scholarly journals Estimation of the Spatial Impact of the Pohang Earthquake on Regional Economies: Development of the SCGE Model

2021 ◽  
Vol 21 (1) ◽  
pp. 269-279
Author(s):  
Euijune Kim ◽  
Byula Kim ◽  
Dong Keun Yoon
Keyword(s):  
Travel Time ◽  
Road Networks ◽  
Economic Losses ◽  
Regional Economies ◽  
Spatial Effects ◽  
Demand Model ◽  
Time Costs ◽  
Computational General Equilibrium ◽  
Regional Product ◽  
Production Capacities

This study analyzes the spatial effects of the Pohang earthquake on regional economies by developing a spatial computational general equilibrium (SCGE) model. The model is composed of regional modules of production and consumption and is linked with a transport demand model at the city and county levels. The effects are measured using changes in the gross regional product (GRP) derived from increases in travel time costs from the collapse of road networks by an earthquake. Three scenarios are considered in this study in terms of external shocks to changes in production capacities (capital stocks) and travel time costs. Counter-factual experiments show that the earthquake in Pohang may have led to economic losses in other regions besides the area in which the earthquake occurred. Results showed a decline in gross domestic product by 0.58%, GRP by 3.69% for southern Daegu in zones directly influenced by the earthquake, and 0.64% for capital regions in zones indirectly influenced by the earthquake. These negative outcomes on the economies depended on much more direct damage to production facilities rather than indirect damage such as the collapse of road networks after an earthquake.

A Regional Supply and Demand Model for Inpatient Hospital Care

1992 ◽  
Vol 24 (8) ◽  
pp. 1097-1116 ◽  
Author(s):  
J A Bikker ◽  
A F de Vos
Keyword(s):  
Travel Time ◽  
Hospital Care ◽  
Hospital Admissions ◽  
Regional Economics ◽  
Supply And Demand ◽  
Market Equilibrium ◽  
Hospital Utilization ◽  
Demand Model ◽  
Time Costs ◽  
Supply And Demand Model

In this paper a regional supply and demand model for hospital admissions is developed which can be used for policymaking and planning purposes. It incorporates spatial factors such as travel-time costs into a model of market equilibrium in which waiting time acts implicitly as the equilibrating device. By distinguishing travel-time costs or distances it is shown that both supply and demand within local markets strongly influence admissions in a way which cannot be observed on aggregated levels: the tension between supply and demand is cushioned by a strong redistribution of patients. The model encompasses several well-known models for patient flows and hospital utilization originating in regional economics.

scholarly journals Trip Travel Time Forecasting Based on Selective Forgetting Extreme Learning Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhiming Gui ◽  
Haipeng Yu
Keyword(s):  
Travel Time ◽  
Extreme Learning Machine ◽  
Road Networks ◽  
Time Estimation ◽  
Learning Ability ◽  
Prediction Algorithm ◽  
Travel Time Estimation ◽  
Trace Data ◽  
Learning Machine ◽  
Travel Time Forecasting

Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.

scholarly journals RealTE: Real-time Trajectory Estimation Over Large-Scale Road Networks

2022 ◽  
Author(s):  
Qibin Zhou ◽  
Qingang Su ◽  
Dingyu Yang
Keyword(s):  
Travel Time ◽  
Real Time ◽  
High Speed ◽  
Road Network ◽  
Large Scale ◽  
Road Networks ◽  
Trajectory Data ◽  
Real Time Traffic ◽  
Histogram Estimation ◽  
Traffic Estimation

Real-time traffic estimation focuses on predicting the travel time of one travel path, which is capable of helping drivers selecting an appropriate or favor path. Statistical analysis or neural network approaches have been explored to predict the travel time on a massive volume of traffic data. These methods need to be updated when the traffic varies frequently, which incurs tremendous overhead. We build a system RealTER⁢e⁢a⁢l⁢T⁢E, implemented on a popular and open source streaming system StormS⁢t⁢o⁢r⁢m to quickly deal with high speed trajectory data. In RealTER⁢e⁢a⁢l⁢T⁢E, we propose a locality-sensitive partition and deployment algorithm for a large road network. A histogram estimation approach is adopted to predict the traffic. This approach is general and able to be incremental updated in parallel. Extensive experiments are conducted on six real road networks and the results illustrate RealTE achieves higher throughput and lower prediction error than existing methods. The runtime of a traffic estimation is less than 11 seconds over a large road network and it takes only 619619 microseconds for model updates.

The impact of corona crisis on the economies of major metropolitan areas

2021 ◽  
pp. 145-159
Author(s):  
Т. Д. Polidi ◽  
A. Y. Gershovich
Keyword(s):  
Metropolitan Areas ◽  
Negative Impact ◽  
The United States ◽  
Economic Losses ◽  
Published Data ◽  
Russian Economy ◽  
Real Gdp ◽  
Regional Product ◽  
The Impact ◽  
Operational Assessment

The article presents the results of an operational assessment of the impact of the COVID-19 crisis on the change in the gross urban product (GUP) in 17 metropolitan areas of Russia with a population of more than 1 million people in 2020. The goal of the authors was to try to answer the most actual questions nowadays (early 2021): how deep was the fall of the largest agglomerations economies in Russia and abroad; did the corona crisis have a more negative impact on the largest metropolitan areas then on the rest of the economy? In order to answer these questions, two main tasks were: 1) to assess GUP in 17 largest metropolitan areas of Russia; 2) to consider foreign estimates of the GUP in 2020. For foreign comparisons, the authors use the first published data on changes in GDP and gross urban/regional product in the United States, Canada and Australia. The assessment of GUP in this work is carried out through the assessment of the component of employee compensation and then the transition to the GUP indicator on the assumption that such a ratio of compensation of employees to GDP in a city equals the average of the said ratios for the 17 metropolitan areas. The assessment showed that the real GDP growth rates in 2020 were negative not in all metropolitan areas, and in most of them economic losses turned out to be less than those of the Russian economy as a whole.

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