Yuangyai, Chumpol

Preferred name

Yuangyai, Chumpol

Alternative Name

Yuangyai, Čhumpol

Yuangyai, C.

Main Affiliation

School of Engineering

chumpol.yu@kmitl.ac.th

Now showing 1 - 5 of 5

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Integrating Spatial Risk Factors with Social Media Data Analysis for an Ambulance Allocation Strategy: A Case Study in Bangkok
(2022-08-01)
Jientrakul, Ranon
;
Yuangyai, Chumpol
;
Boonkul, Klongkwan
;
Chaicharoenwut, Pakinai
;
Nilsang, Suriyaphong
Emergency medical service (EMS) base allocation plays a critical role in emergency medical service systems. Fast arrival of an EMS unit to an incident scene increases the chance of survival and reduces the chance of victim disability. However, recently, the allocation strategy has been performed by experts using past data and experiences. This may lead to ineffective planning due to a lack of consideration of a recent and relevant data, such as disaster events, population density, public transportation stations, and public events. Therefore, we propose an approach of the integration of using spatial risk factors and social media factors to identify EMS bases. These factors are combined into a single domain by using the kernel density estimation technique, resulting in a heatmap. Then, the heatmap is used in a modified maximizing covering location problem with a heatmap (MCLP-Heatmap) to allocate ambulance base. To acquire recent data, social media is then used for collecting road accidents, traffic, flood, and fire incidents. Additionally, another data source, spatial risk information, is collected from Bangkok GIS. These data are analyzed using the kernel density estimation method to construct a heatmap before being sent to the MCLP-heatmap to identify EMS bases in the area of interest. In addition, the proposed integrated approach is applied to the Bangkok area with a smaller number of EMS bases than that of the existing approach. The simulated results indicated that the number of covered EMS requests was increased by 3.6% and the number of ambulance bases in action was reduced by approximately 26%. Additionally, the bases defined by the proposed approach covered more area than those of the existing approach.
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Locating an ambulance base by using social media: a case study in Bangkok
(2019-12-01)
Nilsang, Suriyaphong
;
Yuangyai, Chumpol
;
Cheng, Chen Yang
;
Janjarassuk, Udom
Response time reduction is a fundamental aspect of ambulance location management. To minimize patient mortality and disability, the response time of emergency medical services is critical. Therefore, real-time management is required to determine the location of an ambulance with a low response time or called or a dynamic allocation system. Dynamic allocation is moving the ambulance bases from low demand areas to high-demand areas that is useful in the operational level. However, the dynamic allocation model for real-time management requires re-allocation of ambulances, resulting in high costs and heavy workloads for the ambulance crews. This paper focuses on a covering model based on social media analysis. The model was used for developing an ambulance reallocation system. In addition to dynamic allocation, the proposed model considers real-time data from a social media application (Twitter) to minimize the response time and cost during emergencies and disasters. Twitter has been used in various ways to communicate during and manage emergencies. In this paper, we formulate the Maximal Covering Location Problem (MCLP), develop a solution procedure based on social media (Twitter application) and show the effect of the approach on the optimal solution by comparing it with the classical approach and also demonstrate our approach on Bangkok EMS.
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Allocation strategy for an ambulance base under traffic congestion
(2018-08-14)
Nilsang, Suriyaphong
;
Yuangyai, Chumpol
;
Buatongkue, Sirisuda
;
Cheng, Chen Yang
One of crucial issues for emergency medical service (EMS) is to reduce response time. However, in metropolis city, a traffic congestion is an obstacle for an ambulance to responsively reach at the scene, then patient mortality and disability rates increase. Traffic congestion is considered as a complex spatial-temporal situation. It is often triggered by repeating factors, such as car lane capacity, weather, and unexpected events. Therefore, a real-time traffic condition is required to effectively determine the location of an ambulance. The current ambulance base allocation strategy model considers only demand point, resulting inability to handle high traffic congestion. This paper proposed a covering model based on traffic congestion (using Google map API) to allocate ambulance bases that covering all demand point, while minimizing the number of the ambulance. In addition, our model was applied to the case study of Bangkok EMS.
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Activity detection for multi-factors of ambulance demand areas: A case study in Bangkok
(2021-09-15)
Nilsang, Suriyaphong
;
Yuangyai, Chumpol
One of the big challenges for the management of emergency medical service (EMS) in many urbans is a timely demand response when an emergency call occurs. To minimize patient mortality and disability, the response time of emergency medical services is critical. However, due to the continuous growth of the economic area, residential density, population density, traffic congestion, and epidemic area. Those factors are becoming complex issues for operation level planning, which need accurate and real-time demand area estimates to assign the area of responsibility for ambulance facilities while minimizing response time to emergencies and keep operating costs low. Therefore, in this article, we propose a conceptual framework for integrating multiple factors data both historical data and real-time data from social media to real-time EMS management. We propose an approach for identifying and analyzing hot spots of activity in data collections by using time-varying kernel density estimation (KDE) to convert multiple factors related to ambulance service (point locations and weight) for visualization and detection of abnormal intensities of the activity information and leads to the improvement of the EMS system. In addition, our model was applied to the case study of Bangkok EMS.
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Robust ambulance base allocation strategy with social media and traffic congestion information
(2023-11-01)
Yuangyai, Chumpol
;
Nilsang, Suriyaphong
;
Cheng, Chen Yang
At present, traffic congestion has become a major problem in many metropolitan areas globally, affecting the economy and social conditions in these areas. Of special concern, the response time of ambulances has increased, causing the death or disability of patients during emergencies. Various ambulance allocation strategies have been developed to locate a base that can achieve coverage of the demand point within a prescribed time or distance frame. However, real-time ambulance deployment is required to determine the number of ambulance and their bases. In particular, the dynamic relocation of an ambulance base is complicated, and each relocation does not guarantee that the next period will change again, thus increasing the workloads of the ambulance crew and potentially reducing their capability of responding to an emergency call. Furthermore, these models only considered covering all demand points but lacked the ability to consider uncertain factors, such as traffic congestion, patient conditions, public events, and population movement. Therefore, this study focused on formulating a covering model based on traffic congestion from web-based services and social media analysis, using the Markov-chain traffic speed assignment to allocate ambulance bases and trade-off the number of ambulance facilities between the current period and next period while considering the number of ambulance vehicles. Moreover, the proposed model was demonstrated using a case study of Bangkok emergency medical services. According to the results, obtained through collecting data on social media and traffic speed, the average traveling time of an ambulance can be improved by more than 70% with the trade-off between different periods if several emergency calls are received.

Yuangyai, Chumpol

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