Title MAXIMAL COVERING LOCATION MODELS OF EMERGENCY AMBULANCE CONSIDERING ...

178 Pages·2017·3.81 MB·English

TITLE:
MAXIMAL COVERING LOCATION MODELS OF
EMERGENCY AMBULANCE CONSIDERING
HEAVY TRAFFIC CONGESTION IN URBAN
AREAS( Dissertation_全文 )
AUTHOR(S):
Limpattanasiri, Wisit
CITATION:
Limpattanasiri, Wisit. MAXIMAL COVERING LOCATION MODELS OF EMERGENCY
AMBULANCE CONSIDERING HEAVY TRAFFIC CONGESTION IN URBAN AREAS. 京都大学,
2013, 博士(工学)
ISSUE DATE:
2013-09-24
URL:
https://doi.org/10.14989/doctor.k17873
RIGHT:
MAXIMAL COVERING LOCATION MODELS
OF EMERGENCY AMBULANCE
CONSIDERING HEAVY TRAFFIC CONGESTION
IN URBAN AREAS

2013

LIMPATTANASIRI WISIT
MAXIMAL COVERING LOCATION MODELS
OF EMERGENCY AMBULANCE
CONSIDERING HEAVY TRAFFIC CONGESTION
IN URBAN AREAS

2013
LIMPATTANASIRI WISIT

I
II
PREFACE
Manuscript of this PhD dissertation partly includes contents of the following conference
papers.
Conference Papers
 Limpattanasiri, W., Taniguchi, E., 2011. Comparison of optimizing models for
ambulance location problem for emergency medical service. Proceeding of
Infrastructure Planning, 44, CD-ROM.
 Limpattanasiri, W., Taniguchi, E., 2012. Maximal covering location problem
with uncertainty of travel speed. Proceeding of International Conference on
Transportation and Logistics, 4, CD-ROM.
 Limpattanasiri, W., Taniguchi, E., 2012. A probabilistic maximal coverage
ambulance location model with uncertainty of travel speed. Proceeding of
Infrastructure Planning, 46, CD-ROM.
 Limpattanasiri, W., Taniguchi, E., 2013. Solving maximal covering model of
emergency ambulance location problem in urban areas by dynamic
programming technique. Proceeding of International Conference of Eastern
Asia Society of Transportation Studies, 10, CD-ROM.

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ACKNOWLEDGEMENT
The research work presented in this thesis has been conducted in Logistics Management
Systems Laboratory, Department of Urban Management, and Kyoto University, Japan. I
would like to express my sincere gratitude to all those who directly or indirectly helped
me in order to accomplish this research study.
My first and most earnest acknowledgement goes to my advisor, Professor Eiichi
Taniguchi for his intellectual guidance, his tremendous help, continuous encouragement
throughout the study, his support, and his trust that give to me.
My sincere thanks go to Assistant Professors, Dr. Yuki Nakamura and Dr. Rojee
Pradhananga for enabling me to assimilate and gather the necessary data in this thesis
study. I am especially grateful to Dr. Joel Teo for his constant support and his
friendship.
I am greatly acknowledged to the Kingdom of Thailand Government for financial
support. I am thank you to Faculty of Science and Industrial Technology, Prince of
Songkla University, Surat Thani Campus, Thailand, for this study opportunity.
Last but not least, I want to express my gratitude to my family and my friends for their
unconditional support.

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ABSTRACT
The emergency medical service (EMS) is an important service in the city. The EMS
systems aim to reduce unnecessary death and disability. The key linkage between
emergency case and treatment acts by emergency ambulance service in the role of
transporting emergency medical technicians (EMT) to the scene within effective time
for treatment. The ambulance location problem is a planning process in macro level to
determine the optimal location of ambulance stations in order to provide a high standard
of ambulance service under the limitation of resources and dynamic of the traffic. The
EMS systems typically performance target represents by “reach α% of patients in r
minutes of less”. Every patients should be treated by a physician within 15 minutes
while resuscitation patients should be treated within 4 minutes (golden period for cannot
breathe case).
Ambulance location models can be placed into three kinds of problems are the set
covering problem (SCP), the maximum coverage problem (MCP), and the p-median
problem (PMED). SCP aims to minimize the number of facilities that covered all
demand nodes. MCP aims to maximize the demand covered by a given number of
facilities. PMED aims to minimize the summation of distance between facilities and
demand nodes that covered by the facility. Ambulance location models are defined on
graph = , where is index of demand node set, , with demand nodes, is
index of potential ambulance station set, , with potential stations, and
an is the set of edges. With each edge is associated with a
travel time . Demand node is covered by site if and only if , where

is a preset coverage standard.
In literatures review, there are 9 models based on SCP, 24 models based on MCP, and 2
models based on PMED. No literatures have considered the traffic congestion for
ambulance location models so far. This thesis proposed the formulation of maximal
covering location problem considering heavy traffic congestion (MCLP-htc) in urban
areas based on the MCLP model. The MCLP-htc model determines the pattern of
optimal ambulance location with two hierarchical objectives are maximize the
population covered for regular traffic situation and then maximize the population
covered with speed of heavy traffic congestion situation. The MCLP-htc model assumed
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the traveling speed is normally distributed. The traveling speed is derived by inverse
cumulative distribution function in terms of mean (μ) and standard deviation (σ) with
specified percentile rank ( ). The MCLP-htc model represented the regular traveling
speed with β=0.50 and the speed of heavy traffic congestion situation with β=0.05.

Generally, the MCP problem has possible solutions, where is number of

potential stations and is number of stations to be located. In order to solve the problem
with exact solutions, a set of dynamic programming (DP) algorithm was developed for
MCLP model and MCLP-htc model. The proposed algorithms were implemented by
Java.
The MCLP-htc model and proposed searching algorithms were evaluated and compared
by CPLEX optimizer with 2 hypothetical networks. The 60-Nodes hypothetical network
were generated 60 demand nodes and 15 potential stations randomly in Cartesian
coordination space. The distance between demand nodes and stations computes in
Euclidean system and population of each demand node is randomly generated between
1 and 59. For 60-Nodes hypothetical network assumed the regular traveling speed of
ambulance is the maximum authorized speed. The OsakaNet hypothetical network was
derived from Osaka city. 26 fire stations of Osaka city were assigned to potential
ambulance stations. The demand nodes were assigned by mesh size 300 x 300 meters
and mapped in Google® EarthTM. The population of each demand node is randomly
generated between 0 and 1,000. The distance between demand nodes and potential
ambulance stations were derived by Google® Distance Matrix Service. The travel speed
istribution was erive from VICS’s ata of Osaka city between October 4th 2010 and
November 5th 2010. Two scenarios of regular traveling speed were defined. First
scenario assumed the regular traveling speed of ambulance is the maximum authorized
speed. Second scenario assumed the regular traveling speed of ambulance is the average
speed of the network. All hypothetical networks were tested by varying maximum
number of located stations from 1 to ( – 1). The results of 2 hypothetical networks
confirm that proposed DP searching algorithms reaches the objective function as same
as standard commercial solver, CPLEX optimizer. Proposed DP searching algorithm is
acceptable for planning level. The MCLP-htc model maintains level of population
covered as same as the MCLP model. The number of optimal location patterns for
MCLP-htc model is less than MCLP model.
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The MCLP-htc model was applie in Osaka city’s network. There are 898 demand
nodes defined by Statistic Bureau, Ministry of Internal Affairs and Communication,
Japan using mesh size of 500 x 500 meters. There were 2,550,359 inhabitants in total.
The population data of each demand nodes is the information provided on November
2011. There are 26 fire stations given to the location of potential emergency ambulance
stations. The demand nodes and location of potential ambulance stations were mapped
in Google®™ Earth™ (2012). The travel spee istribution was erive from VICS’s
data of Osaka city between October 4th 2010 and November 5th 2010. Two scenarios of
regular traveling speed were defined. First scenario assumed the regular traveling speed
of ambulance is the maximum authorized speed. Second scenario assumed the regular
traveling speed of ambulance is the average speed of the network. The optimal
ambulance station location pattern for MCLP-htc model increased the level of
population covered within short response time (8 minutes and 4 minutes) compared with
the optimal location for MCLP model while maintaining the same level of population
covered within standard response time (15 minutes). The MCLP-htc model had relaxed
the number of located stations constraint to minimize the number of located stations. A
set of searching algorithm for relaxed MCLP-htc model was designed and developed
based on Dynamic Programming technique.
Proposed mathematical formulation based on the MCLP model and considering heavy
traffic congestion in urban areas is helpful for local authorities to preparing the best
level of population coverage for EMS system under the limitation of resources such as
the EMT teams and the emergency ambulances. For future research, the idea of heavy
traffic congestion and methodology for solving hierarchical objectives system can be
incorporate with cost of location problem, backup coverage problem, availability of
ambulance problem, and reliability of service problem. Meanwhile, the proposed DP
searching algorithm requires an improvement to reduce the computational time.

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Table of Contents
Title Page I
Preface III
Acknowledgement IV
Abstract V
Table of Contents VIII
List of Tables XI
List of Figures XIII

1. INTRODUCTION 1
1.1 Emergency Medical Services (EMS) Systems 2
1.2 Emergency Ambulance Location Problem 10
1.3 Motivation 13
1.4 Purpose and Objective of the Study 14
1.5 Organization of the Thesis 15
References 16

2. LITERATURES REVIEW 19
2.1 Ambulance Location Models 19
2.2 Stochastic Variables of Ambulance Location Problem 63
2.2.1 Uncertainty of Demand 64
2.2.2 Availability of Ambulance 68
2.2.3 Reliability of Service 70
2.2.4 Travel Speed of Ambulance 71
2.3 Heuristic Optimization Methods 72
2.4 Exact Optimization Methods 79
2.5 Summary 81
References 83

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