Optimization Problem of Pricing and Seat Allocation Based on Bilevel Multifollower Programming in High-Speed Railway

This paper studies the multistage pricing and seat allocation problems for multiple train services in a high-speed railway (HSR) with multiple origins and destinations (ODs). Taking the maximum total revenue of all trains as the objective function, a joint optimization model of multistage pricing and seat allocation is established. The actual operation constraints, including train seat capacity constraints, price time constraints in each period, and price space constraints among products, are fully considered. We reformulate the optimization model as a bilevel multifollower programming model in which the upper-level model solves the seat allocation problem for all trains serving multiple ODs in the whole booking horizon and the lower optimizes the pricing decisions for each train serving each OD in different decision periods. The upper and lower are a large-scale static seat allocation programming and many small-scale multistage dynamic pricing programming which can be solved independently, respectively. The solving difficulty can be significantly reduced by decomposing. Then, we design an effective solution method based on divide-and-conquer strategy. A real instance of the China’s Wuhan-Guangzhou high-speed railway is employed to validate the advantages of the proposed model and the solution method.

IoT Based Automatic Seat Vacancy Detection in Travel Buses using Cloud Database

The world has improving with lot of people utilities for living case. In this development technologies make the purpose of surviving easier. Internet of things is the inter-connecting technology used to pass the data to the required people via physical devices which are embedded with the software’s, sensors, electronics etc. IoT lift up smart cities, transportation, industries with new innovations for the development. The proposed system is done in transport sector to effectively manage the vacant seats particularly on travel buses. The vacant seats may happen due to last minute cancellation, the passengers who missed bus, or the passengers who doesn’t cancel their ticket even after they decide not to travel. In present situation, the seat allocation for the travelers is mostly done through online but when it comes to the vacant seats, the ticket checker has to allocate it manually. The system purpose is to verify whether all booked seats are occupied or not using sensors, and it automatically sends the signal to centralized server and make enable that particular seat for fresh booking. So that, the passengers who planned for travel by last minute can able to book ticket through online from the upcoming boarding stations.

Real-Time Office Seat Allocation System using Spring MVC framework

Real-time Seat Allocation System is a web application that assists employees and employers in handling modern-day seating issues at work. Currently, seats are assigned to employees manually, which is a time-consuming process. This might cause issues if seats were not assigned or if none were available for some employees. Furthermore, during pandemics, social distance rules must be followed, making it difficult to manage the workforce. The proportion of individuals allowed in the workplace must adhere properly. As the firm grows, so does the number of employees, which necessitates the expansion of infrastructure, which costs money and time. These issues can be addressed to some extent by a shared office concept, in which employees have access to a desk and can work as needed. This article presents a responsive web application through which company employees can registerusingtheircompanyIDandreserveaseatifoneisavailableonaspecified day and time. Admins can add or remove offices, floors, and seats with certain permissions. To implement the business logic, the application uses Java, Spring MVC web framework with Tomcat server, renders the views using Java Server Pages, and uses front-end technologies like HTML, CSS, and JavaScript for the front-end design of the User Interface. The web application built promotes the concept of using a shared workspace to maximize resource use. It also aids in increasing employee productivity by providing them with workplace seating and schedule flexibility.

Measures of vote-seat disproportionality for incomplete data

Measures of the proportionality of distributions are used across disciplines. ‘Disproportionality indices’ represent an application in politics, comparing the seat allocation in parliaments to the votes expressed for political parties. Disproportionality in elections is particularly high when many votes are expressed for parties not entering parliament; in some elections such ‘wasted votes’ add up to two-digit vote percentages. However, ‘wasted votes’ for small parties below the electoral threshold, as well as votes for non-partisan candidates, are often not listed in detail in election statistics, and are instead lumped together in residual categories such as ‘Others’ or ‘Independents’. This can hide major discrepancies between vote and seat distributions. This risks introducing systematic bias into the analysis of elections. This paper discusses several theoretically based methods to estimate indices of disproportionality for incomplete data, based on different theoretical scenarios concerning the distribution of votes and seats, and inspired by Taagepera’s method of ‘logical boundaries’. Empirical tests, relying on a dataset of 735 parliamentary elections worldwide, show that residual categories substantially affect indices of disproportionality. Several methods can considerably improve the measurement validity compared to the frequently used ‘naive’ procedures.

Edge Computing-Based Internet of Things Framework for Indoor Occupancy Estimation

Indoor occupancy estimation has become an important area of research in the recent past. Information about the number of people entering or leaving a building is useful in estimation of hourly sales, dynamic seat allocation, building climate control, etc. This work proposes a decentralized edge computing-based IoT framework in which the majority of the data analytics is performed on the edge, thus saving a lot of time and network bandwidth. For occupancy estimation, relative humidity and carbon dioxide concentration are used as inputs, and estimation models are developed using multiple linear regression, quantile regression, support vector regression, kernel ridge regression, and artificial neural networks. These estimations are compared using execution speed, power consumption, accuracy, root mean square error, and mean absolute percentage error.