Parking and Ride Induction Methods for Drivers in Commuting Scenes

Parking and ride is a very effective method to improve the traffic condition of commuter channels, and it is necessary to develop effective parking guidance strategies. In this study, considering the travel time, walking distance, parking cruise time, parking fee, and personal attributes of drivers, a probability model of parking and ride selection in commuter scenarios was proposed, and a dynamic price adjustment method based on the equilibrium of parking occupancy in the region was constructed. The parking price was adjusted by determining the target occupancy, thus affecting the parking choice behavior to guide the commuter to park. The example analysis showed that this method adjusted the selection probability of the parking lot by using the dynamic price adjustment method from the perspective of regional parking occupancy equilibrium, solved the model by symmetric duality algorithm and formulated a reasonable parking replacement induction scheme to achieve the goal of occupancy equilibrium. Compared with parking guidance under static pricing, it can avoid the crowding of commuter vehicles into the city center effectively to reduce the congestion of commuter channels.

Dynamic pricing with demand disaggregation for hotel revenue management

In this paper we present a novel approach to the dynamic pricing problem for hotel businesses. It includes disaggregation of the demand into several categories, forecasting, elastic demand simulation, and a mathematical programming model with concave quadratic objective function and linear constraints for dynamic price optimization. The approach is computationally efficient and easy to implement. In computer experiments with a hotel data set, the hotel revenue is increased by about 6% on average in comparison with the actual revenue gained in a past period, where the fixed price policy was employed, subject to an assumption that the demand can deviate from the suggested elastic model. The approach and the developed software can be a useful tool for small hotels recovering from the economic consequences of the COVID-19 pandemic.

A Dynamic Level Technical Indicator Model for Oil Price Forecasting

Investment in commodities and stock requires a nearly accurate prediction of price to make profit and to prevent losses. Technical indicators are usually employed on the software platforms for commodities and stock for such price prediction and forecasting. However, many of the available and popular technical indicators have proved unprofitable and disappointing to investors, often resulting not only in ordinary losses but in total loss of investment capital. We propose a dynamic level technical indicator model for the forecasting of commodities’ prices. The proposed model creates dynamic price supports and resistances levels in different time frames of the price chart using a novel algorithm and employs them for price forecasting. In this study, the proposed model was applied to predict the prices of the United Kingdom (UK) Oil. It was compared with the combination of two popular and widely accepted technical indicators, the Moving Average Convergence and Divergence (MACD) and Stochastic Oscillator. The results showed that the proposed dynamic level technical indicator model outperformed MACD and Stochastic Oscillator in terms of profit.

A Study on the Trading Price Estimation Algorithm for Healthcare Transaction Data

Background: While more attention has been paid of late to utilization plans for big data in the healthcare sector worldwide, few scholars have addressed the value estimation of healthcare data. Accordingly, this study aims to propose an idea of a reasonable price estimation algorithm that can be applied to bidirectional exchange in healthcare data platforms.Methods: This study incorporates three methodologies for the data valuation, namely: cost-based, market-based, and impact-based approaches. The cost-based approach calculates the value of data based on the costs associated with data creation, management, and utilization. On the other hand, the market-based approach evaluates it by comparing the market price of a service similar to the data. Finally, the impact-based approach estimates the data value with an emphasis on improving future revenue generation and productivity as an effect of using the data.Results: The trading prices of healthcare data are determined by the sum of two prices—the fundamental price and the dynamic price. Here, the fundamental price can be further subdivided into the beginning value, complexity value, and network value. The beginning value is determined in proportion to the physical file size of the data, and the fundamental price is estimated by adding the complexity value and network value that can reflect the qualitative value (within 20% of the beginning value) of the data to the beginning value. First, the complexity value can increase if more personal information, more relevant information to the national health insurance system, and more recent and long-term information are included in the dimensions of identification, material, and time information inherent in healthcare data. Second, the network value reflects whether the data can be well linked with data from, not only the healthcare sector, but also from other fields and sectors. The higher the match rate between the attribute value keyword of the data and the healthcare search keyword of journals of excellence and portal services, the higher value is given. Finally, dynamic price reflects real-time preferences for the data and changes in data supply and demand as the actual exchange proceeds through healthcare data trading. To this end, dynamic value is determined within the upper and lower 5% band of the previous month's trading price based on the number of monthly views for the data, the number of downloads of summary data, and the number of actual purchases, and this is reflected in the next month's trading price.Conclusions: If the algorithm for estimating the trading price of healthcare data proposed in this study is applied to actual data trades, it would expand the transactions of healthcare data from both supply and demand sides. Also, in the processes of actual data exchange and the accumulation of actual data trades, continuing studies on the weighting parameters are needed to better reflect reality; such studies would enable the assignment of additional values or penalties.