Ticket Data Research Articles

Efektivitas Penerapan Electronic Traffic Law Enforcement (E-TLE) dalam Penegakan Aturan Lalu Lintas di Kota Denpasar

The application of E-ticket technology has become an innovative effort in enforcing traffic laws in various cities in Indonesia, including Denpasar. This research aims to examine the effectiveness of implementing E-tickets in law enforcement in the city of Denpasar, with a focus on its effect on reducing traffic violations, increasing public compliance with traffic rules, and its impact on the effectiveness of law enforcement. The problem raised by the author is regarding obstacles or obstacles in the implementation and effectiveness of E-TLE in law enforcement in the city of Denpasar. Apart from that, this research also aims to answer the question about how effective the implementation of E-TLE is in law enforcement in the city of Denpasar. The research method used by the author is a historical approach and a comparative approach. Data was collected through surveys of the public, analysis of electronic ticket data, and interviews with related parties, such as law enforcement officers and road users. This method was chosen to obtain a comprehensive understanding of the implementation of E-TLE and its impact on traffic law enforcement in Denpasar. The results of this research include an evaluation of the effectiveness or not of the implementation of E-TLE in Denpasar City. From analysis of electronic ticket data and surveys of the public, researchers can evaluate the level of road user compliance with traffic rules after the E-TLE system is introduced, as well as identify obstacles or obstacles that may hinder the effectiveness of implementing this technology.

Modeling price-sensitive demand in turbulent times: an application to continuous pricing

AbstractPricing drives demand for service industries such as air transport, hotels, and car rentals. To optimise the price, firms have to predict real-time customer demand at the micro level and optimise the price. This paper contributes to revenue management by introducing a nonparametric statistical approach to predict price-sensitive demand and its application to continuous pricing. Continuous pricing lets service companies maximise revenue by using customers’ willingness to pay. However, it requires accurate demand estimations, particularly of customers’ price sensitivity. This paper introduces an augmented generalised additive model to estimate price sensitivity, which identifies substantial variations in price sensitivity, exceeds the predictive performance of state-of-the-art alternatives, and controls for price endogeneity. In addition, the demand model has variable price derivatives enabling continuous pricing. The proposed approach offers a simple and efficient way to implement continuous pricing with a closed-form solution. Our research also highlights the relevance of considering the problem of price endogeneity when estimating price-sensitive demand based on observations from prior pricing decisions. We demonstrate how continuous pricing is applied using empirical airline ticket data. We document a field study, which shows a revenue increase of 6% on average, and outline how the approach applies to turbulent market conditions caused by the COVID-19 pandemic, the surge in inflation since mid-2021, and the start of the Ukraine war in April 2022.

Ticketing and Crowd Management System for Attraction Facilities: An Aquarium Case Study

In tourist facilities, managing ticket sales can reduce congestion imbalances. This study reports on the results of a pilot experiment conducted at Kaiyukan in Osaka, one of the largest aquariums in Japan. The experiment utilized pre-sale ticket data for controlling admission time intervals, smoothing the number of admissions through dynamic pricing, and predicting visitor numbers. The study reviewed the effectiveness of each of these approaches to alleviate congestion inside Kaiyukan. We then report on a method to predict the number of visitors from the pre-sale conditions of Kaiyukan admission tickets. It was found that setting entry times to 15-minute intervals was most operationally advantageous for the Kaiyukan. Moreover, the behavioral effects induced by variable pricing were more effective when prices were changed based on time slots rather than on a daily basis. Compared to the Holt–Winters’ method, we were able to maintain stable prediction accuracy even during consecutive holidays and long school vacation seasons.

Comparing micro-level and macro-level models for epidemic diffusion in the metro system

ABSTRACT Few studies focus on how the ground transport system has increased COVID-19 transmission; the details of its spread remain unclear. The absence of station-level data obstructs healthcare professionals from effectively targeting anti-epidemic measures. This study employs agent-based modeling through GAMA software to identify Taipei metro stations implicated in initial transmission. In addition, a macro-level estimator is applied as a baseline model to compare COVID-19 arrival sequences at each station. Utilizing electronic metro ticket data, passenger travel patterns are discerned. We found (1) the average infection order of all stations, according to both models were not significantly different; (2) however, this difference between two model results became significant when the sample size was decreased. (3) Of all the stations, Taipei Main Station was the first because it has the highest passenger volume and the most connections. These early infected stations are near Taipei Main station and commercial or hub stations.

Using Artificial Intelligence to Detect the Relationship Between Social Media Sentiment and Season Ticket Purchases

Sport marketing researchers and practitioners have suggested that sport organizations that effectively engage in social media conversations with fans are likely to influence fan behavior. Few prior studies have empirically examined the relationship between social media engagement and sport product purchases, particularly event tickets. The current study utilized artificial intelligence to examine eight user sentiments on official sport organizations’ Twitter accounts, then determine if those sentiments were related to season ticket sales. Three years of season ticket data were obtained from 62 NCAA Division I men’s basketball teams and utilized in a regression model, which also identified Twitter sentiment scores from 176,439 posts captured from the official Twitter account of those programs. A final model, which included several control variables, explained 65.7% of the variance in season ticket sales, with the lagged sentiments of “joy” (positive) and “sadness” (negative) having a statistically significant relationship with season tickets sold.

Unleashing the Potential of Boosting Techniques to Optimize Station-Pairs Passenger Flow Forecasting

Station-pair passenger flow forecast modeling is crucial for public transportation to address emerging needs. The accurate prediction and estimation will provide backbone support for various features of transport, viz. (a) Demand forecasting for resource allocation during peak travel periods; (b) Service planning to determine transportation frequency on specific routes; (c) Resource allocation for vehicles and capacity adjustments, including planning for new fleet size; (d) Infrastructure planning to identify areas requiring additional development; (e) Enhancing passenger experience with ample seating and amenities; and (f) Emergency planning for crowd control and passenger safety. Recent advancements in transit information station-pair estimation methods can be implemented with Data Science. Over the years, there has been significant progress in developing innovative modeling approaches to estimate travel behaviour, taking into account various factors such as timeslots, holidays, and other relevant features. In this study, we have synthesized ticket data from Thane Municipal Transport (TMT), collected from e-ticket machines, spanning from January 1 to June 23, 2023, covering all routes and comprising more than 2.80 crore tickets. This paper presents a comprehensive station-pair short-term passenger flow forecast by utilizing three boosting models: extreme Gradient Boosting (XGBoost), Light Gradient Boosting model (LightGBM), and Categorical Boosting model (CatBoost). Their implementation is well-suited based on statistical parameters. The study concludes by comparing the results of the models, considering computation time and statistical parameters, to validate their superiority. By looking into day to day requirements, the LightGBM model is most appropriate for implementation with an accurate result. The research findings contribute to providing valuable data support and a robust scientific foundation for managing passenger flow through route planning and dispatching in urban transit systems.

A Machine Learning and Computer Vision Application to Robustly Extract Winnings from Multiple Lottery Tickets in One Shot

Mega Millions and Powerball are among the most popular American lottery games. This article provides a practical software application that can conveniently examine and evaluate several lottery tickets for prizes using just the images. The application accepts as input a directory containing the images of lottery tickets and utilizes machine learning and computer vision to extract lottery ticket data, lottery name, lottery draw date, 5-digit lottery numbers, 2-digit lottery "ball" numbers, and the lottery multiplier. The application also retrieves winning lottery data that corresponds to the lottery draw date using a public database API. This is compared with data collected from each lottery ticket image to establish matches, and the corresponding prize amount is computed. The current version of the application supports GPU usage, and image orientation has no impact on its functionality. It is believed that a considerable portion of the U.S. public participating in the Powerball and Mega Millions lotteries will find such an application beneficial and handy.

Improving IT Support Efficiency Using AI-Driven Ticket Random Forest Classification Technique

This research project aims to improve IT support efficiency at Indonesian company XYZ by using AI-based IT support ticket classification integration. This method involved collecting over 1,000 support tickets from the company's IT ticketing system, GLPI, and pre-processing the data to ensure the quality and relevance of the data for analysis. Claims data is enriched with relevant features, including textual information and categorical attributes such as urgency, impact, and requirement expertise. To improve the ticket preference matrix, AI-based language models, especially OpenAI's GPT-3, are used. These templates help to reclassify and improve the work of IT support teams. In addition, the ticket data is used to train the Random Forest classifier, allowing automatic classification of tickets based on their specific characteristics. The performance of the ticket classification system is evaluated using a variety of metrics, and the results are compared with alternative methods to assess effectiveness. of the Random Forest algorithm. This evaluation demonstrates the system's ability to correctly classify and prioritize incoming tickets. The successful implementation of this project at Company XYZ is a model for other organizations looking to optimize their IT support through AI-driven approaches. By providing simplified ticket classification and admission ticket reclassification based on AI algorithms, this research helps leverage AI technologies to improve IT support processes. Ultimately, the proposed solution benefits both support providers and users by improving efficiency, response times, and overall customer satisfaction.

401 Creating a Dashboard to Increase Efficiency in Tracking and Reporting on Research Data Requests

OBJECTIVES/GOALS: The purpose of the project was to create a Tableau dashboard to track metrics on requests for research data at Atrium Health Wake Forest Baptist. The objectives included: 1) define and identify request fulfillment metrics, 2) build a dashboard to capture metrics, and 3) integrate the dashboard into metrics tracking and reporting activities. METHODS/STUDY POPULATION: Project managers and team leaders in the Office of Informatics collaborated to determine which measures would be most relevant and impactful to report on. Metrics that were collected included: total count of tickets fulfilled over time, number of tickets currently open, sum of outstanding quoted hours, quoted hours vs. actual hours needed to fulfill ticket, and hours billed. Tableau's direct connection feature was used to extract the Trac ticket data from its Postgres database and the dashboard was published to Tableau Server. After the initial draft was created, several rounds of revisions were made as new data insights were discovered through further investigation of the data. RESULTS/ANTICIPATED RESULTS: Each morning, Tableau Server runs an automatic refresh of the data. On the dashboard homepage, users can see a quick view of all available metrics; to minimize noise, only the current statuses, active tickets, and stats for the most recent monitoring periods are displayed. Many of the charts give the user the option to link out to a page with related supplemental information (historic data, ticket status history, etc.). With the help of the dashboard, project managers and team leaders can now monitor how long tickets are in each status, increase quote accuracy using the hours quoted and hours billed charts, and examine ticket complexity over time. DISCUSSION/SIGNIFICANCE: Prior to dashboard creation, metrics were sparse and difficult to assemble. By providing information on the quantity, size, and complexity of data requests, the dashboard enables the Office of Informatics to monitor how the process is functioning overall, make informed decisions about resource allocation, and provide quick interventions.

下车信息对公交乘客精细化分类影响研究

The widely used bus one ticket system lacks off boarding information, and the passenger classification based on the dataset of one-ticket bus system needs to be refined to evaluate the classification effect. This study uses the large-scale bus ticket data with alighting information to compare and analyze the transit rider classification models with and without the alighting information to explore the impact of the missing alighting information on the fine classification of transit riders. The classification indexes and combined clustering method are adopted to refine the classification of bus passengers. A case study of Beijing conventional buses which contain boarding and alighting data of 28 consecutive days is presented and used to analyze the impact of bus alighting data and classification indexes on the transit rider classification results. The result indicates that both models successfully complete the fine classification of the riders and reflect the spatio-temporal characteristics of each type of riders. The model with alighting information is able to identify the distance-concerned type with few samples such as the long-distance transit riders accounting for 0.25%. The standard deviation of the average proportion of categories in the model without alighting information is much smaller than that in the model with alighting information. The results obtained from the model without alighting information are more balanced, and the regularity of various categories of passengers in multiple dimensions is significantly different, which can better reflect the macro characteristics of classification.

A DEEP LEARNING BASED IT SERVICE DESK TICKET CLASSIFIER USING CNN

Assignment of problem tickets to a proper resolver group is an important aspect and crucial step in any IT Service management tools like IT Service desk systems. Manual categorization of tickets may lead to dispatching of problem tickets to an inappropriate expert group, reassignment of tickets, delays the response time and interrupts the normal functioning of the business. Traditional supervised machine learning approaches can be leveraged to train an automated service desk ticket classifier by using the historical ticket data. Sparsity, non-linearity, overfitting and handcrafting of features are some of the issues concerning the traditional ticket classifiers. In this research work, a deep neural network based on Convolution Neural Network (CNN) is proposed for the automated classification of service desk tickets. CNN automatically extracts the most salient features of the ticket descriptions represented using word embeddings. The extracted features are further used by the output classification layer for efficient ticket category prediction. To corroborate the efficacy of the proposed ticket classifier model, we empirically validated it using a real IT infrastructure service desk data and compared the results with the traditional classifier models like Support Vector machines, Naive Bayes, Logistic Regression and K-nearest neighbour. The proposed CNN model with proper hyperparameters tuning outperforms the traditional classifiers in terms of overall model performance. Assignment of tickets to the correct domain groups, speedy resolution, improved productivity, increased customer satisfaction and uninterrupted business are some of the benefits of the proposed automated ticket classifier model.

College Student Social Dynamic Analysis and Educational Mechanism Using Big Data Technology.

With the advancement of the “big data” technology, college students inadvertently purchase personal advice while taking advantage of the exciting Internet to access information quickly and easily. In order to objectively achieve the real office of college students' material enlightenment penetration in the mobile-friendly network, we choose the popular mobile social network, and we apply the natural clustering algorithm rules to segment the college students. Further, we identify college students, based on which we construct information leakage and apply the risk assessment design. The comprehensive entrepreneurial evaluation of the microblog platform combined with the user's mobile complaints is utilized to conduct a psychological analysis on the key components and key communication channels of college students' complaint leakage. We obtain ticket data using the social prospect method and refer to four dogmatic characteristic elements of query motivation. And we also collect dimensions through surrogate analysis. Based on the reference feature factor, four different user groups are rapidly moved. Dining, social, teaching and large users, and the model features of various usage profiles are described in combination with eight categories of user feature importance. The |objective is to improve college students' awareness of social network and mobile partner network mobile information to a certain extent. We attempt to protect college students from fraud and installation plans, to standardize the management of online social platform of advertisements, and to progressively promote the disposal of network movable property due to security changes.

Application of Deep Learning and Neural Network to Speeding Ticket and Insurance Claim Count Data

With the popularity of big data analysis with insurance claim count data, diverse regression models for count response variable have been developed. However, there is a multicollinearlity issue with multivariate input variables to the count response regression models. Recently, deep learning and neural network models for count response have been proposed, and a Keras and Tensorflow-based deep learning model has been also proposed. To apply the deep learning and neural network models to non-normal insurance claim count data, we perform the root mean square error accuracy comparison of gradient boosting machines (a popular machine learning regression tree algorithm), principal component analysis (PCA)-based Poisson regression, PCA-based negative binomial regression, and PCA-based zero inflated poisson regression to avoid the multicollinearity of multivariate input variables with the simulated normal distribution data and the non-normal simulated data combined with normally distributed data, binary data, copula-based asymmetrical data, and two real data sets, which consist of speeding ticket and Singapore insurance claim count data.

THE IMPACT OF THE METRO SYSTEM ON THE SPREAD OF COVID-19

Abstract. At the beginning of the COVID-19 pandemic, most scholars focused on how international transportation (such as airlines) spread the virus to different countries. At this point, scholars have begun to pay more attentions on how COVID-19 locally transmission via ground transportation systems. Because many people use these ground services to commute in urban areas, a high passenger volume may lead to a domestic large outbreak. Without detailed disease spreading path, healthcare professionals are still not sure where and how to apply these anti-epidemic measures. Therefore, this study chose the Taipei metro system as our study area to investigate the relationship between metro station passenger volume and COVID-19 transmission. By using the electric metro ticket data, we know the movement of metro passengers in Taipei, and this OD movement dataset was used to estimate the spreading path of the COVID-19. In order to simulate possible Covid-19 spreading cases in the real world, two different methods (the agent-based model (a microlevel simulation) and the effective distance method (a macro-level estimator)) were applied. Then, we compared the COVID-19 arrival order for each station. In our result, the average infectious order of stations of agent based model and shortest path effective distance is similar. Among all stations, Taipei Main Station is the first infectious station, and the top 15 infectious stations are similar according to result of the two method. Our result may help the authority choose proper methods to simulate the epidemic local transmission and then allocate resources effectively in the future.

Analisis Sistem Antrian pada Pelayanan Help Desk UPT TIK Institut Teknologi Sumatera Menggunakan Teori Antrian

The analysis of the queuing system at the Help Desk service of the UPT TIK Institut Teknologi Sumatera includes the number of ticket arrivals, the average length of service time, and the number of service facilities (admin). The data for the number of Help Desk tickets are Poisson distributed and the service time is the Exponential distribution. The size of the steady-state in the service has a value of so that an analysis of the performance size of the queue system can be carried out. The data used in the study are Help Desk ticket data for the period November 2020-February 2021. The results obtained indicate that the queue model for the Help Desk service can be added to the server (admin). The addition of a server in the service can reduce the workload significantly. The queue model that can be applied is M/M/3:FCFS/∞/∞ by adding one server (admin), from the previous model, namely M/M/2:FCFS/∞/∞.

Intelligent Filling Method of Power Grid Working Ticket Based on Historical Ticket Knowledge Base

The traditional power grid ticket filling method has a large workload, low efficiency, and cannot achieve comprehensive and effective reference of historical tickets. This paper proposes a method of intelligent filling in a power grid working ticket based on a historical ticket knowledge base. Firstly, the historical ticket data are preprocessed, then the historical ticket information is mined by the association rule algorithm, and the method of establishing the historical ticket knowledge base is proposed. Based on the improved word bag model, an intelligent grid work ticket filling model is established based on the historical ticket knowledge base, and the correctness of the method is verified by an example. The results show that the accuracy of the proposed method is at least 18% higher than that of the traditional model, and the matching efficiency is 50% higher than the evaluation results of the three models based on semantic expressions. The method enables the identification and extraction of similar and associated work tickets, improves the efficiency of filling work tickets for power grids, and promotes the intelligence of the safety procedures for power grid operations.

A multiapproach generalized framework for automated solution suggestion of support tickets

Nowadays, customer support systems are one of the key factors in maintaining any big company's reputation and success. These systems are capable of handling a large number of tickets systemically and provides a mechanism to track/logs the communication between customer and support agents. Companies invest huge amounts of money in training support agents and deploying customer care services for their products and services. Support agents are responsible for handling different customer queries and implementing required actions to solve a particular issue or problem raised by the service/product user. In a bigger picture, customer support systems could receive a large amount of ticket raised depending upon the number of users and services being offered. Customer care service gets directly affected due to the high volume of tickets and a limited number of support agents. Therefore, providing support agents with the recommendations about the possible resolution actions for a new ticket would be helpful and can save a lot of time. This study is focused on the development of an end-to-end framework for suggesting resolution actions rather than recommending free form resolution text against a newly raised ticket. To develop such a system, the pipeline is broadly divided into four components that are data preprocessing, actions extractor, resolution predictor, and evaluation. In actions extractor module, we have proposed a technique to identify and extract actionable phrases from resolution text. For resolution predictor, we have proposed two different pipelines that are referred as “Similarity Search Model” and “End-to-End Model.” The similarity search method is based on a ticket similarity search to find the most relevant historical tickets which then leads to corresponding resolution actions. On the other hand, end-to-end model make use of actions extractor module directly and implemented in a way to directly predict resolution actions. To compare and evaluate the mentioned methods on the same ground, we also proposed an actions evaluation criterion which uses BertScore and METEOR score jointly to compute the score against actual and predicted actions for a particular test ticket. The analysis and experiments are performed on the real-world IBM ticket data set. Overall, we observed that end-to-end model outperformed similarity search-based methods and achieved better performance and scores comparatively. The trained models and code are available at https://bit.ly/2GbUBVk.

The value of space during a pandemic

To encourage social distancing during the COVID-19 pandemic, Delta Air Lines did not sell the middle seat on its flights that had them. In the second half of 2020 its principal rivals, American Airlines and United Airlines, continued to sell the middle seat. Analysis of U.S. Department of Transportation airline ticket data on 1358 domestic routes finds that Delta raised its fares by 15%. Therefore, passengers paid $23 to prevent a stranger from sitting next to them.

Sistem Informasi Pengelompokan Pembayaran Denda Tilang Menggunakan Algoritma K-Means Clustering

This study Violation can be defined as a situation where there is a mismatch between the rules and implementation. The regulation is contained in Law Number 22 Year 2009, concerning "Road Traffic and Transportation" which has been stipulated by the state and is valid legally. The current problem shows, there is no information service system for ticket data management, unable to manage the process of grouping the results of the payment of fines properly, because the process is still manual by checking the data of offenders who have made payments, so it takes longer to process the grouping and data management payment results. The objectives of this study are: Knowing the success of the k-means clustering algorithm in the process of grouping the ticket data to the process of grouping the results of the payment of fines. This research uses the clustering method with the k-means algorithm which is one of the algorithms for the formation of data clusters. This algorithm works by dividing the data into k-clusters by grouping data based on certain classes which are then formulated the results by analyzing the articles that are violated and average. the total amount of the penalty payment. Research shows that information services for ticket data management using the k means clustering algorithm can simplify the process of grouping the payment of fines by more than 70%. This can be proven through the results of the hypothesis test which states that t count is smaller than t table with a value of -2.430 <2.178 so that Ha can be accepted and H0 is rejected.

Increasing value through gear flexibility: a case study of US west coast sablefish

This paper explores the potential economic gains of allowing additional flexibility in gear choice, within rights-based management programs. A case study of US west coast sablefish (Anoplopoma fimbria) provides an example of a commercially important species where gear-switching is currently occurring within the individual fishing quota (IFQ) program, allowing us to isolate the economic potential of gear flexibility along two important margins: size and quality. We conduct a hedonic price analysis of ex-vessel prices using panel fish ticket data and linear mixed-effect econometric models to examine the influences of gear, size, condition, fishing sector, port group, landing month, and year on the price of sablefish. We generate a counter-factual scenario that represents the IFQ fishery where the use of fixed gear is prohibited by predicting what the size composition of catch would have been if the sablefish had been caught with trawl gear. We find that the flexibility of targeting sablefish with fixed gear between 2011 and 2016 generated an annual mean 10.45% increase in total revenue, or US$1.17 million, compared with the trawl-only scenario. These results show sablefish value increases through implementing gear flexibility, which contributes to a broader conversation of allocative efficiency.