Journal of the Eastern Asia Society for Transportation Studies, Vol.12, 2017

Analysis of Pedestrian Crash Zones in : A Case study of Primary and Secondary school

Somluk BUNNARONG a, Prapatpong UPALAb

a,b Multidisciplinary Design Research Program, Faculty of Architecture, King ’s Institute of Technology Ladkrabang (KMITL), 10520, Bangkok, a E-mail; [email protected] b E-mail; [email protected]

Abstract: Road accidents are major problems resulting in loss of life and property. Schools are accident-prone locations because majority of road users are students understand about traffic rules. This research aimed to investigate the occurrence of road and pedestrian accidents at primary and secondary schools in Bangkok, Thailand. This research was collected statistical data on road traffic injuries and imported the collected data into Geographic Information System (GIS). Then, Kernel Density Estimation (KDE) was applied to analyze data of 12 schools in Bangkok Metropolis Administration areas. The results showed that a majority of road accidents occurred in urban areas, crowded areas and intersections with heavy traffic. The suggested solutions were that knowledge of understanding of traffic rules should be promoted among students and a traffic sign consistent with student’s learning behavior should be provided.

Keywords: Pedestrian Crash Zones, Accident-prone areas, Road safety, School zone

1. INTRODUCTION

Road accidents are a significant problem currently faced in the real world. The World Health Organization (WHO) has reported road accident as the primary cause of death among people aged 15-29 years and the second leading cause of death in children aged 5-14 years. Furthermore, over 50 percent of all deaths caused by road accidents occur in pedestrians, cyclists, and motorcyclists. If no prevention plans are implemented to correct the problem mentioned above, the rate of death by a traffic accident in poor to moderately affluent countries is expected to double by 2020. Hence, the United Nations has called for all of its members to adhere to the Moscow Declarations, which designated years 2011-2020 as the Decade of Action for Road Safety 2011-2020 whose goal is to lower deaths and injuries caused by road accidents by half, or fewer than ten per 10 per 100,000 populations by 2020 (WHO, 2015). A report of the World Health Organization (2013) shows that Thailand was ranked the world’s third highest in road accident death rate (Recorded in 2011). There were 38.1 deaths per 100,000 populations. The main causes of road traffic injuries were drunk driving, not wearing helmets and drowsy driving. The Thai government has solved to this problem continues, so the road accidents in Thailand decreased during 2009 – 2014. In 2007, there were 62,769 cases of road accidents, decreasing from 2009 when the number of road accidents reached 84,806 cases. However, the number of deaths and injuries is still high. Comparing the rate to population proportion in 2014, there were 9.79 deaths per 100,000 populations. Which is higher when compared to high-income countries and certain Asian countries such as Singapore’s rate of 4.8 per 100,000 populations and Korea’s rate 5.5 per

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Journal of the Eastern Asia Society for Transportation Studies, Vol.12, 2017

According to statistics of road traffic injuries in Thailand reported by ThaiRSC - Road Accidents Data Center for Road Safety Culture (ThaiRSC, 2017), Bangkok had the highest road accidents from 2016 for 28,537 cases or 10% of the entire country. Bang Khun Tian District had the highest road accidents for 1,296 cases (4.54%). had the second highest road accidents for 1,174 cases (4.11%). had the lowest road accidents for 37 cases (0.13%). Shown in Figure 3. Accordingly, school grounds are considered areas in which there is the high likelihood of accidents as a result of students representing the majority of pedestrians, quiet road usage caution, lack of awareness about the dangers posed by cars and insufficient knowledge about traffic signs. Also, drivers are unable to predict the movements of children. Therefore, it is particularly dangerous when children crossroads and walk on pedestrian during heavy traffic (Ratanavaraha, 2011; OTP, 2004).

Figure 3. The map of road and pedestrian accidents in Bangkok, 2016 (Resource: ThaiRSC data applied to in GIS by authors.)

According to a review conducted in research involving traffic sign systems, children have limited perception, especially children younger than 11 years of age. Furthermore, signs should be designed using specific shapes, colors, and materials (Waterson, 2012) with interpretations for different perceptions based on cultural backgrounds, education, and age (Hashim et al., 2014). Vendors are blocking the way on pedestrian, so students have to walk on the roads. In addition, traffic signs and pedestrian are damaged and no crosswalks are provided (Bunnarong and Upala, 2017). Consistent with the public participation to improving pedestrian environment and traffic sign system in the Walk and Bike Friendly Cities project, Ranong Province, Thailand. In conclusion, all stakeholders summarizing the physical problems resulting as follows: rough path surfaces, slippery spots caused by algae, many paths with different heights, damaged and obstructive street furniture, pedestrian crashing from cars, These are factors causing more accidents on the pedestrian (Upala and Bunnarong, 2017). According to Sattanon and Upala (2017) concluded the top five factors that help with the safety of children are: (1) traffic management around the school to ensure safety and give

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importance to walking and bicycle use; (2) improving the internal environment of the school to provide security; (3) improving student pickup-delivery locations (4) training teachers, parents, and students about safety; and (5) organizing snack shops and walkways Therefore, if the government is about to encourage people to travel by non-motorized and use public transport instead of using personal cars, this is one important guideline the designers prepare environmental of children and considering safety and security to decrease parents’ anxiety can motivate children to choose the journey to school (Rezasoltani et al., 2015). The problems in pedestrian shown in Figure 4.

• Rough path surfaces • Traffic and Safety Problems • Shop stalls obstructing pedestrian • Knowledge about traffic signs etc. • Lack of sidewalks etc.

Figure 4. Problems in pedestrian at schools

It is highly questionable for traffic safety engineers that where they should employ safety counter measures to give strong impact. Therefore, the dangerous locations or black spots are identified and analyzed. In order to improve pedestrian safety, locations where pedestrian often meets with accidents have to be indicated to employ specific safety countermeasures for pedestrians. From several Authors showed that were conducted studies by using Geographic Information Systems (GIS) to examine the issue of safety analysis. The results shown that GIS is an effective tool for geographic context analysis of crashes. The research methodology began by selecting schools in Bangkok according to Urban Zone that had the highest rate of accidents. Then, the authors recorded locations of accidents in GIS and used Kernel density estimation (KDE) to analyze the results. The results showed accident -prone areas around school according to color intensity. After that, the results were displayed on a map. The analysis contributed to black spot solution which should be carried out along with the physical survey of the transportation network. An effective way to gain an understanding of traffic crashes that have spatial dimension is to utilize statistical analysis combined with spatial attributes. Spatial data management and analysis operations are largely

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undergone by Geographical Information System (GIS). This paper mainly aimed to study the potential of using GIS to identify locations of accident-prone of primary and secondary schools in Bangkok.

2. LITERATURE REVIEW

Many studies focused on the spatial analysis of accident-prone locations through the use of GIS. The methods are to geo-code crash location and interpret collected data by GIS combined with statistical analysis. The results can be related to the actual occurrence in the areas such as a relation between accident location and land use, traffic situation and width of roads to set guidelines for traffic engineering or to reduce problems. To geo-code accident locations, GIS has been utilized in transportation safety application. Levine et al. (1995), Affum and Taylor (1995), Austin et al. (1997), Kim and Levine (1996) and Miller (1999) improved pin maps of crash and database queries by using GIS. Nevertheless, to demonstrate spatial crash distribution at the road network level, other effective analytical tools in GIS software including buffer, nearest neighbor method, simple density and kernel density estimation method of crash cluster detection were combined with GIS. (Levine et al., 1995; Pulugurtha et al., 2007) Anderson (2009) identifying road accident hotspots in London, The methodology using GIS and Kernel Density Estimation to study the spatial patterns of injury related road accidents. Rankavat and Tiwari (2013) analyzed pedestrian accent-prone locations in India, utilizing GIS and Kernel density estimation technique to explain the occurrence of accidents and to formulate preventive plans for accident reduction. Moura et al. (2017) applied GIS for measuring walkability of pedestrian and interpreted the research results into a map. This enabled designers to understand the results and use it for effective pedestrian development. Schneider et al. (2004) explain that utilizing GIS in the research could reduce problems of accident locations and helped formulate preventive plans effectively. Ziari and Khabiri (2005), Lai and Chan (2004) and Steenberghen et al. (2004) designed pedestrian and bicycle lanes, applying the results from GIS analysis to generate a contour map identifying areas of high crash occurrence determined by crash density and clusters of crashes involving pedestrians and bicyclists. Satiennam and Tanaboriboon (2003) were GIS spatial to create the pedestrian crash density and examined the quality of pedestrian where accidents occurred in Muang Khon Kaen District. Jang et al. (2013) used Kernel density spatial technique by GIS to measure the center of accident occurrence.

3. METHODS

Research methods are as follows.

3.1 Sampling Group The sample of this study included 436 schools in primary and secondary levels in 50 Bangkok Metropolis Administration areas in Bangkok. Multi-stage sampling technique was applied to select the sample, using the highest statistics of the accident as a filter. The study began with: 1) Divide schools in 6 districts group Identifying administrative areas according to Bangkok Metropolis Administration (BMA) consisting of six groups of districts (BMA, 2013), then investigating the 6 areas with high road and pedestrian accidents. A list of 6 districts group in BMA shown in Table 1 and Figure 5.

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Table1. The Districts group in Bangkok No Districts group Sub districts

1 Rattanakosin (RTK) Phra Nakhon, Bang Rak, Bang Sue, Dusit, Pathum Wan, Phaya Thai, Sattru Phai, Ratchathewi, Samphanthawong 2 Burapa (BRP) Bang Kapi , Lak Si, Bang Khen, Bueng Kum, Chatuchak, Don Mueang, Lat Phrao, Sai Mai, Wang Thonglang 3 Sinakrarin (SKR) Khan Na Yao, Khlong Sam Wa, Lat Krabang, Min Buri, Nong Chok, Prawet, Saphan Sung, Suan Luang 4 Chapraya (CPY) Bang Kho Laem, Bang Na, Din Daeng, Huai Khwang, Khlong Toei, Phra Khanong, Sathon, Vadhana, Yan Nawa 5 Kongtontay (KTT) Bang Bon, Bang Khun Thian, Chom Thong, Khlong San, Rat Burana, Thon Buri, Thung Khru 6 Kongtonnoy (KTN) Bang Khae, Bang Phlat, Bangkok Noi, Bangkok Yai, Nong Khaem, Phasi Charoen, Taling Chan, Thawi Watthana

Figure 5. The map shown in 6 districts group in Bangkok

2) The selection sub districts in Bangkok After that selecting representatives of each sub district based on the ThaiRSC statistics of road and pedestrian accidents in 2016. The districts with highest accidents have been chosen. There were 6 selected districts including , , Prawet District, , , , shown in Table 2 and Figure 6.

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Table 2. Sampling of sub districts and accidents data in Bangkok, 2016

No. Group of Districts Sub districts Accidents (cases) 1 Rattanakosin (RTK) Bang Sue 402 2 Burapa (BRP) Lat Phrao 1,080 3 Sinakrarin (SKR) Prawet 1,174 4 Chapraya (CPY) Huai Khwang 962 5 Kongtontay (KTT) Bang Khun Thian 1,296 6 Kongtonnoy (KTN) Nong Khaem 938

1 2

4

6 3

5

No. Sub districts No. Sub districts 1 Bang Sue 4 Huai Khwang 2 Lat Phrao 5 Bang Khun Thian 3 Prawet 6 Nong Khaem

Figure 6. Sampling of sub districts in Bangkok (Resource: ThaiRSC data applied to in GIS by authors.)

3) The selection schools Schools were selected from the 6 sub districts areas, considering from statistics of road accidents on walking distance for Thai people within 800 meters away from the schools (UDDC, 2014), then selected the areas with the highest road accidents of each study site. Therefore, 12 schools from 6 sub districts were finally selected, then selected schools were analysis of pedestrian crash zones. Sampling in 12 schools in Bangkok shown in Table 3.

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Table 3. A list of schools and statistics of road and pedestrian accidents within 800 meters Districts School name Accidents within 800 m. (cases) Bang Sue Bangpho school 121 Yothinborana2 school 105 Lat Phrao Lat Phrao school 135 Satriwithaya 2 school 54 Prawet Wat Krathum Suea Pla school 78 Ratdamri school 26 Huai Pracharatbamphen school 88 Khwang Triamudomsuksapattanakarn Ratchada school 156 Bang Khun Thian Bangkuntiansuksa school 131 Rattanakosinsomphodbangkhunthian school. 120 Nong Khaem Watudomrungsee school 153 Matthayom Wat Nongkhaem school 51

3.2 Data Collection

The digital map of Bangkok, Thailand was support by City planning department of Bangkok metropolitan (CPD), was imported in the data frame of the Arc Map window. The coordinate system used throughout this project is WGS 1984. The accidents’ data in Bangkok in 2016 was collected from ThaiRSC (Road Accident information from Compulsory Insurance claims data from Road Accident Victims Protection Co., Ltd. nationwide). Recorded in Excel sheet was imported in GIS by giving X and Y coordinates of each accident. The data was recorded in Shape file format (.shp). Further analysis was done using SQL (structured query language) in the attribute table of accident points and spatial analysis using spatial statistics tool.

3.3 Kernel density estimation (KDE)

Kernel Density is a spatial analyst tool for point pattern analysis which is the core of the geographical analysis (Maurizio et al., 2007) Analyzing spatial data by GIS shows the results in the form of the raster. The principle of this method is to calculate radius of each point and link a point to another through bandwidth. The radius of the circular neighborhood affects the resulting density map. If the radius is, increased there is a possibility that the circular neighborhood would include more feature points which result in a smoother density surface (Silverman, 1986). Cell size chosen was 1m x1m. It then applies a quadratic kernel function this study defined the Principle of Kernel function is 1 d  yxf ),(  k i 2 i1   (1) hhn  h  where f (x,y) : The density estimate at the accident location (x, y), nh : number of school in sub districts, h : bandwidth or kernel size, K : kernel function, and di : distance between the accidents location (x, y) and the location of the ith observation.

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The research method shown in Figure 7.

Sampling Group Data Collection Analysis of Pedestrian Crash Zones

436 schools in Bangkok Digital map of Bangkok Kernel density (Resourced: CPD) estimation (KDE)

Accident -prone areas in Accidents’ data Descriptive statistic 12 schools (Resourced: ThaiRSC)

Figure 7. Research Method

4. RESULT

4.1 Schools with highest accidents

The statistical data in 2016 reported by ThaiRSC shows that among the 6 primary schools and 6 secondary schools in 6 sub-district areas, having accidents within 800 meters around the schools, Watudomrungsee School had the highest accidents (153 cases) in primary school level and Triamudomsuksapattanakarn Ratchada School had the highest accidents (156 cases) in secondary school level. Details of schools and statistics of road accidents are as follows Table 4.

Table 4. Summary of schools with the highest road accident statistics within 800 meters according of school, 2016 Districts Sub School name Primary Secondary Accidents Group Districts school school within 800 m. (cases) Rattanakosin Bang Sue Bangpho school • 121 )RTK( Yothinborana2 school • 105 Burapa Lat Phrao Wat Lat Phrao school • 135 (BRP) Satriwithaya 2 school • 54 Sinakrarin Prawet Wat Krathum Suea Pla school • 78 (SKR) Ratdamri school • 26 Chapraya Huai Pracharatbamphen school • 88 (CPY) Khwang Triamudomsuksapattanakarn • 156 Ratchada school Kongtontay Bang Khun Bangkuntiansuksa school • 131 (KTT) Thian Rattanakosinsomphod • 120 bangkhunthian school Kongtonnoy Nong Watudomrungsee School • 153 )KTN( Khaem Matthayom Wat Nongkhaem • 51 school

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The X and Y coordinate of accidents location can be used for accidents distribution on the map. It can provide understanding of accident density and the environment of accident locations for traffic engineering plan. It can be related to attribute data that collects injuries, deaths, gender and time of accidents from location. This research will correlate coordinate data to GIS by ArcMap for analysis of Pedestrian Crash Zones by spatial Analysis tools in GIS. This will be discussed in this paper later. Accident’s data shown in Figure 8.

4.2 Pedestrian Crash Zones

The analysis of Pedestrian Crash Zones used the statistics of road and pedestrian accidents within 800 meters around the schools in 2016. The Cell Size was 1m. x 1m. According to Kernel’s, the distribution patterns were different based on the environment in each area. Once high density zones are defined, pedestrian safety programs can be focused in them with greatly increased efficiency (NHTSA, 1998). Based on school types, the most accident-prone primary school was Bangkuntiansuksa school and Bangpho school, and the most-accident-prone , secondary school was Triamudomsuksapattanakarn Ratchada school. The density map as shown in Figure 9. According to their hierarchy of severity of risk shown in Figure 10. The accident-prone areas by marking black circles. The most three accident-prone locations were Bangpho school and Yothinborana2 school, Bangkuntiansuksa school and Rattanakosin somphod bangkhunthian school, Triamudomsuksa pattanakarn Ratchada school. Finding results the accident-prone set in urban area, at intersections of arterial road and local roads, at commercial areas with heavy traffic. Further studies will collect engineering data to relate to hotspots in the future. This research was explained the percentage of pedestrian accidents consisting of the statistics of accident occurrence, deaths, injuries, Fatal per Accidents Index and Accidents per Pedestrian. According to GIS analysis, there were Triamudomsuksa pattanakarn Ratchada School, Bangpho school, Yothinborana2 school, Bangkuntian suksa school and Rattanakosin somphod bangkhunthian school had many accident-prone because of the borders the arterial road, crowded areas and intersections with heavy traffic. The Fatal per Accidents Index showed that Ratdamri school had the highest (0.077) , followed by Satriwithaya 2 school (0.056), showed that violence. The Accidents per Pedestrian showed that Watudomrungsee School had the highest (4.23), followed by Bangkuntiansuksa school (3.95), showed that the frequency of accidents. This indicated the danger for students as pedestrians caused by several factors including motor vehicle driver, roads and environment. This will be further investigated in the future. Details shown on Table 5.

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Bangpho school Yothinborana2 school Wat Lat Phrao school

Satriwithaya 2 school Wat Krathum Suea Pla Ratdamri school school

Pracharatbamphen school Triamudomsuksa Bangkuntiansuksa school pattanakarn Ratchada school

Rattanakosinsomphod Watudomrungsee school Matthayom bangkhunthian school. Wat Nongkhaem school

Figure 8. Data X Y Coordinate of road and pedestrian accidents within 800 meter according of school, 2016

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Bangpho school Yothinborana2 school Wat Lat Phrao school

Satriwithaya 2 school Wat Krathum Suea Pla school Ratdamri school

Pracharatbamphen school Triamudomsuksa Bangkuntiansuksa pattanakarn Ratchada school school

Rattanakosinsomphodbang Watudomrungsee school Matthayom khunthian school. Wat Nongkhaem school LEGEND

MAX MIN

Figure 9. Density map for road and pedestrian accidents within 800 meter of school, 2016

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a) Accident-prone in Bangpho school and Yothinborana2 school

b) Accident-prone in Bangkuntiansuksa school and Rattanakosinsomphodbangkhunthian school

c) Accident-prone in Triamudomsuksapattanakarn Ratchada school

LEGEND

MAX MIN

Figure 10. Density map of three hotspots for pedestrian accidents and surrounding

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Table 5. Details of road and pedestrians accidents within 800 meter around schools, 2016 Accidents Fatal per per School name Accidents Fatal Injury Accidents (Cases) (Cases) (Cases) Pedestrian Index (km.) Bangpho school 121 4 140 0.033 3.65 Yothinborana2 school 105 4 120 0.038 3.14 Wat Lat Phrao school 135 1 162 0.007 3.20 Satriwithaya 2 school 54 3 58 0.056 1.80 Wat Krathum Suea Pla school 78 2 86 0.026 2.30 Ratdamri school 26 2 32 0.077 0.63 Pracharatbamphen school 88 - 104 - 1.95 Triamudomsuksapattanakarn 156 2 181 0.013 3.77 Ratchada school Bangkuntiansuksa school 131 - 162 - 3.95 Rattanakosinsomphod 120 3 141 0.025 3.31 bangkhunthian school Watudomrungsee school 153 2 158 0.013 4.23 Matthayom Wat Nongkhaem school 51 1 65 0.020 2.46 Total 1,218 24 1,409

The correlation between time of accidents and ages of accident victims are discussed as follows. It was found that the primary and secondary school and had highest accidents at 6-10 AM. The most victims were 16-25 years, the significant time when the accidents took at 6-10 AM. because it was the time for delivering and picking up students. Data are shown in Figure 11-12.

5. CONCLUSION & DISCUSSION

This research was collected data from the statistics of road and pedestrians accidents in 2016 by ThaiRSC and calculated the collected data in GIS by ArcMap so as to analyze the distribution of accident statistics around primary and secondary schools in Bangkok. GIS can effectively help in the processing of accident data, and for performing complex spatial analysis. GIS helps tremendously in the visualization of the problem of road and pedestrians accidents. The analysis of the distribution of accidents by GIS, applying Kernel Density Estimation (KDE) of 12 schools from 6 sub districts areas showed that Bangpho school and Yothinborana2 school, Bangkuntiansuksa school and Rattanakosin somphod bangkhunthian school, Triamudomsuksa pattanakarn Ratchada school was the most accident-prone area. The accident locations were different depending on the environment. Importantly, the significant areas of road and pedestrian accidents were at intersections of arterial road and local roads, at commercial areas with heavy traffic. The results proved the use of spatial statistical techniques and demonstrated statistically significant consistent patterns of clusters for pedestrian crashes.

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The research findings could provide understanding of the correlation between built environment and pedestrian safety, establish priority over the high-density zones for intervention efforts and develop research hypotheses for pedestrian crash studies. Since the mentioned accident-prone areas were related to schools, solutions to traffic engineering problems should be provided as preventive measures for students. In addition, warning measures including traffic signs on pedestrian consistent with students’ learning behavior should be improved immediately. Analysis of road and pedestrian accident areas can explain correlation of incidents occurring in traffic engineering. If the X and Y coordinate of location using GPS saved in ThaiRSC is inaccurate, it might affect inaccurate accident areas. The techniques mentioned in this research are tools that support effective decision. However, site investigation and traffic engineering data should be carried out and collected to explain findings and to develop solutions to problems in the future.

6. RECOMMENDATION

The results of analysis of pedestrian crash zones led to identification of accident - prone areas around school. The results showed that GIS was an effective tool that could identify locations accurately. In addition, warning measures including traffic signs on pedestrian consistent with students’ learning behavior should be improved immediately. However, in formulating preventive plan, qualitative research such as observation, exploration, in-depth interview should be applied to examine accuracy to explain reasons. Furthermore, quantitative research should be employed to specifically collect road accident statistics on students, traffic behavior and traffic volume. This could lead to effective solution and future research development. The suggested solutions were that knowledge of understanding of traffic rules should be promoted among students and a traffic sign consistent to student’s learning behavior should be provided.

REFERENCES

Affum, J. K., Taylor, M. A. P. (1995) Technology tools for transportation professionals – moving into the 21st century, The 1995 International conference, Integrated GIS database for road safety management. Institute of Transportation Engineers, Washington, D.C., 189-193. Anderson, T. (2009) Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Analysis and Prevention, 41, 359–364. Austin, K., Tight, M.,, Kirby, H.(1997) The use of geographical information systems to enhance road safety analysis. Transportation Planning and Technology, 20 (4), 249-266. Bangkok Metropolis Administration (BMA) (2013) The format of the public administration, Bangkok. Available at: http://www.bangkok.go.th. Bunnarong, S., Upala, P. (2017) Improving Pedestrian Environment and Traffic Sign System with the Participatory Design at Anuban Ranong School. AicQoL2017 Bangkok: Proceedings on the Quality of Life 5th AMER International Conference, Bangkok, Thailand, 25-27 February 2017. Hashim, M.J., Alkaabi, M. S. K. M., Bharwani, S. (2014) Interpretation of way-finding healthcare symbols by a multicultural population: Navigation signage design for global health. Journal of Applied Ergonomics, 45, 503-509. International Traffic Safety Data and Analysis Group (IRTAD) (2015) Road Safety Annual Report 2015, 39pp.

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Jang, K., Park, S. H., Kang, S., Song, K.H., Kang, S., Chung, S. (2013) Evaluation of Pedestrian Safety: Geographical Identification of Pedestrian Crash Hotspots and Evaluating Risk Factors for Injury Severity, TRB Annual Meeting, 2393. Kim, K., Levine, N. (1996) Using GIS to improve highway safety. Computers, Environment and Urban Systems, 20(4), 289-302. Lai, P., Chan, W. (2004) GIS for Road Accident Analysis in Hong Kong, The International association of Chinese professionals in Geographic Information Science, 10, 58-67. Levine, N., Kim, K., Nitz, L. (1995) Spatial analysis of Honolulu motor vehicle crashes:part I: spatial patterns, Accident Analysis and Prevention, 27(5), 663-674. Maurizio, G., Paul, L., Phil, A. (2007) Kernel density estimation and percent volume contours in general practice catchment area analysis in urban areas. GISRUK 2007: Proceedings of the Geographical Information Science Research UK 15th Annual Conference. Maynooth, Ireland, 11th-13th April 2007. Miller, J. S. (1999) What value may geographic information systems add to the art of identifying crash countermeasures?, Virginia Transportation Research Council (VTRC99-R13), 40pp. Moura, F., Cambra, P., Goncalvesa, A. B. (2017) Measuring walkability for distinct pedestrian groups with a participatory assessment method: A case study in Lisbon, Journal of Landscape and Urban Planning, 157, 282–296. National Highway Traffic Safety Administration (NHTSA) (1998) Zone Guide for Pedestrian Safety, 16pp. Pulugurtha, S. S., Krishnakumar, V. K. , Nambisan, S. S. (2007) New methods to identify and rank high pedestrian crash zones: An illustration, Accident Analysis and Prevention, 39( 4), 800-811. Rankavat, S., Tiwari, G. (2013) Pedestrian Accident Analysis in Delhi using GIS, Journal of the Eastern Asia Society for Transportation Studies, 10, 1146-1157. Ratanavaraha, V. (2011). The Study of Safety Management at School Zone for Nakhon Ratchasima Province. Nakhon Ratchasima: Institute of Engineering Suranaree University of Technology, 54pp. Rezasoltani,M., Behzadfar, M. & Said, I. (2015) A Model Development for Children’s Walking in Neighborhood, Procedia - Social and Behavioral Sciences, 201, 30 – 38. Satiennam, T., Tanaboriboon, Y. (2003) A Study on Pedestrian Accident and Investigation of Pedestrian’s Unsafe Conditions in Khon Kaen Municipality, Thailand. Journal of the Eastern Asia Society for Transportation Studies,5, 95-110. Sattanon, K, Upala, P. (2017) Assessment of Parent’s Anxiety within Safety of Children: Primary school in upper part of Thailand. AicQoL2017 Bangkok: Proceedings on the Quality of Life 5th AMER International Conference, Bangkok, Thailand, 25-27 February 2017. Schneider, R. J., Ryznar, R. M., Khattak, A. J. (2004) An accident waiting to happen: a spatial approach to proactive pedestrian planning, Accident Analysis & Prevention, 36, 193-211. Silverman, B. W. (1986) Density Estimation for Statistics and Data Analysis. Chapman and Hall Ltd., New York, NY, 170 pp. Steenberghen, T., Dufays, T., Thomas, I., Flahaut, B. (2004) Intra-urban location and clustering of road accidents using GIS: a Belgian example. International Journal of Geographical Information Science, 18, 169-81. ThaiRSC - Road Accidents Data Center for Road Safety Culture (ThaiRSC) (2017) Accidents statistic in Thailand. Available at: http://www.thairsc.com/

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The Office of Transport and Traffic Policy and planning (OPT) (2004). The project of traffic safety standards and transportation, 57pp. The Office of Transport and Traffic Policy and planning (OPT) (2015). Statistic report of road accident 2014 in Thailand, 22pp. Urban Design and Development Center (UDDC) (2014) Good Walk project, Bangkok. Available at: http://www.uddc.net/th/node/288. Waterson, P., Pilcher, C., Evans, S., et al. (2012) Developing safety signs for children on board trains. Journal of Applied Ergonomics, 43, 254-265. World Health Organization (WHO) (2013) Global status report on road safety 2013, 303 pp. World Health Organization (WHO) (2015) Global status report on road safety 2015, 303pp. Upala, P., Bunnarong, S. (2017) Participatory Design Process for Improving Pedestrian Environment and Traffic Sign System: A Case Study of Anuban Ranong School, Area Based Development Research Journal, 9(1), 52-69. Ziari, H., Khabiri, M. (2005) Applied Gis software for improving pedestrian & bicycle safety, Transport, 20, 160-164.

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