Search Results (35)

Optimizing passenger train line planning is a complex task that involves balancing operational costs and passenger service quality. This study investigates the adjustment and optimization of train line plans to better align with passenger demand and operational constraints, while minimizing systematic costs. These costs include train operation expenses (e.g., line usage fees and station service fees), passenger travel costs, and hidden costs such as imbalances in station stops. Line usage fees refer to charges for using railway tracks, whereas station service fees cover services provided at train stations. The optimization process employs a Simulated Annealing Algorithm to adjust train compositions, capacity configurations, and stop patterns to better match passenger demand. The results indicate a 13.89% reduction in the objective function value, reflecting improved overall efficiency. Notably, most costs are reduced, including train operating costs and passenger travel costs. However, ticketing service fees—which are calculated as a percentage of passenger fare revenue—increased slightly due to additional backtracking in passenger travel paths, which raised the total fare collected. Overall, the optimization improves the operational performance of the train network, enhancing both efficiency and service quality. Full article

Passenger flow forecasting is crucial for optimizing urban transit operations, especially in developing countries such as India, where congestion, infrastructure constraints, and diverse commuter behaviors pose significant challenges. Despite its importance, limited research explored forecasting models for Indian urban transit systems, particularly incorporating the effects of holidays and disruptions caused by the COVID-19 pandemic. To address this gap, we propose TBATS Boosting, a novel hybrid forecasting model that integrates the statistical strengths of trigonometric, Box–Cox, ARMA, trend, and seasonal (TBATS) with the predictive power of LightGBM. The model is trained on a five-year real-world dataset from e-ticketing machines (ETM) in Thane Municipal Transport (TMT), incorporating holiday and pandemic-related variations. While Route 12 serves as a primary evaluation route, different station pairs are analyzed to validate their scalability across varying passenger demand levels. To comprehensively evaluate the proposed framework, a rigorous performance assessment was conducted using MAE, RMSE, MAPE, and WMAPE across station pairs characterized by heterogeneous passenger flow patterns. Empirical results demonstrate that the TBATS Boosting approach consistently outperforms benchmark models, including standalone SARIMA, TBATS, XGBoost, and LightGBM. By effectively capturing complex temporal dependencies, multiple seasonalities, and nonlinear relationships, the proposed framework significantly enhances forecasting accuracy. These advancements provide transit authorities with a robust tool for optimizing resource allocation, improving service reliability, and enabling data-driven decision making across varied and dynamic urban transit environments. Full article

The disruptive innovation of artificial intelligence (AI) chatbots is affecting educational dominance, which must be considered by higher educational institutions. Open and Distance Learning (ODL) becomes imperative for the effective and interactive communication between the institutions and learners. Drawbacks of isolation, motivation, insufficient time to study, and delay feedback mechanisms are some of the challenges encountered by ODL learners. The consequences have led to an increase in students’ attrition rate, which is one of the key issues observed by many authors facing ODL institutions. The National Open University of Nigeria (NOUN), one of the ODL institutions in Nigeria, is limited to an existing e-ticketing support system which is manually operated. A study on 2000 students of the NOUN using an online survey method revealed that 579 students responded to the questionnaire, equalling 29%. Further findings revealed significant delay time responses and inadequate resolutions as major barriers affecting the e-ticketing system in the NOUN. However, despite the quantitative method employed in the study, an artificial intelligence chatbot for automatic responses was also developed using Python 3.8+, ChatterBot (Version 1.0.5) Chatbot Framework, SQLite (default ChatterBot Storage, NLTK, and Web Interface: Flask (for integration with a web application). In testing the system, out of the 579 respondents, 370, representing 64% of the respondents, claimed that the chatbot was extremely helpful in resolving their issues and complaints. The adaptation of an AI chatbot in an ODL institution as a support system reduces the attrition rate, thereby revolutionising support services’ potential in Open and Distance Learning systems. Full article

While studies have typically examined the driving habits of young drivers living in large urban cities, few have examined the habits of young drivers living in smaller cities with large rural surrounding areas. Three surveys were disseminated to 193 young drivers, 65 police officers, and 62 driving instructors to examine the driving habits and challenging driving situations young drivers experience. Almost a fifth (18.1%) reported consuming alcohol prior to driving; alcohol consumption prior to driving was significantly associated with eating food/drinking beverages while driving, cellphone use, and speeding. The most challenging situations young drivers reported were night driving, encountering wild animals on the road, and driving in extreme weather conditions (e.g., ice, snow). Driving instructors reported that young drivers had challenges with lane positioning, speed control, and navigating traffic signs and signals. Additionally, police officers reported issuing tickets to young drivers primarily for failure to stop, distracted driving, impaired driving, and speeding. Young drivers living in smaller cities and rural communities have unique challenges, including interactions with wildlife, driving on gravel roads, and driving in poor weather and road conditions (e.g., ice, snow). Opportunities for young drivers to be exposed to these scenarios during driver training are critical for increasing awareness of these conditions and reducing crash risk. Full article

Financial decision making requires a sound handling of chance events. However, various studies have suggested that people are prone to illusion of control, i.e., the belief that prospects of a chancy event are better if they are involved in the randomisation process. This paper reports results from an experiment ($N=420$) suggesting that psychological characteristics moderate risk-taking behaviour under such circumstances. For example, we find that subjects high in sensation seeking buy more tickets of a risky lottery if they determine the winning numbers themselves and the random event lies in the future. The findings suggest that “illusion of control” effects are at least partly driven by underlying (idiosyncratic) emotions/preferences rather than an actual belief in control. Regarding applications, the results emphasise the importance of individual characteristics for the behaviour of decision makers in a financial context. Full article

Electronic tickets (e-tickets) are gradually being adopted as a substitute for paper-based tickets to bring convenience to customers, corporations, and governments. However, their adoption faces a number of practical challenges, such as flexibility, privacy, secure storage, and inability to deploy on IoT devices such as smartphones. These concerns motivate the current research on e-ticket systems, which seeks to ensure the unforgeability and authenticity of e-tickets while simultaneously protecting user privacy. Many existing schemes cannot fully satisfy all these requirements. To improve on the current state-of-the-art solutions, this paper constructs a blockchain-enhanced privacy-preserving e-ticket system for IoT devices, dubbed PriTKT, which is based on blockchain, structure-preserving signatures (SPS), unlinkable redactable signatures (URS), and zero-knowledge proofs (ZKP). It supports flexible policy-based ticket purchasing and ensures user unlinkability. According to the data minimization and revealing principle of GDPR, PriTKT empowers users to selectively disclose subsets of (necessary) attributes to sellers as long as the disclosed attributes satisfy ticket purchasing policies. In addition, benefiting from the decentralization and immutability of blockchain, effective detection and efficient tracing of double spending of e-tickets are supported in PriTKT. Considering the impracticality of existing e-tickets schemes with burdensome ZKPs, we replace them with URS/SPS or efficient ZKP to significantly improve the efficiency of ticket issuing and make it suitable for use on smartphones. Full article

Although the most widely studied datasets in fraud-detection systems belong to the banking sector, the aviation industry is susceptible to fraud activities that seriously harm airline companies. Therefore, big airline companies have started to purchase or develop their own fraud-detection systems in order to prevent their financial loss and prestige decline. Chronological order and temporal flow are intrinsically of high importance in fraud detection in the banking sector as well as in airline sale channels. Therefore, the transactions in the datasets used in fraud-detection systems should be evaluated not only according to the information they contain but also according to the past transactions they are linked to. One of the best ways to raise awareness about the connected past transactions to the fraud-detection system is to profile the data fields whose historical data is important and dynamically place these profiles on each transaction. In this study, we first draw the baseline, i.e., the first touch in this field, for fraud detection in aviation and then introduce a novel multi-modal profiling mechanism based on deep learning for the detection of fraudulent airline ticket activities. We achieved great success by feeding the new features obtained from those profiles into a deep neural network that is fine-tuned by adjusting the well-known hyperparameters regarding the aviation data. Thanks to the combination of profiling and deep learning, the F1 score of the proposed system reaches up to 89.3% and 93.2% in terms of quantity-based success and cost-based success, respectively. Full article

A well-functioning public transport system is one of the foundations of the Smart city concept and sustainable cities. The COVID period offers an excellent opportunity to examine the impact of different factors, because throughout this period society was faced with both health and governmental impacts that had different effects. Public transport use in the EU member states from the turn of the millennium until the beginning of the pandemic ranged from 17–18% to 27% of total journeys. The COVID-19 virus caused a 5–7% decrease in public transport use. The present study analyses the relationship between these effects and the number of paying passengers on public transport in Debrecen. Four hypotheses were put forward, i.e., that (1) the evolution of travel tickets, (2) general passes, (3) discounted passes and (4) replacement tickets were influenced by the evolution of the active COVID-19 case rate. The data were collected from the Debrecen Transport Company (DKV) for the period 1 January 2020–12 December 2021, and the Worldometers.info database. Statistical analyses were performed using an autoregressive and moving average (ARMA) model. We found that COVID’s active case numbers did not affect sales, but some of the government measures did have an effect. The results of this research can be used to inform future decisions to maintain and improve urban transport. Full article

Designed using vehicle requirements, Scalable service-Oriented MiddlewarE over IP (SOME/IP) has been adopted and used as one of the Ethernet communication standard protocols in the AUTomotive Open System Architecture (AUTOSAR). However, SOME/IP was designed without considering security, and its vulnerabilities have been demonstrated through research. In this paper, we propose a SOME/IP communication protection method using an authentication server (AS) and tickets to mitigate the infamous SOME/IP man-in-the-middle (MITM) attack. Reliable communication between the service-providing node and the node using SOME/IP communication is possible through the ticket issued from the authentication server. This method is relatively light in operation at each node, has good scalability for changes such as node addition, guarantees freshness, and provides interoperability with the existing SOME/IP protocol. Full article

E-tourism refers to the broader integration of Information and Communication Technologies (ICTs) in the tourism sector and infrastructure. In this context, tourists can choose the travel destination, use price comparison sites and buy online tickets at low prices, while tourism entrepreneurs enhance their marketing strategies in response to the increasing demands of modern tourism Over the last few decades, alternative tourism has gained considerable interest among tourists and enterprises in the tourism industry. At the same time, it acts as an enabler and a facilitator of sustainable development. This paper presents an overview of the current situation of content characteristics of the websites of enterprises involved with alternative tourism services in Greece. The websites are evaluated according to 30 characteristics through multicriteria method. Furthermore, the final ranking of websites is presented to identify the most successful strategies, as well as the website that need improvements. According to the results, an e-tourism website model is suggested for the entrepreneurs to exploit the opportunities that arise in the digitalization transformation of the tourism industry. Full article

The sharing economy relating to e-hospitality is threatened globally with sanctions and closure owing to incessant noise and partying complaints, as well as complaints relating to reckless driving, tax evasion, and its social and economic effect on residents and accommodation vendors of longer stay rentals. Because the government is seeking a balance in regulating the e-hospitality sector, we sought to explore how professionalism of the e-hospitality platforms could potentially contribute to the sustainable growth of the sector in local and regional communities. In our study we developed a conceptual narrative that distinguishes two dimensions of professionalism for the sharing economy, namely the ticket clipper and end-to-end model. Data for the research was obtained from Vacation Rental Data (Airdna). Airdna provides a databank for both Airbnb and VRBO/Stayz. For the study a dataset from Airdna for HomeAway, also popularly known as Stayz, was utilized as a representative sample from a tourism town in Western Australia. For analysis of the dataset, path/panel regression was utilized, with a hierarchical linear model subsequently adopted for cross-section and multi-sectional analysis. Findings in the study demonstrate that professionals tend to improve the overall rating, and where the overall rating mediates the relationship between management firm (property/apartment/accommodation venue) and price. It was further observed that no relationship exists between overall rating and the number of HomeAway supply types; nevertheless, professionals promote the image and reputation of the property. Contrary, bad, or negative e-hospitality reviews lead to avoidance by prospective visitors. Lastly, results from the study took the form of two theoretical contributions, namely the ticket clipper model and the end-to-end model. More complaints were received concerning ticket clippers and it was noted that this model has caused severe shutdown in several cities and regions. The end-to-end model appears to be more sustainable. Moreover, literature suggests that there are more complaints from residents concerning ticket clippers and it was noted that this model has caused severe shutdown in several cities, nonetheless the end-to-end model appears to be more sustainable. Full article

One of the main challenges for ticketing in Mobility as a Service is the integration of the public and individual transport modes into a unified ticketing service. To realize this concept, a trustworthy identification of transport modes that is resilient to possible attacks is required. In this work, we propose two smartphone-based methods to seamlessly identify the use of trams, buses, subways, walking and bicycles, which are able to detect GNSS-based attacks and continue to provide a trustworthy identification of transport modes. We have recorded real-world measurements with commercial smartphones using the transport network in Munich and Paris. Our results show that it is possible to provide trustworthy identification of transport modes even when the system is under attack. In conclusion, in this work we demonstrate the vulnerability of smartphone-based ticketing to GNSS-based attacks, propose two methods to overcome this vulnerability and demonstrate the validity of our methods with real-world measurements. Full article

Increasing the agricultural technical efficiency is crucial to poverty reduction for smallholder farmers in developing countries. This study attempts to examine the impact of farmers’ lottery participation on technical efficiency from the perspective of the farmers’ revealed risk preferences, and to explore the influence mechanism between lottery participation and technical efficiency, based on the primary data collected from banana farmers in rural China. We used data envelopment analysis (DEA) to measure the combined technical efficiency of the farmers and constructed an endogenous switching regressions (ESR) model to analyse the impact of lottery participation on the technical efficiency of banana farms. A quantile regression model was used to analyse the heterogeneous effects under the different levels of the farmers’ technical efficiency. Then, a mediation model was employed to clarify the influence mechanism of lottery participation on technical efficiency in terms of two pathways: the adoption of biopesticide and the soil improvement technique. The empirical results show that farmers’ lottery participation revealed their risk preferences and several factors affected banana farmers’ participation in the lottery. Specifically, male farmers are more likely to participated in the lottery than female; farmers’ working hours negatively affected their lottery participation; and the use of a smartphone significantly increased the likelihood of farmers buying lottery tickets. We also found that farmers who participate in the lottery have higher technical efficiency in banana production, and the average treatment effect of lottery participation on the technical efficiency was 21.5%, indicating that the farmers with revealed risk preferences can significantly promote technical efficiency. The effect of risk preferences on economic performance is more significant for farmers at the middle technical efficiency level. The explanation is that the adoption of new technologies (e.g., biopesticides) played a mediating effect between farmers’ lottery participation and their technical efficiency. New technologies are more likely to be adopted by farmers who participate in the lottery, resulting in higher technical efficiency. Therefore, policymakers and stakeholders can better design technology extension programs according to the different attitudes of the target farmers towards risks in developing regions. Full article

Large-scale pre-trained language models such as BERT have brought much better performance to text classification. However, their large sizes can lead to sometimes prohibitively slow fine-tuning and inference. To alleviate this, various compression methods have been proposed; however, most of these methods solely consider reducing inference time, often ignoring significant increases in training time, and thus are even more resource consuming. In this article, we focus on lottery ticket extraction for the BERT architecture. Inspired by observations that representations at lower layers are often more useful for text classification, we propose that we can identify the winning ticket of BERT for binary text classification through adaptive truncation, i.e., a process that drops the top-k layers of the pre-trained model based on simple, fast computations. In this way, the cost for compressing and fine-tuning, as well as inference, can be vastly reduced. We present experiments on eight mainstream binary text classification datasets covering different input styles (i.e., single-text and text-pair), as well as different typical tasks (e.g., sentiment analysis, acceptability judgement, textual entailment, semantic similarity analysis and natural language inference). Compared with some strong baselines, our method saved 78.1% time and 31.7% memory on average, and up to 86.7 and 48% in extreme cases, respectively. We also saw good performance, often outperforming the original language model. Full article

For many years, the air travel market has been one of the most regulated sectors in economy. The airline industry was fast-growing until the COVID pandemic. However, it is still a main business sector considered by economists and policymakers highly competitive. Air transport has specific features contributing to the final price of a travel ticket and differentiating airline companies. The aim of this paper is to analyze if the concept of yield management is applied by the Romanian national full-service airline TAROM and to highlight which criteria could be used for evaluating airline companies, the differences between a full-service carrier (FSC) and a low-cost carrier (LCC) model, and the types of pricing techniques used by airline companies, of which consumers could take advantage. The methodology is based on the dynamic pricing analysis by a continuous online simulation method for the Romanian full-service carrier and two low-cost carriers. The research results confirm that for the investigated companies, yield management involves strategic control of available resources meaning to sell the product to the right customer at the right time for the right price and thus fill in a gap in international research. Being extremely competitive and customer-oriented, airline companies serve as an example for other businesses in which yield management can be applied. Full article