, identify the sources of inefficiency and make appropriate policy recommendations based on the findings obtained.Design/methodology/approachThe study is based on DEA and Malmquist Index Analysis. The number of personnel and terminal size data of ten selected decision-making units (DMU) are used as inputs, while passenger, cargo and aircraft traffic data are used as outputs. A five-year period, spanning from 2018 to 2022, is considered as the data set in the study.FindingsUpon analysis of the data from the ten airports included in the study, it was found that the current input-output combination yielded efficient results, with the exception of certain characteristics, such as the impact of seasonal conditions or tourism. Concurrent with the growth in aircraft, passenger and freight traffic, the number of personnel employed at these airports has also increased. It was concluded that technological efficiency is of paramount importance for each airport, and that investments in technology should be increased.Practical implicationsA separate assessment was conducted for each of the ten airports included in the study sample. Each airport was evaluated in terms of its strengths and weaknesses, and areas of low efficiency were identified. Consequently, more general conclusions were reached than airport-specific evaluations.Originality/valueIn order to ensure the long-term sustainability of the sector, it is essential that the efficiency measurements of airports are calculated using up-to-date data on a regular basis. The results obtained from these calculations provide guidance for the strategic plans to be implemented in the long term, as well as for the solution proposals for operational problems. In this context, this study not only provides information to policymakers and airport managers about the current situation, as it includes recent data, but also contributes to the literature in this sense, as it includes policy recommendations.

Secure and Privacy-Protected Bioinformation Implementation in Air Passenger Transport Based on DLT

Aviation information systems are a key component in ensuring efficient and smooth air transport operations. In this regard, the transfer of passenger information between parties is of paramount importance. With the continuous improvement of biometrics technology, this kind of individual identification that can provide accurate and unforgeable identification is widely used in various fields. This research presents the significance and effective application scenarios of facial recognition in biometrics in air transport operations. Due to the characteristics of aviation information systems, Distributed Ledger Technology (DLT) is used in this study for secure and private transmission of facial recognition information. Distributed systems can give a transparent and secure platform to multiple parties to access sensitive passenger data. This study uses the Corda framework as the DLT that supports CorDapp development. Based on the above techniques, this study proposes two feasible application scenarios. One is a baggage match detection system to prevent misplaced baggage, and the other is an iAPIS system that transmits passenger information in real-time communication between airlines and border control agencies. This article details how to apply the research in these two scenarios, as well as the benefits and implications of the applications. Finally, this article presents an outlook for future development and feasible directions for improvement.

PERAMALAN JUMLAH PENUMPANG BUS RAPID TRANSIT (BRT) BANJARBAKULA DENGAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE WITH EXOGENOUS VARIABLE (ARIMAX) DENGAN EFEK VARIASI KALENDER

Banjarbakula Bus Rapid Transit (BRT) is an inner-city bus-based mass transit system that provides a sense of comfort, safety, speed in mobility, and low cost in serving the citizens of Banjarmasin City and Banjarbaru City. Based on data on the number of passengers on the Banjarbakula BRT for the period April 2020 - February 2023, public interest in using the Banjarbakula BRT as a mode of transportation is quite high. However, the limited units and operational schedules make the Banjarbakula BRT unable to fully meet the needs of the public. Forecasting the number of passengers of BRT Banjarbakula for the next 12 periods is one of the measures to prepare the infrastructure, quality and units of BRT Banjarbakula in order to facilitate the public and create a better transportation system. In the Banjarbakula BRT passenger data, there is an increase in the number of passengers at certain times such as during religious holidays and school holidays, so this increase in passenger numbers is thought to be due to the influence of the calendar variation effect. This research intends to forecast the number of passengers of BRT Banjarbakula using the best ARIMAX model with the effect of calendar variation. The results indicate that the ARIMAX (0, 1, 1) model is the best ARIMAX model to forecast the number of passengers of BRT Banjarbakula for the next 12 periods. The forecast results indicate an increase in the month where the Christmas celebration and also the memorial haul guru sekumpul, so that the variable Christmas celebration and memorial haul guru sekumpul significantly affect the number of passengers of BRT Banjarbakula.Keywords: Forecasting, BRT Banjarbakula, ARIMAX with calendar variation effects

Security and Privacy for Smart Transportation Management using Big Data Analytics

Security and privacy are vital aspects of smart transportation management with big data analytics because they assure the security of sensitive information, prevent unwanted access to essential systems, and retain public trust in the safety and dependability of the transportation infrastructure. Protecting data from cyber threats, ensuring secure communication and data transmission, protecting passengers' personal information and addressing privacy concerns related to data collection and usage to maintain transparency and accountability in data handling practices are all obstacles to smart transportation management using big data analytics. This paper proposes a Secure Data Encryption Control based Big Data Framework (SDEC-BDF) to strike a middle ground between data analytics and privacy protection, establishing the way for more private and secure transportation systems that benefit everyone involved. The intention of this approach is to offer strong security while simultaneously safeguarding people's privacy. The method has many potential uses in the Intelligent transportation sector (ITS), including traffic control, passenger security, fleet management, preventative maintenance, and road network design. It ensures privacy and security while facilitating effective data analysis. Furthermore, it protects the public confidence in the security and dependability of the transportation system, protects sensitive passenger data, and stops hackers from breaking into vital systems. The simulation analysis is conducted on the assumption that the system can maximize its security, privacy, and efficiency to create a more trustworthy transportation network.

Online Tourism Management System

In order to handle travel planning and booking, better management systems are required as vacations and tourism gain importance in today's society. Although digital solutions offer better processes, many travel agencies still don't fully utilize them. The purpose of this study is to address gaps in the travel and tourism sector by introducing an online tourist management system. The goal of this platform is to increase corporate productivity and enhance customer experience by centralizing booking and information transmission. Techniques: The prototype runs Microsoft SQL Server 2008 on a front end composed of HTML and PHP. With detailed information, it assists clients worldwide in making reservations for places and activities. Administrators might, nevertheless, offer hotel and travel agency vacation packages. Following confirmation, customers can view their booking details in "my booking." Outcome: Passenger data is centralized via the integrated platform.

Model for Intercity Railway Schedule Optimization Incorporating Metrorail Transfers

The promotion of multimodal coordination is of great significance for the joint development of diverse urban transportation systems. Focusing on a comprehensive urban passenger transport hub that converges both intercity modes and multiple inner-city modes, our study aims to incorporate intercity railway and Metrorail coordination into train scheduling optimization to enhance transfer efficiency and provide a seamless service for passengers in urban railway networks. By considering the variety of passenger demands in different transport modes (e.g., rail transit and traditional mainline railway), train loading capacity, and potential disruption scenarios, we specifically developed a mixed-integer nonlinear programming (MINLP) model to simultaneously generate the coordination strategies, arrival/departure times of trains at each station, and the number of carriages in service in the network. To enhance computational efficiency, we proposed several improvement strategies involving the model linearization to reformulate the MINLP model into a mixed-integer linear programming (MILP) model and the determination of appropriate big- M values. Finally, numerical experiments based on historical passenger data from a realistic urban railway network in China were implemented to verify the effectiveness of our approach. The results demonstrate that our intercity railway and Metrorail coordination strategies could, in practice, significantly improve passenger transfer efficiency by over 35% compared with the train schedule without coordination.

How to Reduce the Number of Inspectors: Operation Mode and Modeling Simulation of Centralized Image Recognition in Railway Passenger Security Inspection

Security inspection is an essential step for passengers entering railway stations in China. Under the existing security inspection operation mode, each X-ray machine needs an image inspector to recognize the X-ray image, which poses the issue of resource optimization for inspectors. Based on the networking of security inspection X-ray machines, an innovative centralized image recognition (CIR) operation mode for railway security inspections is proposed, and it is worth noting that network and device failures are risk factors for this mode. The method for assigning image recognition personnel is designed to reduce labor force needed for security inspection. Using a combination of agent-based modeling method and computer simulation technology, the four elements of passengers, security inspection X-ray machines, X-ray image inspectors, and secondary baggage inspection personnel are modeled, and CIR indicators to study the effectiveness of the operation are designed. Based on the real departing passenger data of the Zhangjiakou section of the Beijing Zhangjiakou Line in China, simulation is conducted in two scenarios: large passenger flow and small passenger flow. The research is conducted from three aspects: operational efficiency, travel experience, and system complexity. Experiments have shown that, with a slight increase in passenger waiting time, the average number of X-ray image inspectors is reduced by more than 70% compared with the existing operation mode. The best pick-up location for passengers is related to passenger flow. The proposed model can analyze the effectiveness of CIR operation mode and provide decisive support for the application of CIR operation mode.

Planning, Optimization and Development of River Transportation in Supporting The Development of Mega City IKN, Balikpapan and Samarinda

This research aims to plan, increase the potential and optimize river transportation in the IKN area, Balikpapan City and Samarinda which can become one of the driving forces of the economy by moving people and goods more efficiently and integrated between other modes of transportation such as road, sea and air networks. River transportation is one of the main choices for the National Capital Region (IKN), Balikpapan City and Samarinda City as the IKN Megapolitan area. Because, the IKN Megapolitan area can be fully accessed by river transportation, and can reduce the burden on land transportation which can cause congestion. This river transportation is specifically related to the movement of passengers and logistics of industrial goods and services as well as food products to the IKN area. Indicators of regional characteristics, such as river physics, existing facilities, ship call data, origin destination data or OD matrix, regional spatial layout, and passenger and goods data are important in river development analysis and strategies. The method used uses descriptive analysis for qualitative data, while statistical analysis analyzes needs and analyzes strategies for developing river transportation in the future. The expected results are in the form of strategy documents, planning and optimization of river transportation development in the IKN region and its surroundings.

International mobility between the UK and Europe around Brexit: a data-driven study

Among the multiple effects of Brexit, changes in migration and mobility across Europe were expected. Several studies have analysed these aspects, mostly from the point of view of perceptions, motivations, economic effects, scenarios, and changes in migration from Central and Eastern European countries. In this study we propose an analysis of migration and cross-border mobility using an integrated data-driven approach. We investigate official statistics from Eurostat, together with non-traditional data, to give a more complete view of the changes after Brexit, at EU and regional level. Specifically, we employ scientific publication and Crunchbase data to study highly-skilled migration, Twitter and Air Passenger data to investigate monthly trends. While main trends are preserved across datasets, with a general decrease in migration towards the UK immediately after the referendum approval, we are able to also observe more fine grained trends specific to some data or regions. Furthermore, we relate the changes in mobility observed from Air Passenger data with attention to Brexit from Google Trends data.

Temporal variations in international air travel: implications for modelling the spread of infectious diseases.

The international flight network creates multiple routes by which pathogens can quickly spread across the globe. In the early stages of infectious disease outbreaks, analyses using flight passenger data to identify countries at risk of importing the pathogen are common and can help inform disease control efforts. A challenge faced in this modelling is that the latest aviation statistics (referred to as contemporary data) are typically not immediately available. Therefore, flight patterns from a previous year are often used (referred to as historical data). We explored the suitability of historical data for predicting the spatial spread of emerging epidemics. We analysed monthly flight passenger data from the International Air Transport Association to assess how baseline air travel patterns were affected by outbreaks of Middle East respiratory syndrome (MERS), Zika and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) over the past decade. We then used a stochastic discrete time susceptible-exposed-infected-recovered (SEIR) metapopulation model to simulate the global spread of different pathogens, comparing how epidemic dynamics differed in simulations based on historical and contemporary data. We observed local, short-term disruptions to air travel from South Korea and Brazil for the MERS and Zika outbreaks we studied, whereas global and longer-term flight disruptions occurred during the SARS-CoV-2 pandemic. For outbreak events that were accompanied by local, small and short-term changes in air travel, epidemic models using historical flight data gave similar projections of the timing and locations of disease spread as when using contemporary flight data. However, historical data were less reliable to model the spread of an atypical outbreak such as SARS-CoV-2, in which there were durable and extensive levels of global travel disruption. The use of historical flight data as a proxy in epidemic models is an acceptable practice, except in rare, large epidemics that lead to substantial disruptions to international travel.

Research on the Prediction of Flight Passenger Flow in Holidays based on Machine Learning

This article focuses on predicting holiday flight passenger flow to optimize airline resource allocation. Analyzing daily passenger data during key holidays, including New Year’s Day, Qingming Festival, Labour Day, Dragon Boat Festival, Mid-Autumn Festival, and National Day, the study employs four machine learning models: Random Forest, Multilayer Perceptron, LightGBM, and Stacking. Findings reveal that all models effectively capture holiday flow patterns, with LightGBM demonstrating superior prediction accuracy. Moreover, creating unified models for all holidays outperforms individual holiday-specific models. The study delves into the factors influencing the varying performance of the best model across different features, providing insightful analysis and discussion.

Exploring Travel Mobility in Integrated Usage of Dockless Bike-Sharing and the Metro Based on Multisource Data

Dockless bike-sharing (DBS) is a green and flexible travel mode, which has been considered as an effective way to address the first-and-last mile problem. A two-level process is developed to identify the integrated DBS–metro trips. Then, DBS trip data, metro passenger data, socioeconomic data, and built environment data in Shanghai are used to analyze the spatiotemporal characteristics of integrated trips and the correlations between the integrated trips and the explanatory variables. Next, multicollinearity tests and autocorrelation tests are conducted to select the best explanatory variables. Finally, a geographically and temporally weighted regression (GTWR) model is adopted to examine the determinants of integrated trips over space and time. The results show that the integrated trips account for 16.8% of total DBS trips and that departure-transfer trips are greater than arrival-transfer trips. Moreover, the integrated trips are concentrated in the central area of the city. In terms of impact factors, it is found that GDP, government count, and restaurant count are negatively correlated with the number of integrated trips, while house price, entropy of land use, transfer accessibility index, and metro passenger flow show positive relationships. In addition, the results show that the GTWR model outperforms the OLS model and the GWR model.

Occupational Encounters: The Interpersonal Impact of Bus Drivers' Engagement With Disabled Passengers.

Whether mundane or meaningful, interpersonal encounters are prerequisites for (and have a significant impact on) many occupations. This study sought to understand the interpersonal impact and occupational relevance of encounters, through exploring encounters between bus drivers and disabled passengers. The Person-Environment-Occupation-Performance (PEOP) Model was applied as part of a Framework Analysis, separately examining bus driver and disabled passenger data. The Interpersonal-Person-Environment-Occupation-Performance (I-PEOP) Model prototype was then developed to enable simultaneous exploration of the factors impacting such encounters. Application of the I-PEOP and PEOP Models found encounters between bus drivers and disabled passengers were pivotal to the exclusion or inclusion of disabled passengers in their communities. Understanding the occupational relevance of encounters, and the factors that determine their interpersonal impact, prompts occupational therapists to identify and address how intersectionality, power dynamics, occupational roles, and inter-perceptions impact an individual's community mobility and inclusion.

Study on physiological representation of passenger cognitive comfort: An example with overtaking scenarios

During autonomous driving, the subjective cognition of scenario risks may lead to cognitive discomfort of occupants. This is a new concept of ride comfort for vehicles, which is being included in the performance evaluation of autonomous vehicles. The observation methods, generation mechanisms, prediction methods, and optimization methods of this cognitive discomfort remain to be explored. In order to quantify the risk cognition of passengers and evaluate cognitive comfort, this paper proposes a physiological representation method using overtaking scenarios as an example. Firstly, physiological data of passengers are collected through road test, and 19 physiological indicators are extracted, including 6 galvanic skin response (GSR) indicators, 5 electrocardiogram indicators, and 8 eye movement indicators. Secondly, the physiological indicator set related to the subjective evaluation results of comfort is constructed through correlation analysis. Finally, a multiple linear regression model is established to represent the level of passenger discomfort, with physiological indicators as inputs and subjective comfort indicators as outputs. The indicators that contribute significantly to the output results in the model are the maximum amplitude of GSR, the rise time of GSR, the maximum pupil diameter, and the average fixation time. The validation results indicate that the overall accuracy of the model is 86.90 %.

A collaborative privacy-preserving approach for passenger demand forecasting of autonomous taxis empowered by federated learning in smart cities

The concept of Autonomous Taxis (ATs) has witnessed a remarkable surge in popularity in recent years, paving the way toward future smart cities. However, accurately forecasting passenger demand for ATs remains a significant challenge. Traditional approaches for passenger demand forecasting often rely on centralized data collection and analysis, which can raise privacy concerns and incur high communication costs. To address these challenges, We propose a collaborative model using Federated Learning (FL) for passenger demand forecasting in smart city transportation systems. Our proposed approach enables ATs in different regions of the smart city to collaboratively learn and improve their demand forecasting models through FL while preserving the privacy of passenger data. We use several backpropagation neural networks as local models for collaborating to train the global model without directly sharing their data. The local model shares only the model updates with a global model that aggregates them, which is then sent back to local models to improve them. Our collaborative approach reduces privacy concerns and communication costs by facilitating learning from each other’s data without direct data sharing. We evaluate our approach using a real-world dataset of over 4500 taxis in Bangkok, Thailand. By utilizing MATLAB2022b, the proposed approach is compared with popular baseline methods and existing research on taxi demand forecasting systems. Results demonstrate that our proposed approach outperforms in passenger demand forecasting, surpassing existing methods in terms of model accuracy, privacy preservation, and performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-squared (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document}). Furthermore, our approach exhibits improved performance over time through the collaborative learning process as more data becomes available.

A data-driven prioritisation framework to mitigate maintenance impact on passengers during metro line operation

The application of artificial intelligence (AI) techniques may lead to significant improvements in different aspects of rail sector. Considering asset management and maintenance, AI can improve data analysis and asset status forecasting and decision-making processes, fostering predictive and prescriptive maintenance strategies. A prescriptive approach should be able to predict future scenarios as well as to suggest a course of actions. Nevertheless, the decision-making in rail asset management is often based on the classical asset-oriented approach, concentrating on the function of the asset itself as a main key performance indicator (KPI), whereas a user-oriented approach could lead to improved performance in terms of level of service. This paper is aimed at integrating the passengers’ perspective in the decision-making process for asset management to mitigate the impact that service interruptions may have on the final users. A data-driven prioritisation framework is developed to prioritise maintenance interventions taking into account asset status and criticality. In particular, a three-step approach is proposed, which focuses on the analysis of passenger data to evaluate the failure impact on the service, the analysis of alarms and anomalies to evaluate the asset status, and the suggestion of maintenance interventions. The proposed approach is applied to the maintenance of the metro line M5 in the Italian city of Milan. Results show the usefulness of the proposed approach to support infrastructure managers and maintenance operators in making decisions regarding the priority of maintenance activities, reducing the risk of critical failures and service interruptions.

Forecasting Airline Passenger Growth: Comparative Study LSTM VS Prophet VS Neural Prophet

To conduct an exhaustive examination of airline passenger growth prediction methods, this study compares the performance of three distinct strategies: LSTM, Prophet, and Neural Prophet. To forecast passenger volumes accurately, the aviation industry needs robust prediction models due to rising demand. This research evaluates the performance of LSTM, Prophet, and Neural Prophet models in passenger growth forecasting by utilizing historical airline passenger data. A comprehensive examination of these methodologies is conducted via a rigorous comparative analysis, encompassing prediction accuracy, computational efficiency, and adaptability to ever-changing passenger traffic trends. The research methodology consists of various approaches for preprocessing time series data, engineering features, and training models. The findings elucidate the merits and drawbacks of each method, furnishing knowledge regarding their capacity to capture intricate patterns, fluctuations in passenger behavior across seasons, and abrupt shifts. The results of this study enhance comprehension regarding the relative efficacy of LSTM, Prophet, and Neural Prophet in prognosticating the expansion of airline passenger numbers. As a result, professionals and scholars can gain valuable guidance in determining which methodologies are most suitable for precise predictions of forthcoming passenger demand. This comparative study serves as a significant point of reference for enhancing aviation prediction models to optimize the industry's resource allocation, operational planning, and strategic decision-making.