CORRELATION IMPRESSION OF MOBILITY MODE AND DISTANCE FACTOR TO SCHOOL

Similar documents
THE DEVELOPMENT OF MALAYSIAN HIGHWAY RAIL LEVEL CROSSING SAFETY SYSTEMS: A PROPOSED RESEARCH FRAMEWORK. Siti Zaharah Ishak

Relationship Between Child Pedestrian Accidents and City Planning in Zarqa, Jordan

IMPROVED SAFETY SURFACE ACCESS AT LOW COST AIRPORTS: KUALA LUMPUR INTERNATIONAL AIRPORT, MALAYSIA CASE STUDY

Pedestrian Level of Service at Intersections in Bhopal City

CAPACITY ESTIMATION OF URBAN ROAD IN BAGHDAD CITY: A CASE STUDY OF PALESTINE ARTERIAL ROAD

Road Accident Analysis and Identify the black spot location On State Highway-5 (Halol-Godhra Section)

Combined impacts of configurational and compositional properties of street network on vehicular flow

The Application of Pedestrian Microscopic Simulation Technology in Researching the Influenced Realm around Urban Rail Transit Station

2. Context. Existing framework. The context. The challenge. Transport Strategy

Keywords: multiple linear regression; pedestrian crossing delay; right-turn car flow; the number of pedestrians;

Planning Daily Work Trip under Congested Abuja Keffi Road Corridor

Evaluation of shared use of bicycles and pedestrians in Japan

Analysis of the Pedestrian System In Jayapura City (A Case Study of Pedestrian Line on Percetakan Street)

Deaths/injuries in motor vehicle crashes per million hours spent travelling, July 2007 June 2011 (All ages) Mode of travel

Anatomy of Injury Severity and Fatality in Indonesian Traffic Accidents

A location model for pedestrian crossings in arterial streets

A Study on Weekend Travel Patterns by Individual Characteristics in the Seoul Metropolitan Area

Bike Counter Correlation

Determining bicycle infrastructure preferences A case study of Dublin

Modelling of Tun Mustapha Residential College s Students Readiness to Cycling Mode in University

ANALYSIS OF ACCIDENT SURVEY ON PEDESTRIANS ON NATIONAL HIGHWAY 16 USING STATISTICAL METHODS

Cycling and risk. Cycle facilities and risk management

Safety and accident prevention plan in Burgos

Children s expectations and beliefs toward the relative safety of riding bicycles at night

Discerning Pedestrian Accidents in Dhaka-Barisal National Highway in Bangladesh

Safety culture in professional road transport in Norway and Greece

ANALYSING TRAFFIC ELEMENTS IN INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA BY REVEALED PREFERENCE APPROACH


Macroscopic Determination of Speed and Flow Using Moving Car Observer Method in Johor Bahru, Malaysia

PEDESTRIAN AND DRIVER KNOWLEDGE OF PRIORITY RULES FOR VARIOUS TYPES OF CROSSINGS J. Hatfield 1, R.F.S. Job 2 & K. Smith 3

People killed and injured per million hours spent travelling, Motorcyclist Cyclist Driver Car / van passenger

Children s risks on their way to school: the example of Tallinn

ANALYSIS OF THE TRENDS IN ACCIDENT RATES IN SRI LANKA

Pedestrian Travel Pattern At Johor Bahru Commercial Centre

EFFECTIVENESS OF AUTOMATED ENFORCEMENT SYSTEM (AES) IN

The Willingness to Walk of Urban Transportation Passengers (A Case Study of Urban Transportation Passengers in Yogyakarta Indonesia)

Road Accidental Analysis: A Case Study of Rajasthan State, India

May 12, 2016 Metro Potential Ballot Measure Issue Brief: Local Return

Relative Vulnerability Matrix for Evaluating Multimodal Traffic Safety. O. Grembek 1

Cyclist Safety in Australia

Life Science Journal 2016;13(5)

The Walkability Indicator. The Walkability Indicator: A Case Study of the City of Boulder, CO. College of Architecture and Planning

Efficient methodology of route selection for driving cycle development

Dilemma Zone Conflicts at Isolated Intersections Controlled with Fixed time and Vehicle Actuated Traffic Signal Systems

3 ROADWAYS 3.1 CMS ROADWAY NETWORK 3.2 TRAVEL-TIME-BASED PERFORMANCE MEASURES Roadway Travel Time Measures

Road safety training for professional drivers: worldwide practices

Analysis of Unsignalized Intersection

MEASURING CONTROL DELAY AT SIGNALIZED INTERSECTIONS: CASE STUDY FROM SOHAG, EGYPT

Safe Routes to School Program in California: An Update

UNIT V 1. What are the traffic management measures? [N/D-13] 2. What is Transportation System Management (TSM)? [N/D-14]

ROAD SAFETY DEVELOPMENT INDEX (RSDI)

A GIS APPROACH TO EVALUATE BUS STOP ACCESSIBILITY

Prediction model of cyclist s accident probability in the City of Malang

SITUATIONS AND CHALLENGES OF ROAD SAFETY IN CHINA

Investigating Commute Mode and Route Choice Variability in Jakarta using multi-day GPS Data

An Analysis of the Travel Conditions on the U. S. 52 Bypass. Bypass in Lafayette, Indiana.

Spatial and Temporal Patterns of Pedestrian Crashes Along The Urbanization Gradient

DOI /HORIZONS.B P23 UDC : (497.11) PEDESTRIAN CROSSING BEHAVIOUR AT UNSIGNALIZED CROSSINGS 1

INTERSECTION SAFETY RESEARCH IN CRACOW UNIVERSITY OF TECHNOLOGY

BLACK SPOTS MANAGEMENT - SLOVENIAN EXPERIENCE

AIS data analysis for vessel behavior during strong currents and during encounters in the Botlek area in the Port of Rotterdam

Napier City road trauma for Napier City. Road casualties Estimated social cost of crashes* Major road safety issues.

Safety Behavior for Cycling : Application Theory of Planned Behavior

EFFICIENCY OF TRIPLE LEFT-TURN LANES AT SIGNALIZED INTERSECTIONS

PRELIMINARY DRAFT FIRST AMENDMENT TO VISION 2050: A REGIONAL LAND USE AND TRANSPORTATION PLAN FOR SOUTHEASTERN WISCONSIN

DEVELOPMENT OF A SET OF TRIP GENERATION MODELS FOR TRAVEL DEMAND ESTIMATION IN THE COLOMBO METROPOLITAN REGION

MULTIMODAL INJURY RISK ANALYSIS OF ROAD USERS AT SIGNALIZED AND NON- SIGNALIZED INTERSECTIONS

Keywords: Highway Intersection, Intersection Accidents, Crash Type, Crash Contributing, Statistical Analysis, Design Factors

Delivering Accident Prevention at local level in the new public health system

BICYCLE INFRASTRUCTURE PREFERENCES A CASE STUDY OF DUBLIN

HUMAN (DRIVER) ERRORS

Regression Analysis of Travel Indicators and Public Transport Usage in Urban Areas

Strategy of Infra-Structure Development on. Junction Road Network in Karanglo for Handling. Congestion of Lawang-Malang-East Java

This objective implies that all population groups should find walking appealing, and that it is made easier for them to walk more on a daily basis.

Cambridgeshire and Peterborough Road Safety Partnership Handbook

Application of Transyt-7f on Signalized Road Junction Networks in Shah Alam and Petaling Jaya

SECTION 1. The current state of global road safety

A SYSTEMS APPROACH TO IMPROVING PEDESTRIAN SAFETY IN RURAL COMMUNITIES ABSTRACT

Walking and Cycling Action Plan Summary. A Catalyst for Change The Regional Transport Strategy for the west of Scotland

METHODOLOGY. Signalized Intersection Average Control Delay (sec/veh)

A prediction model for pedestrian fatalities based on explanatory factors

Proposed. City of Grand Junction Complete Streets Policy. Exhibit 10

OVERVIEW OF CURRENT ROAD SAFETY SITUATION IN MALAYSIA

Cyclist-reported habits of helmet usage and differences in riding postures by using helmets

Analysis of Causal Factors of Accidents near Mahipalpur Junction on NH-8 in Delhi

The calibration of vehicle and pedestrian flow in Mangalore city using PARAMICS

4. WIDER SOCIAL DETERMINANTS OF HEALTH

Journal of Chemical and Pharmaceutical Research, 2014, 6(3): Research Article

Road safety and bicycle usage impacts of unbundling vehicular and cycle traffic in Dutch urban networks

CALIBRATION OF THE PLATOON DISPERSION MODEL BY CONSIDERING THE IMPACT OF THE PERCENTAGE OF BUSES AT SIGNALIZED INTERSECTIONS

TECHNICAL NOTE THROUGH KERBSIDE LANE UTILISATION AT SIGNALISED INTERSECTIONS

Field and Analytical Investigation of Accidents Data on the Egyptian Road Network

Bicycle - Motor Vehicle Collisions on Controlled Access Highways in Arizona

ARE SIGNALIZED INTERSECTIONS WITH CROSSWALKS SAFER IN INDIA? A STUDY BASED ON SAFETY ANALYSIS USING VIDEO DATA

Franklin Parking Analysis

PEDESTRIAN COLLISIONS IN LOS ANGELES 1994 through 2000

Trial 3: Interactions Between Autonomous Vehicles and Pedestrians and Cyclists

Application of Traffic Management Plan a Sustainable Solution of Traffic Congestions in Pabna City, Bangladesh

WHAT CAN WE LEARN FROM COMPETITION ANALYSIS AT THE 1999 PAN PACIFIC SWIMMING CHAMPIONSHIPS?

Cycle journeys on the Anderston-Argyle Street footbridge: a descriptive analysis. Karen McPherson. Glasgow Centre for Population Health

Transcription:

IMPACT: International Journal of Research in Applied, Natural and Social Sciences (IMPACT: IJRANSS) ISSN (P): 2347-4580; ISSN (E): 2321-8851 Vol. 4, Issue 7, Jul 2016, 181-188 Impact Journals CORRELATION IMPRESSION OF MOBILITY MODE AND DISTANCE FACTOR TO SCHOOL INTAN S. AZIZ 1 & ZAHARAH M. YUSOFF 2 1 Postgraduate Scholar, Centre of Surveying Science and Geomatics, Universiti Teknology MARA, Malaysia 2 Lecturers, Centre of Surveying Science and Geomatics, Universiti Teknology MARA, Malaysia ABSTRACT City living and urban lift has been known to interpolate the existing road safety standards. Quantity of vehicle on the road multiplied as a result of urbanization process. Daily routine of the heavy volume traffic around the school vicinity makes parents and school children find it is not safe to either walk or cycle to school. The outcome indicates the amount of walking and cycling to school decreased throughout the years. In Malaysia, school siting parameter was established by the Department of Town and Country Planning (DTCP) yet safety aspect of the route involves from house to school was not thoroughly discussed. The concern of road safety and school children becomes the primary subject in this paper, where the relationship of transportation mode and house location was examined. This study uses Geographic Information System (GIS) and Statistical Package for the Social Sciences (SPSS) software in learning the behavioral pattern. The research will be based on interviews, site visits and questionnaire distribution in selected three primary schools in Johor Bahru, Malaysia. The data obtained from questionnaires used in figuring out the correlation between the mobility mode and distance factor. This will give some insight on the effect of school siting parameter to school children road safety. KEYWORDS: Distance, Mobility Mode, Children, Primary School INTRODUCTION The expansion of urban area makes transportation is essential in daily life. As the technology of transportation increased parallel with the urban population the awareness of road safety is highly important. It is notable that traffic accidents contribute great percentage in accidents category throughout the world, especially in a place where traffic congestion is frequent. Traffic accident issue is highly debated in World Health Organization (WHO), 2013 and it is reported that about 1.24 million people die due to road accidents [1]. While in Malaysia, it is stated that an average 17 death by traffic accidents daily [2]. Research by [1], [2] and [3] found that there is a rising statistic of accidents among young people. [3] reported that road traffic injuries rank as no 1 leading cause of death for child ages 15-19 years old and followed with child ages 5-14 years old. Road safety aspect plays an important role in reducing the case of accident and it has to start with school children. Children travel to school every day, by various mobility modes. Lately school safety aspect towards children is always in the discussion by the school authority, public and the government. In fact, school siting parameter was made to ensure that school safety aspect was practice by all. [4], [5] and [6] has found out that there are many components should be included in the safety matter and it must appear in the document. The road safety aspect is one of the important primary elements. Impact Factor(JCC): 2.7341 - This article can be downloaded from www.impactjournals.us

182 Intan S. Aziz & Zaharah M. Yusoff A study done by [7] suggests eight factors to influence school children to walk to school. Those factors are distance to school, school characteristic and policies, schedule constraints, values, weather, and parents fear of traffic and crime. However, [8] reported that walking to school trend is decreasing and it is noticeable. Related finding [9], said that statistically 50% of school children walk and cycle to school every day in United States of America. However, the number drops to lesser than 15%. According to [10], the analyses of road crashes data in 2010 reported that approximately a staggering number of 70,000 pedestrians were injured, and 14,000 of those injured were in the age of below 14 years old. This study is to investigate the relationship between mobility mode and the distance factor with supported by three objectives which are To discover the mobility mode pattern used by school children, To determine the coverage area by selected primary school Finding the correlation coefficient analysis between mobility mode and distance factor. CASE STUDY The study area was at Johor Bahru located at the southern side of Malaysia with the GPS coordinate is 2.3167 N, 111.5500 E. Eight districts of Johor Bahru (1 29 00 N 103 44 00 E) were discovered. Johor Bahru has a population of 1.73 million people and the figure keeps on growing [11]. The town is administrated by the Johor Bahru City Council (JBCC), where it covers up to fifteen areas. There are three schools selected for this study which located at the urban area. The selected schools are SK Kompleks Uda, SK Taman Bukit Mewah and SK Taman Daya 2. METHODS This study comprise of four main phases gradually. The main data for this study was obtained from a set of questionnaire constructed. The questionnaire was design in a simple structure to suit with the target respondent is which below age 12 years old. There are three main sections based on the mobility mode, where the school children will answer accordingly. The mobility mode and the house location are the main criteria for this study. The data was then will be process using the SPSS software in order to find out the correlation between mobility mode and distance factor. The Pearson correlation coefficient was used. Several assumptions were taken into account that are; The population distribution is normal. There is a linear relationship between the variables. The scored obtained from the population are randomly sampled. The variables are measured on an interval. The formula for Pearson s correlation coefficient is as follows; Index Copernicus Value: 3.0 - Articles can be sent to editor@impactjournals.us

Correlation Impression of Mobility Mode and Distance Factor to School 183 Figure 1: Pearson s Correlation Coefficient If the r value obtained is approaching 1, it means that the relationship between the variables is strong. The changes in one variable will affect the second variable. If the r value obtained is approaching to 0, it means that the relationship between the variables is weak. The changes in one variable will not affect the second variable. If the r value is positive, it implies that the increase value in one variable will increase the value of the second variable. If the r value is negative, it implies that the increase value in one variable will decrease the value of the second variable RESULTS The purpose of this analysis is to examine whether there is a relationship between the choice of mobility mode and the distance factor to school. Data obtained were analyzed using Geographic Information System (GIS) and Statistical Package for the Social Sciences (SPSS) software. Results obtained are as follow; RESEARCH FINDING 1 There were three mobility mode to school, namely by school bus/van, parents vehicle and walking. From the analysis it shows that all school show parent s vehicle is the preferred mobility while the least mode is walking. Majority of children chose to ride with parent s vehicle, where the percentage for SK Kompleks Uda is 53.3%, SK Taman Bukit Mewah and SK Taman Daya 2 both show 39.6%. Percentage for using school bus/van for SK Kompleks Uda is 32.6%, while SK Taman Bukit Mewah shows 17.7% and SK Taman Daya 2 shows 42.7%. While the percentage of school bus/van and parent s vehicle is encouraging, the walking percentage shows a different form. The result shows only 14.1% from SK Kompleks Uda, and 12.5% from SK Taman Daya 2. However, in SK Taman Bukit Mewah, 33.3% of the students have said to walk to school, that indicate walking is the second choice of mobility mode after parent s vehicle for the school. Impact Factor(JCC): 2.7341 - This article can be downloaded from www.impactjournals.us

184 Intan S. Aziz & Zaharah M. Yusoff Figure 2: Mobility Mode Percentage for Each School RESEARCH FINDING 2 In finding out the coverage area, the students were asked to fill in their address in the questionnaire. The results were then processed in ArcGIS to map the house location from school. Figure 4 below is the maps of coverage area for each school. Figure 3: School Coverage Area within 400m to 800m Index Copernicus Value: 3.0 - Articles can be sent to editor@impactjournals.us

Correlation Impression of Mobility Mode and Distance Factor to School 185 It can be concluded that majority of school children live in the school coverage area. The coverage area for primary school is 400m 800m or 10 minutes walk, as proposed by the Federal Department of Town and Country Planning Malaysia. SK Kompleks Uda showed that among 92 school children surveyed and 27% of them live at Jalan Susur Barli 1. This result is expected as they live in a low cost flat, namely Flat Fasa 13 where it is nearbt the school. The other 15% lives at Jalan Padi Emas, a residential area that is approximately 20 minutes walk or 5 minutes drive by car. However there are several students live far from the coverage area like Tampoi. In SK Taman Bukit Mewah, the pattern continues as 60% from the 96 school children live in Taman Bukit Mewah. The rest 15% lives in Taman Munsyi Ibrahim, a residential area in about 15 minute s walks or 5 minutes drive. There are several school children claim they live far such as Taman Pulai Indah, Taman Mutiara Rini, Taman Johor Jaya and Taman Pelangi. Taman Pelangi is the farthest as it took approximately 20 minutes driving, in a normal traffic. SK Taman Daya 2 shows a slightly different pattern from SK Kompleks Uda and SK Taman Bukit Mewah. 96 students were surveyed, and 60% of them live just besides the school, which is Taman Daya. However, a big fraction of 12% lives at Taman Setia Indah that located out from the coverage area. While one student live at Felda Taib Andak, located outside of the district with 40 minutes drive. RESEARCH FINDING 3 The distance factor is the distance from house to school, which the data are collected from the House Location and Distance from House to School question in the questionnaire survey. All of the schools were analysed using Pearson s correlation analysis individually. Table 1: Table of Correlation Result for Mobility Mode and Distance Factor Pearson s Correlations Sig. (2-tailed) SK Kompleks Uda 0.031 0.769 SK Taman Bukit Mewah -0.094 0.389 SK Taman Daya 2 0.029 0.784 The results from all of the school show that there is a weak relationship between the two variables, which are transportation mode and distance shown on the Table 1 above. All of the school bears a result that is very small number and approaching to 0. Even though SK Kompleks Uda and SK Taman Daya 2 show positive number results, yet the value is still far from 1. In this analysis, it means that the choice of transportation mode was not affected by the distance of house from school. RESEARCH FINDING 4 After finding out the school s coverage area and the correlation between distances with mobility mode, a route network analysis is done to establish the shortest path from school to house. From the previous results, all of the schools show that the majority of students live inside the coverage area. Using the road network analysis in ArcGIS, the shortest path from house to school was generated. The parameter involved was using the road that bears the least time of walking. SK Kompleks Uda shows that there are a lot of route taken by children. Majority of the children lives at Flat Fasa 13, and the route taken was shown on the map. However, the route needs the children to go through a critical junction, which depicts a heavy volume of traffic. The junction nevertheless has a traffic light for vehicle and pedestrian crossing (zebra crossing). The route for the rest of the house location shows the same pattern, where although it is considered as a safe distance, they need to go through a critical junction. Impact Factor(JCC): 2.7341 - This article can be downloaded from www.impactjournals.us

186 Intan S. Aziz & Zaharah M. Yusoff The result for SK Taman Bukit Mewah shows no differ from SK Kompleks Uda, where children need to go through a critical junction. However, this junction located just outside of the school, where it is besides commercial lots. This junction is critical considering the fact that the commercial lots have a food chain restaurant where a lot of adults with motorized vehicle go there. What makes this problem more apparent is that a lot of them place their vehicle just at the pavement, where supposedly it is the place for children to walk safely on the road. K Taman Daya 2 demonstrates the same pattern where the school children need to go through a critical junction on their route to school. This school shows only two residential a located inside the coverage area, which are Taman Delima 2 and Taman Delima. Both route shows that the children needs to go through one critical junction, and several junctions with crossing. However, from site observation it is shown that the road outside school is a major road, where heavy vehicle are allowed to use it. This makes the road is not safe for children to use without an adult supervision. Figure 4: Shortest Route to School CONCLUSIONS From the perspective of the results obtained, current pattern suggest that distance from house to school does not affect the urge to choose mobility mode by school children. The finding from correlation coefficient analysis shows that all school bear a result that is adverse to the theory that only students who lived far from school take school bus/ van or parents vehicle. From the interview with school children and parents, the road condition has become the affective factor for not choosing to walk to school. In addition to the road condition, the heavy traffic volume has been known to alter the perception of walking to school amongst children and parents. This trend is predicted to remain if there is no effort to enhance road safety aspect in the school siting parameter. Index Copernicus Value: 3.0 - Articles can be sent to editor@impactjournals.us

Correlation Impression of Mobility Mode and Distance Factor to School 187 ACKNOWLEDGEMENTS This study is a part of main research that was funded by Ministry of Higher Education Malaysia (MOHE). It is an on-going Master by research. We would like to thank the Ministry of Science, Technology and Innovation Malaysia (MOSTI), University Technology MARA (UiTM), the Johor Bahru City Council (JBCC), school personnel and all individuals who have been involved in this research directly nor indirectly. REFERENCES 1. WHO. (2013). Supporting A Decade Of Action. 2. MIROS Crash Investigation and Reconstruction Annual Statistical Report 2007 2010. (2012). 3. Peden, M., & Oyegbite, K. (n.d.). World report on child injury prevention World report on child injury prevention. 4. Khalil, R. F., & Ibrahim, H. G. A. (2012). Optimizing School Siting in Doha, Qatar. 5. Cooner, S. A. (2003). Traffic Operations and Safety at Schools : Review of Existing Guidelines. 6. Samad, a. M., Hifni, N. a., Ghazali, R., Hashim, K. a., Disa, N. M., & Mahmud, S. (2012). A study on school location suitability using AHP in GIS approach. 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, 393 399. doi:10.1109/cspa.2012.6194756 7. Stewart, O., Vernez Moudon, A., & Claybrooke, C. (2012). Common ground: Eight factors that influence walking and biking to school. Transport Policy, 24, 240 248. doi:10.1016/j.tranpol.2012.06.016 8. McMillan, T. E. (2007). The relative influence of urban form on a child s travel mode to school. Transportation Research Part A: Policy and Practice, 41(1), 69 79. doi:10.1016/j.tra.2006.05.011 9. States, U. (2009). SAFE ROUTES TO SCHOOL NATIONAL PARTNERSHIP WHAT IS SAFE ROUTES TO SCHOOL? Background and Statistics, 2009 2011. 10. States, U., Statistics, H., States, U., States, U., Fatalities, T., Children, A., & Group, A. (2012). 2010 Data, (July). 11. The Star Impact Factor(JCC): 2.7341 - This article can be downloaded from www.impactjournals.us