Ambulance Location for Service Coverage in an Urban-rural Area in Chiang Rai using Machine Learning

Authors

  • Krit Sittivangkul Logistics and Supply Chain Management Program, School of Management, Mae Fah Luang University, Chiang Rai, Thailand, Urban Mobility Laboratory (UML), School of Management, Mae Fah Luang University, Chiang Rai, Thailand
  • Sunida Tiwong Logistics and Supply Chain Management Program, School of Management, Mae Fah Luang University, Chiang Rai, Thailand, Urban Mobility Laboratory (UML), School of Management, Mae Fah Luang University, Chiang Rai, Thailand
  • Tosporn Arreeras Logistics and Supply Chain Management Program, School of Management, Mae Fah Luang University, Chiang Rai, Thailand, Urban Mobility Laboratory (UML), School of Management, Mae Fah Luang University, Chiang Rai, Thailand
  • Samatthachai Yamsa-ard Logistics and Supply Chain Management Program, School of Management, Mae Fah Luang University, Chiang Rai, Thailand, Digital Transformation Research Center for Agro-Industry and Business (DTRAB), Mae Fah Luang University, Chiang Rai, Thailand https://orcid.org/0009-0004-8828-979X

DOI:

https://doi.org/10.13052/jmm1550-4646.2152

Keywords:

Ambulance coverage, Services, Rural-Urban Area, Ambulance Location, Machine Learning

Abstract

Ambulance response time is a critical factor in saving lives and is heavily influenced by ambulance placement strategies. Currently, Nang Lae Subdistrict is served by only one ambulance, yet the managing agency does not systematically analyze high-frequency accident zones or areas with frequent emergency calls. This oversight raises concerns about whether the current ambulance station provides optimal coverage. To address this, call data from all ambulance dispatches over four months (July–November 2023) were analyzed. The findings revealed 96 cases within a 5-kilometer radius of Nang Lae Subdistrict Municipality and 18 cases within a 10-kilometer radius. Cluster analysis was conducted to determine optimal ambulance placement, identifying two potential locations approximately 1.55 km apart. These results can inform strategic improvements in ambulance deployment, particularly during high-demand periods such as festivals, where accident rates surge and faster response times are crucial. Additionally, the study observed instances of ambulances servicing areas beyond their designated zones, suggesting a need for better resource allocation.

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Author Biographies

Krit Sittivangkul, Logistics and Supply Chain Management Program, School of Management, Mae Fah Luang University, Chiang Rai, Thailand, Urban Mobility Laboratory (UML), School of Management, Mae Fah Luang University, Chiang Rai, Thailand

Krit Sittivangkul received the B.Sc. in food process engineering in 2008, and the master’s degree in industrial engineering from Chiang Mai University in 2012, respectively. He is currently working as an Assistant Professor in Management Technology at the major of Logistics and Supply Chain Management, School of Management, Mae Fah Luang University. His research interests include data mining, machine learning, logistics and supply chain management, manufacturing system, and urban mobility.

Sunida Tiwong, Logistics and Supply Chain Management Program, School of Management, Mae Fah Luang University, Chiang Rai, Thailand, Urban Mobility Laboratory (UML), School of Management, Mae Fah Luang University, Chiang Rai, Thailand

Sunida Tiwong is a Lecturer at Logistics and Supply Chain Major, School of Management, Mae Fah Luang University, Chiang Rai, Thailand. Dr. Tiwong holds a Master’s Degree and a Ph.D. in Industrial Engineering, Faculty of Engineering from Chiang Mai University, Chiang Mai, Thailand. She has published in journals and conferences. Her research interests include logistics and supply chain management, industry 4.0, logistics modeling, cross-border e-commerce, cross-border trade and lifecycle management. She is a member of and Urban Mobility Lab, Mae Fah Luang University.

Tosporn Arreeras, Logistics and Supply Chain Management Program, School of Management, Mae Fah Luang University, Chiang Rai, Thailand, Urban Mobility Laboratory (UML), School of Management, Mae Fah Luang University, Chiang Rai, Thailand

Tosporn Arreeras received the B.Eng. in transportation engineering from Suranaree University of Technology, Thailand. He received M.Eng. in civil engineering from King Mongkut’s University of Technology North Bangkok, Thailand, and D.Eng. in civil engineering from Muroran Institute of Technology, Hokkaido, Japan. Currently, he is an assistant professor in transport engineering and lecturer at School of Management, Mae Fah Luang University, Thailand. His research interests include transportation, urban, tourism, and logistics.

Samatthachai Yamsa-ard, Logistics and Supply Chain Management Program, School of Management, Mae Fah Luang University, Chiang Rai, Thailand, Digital Transformation Research Center for Agro-Industry and Business (DTRAB), Mae Fah Luang University, Chiang Rai, Thailand

Samatthachai Yamsa-ard is a lecturer at Mae Fah Luang University’s School of Management with a Ph.D. in Management from NEOMA Business School, France. Specializing in digital transformation and value chain management, he combines academic expertise with practical experience from roles in the manufacturing sector, focusing his research on supply chain optimization and logistics innovation.

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Published

2025-10-03

How to Cite

Sittivangkul, K. ., Tiwong, S. ., Arreeras, T. ., & Yamsa-ard, S. . (2025). Ambulance Location for Service Coverage in an Urban-rural Area in Chiang Rai using Machine Learning. Journal of Mobile Multimedia, 21(05), 831–854. https://doi.org/10.13052/jmm1550-4646.2152

Issue

2025: Vol 21 Iss 5

Section

ECTI