Network Design Algorithm Implementation for Resilient Transportation System under Continuous Risk Perturbation with Big Data Analysis

With the rapid economic development and urbanization process accelerating, motor vehicle ownership in large cities is increasing year by year; urban traffic congestion, parking difficulties, and other problems are becoming increasingly serious; in ordinary daily life, continuous risk of disturbance, having a flexible transportation system network is more able to alleviate daily congestion in the city, and the main thing about flexible transportation network is its algorithm. It is worth noting that congestion in many cities is generally reflected in the main roads, while many secondary roads and branch roads are underutilized, and the limited road resources in cities are not fully utilized. As an economic and effective road traffic management measure, one-way traffic can balance the spatial and temporal distribution of traffic pressure within the road network, make full use of the existing urban road network capacity, and solve the traffic congestion problem. Therefore, it is of great theoretical and practical significance to develop a reasonable and scientific one-way traffic scheme according to the characteristics of traffic operation in different regions. Based on the fixed demand model, the influence of traffic demand changes is further considered, the lower-level model is designed as an elastic demand traffic distribution model, the excess demand method is used to transform the elastic demand problem into an equivalent fixed demand problem based on the extended network, and the artificial bee colony algorithm based on risk perturbation is designed to solve the two-level planning model. The case study gives a one-way traffic organization optimization scheme that integrates three factors, namely, the average load degree overload limit of arterial roads, the detour coefficient, and the number of on-street parking spaces on feeder roads, and performs sensitivity analysis on the demand scaling factor.

The price elasticity of the demand and revenue increase for some Mexican planting seeds

The study of the price elasticity of demand (PED) has been and is a current research topic, as it greatly helps the managers of companies to make decisions about the price of their products and the expected repercussions in changing them. The objective of this study was to estimate PED and its impacts on the variation of income (VI) of six planting seed crops from Mexico, such as yellow corn, other corn (except yellow and sweet corn), sorghum, chickpea, pumpkin and cucumber. The data were gathering from the Foreign Agriculture Service (FAS) (1998-2018 period) through the tables provided and published on the Internet (secondary data). In this study, the arc method was applied to calculate PED and VI = ((Pf * Qf * 100) / (Pi * Qi)) - 100. Five of six the planting seed products have a relatively elastic demand price in most of the years, on the contrary the revenue increase had positive and negative values, without a defined trend. Sorghum planting seed has in some cases elastic demand and in other inelastic demand. It is concluded that there are no clear effects of the PED on the sales revenue of the aforementioned products; an aspect that contradicts the theory of PED and its impact on income.

Bi-level optimization of long-term highway work zone scheduling considering elastic demand

Purpose More and more work zone projects come with the needs of new construction and regular maintenance-related investments in transportation. Work zone projects can have many significant impacts socially, economically and environmentally. Minimizing the total impacts of work zone projects by optimizing relevant schedules is extremely important. This study aims to analyze the impacts of scheduling long-term work zone activities. Design/methodology/approach Optimal scheduling of the starting dates of each work zone project is determined by developing and solving using a bi-level genetic algorithm (GA)–based optimization model. The upper level sub-model is to minimize the total travel delay caused by work zone projects over the entire planning horizon, whereas the lower level sub-model is a traffic assignment problem under user equilibrium condition with elastic demand. Findings Sioux Falls network is used to develop and test the proposed GA-based model. The average and minimum total travel delays (TTDs) over generations of the proposed GA algorithm decrease very rapidly during the first 20 generations of the GA algorithm; after the 20th generations, the solutions gradually level off with a certain level of variations in the average TTD, showing the capability of the proposed method of solving the multiple work zone starting date optimization problem. Originality/value The proposed model can effectively identify the near-optimal solution to the long-term work zone scheduling problem with elastic demand. Sensitivity analysis of the impact of the elastic demand parameter is also conducted to show the importance of considering the impact of elastic demand parameter.

The Effects of Payer-Mix on Utilization during the COVID-19 Pandemic, in Urgent and Primary Care.

BackgroundHealthcare utilization has changed dramatically during the COVID-19 crisis with the most dramatic drops coming in April 2020. While a lot of research has focused on utilization among the privately insured, or on comparing specialty-visits, comparatively less has been published on the effect of payer-mix, particularly Medicaid, on utilization. MethodsMonthly patient volume was gathered across 3 ambulatory primary and urgent care clinics. The timeframe included appointments, walk-ins and virtual visits from January through June 2020, including the nadir in April. Patient volume was then compared to average payer mix over that same time, at each clinic. A simple linear regression was then run, comparing changes in patient volume and percent Medicaid. ResultsTwo clinics had similar payer mixes, and saw similar decreases in utilization. A third clinic with twice as many Medicaid patients, saw only half the reduction in patient volume, during the nadir in April 2020. ConclusionGiven the limited number of data points, a more robust statistical analysis was not possible. A simple regression line indicated a correlation between the proportion of Medicaid patients and demand resilience for healthcare resources. At scale, the Medicaid population may be less vulnerable to variations in utilization, exhibiting less elastic demand, despite a pandemic, for a myriad of reasons.