Passenger Volume Research Articles

Accelerating the penetration of clean electricity to promote the low carbonization of high-speed railways: A probabilistic framework

Transitioning from carbon-intensive travel modes to high-speed railways (HSR) is widely recognized as a crucial pathway to achieving the ‘carbon peak and carbon neutrality’ goals in the transport sector. However, it remains unclear whether HSR can meet carbon peak goals on time, under the high uncertainty of future electricity development. This study integrates scenario analysis and Monte Carlo simulation into life cycle assessment, proposing a probabilistic framework for dynamically simulating the carbon dioxide (CO2) emissions over the entire life cycle of HSR. Monte Carlo simulation-Latin hypercube sampling method is introduced to model the uncertainty in the development of electricity. The results show that with the gradual increase in the proportion of clean electricity, the time when the CO2 emission intensity of HSR is lower than that of electric cars and electric coaches is most probable to occur in the 2nd–3rd years and the 5th–14th years of operation, respectively. The emission trend of HSR is primarily influenced by the passenger growth rate and the depth of transition to clean electricity, with minimal impact from the loading factor and initial passenger volume. When the passenger growth rate reaches 5 % by 2030, transitioning solely to clean electricity can peak CO2 emissions from HSR by 2030. However, with passenger growth rates of 6 %, 7 %, 8 %, and 9 %, the peak time is most probable to be delayed by 6–7 years, 13–14 years, 16 years, and 19 years, respectively. These findings suggest that achieving CO2 emission peak goals in the transport system requires collaborative efforts across multiple sectors, including transport, energy, and industry sectors. The results contribute to a deeper understanding of the pathways for achieving carbon peak in the transport sector.

Research on passenger flow control at metro transfer stations based on real-time flow calculation of streamlines

PurposeThe volume of passenger traffic at metro transfer stations serves as a pivotal metric for the orchestration of crowd flow management. Given the intricacies of crowd dynamics within these stations and the recurrent instances of substantial passenger influxes, a methodology predicated on stochastic processes and the principle of user equilibrium is introduced to facilitate real-time traffic flow estimation within transfer station streamlines.Design/methodology/approachThe synthesis of stochastic process theory with streamline analysis engenders a probabilistic model of intra-station pedestrian traffic dynamics. Leveraging real-time passenger flow data procured from monitoring systems within the transfer station, a gradient descent optimization technique is employed to minimize the cost function, thereby deducing the dynamic distribution of categorized passenger flows. Subsequently, adhering to the tenets of user equilibrium, the Frank–Wolfe algorithm is implemented to allocate the intra-station categorized passenger flows across various streamlines, ascertaining the traffic volume for each.FindingsUtilizing the Xiaozhai Station of the Xi’an Metro as a case study, the Anylogic simulation software is engaged to emulate the intra-station crowd dynamics, thereby substantiating the efficacy of the proposed passenger flow estimation model. The derived solutions are instrumental in formulating a crowd control strategy for Xiaozhai Station during the peak interval from 17:30 to 18:00 on a designated day, yielding crowd management interventions that offer insights for the orchestration of passenger flow and operational governance within metro stations.Originality/valueThe construction of an estimation methodology for the real-time streamline traffic flow augments the model’s dataset, supplanting estimated values derived from surveys or historical datasets with real-time computed traffic data, thereby enhancing the precision and immediacy of crowd flow management within metro stations.

Revealing the multiscale impact of external factors on dockless bike sharing and metro integration: Evidence from Shenzhen

Dockless bike sharing (DBS) and metro integration offer innovative solutions for the first-mile and last-mile challenges of urban transportation. However, accurately identifying transfer passenger volume, analyzing DBS and metro integration characteristics, and understanding the spatial heterogeneity of factors affecting this volume remain areas to explore. This study, using Shenzhen as a case, constructs a framework to identify transfer passenger volume by considering built environment elements around metro stations and applying a Thiessen polygon. Transfer passenger volumes are classified into six categories, and their spatio-temporal characteristics are analyzed. A multiscale geographically weighted regression (MGWR) model investigates the impacts of socio-economic attributes, built environment, and transportation-related factors on transfer passenger volume, revealing spatial heterogeneity. Results show that the demand for DBS and metro integration is higher in the morning peak than in the evening peak, with access transfer passenger volume peaks occurring earlier than egress peaks. Spatially, transfer passenger volume shows heterogeneity, with higher demand in the urban core than in the suburbs. Commercial and residential and enterprises have the most significant impact on various types of transfer passenger volume, showing the highest spatial heterogeneity in enterprises. In contrast, science education and culture positively influence evening peak access transfer volume and exhibit the weakest spatial heterogeneity. Population density, sports and leisure, shopping and entertainment, and tourist attractions display localized positive or negative impacts. These findings can guide operators and planners in optimizing public transportation resources to enhance DBS and metro integration.

Analisis Tingkat Risiko Penyebab Rel Patah pada Jalur Kereta Api Wilayah Divre IV Tanjung Karang

The increase in the volume of passengers and goods in rail transportation in Indonesia has an impact on the condition of railway infrastructure, especially railway infrastructure. The main focus of this condition is the risk of rail damage, especially broken rails. So a holistic assessment approach is needed to identify the root causes and formulate appropriate treatment and improvement strategies. The aim of the research is to identify the factors causing the rail to break and analyze the causes. This research uses a risk level value analysis method by multiplying the results of probability and impact, thus producing a risk value. Risk variables such as extreme rain events, expansion in the alignment area, and mud pumping are high level risks. This is followed by medium, low and very low risks, according to the classification of each risk variable analyzed. Therefore, the priority scale for handling broken rail repairs is grouped based on the risk level value starting from the highest risk to the lowest risk. The conclusion is that risk variables such as extreme rain produce a risk value of IDR 296,881,200 with a probability value of 0.99% and an impact of IDR 29,988,000,000, expansion in the straight area produces a risk value of IDR 376,992,000 with a probability value of 1.10% and impact IDR 34,272,000,000, and mud pumping produces a risk value of IDR 394,128,000 with a probability value of 1.15% and impact IDR 34,272,000,000, these three risk variables are classified as high risk.

ANALYSIS OF THE INDICATORS OF THE DEPOT REPAIR OF PASSENGER CARS

The article presents the results of the analysis of the technical characteristics of passenger cars and the main indicators of depot repairs of passenger cars. The indicators of depot repairs of passenger cars on the Southern Railway by types and manufacturers of cars for the period from 2015 to 2020 were studied. The data analysis indicates that in the overall structure of the volume of depot repairs of passenger cars by type, compartment cars predominate, while interregional passenger cars have the smallest share. In the overall structure of the volume of passenger cars depot repairs by manufacturing enterprises, cars of the Kryukovsky Railway Car Building Works predominate, while the smallest share is made in Germany. The largest number of depot repairs were carried out for sleeping, compartment and interregional cars built by the Kryukovsky Railway Car Building Works. The age of the cars that underwent depot repairs during the period from 2015 to 2020 ranges 10 to 20 years. The age of the majority of non-compartment cars that underwent depot repairs during the specified period ranges 30 to 40 years. The analysis shows that the cost of depot repairs of a sleeping car built by the Tver Carriage Works is lower than the cost of depot repairs of a sleeping car built by the Kryukovsky Railway Car Building Works. The cost of depot repairs of German and Russian compartment cars is lower than the cost of depot repairs of a compartment car built by the Kryukovsky Railway Car Building Works. It was established that the cost of depot repairs of a compartment car built by the Kryukovsky Railway Car Building Works is lower, and the cost of an interregional car is higher than the cost of depot repairs of a sleeping car of the same manufacturer. The analysis shows an almost annual increase in cost over the period, which is due to an increase in material and labor costs.

Forecasting short-term subway passenger flow using Wi-Fi data: comparative analysis of advanced time-series methods

Accurately monitoring passenger demand fluctuations is crucial for streamlined operations of subway systems and informed decision-making. This study presents a detailed Time Series Analysis of the Toronto subway system using Wi-Fi data connection from devices as a predictor of passenger volume. Various time series models were tested for short-term forecasting, including Linear Regression, Exponential Smoothing, ARIMA, Random Forest, N-BEATS, and T-GCN. An end-to-end modeling implementation process was carried out, and the performance of each model was evaluated. The primary objective was to assess the effectiveness of short-term prediction models for univariate time series at the system level and discuss deployment challenges. While conventional time series models are fast to implement and interpretable, they require a more in-depth data exploration phase for validation, making scaling at the system level difficult. Additionally, maintenance is more challenging with conventional models, and their exploratory analysis phases need to be repeated when the models degrade over time. Prediction difficulty varied across each subway station, indicating the need for a more thorough calibration or hybrid approach, especially for transfer stations. Despite the different uses and qualities of each model in our scenario, Random Forest and Exponential Smoothing emerged as the best performers and could be a satisfactory option for robust demand forecasting at the system level.

Contactless Service: Artificial Intelligence Applications of Airports

Airports are an essential part of the airline value chains. Artificial intelligence (AI) technologies are widely used at airports; they utilize two technologies: face detection and recognition. The study examines how passengers' performance expectancy and emotions affect satisfaction and willingness to adopt the AI service of passengers via familiarity. Based on the AI device, it uses acceptance and socio-technical systems perspectives; participants who adopted the AI service in airports in Beijing, Taipei, and Singapore who have the potential to use AI service usage experience more than three times; 387 completed questionnaires. A satisfaction model was analyzed using the partial least squares approach; this study used purposed sampling to collect data based on the passenger volume analysis by those airports, and 387 completed questionnaires were analyzed. With the rise of AI innovation services in airports, passengers can quickly complete boarding procedures through AI innovative services. The research findings point out that passengers' performance expectancy and emotions positively affect familiarity with AI services for passengers. AI innovations provide a service change driver for and have the potential to replace human tasks and intelligence and analytics applications at airports. AI services are a powerful tool for airports to serve their passengers efficiently, and airports will collaborate with airlines to serve passengers with AI.

Collaborative optimization of multi-modal transport solutions for urban-rural bus routes.

Faced with the land use heterogeneity and trip distribution non-equilibrium along the corridor connecting urban district and rural area, the operation characteristics of the all-stop bus, the express bus and the shuttle bus running on the transportation corridor should be given full consideration, as well as the tradeoff between passenger trip cost and bus operation cost during the peak-hour period. In order to realize the reliable decision of bus resources configuration for urban-rural public transportation, a bi-objective programming model of multi-modal urban-rural bus operation scheme is established, where the decision variables include the departure frequency of different bus modals, the terminals of shuttle buses, and the stop scheme of express buses, under the constraints of departure interval, passenger volume, vehicle occupancy, section capacity usage and modal competition. To validate the feasibility of proposed optimization model, an urban-rural bus corridor from Rural Jasmine Ecological Park in Hanjiang District to West Xiangyang Bridge in Songqiao Town was chosen as a study case, and the operation organization scheme of three bus modals were solved by the mathematical solver Lingo using mixed integer programming. Meanwhile, the scheme differences under different subobjective weights were discussed, taking into account the uncertain passenger demand and operation supply. The result shows that the increase of passenger trip cost weight will increase the departure frequency of all three bus modals, while the increase of operation cost weight will decrease the total operating vehicles, but the stop frequency at middle stations for express buses will increase accordingly. Compared to the current operation scheme, the optimized scheme can greatly enhance the section capacity usage at a slight sacrifice of passenger trip time with fewer bus vehicles.

Can the digital economy empower the transportation sector to improve green total factor productivity?

The digital economy has the potential to facilitate digitization and green transformation within the transportation sector, but further research is warranted on how it can enhance the sector’s green total factor productivity (GTFP). Utilizing provincial data from China spanning 2013 to 2021, this study investigates the impact of the digital economy on the transportation sector’s GTFP. The findings are as follows: (1) The transportation sector exhibits a U-shaped relationship between the digital economy and GTFP. The inflection point of the U-shaped curve is at 0.441, with 81.11% of the samples falling to the left of this point. It is primarily through technological progress and innovation, rather than changes in technological efficiency, that the digital economy influences the improvement of GTFP. (2) The U-shaped relationship exhibits heterogeneity and varies depending on the mode and content of transportation. The U-shaped relationship is more pronounced in regions with lower total traffic volume on highways and railways or higher traffic volume on waterways. For transportation content, DEEI and GTFP show a significant U-shaped relationship in areas with high traffic volumes of passengers and freights. (3) Government intervention can attenuate the U-shaped relationship, which leads to a flatter curve. The market-driven level has the capability to strengthen the U-shaped relationship, resulting in a steeper curve. In conclusion, these findings contribute to deeper insights into the U-shaped relationship between the digital economy and GTFP, offering valuable guidance for policymakers seeking to improve GTFP in the transportation sector.

Robust optimization of train timetables with short-length and full-length services considering uncertain passenger volume and service choice behavior

Train timetables with a combination of short-length and full-length services can adapt to spatial and temporal variations in demand. However, with such timetables, some passengers may choose to catch an earlier short-length service and then transfer to a full-length service to reach their destinations rather than waiting for a crowded full-length service. This uncertain service choice behavior frequently occurs, which, however, was not well considered in most studies on demand-oriented timetabling. Therefore, this study presents a robust timetabling approach based on the scenario-based method considering uncertain passenger volumes and service choice behavior for choosing different types of train services. A customized decomposition-based method with an iterative solution procedure is designed to solve the proposed model. In each iteration, a multi-agent-based simulation algorithm is developed to update passengers’ travel utility and the service choice preference proportion based on the previous iteration’s timetabling results. To obtain high-quality solutions in an acceptable computing time, a hybrid “ALNS + GUROBI” algorithm is developed to handle the timetabling problems for large-scale cases. The proposed method optimizes the train timetables for Xi’an Metro Line 3 in China. Our results indicate that the proposed method can account for uncertain passenger volumes and service choice behavior by adjusting the short-length plans and train headways to match the transport capacity to passenger demand.

AI-driven ventilation control policy proximal optimization coupled with dynamic-informed real-time model calibration for healthy and sustainable indoor PM2.5 management

Indoor air quality (IAQ) is an important factor for determining quality of life and urban sustainability. In underground subway stations, improving IAQ through ventilation systems remains challenging due to the complexity and nonstationary nature of IAQ resulting from diverse influential factors such as subway schedules, passenger volume, and outdoor air quality (OAQ). Therefore, this study aimed to develop a novel artificial intelligence (AI)-driven ventilation system for healthy and sustainable IAQ management in subway stations. First, an IAQ mechanistic model coupled with genetic algorithm (GA)-driven rolling-horizon calibration was developed from the collected IAQ big dataset, and global sensitivity analysis was then employed to identify the dominant variables in IAQ dynamics. Subsequently, proximal policy optimization (PPO), one of the reinforcement learning (RL) algorithms, was employed to control the ventilation system in both the lobby and platform areas of a subway station. The results demonstrated that the IAQ mechanistic model can capture IAQ dynamics with acceptable modeling performance, achieving around 19 % of mean absolute percentage error (MAPE). Furthermore, the PPO-driven ventilation control system can reduce energy consumption by around 22 % while maintaining IAQ at an acceptable level.

Optimization Strategy for Thermal Comfort in Railway Stations above Ground Level in Beijing

Urban rail transit, a convenient and fast public transportation mode with rapid construction and development, occupies fewer land resources and accommodates large passenger volumes. However, thermal comfort should be given more attention. Stations above ground level experience poor thermal comfort on the platforms, especially in hot summers. This study combines field research with a simulation analysis to propose a strategy for improving thermal comfort on above-ground urban rail transit platforms. This study analyzed the effects of the skylight opening rate, side window opening rate, design of transparent maintenance structure shading, and the platform profile shape on the thermal comfort of above-ground stations using field research, comparative experiments, and a simulation analysis with the PHOENICS (Command Prompt) software. The results indicate that adding longitudinal sunshade louvers to the skylight of the station platform is a cost-effective method to reduce the average temperature and PMV value, thereby improving thermal comfort. Increasing the skylight opening rate can result in a temperature rise. Adjusting the opening rate of the side windows to 20% and adding sun-shading louvers can also significantly enhance the station’s thermal comfort. Taking Wudaokou Station on Beijing Line 13 as an example, the simultaneous installation of additional longitudinal skylight shading and side window shading and increasing the side window opening rate to 20% on the platform resulted in a 2.6 °C decrease in the average temperature, a 4.7% increase in the average wind speed, and a 0.62 decrease in the PMV value, significantly enhancing thermal comfort for passengers. This study confirms that optimizing shading and ventilation systems can significantly reduce the platform temperature and improve passengers’ thermal comfort. This study provides theoretical support and innovative methods for optimizing thermal environments in similar environments.

Spatiotemporal Heterogeneity Analysis of Provincial Road Traffic Accidents and Its Influencing Factors in China

To objectively evaluate the road traffic safety levels across different provinces in China, this study investigated the spatiotemporal heterogeneity characteristics of macro factors influencing road traffic accidents. Panel data from 31 provinces in China from 2009 to 2021 were collected, and after data preprocessing, traffic accident data were selected as the dependent variables. Population size, economic level, motorization level, highway mileage, unemployment rate, and passenger volume were selected as explanatory variables. Based on the spatiotemporal non-stationarity testing of traffic accident data, three models, namely, ordinary least squares (OLS), geographically weighted regression (GWR), and geographically and temporally weighted regression (GTWR), were constructed for empirical research. The results showed that the spatiotemporal heterogeneity characterizing the macro factors of traffic accidents could not be ignored. In terms of impact effects, highway mileage, population size, motorization level and passenger volume had positive promoting effects on road traffic accidents, while economic level and unemployment rate mainly exhibited negative inhibitory effects. In terms of impact magnitude, highway mileage had the greatest impact on traffic accidents, followed by population size, motorization level, and passenger volume. Comparatively, the impact magnitude of economic level and unemployment rate was relatively small. The conclusions were aimed at contributing to the objective evaluation of road traffic safety levels in different provinces and providing a basis for the formulation of reasonable macro traffic safety planning and management decisions. The findings offer valuable insights that can be used to optimize regional traffic safety policies and strategies, thereby enhancing road safety.

A Queue-SEIAR model: Revealing the transmission mechanism of epidemics in a metro line from a meso level

The COVID-19 pandemic has affected communities worldwide. The metro, an essential means of public transportation in many cities, is particularly vulnerable to the spread of the virus due to its limited space and complex passenger flow structure. As the basis of quick and effective management decision-making, it is very important but intractable to accurately and quickly capture the transmission mechanism of epidemics in the metro line. This study addresses this challenge by proposing a meso-level Queue-SEIAR model. The Queue-SEIAR model integrates a feedback queuing network model, which captures the nonlinear stochastic effect of the congestion propagation on passenger mobility dynamics in the metro line, with an extended SEIAR (Susceptible, Exposed, Infected, Asymptomatic, and Recovered) epidemic model in multiple-subgroups situations. The risk of infection within a metro line is measured using the Total Number of Newly Exposed Travelers (TNNET). The Euler’s method is used to solve the Queue-SEIAR model, with time complexity that is independent of passenger volumes and station and train capacities, making it suitable for the analysis and decision-making of large-scale metro lines. The Queue-SEIAR model is validated versus the micro-level agent-based simulation. Numerical experiments reveal some interesting findings: (1) The gap between the Queue-SEIAR model and the benchmark Macro-level Epidemic Model (MEM) in terms of TNNET will become larger with congestion; (2) From the Queue-SEIAR model, low-demand and low-risk stations may become high-risk because of the congestion propagation, while MEM does not reveal such a pattern; (3) There exists a moderate allowed entering proportion, train dwell time, and social distance to minimize the TNNET; (4) The optimal social distance increases as the train headway or travel demand increases; and (5) The epidemic prevention effectiveness of side platforms surpasses that of island platforms.

Examining the determinants of Indian airlines’ revenues

The Indian aviation sector has undergone remarkable growth, driven by the emergence of low-cost carriers and diverse business models. This has resulted in a surge in passenger numbers and aircraft orders, establishing India as a vibrant global aviation market. However, this rapid expansion is accompanied by significant financial challenges, leading to distress and insolvency among numerous airlines. Despite optimistic growth forecasts, high operating costs and relatively low revenue returns pose substantial hurdles. Motivated by these challenges, this study aims to uncover the key factors influencing revenue generation in the Indian aviation industry by analysing expenditure components and their impacts on costs. The objective of this study is to address the research gap stemming from the lack of previous studies on Indian airlines that address endogeneity issues related to airline expenditures. By utilising data from 2007 to 2022 sourced from the audited annual reports of each airline, we aim to provide essential insights into the revenue dynamics of Indian airlines through the application of various econometric models including instrumental variables (IV) regression and generalised method of moments (GMM) models for improving causality and addressing endogeneity. Our findings reveal a positive correlation between unit revenue and factors such as unit expenditure, staff numbers, and passenger volume, while also highlighting the positive impacts of airline alliances and regional connectivity schemes. This research not only sheds light on industry intricacies but also underscores the imperative to address key variables to enhance the sector's sustainability and resilience in the face of ongoing challenges, offering valuable contributions to both academia and industry stakeholders for informed decision-making and strategic planning.

Aircraft-related noises increased but compensation decreased in Chinese airports

The rapid development of the aviation industry has brought convenience and noise hazards, altering the modes of travel but adversely affecting public health. Noise pollution is one of the essential factors affecting public health. This study offers a comprehensive examination of noise pollution data associated with the aviation industry, focusing on an array of airlines operating over 400 domestic routes within China from 2014 to 2019. It scrutinizes the noise emissions during the Lateral, Flyover, and Approach phases. It employs European Union standards to derive a formula for noise compensation relative to passenger volume, elucidating the quantum of noise indemnity allocable per passenger. (1) An upward trend in noise emissions is observed annually within China's civil aviation sector, with marked disparities in emission levels across various airports and airlines. (2) The mean annual value of noise compensation has shown a general decline, with China Express Airlines assuming a principal role in these compensations. (3) There is a correlation between noise compensation, noise emissions, and passenger volumes. Utilizing aircraft with low-emission and high passenger capacities significantly reduces airline compensation costs and safeguards the health of passengers and nearby residents.

The effect of quarantine policy on pollution emission and the usage of private transportation in urban areas

Governmental policies, regulations, and responses to the pandemic can benefit from a better understanding of people's resulting behaviours before, during, and after COVID-19. To avoid the inelasticity and subjectivity of survey datasets, several studies have already used some objective variables like air pollutants to estimate the potential impacts of COVID-19 on the urban transportation system. However, the usage of reactant gases and a narrow time scale might weaken the results somehow. Here, both the objective passenger volume of public transport and the concentration of private traffic emitted black carbon (BC) from 2018 to 2023 were collected/calculated to decipher the potential relationship between public and private traffic during the COVID-19 period. Our results indicated that the commuting patterns of citizens show significant (p < 0.01) different patterns before, during, and after the pandemic. To be specific, public transportation showed a significant (p < 0.01) positive correlation with private transportation before the pandemic. This public transportation was significantly (p < 0.01) affected by the outbreaks of COVID-19, showing a significant (p < 0.01) negative correlation with private transportation. Such impacts of the virus and governmental policy would affect the long-term behaviour of individuals and even affect public transportation usage after the pandemic. Our results also indicated that such behaviour was mainly linked to the governmental restriction policy and would soon be neglected after the cancellation of the restriction policy in China.

Offline planning and online operation of zonal-based flexible transit service under demand uncertainties and dynamic cancellations

This paper introduces a comprehensive framework for planning and operating a zonal-based flexible transit (FT) service, a public transit mode designed to accommodate uncertain demand patterns. The framework addresses both offline planning based on stochastic demand distributions and cancellations, as well as online routing considering real-time orders and cancellation behaviour. Offline interzonal route planning is formulated as a two-stage recourse problem, while the intrazonal routing problem is modelled using a Markov decision process (MDP) that incorporates online information. To solve the problem, a service reliability-based decomposition method is employed to divide the problem into three mixed-integer subproblems. The first subproblem focuses on designing interzonal routes up to a specific demand level, taking into account a designated cancellation probability as determined by reliability measures. An insertion heuristic is developed for this subproblem to improve the solution efficiency. The second subproblem allocates passengers from certain categories to vehicles based on the passenger volume designated by reliability measures. Lastly, the third subproblem refines the vehicle intrazonal route according to the passenger assignment from the previous subproblem. The reliability measures are optimised iteratively until no further improvements are observed in consecutive iterations. The proposed FT service’s performance is evaluated using numerical simulations based on real New York City (NYC) taxi demand data, illustrating the effectiveness of the integrated planning and operational approach in accommodating uncertainties in on-demand transit systems.

How do airports align with sustainability? An analysis of the world's 150 busiest airports

AbstractThis study represents the first in‐depth analysis of the global airport industry's compliance with the Sustainable Development Goals (SDGs), assessing the SDG alignment of the world's 150 busiest airports across 48 countries, which collectively account for approximately 60% of global air traffic in 2019. We discovered a 42% overall alignment with the SDGs, illustrating the aviation sector's relatively slow incorporation of green technologies and sustainable practices, especially when contrasted with industries such as Oil and Gas (O&amp;G), and banking. Notably, a subset of 23% of airports demonstrates high alignment, characterized by implementing targeted SDG actions and establishing comprehensive monitoring and evaluation frameworks, indicating a significant, albeit challenging, shift toward sustainability. Our analysis also pinpoints that airports in Europe and those handling larger passenger volumes exhibit more substantial alignment, attributing this to the influence of Europe's stringent sustainability regulations and the prioritization of passenger‐focused operations on enhancing SDG commitments. Conversely, an airport's alignment with the SDGs is not significantly predicted by its host country's national SDG scores, suggesting that regional sustainability pressures exert a more direct influence on airport practices than national achievements. These findings lead to recommendations for standardizing SDG reporting and developing specific metrics for tracking progress at airports, alongside advocating for governmental and international entities to establish regulatory frameworks, financial incentives, and certification programs to enhance global aviation sustainability.

Tindak Pidana Penganiayaan oleh Pengemudi Ojek Konvesional terhadap Pengemudi Transportasi Online

This study aims to determine the factors behind the rampant acts of violence by conventional motorcycle taxi drivers against online transportation drivers and to determine the forms of violence experienced by online transportation drivers and the resolution of efforts to overcome them. This study uses an empirical research type, the location of this research is in the Makassar City area and the Makassar Police Headquarters, South Sulawesi Province, located on Jalan Ahmad Yani, by conducting two data collection techniques through literature and field collection techniques such as interviews and observations. The results of this study are the factors behind the rampant acts of violence by conventional motorcycle taxi drivers against online transportation drivers, namely based on factors of jealousy, fighting over passengers, envy, differences in services provided by each online transportation driver with conventional motorcycle taxis and differences in fares and ease of using transportation. While the form of violence experienced by online transportation drivers and the resolution of efforts to overcome it is because online transportation drivers and conventional motorcycle taxis often argue, which usually ends in chaos. Online drivers' motorbikes were also damaged and helmets confiscated for violating conventional motorcycle taxi base areas with agreed pick-ups. At some point, traditional motorcycle taxis lost their market share compared to online transportation, forcing a decrease in passenger volume, but mediation between the two parties involved was able to resolve violent violations in the Makassar City area without having to take legal action. The agreement reached by both parties during the mediation process was the final result.