make the spss analysis

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timer Asked: Apr 18th, 2017

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Make the SPSS analysis of given Data in SPSS Analysis.docx file, Follow the "4. Data Collection and Model Formulation,5. Results and Analysis and 6. Discussion of Results" in “example work.pdf” file. (Prepare the 5. Results and Analysis and 6. Discussion of Results.)

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Follow the 4. Data Collection and Model Formulation in “traffic accidents in Oman example work.pdf” file. For making the analysis of below given data. (Prepare the 5. Results and Analysis and 6. Discussion of Results.) PROJECT TITLE DEVELOPING OF CRASH PREDICTION MODELS FOR SULTANATE OF OMAN year Total Accidents Fatalities Injuries 2006 6869 681 7548 2007 8816 798 8531 2008 7982 951 10558 2009 7253 953 9783 2010 7571 820 10066 2011 7719 1056 11437 2012 8209 1139 11618 2013 7829 913 10802 2014 6717 816 3835 2015 6279 675 3624 Total 75244 8802 87802 5. Data Collection 5.1Statistical Data Traffic accident data was collected from the Ministry of Health and Royal Oman Police for the last 9 years from 2006 to 2015. SPSS (Statistical Package Computer Program) will be used to determine the prediction accident models for the last. 9 years according to the accident report data. The following tables and figures show the statistical data of the dependent and independent variables. Table 2: The Eleven Independent Variables Variable Independent Variable x1 Speed x2 Neglect x3 Fatigue x4 Overtaking x5 Sudden Stopping x6 Improper Act x7 drunk Driving x8 Weather Condition x9 Safety Distance x 10 Vehicle DEFECTS x 11 Road Defects year Total Accidents Fatalities Injuries 2006 6869 681 7548 2007 8816 798 8531 2008 7982 951 10558 2009 7253 953 9783 2010 7571 820 10066 2011 7719 1056 11437 2012 8209 1139 11618 2013 7829 913 10802 2014 6717 816 3835 2015 6279 675 3624 Total 75244 8802 87802 Table 3: Total Number of Accidents, Fatalities and Injuries (2006-2015) Table 4: Total Number of Accident Cause (2006-2015) cause year 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Speed 5236 5344 4734 3744 4032 3861 4328 3952 3510 3411 Neglect 3649 2098 922 615 494 569 721 845 606 655 Fatigue 3 13 13 18 18 27 25 13 10 40 Drunk Driving 76 93 167 172 203 167 134 136 103 53 Overtaking 165 194 419 374 248 397 384 314 224 228 Weather Condition 25 51 36 130 66 22 27 51 19 22 Sudden Stopping 11 36 70 85 47 60 74 25 11 3 Safety Distance 350 269 378 240 252 566 542 553 507 462 Improper Act 263 574 1064 1639 1967 1726 1609 1579 1479 1217 Vehicle Defects 80 128 154 178 188 232 260 243 169 155 Road Defects 11 16 25 58 56 92 105 118 79 33 TOTAL 9869 8816 7982 7253 7571 7719 8209 7829 6717 6279 Table 5: Number of Fatalities with the Accident Causes (2006-2015) cause year 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Speed 386 458 566 499 487 593 595 471 428 383 Neglect 58 78 74 49 44 70 94 95 87 74 Fatigue 4 9 4 22 6 20 37 12 7 14 Drunk Driving 7 6 7 9 5 15 6 4 10 3 Overtaking 88 122 158 159 97 165 175 125 126 97 Weather Condition 4 6 2 6 5 5 8 2 0 1 Sudden Stopping 0 0 1 2 0 0 0 0 0 1 Safety Distance 3 2 9 9 12 22 16 22 15 8 Improper Act 82 55 80 121 106 92 141 105 86 74 Vehicle Defects 35 58 40 61 47 57 55 61 32 14 Road Defects 14 4 10 16 11 17 12 16 25 6 TOTAL 681 798 951 953 820 1056 1139 913 816 675 Table 6: Number of Injuries With the Accident Causes (2006-2015) cause year 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Speed 4454 5038 5395 4074 4340 4932 5424 6923 1752 1728 Neglect 2119 1650 1266 642 457 573 672 845 355 339 Fatigue 4 10 21 30 24 27 33 13 9 49 Drunk Driving 50 77 94 97 110 99 91 136 28 11 Overtaking 357 445 941 913 600 975 908 314 267 196 Weather Condition 39 70 84 198 85 34 51 57 14 17 Sudden Stopping 11 20 82 142 68 106 100 21 9 42 Safety Distance 77 152 576 398 400 1084 941 553 390 285 Improper Act 273 770 1824 2925 3560 3118 2849 1579 835 842 Vehicle Defects 144 289 243 266 333 343 413 243 109 95 Road Defects 20 10 32 89 89 146 136 118 67 20 TOTAL 7548 8531 10558 9783 11437 11437 11618 10802 10802 3624 Seventh Traffic Safety Conference, 12-13 May 2015, Amman Jordan Development of Traffic Accident Models in Sultanate of Oman Eman Hamed AlHarrasi, Master Degree in Engineering Project Management, Faculty of Engineering, Department of Civil Engineering, Al-Isra University Basim Jrew, Ph.D. Professor Faculty of Engineering, Department of Civil Engineering, Isra University Amman, Jordan, Tel 00962-6-4711710, Mobile 00962-7-95974742 E-mail: basim_jrew@yahoo.com Mohammad Abojaradeh*, Ph.D. Associate Professor, P.E. Faculty of Engineering, Department of Civil Engineering, Zarqa University P.O. Box 2000, Zarqa 13110, Jordan Tel: 00962-5-3821100, E-mail: abojaradeh@yahoo.com Abstract According to Royal Oman Police statistics, more than five hundred people die per year in Oman because of car accidents. The World Health Organization WHO estimated road traffic death rate per 100,000 populations to be equal to 30.4 making Oman to be one of the highest countries in traffic rate accident. The main objectives of this study are: to analyze traffic accidents in Oman and their main causes in order to reduce the frequency and severity of traffic accidents. Also, to study the effect of driver behavior mistakes on traffic safety. The study will predict traffic accident statistical regression models. These models relate the total number of accidents, total fatalities, and total injures as dependent variable with possible causes of accidents as independent variables, focusing on causes that are related to driver behavior as independent variables. Statistical Package for Social Sciences computer software SPSS was used for the statistical analysis. Traffic accident data for the last 11 years (2003-2013) was collected from the Royal Oman Police. The traffic accident causes (independent variables) in Oman are classified according to Royal Oman Police as: speeding, neglect, fatigue, drunk driving, overtaking, weather condition, sudden stopping, safety distance, improper act, vehicle defects and road defects. The analysis showed that the main causes of accidents are related to the driver behavior. Road defects, vehicle defects and weather condition were not considered as main causes of accidents in Oman. Based on the model analysis, countermeasure programs are needed to apply for the driver behavior that are resulted in the traffic accident statistical regression models. Key Words: Traffic Accidents, Driver Behavior, frequency, severity, Regression Models, World Health Organization, Royal Oman Police, Sultanate of Oman. 1|Page Seventh Traffic Safety Conference, 12-13 May 2015, Amman Jordan 1. Introduction Nearly 1.3 million people die each year as a result of a road traffic collision, more than 3000 deaths each day and more than half of these people are not traveling in a car. Twenty to fifty million more people sustain non-fatal injuries from a collision, and these injuries are an important cause of disability worldwide. Ninety percent of road traffic deaths occur in low- and middle-income countries, which claim less than half the world's registered vehicle fleet. Road traffic injuries are among the three leading causes of death for people between 5 and 44 years of age. Unless immediate and effective action is taken, road traffic injuries are predicted to become the fifth leading cause of death in the world, resulting in an estimated 2.4 million deaths each year. This is, in part, a result of rapid increases in motorization without sufficient improvement in road safety strategies and land use planning. The economic consequences of motor vehicle crashes have been estimated between 1% and 3% of the respective gross national product (GNP) of the world countries, reaching a total over $500 billion. Reducing road casualties and fatalities will reduce suffering; unlock growth and free resources for more productive use. (WHO, 2010) There are many causes for traffic accidents, the human factor, the driver, is the primary cause of those accidents. The main cause of traffic accidents is disobeying the traffic safety laws, which include speeding, driver distraction, driving under the influence of drugs or alcohol, close following between the running cars, yielding for pedestrians and other vehicle etc. The causes of crashes are usually complex and involve several factors. The main factors can be divided into four separate categories: the driver, the vehicle, the roadway, and the environment. 1.1 Study Objectives The main objectives of this study are: to analyze traffic accidents in Oman and their main causes in order to reduce the frequency and severity of traffic accidents. Also, to study the effect of driver behavior mistakes on traffic safety. 1.2 Traffic Accident in Sultanate of Oman Oman has, in contrast to many other countries with high numbers of fatalities in road crashes, a well-developed road system as well as a relatively new car fleet. According to official reports of the Ministry of Health (MoH) in Oman, road traffic accident(RTA) problem is the number one cause of inpatient deaths and the leading cause of serious injury, disability and premature death among adults (MoH, 2009). The World Health Organization (WHO) has ranked Oman at fourth place in the Arabian Gulf Co-operation Council (GCC) states and 57th worldwide as far as the occurrence of traffic accident, injuries and deaths (WHO, 2009). (Mazharul, AlHadhrami, 2012). WHO present trends in road traffic deaths in Oman from 2001 to 2010 as shown in Figure 1, (WHO, 2013). 2|Page Seventh Traffic Safety Conference, 12-13 May 2015, Amman Jordan Figure 1: Trends in Road Traffic Deaths in Oman 2. Review of Related Literature There are many studies have been conducted in order to study the traffic accident causes, effects and costs. The following are some of these studies; Abojaradeh and Jrew (2012) concentrated on the driver behavior mistakes on the traffic safety in Jordan. They went through a questionnaire to find the main causes of the traffic accidents and their effect. It was found that there is a direct relationship between driver behavior and the exposed accidents. (Abojaradeh, Jrew, 2012) Abojaradeh and Jrew et al., (2009) focuses on major streets and intersections in five main areas in the Greater Amman Municipality. The study area includes the following areas: Marj AlHamam in Western Amman and Abu Nusair, and Suweileh in North Amman, Jubeeha in Middle Amman, and Marka in Eastern Amman. The results of the prediction models indicates that the close following, lane violation, and not taking enough precaution are the most causes of total accidents. The lane violation and wrong u- turn, and sudden turning are the most causes of accident injuries. (Abojaradeh, Jrew, 2009) Mazharul and AlHadhrami (2012) focused on the increase of motorization and road traffic accidents in Oman. They said that the growth of automobile is faster than the growth of the Omani population. It has been observed that during the 10 year study period from 2000 to 2009, the population of Oman increased by about 2.0% per annum, while the automobile fleet in the country increased by 4.3% per annum. At the same period, the new registration of automobile increased by 10% per annum. (Mazharul, AlHadhrami, 2012) Jaddan, et al., (2013) studied the magnitude, cost and potential countermeasures of traffic accident in Jordan. The study investigates the present and future magnitude of road traffic accidents in order to provide a better understanding of the road safety trauma and assist strategic planning and optional allocation of resources. The nimber of accidents has increased five oldfold between 1995 and 2009 reaching 122,793 in 2009 with an estimated cost of JOD 336 million (equivalent to about $504 million). (Jaddan, et al., 2013) Hong, et al., (2005), they developed models considering the characteristics of roadway alignments and traffic characteristics. The created models can be used to estimate the accidents 3|Page Seventh Traffic Safety Conference, 12-13 May 2015, Amman Jordan rates on new or improved roads. In the study, roads were classified into 4 groups based on lanes, roads level and the existence of median barriers. The regression analysis had been performed for each group with actual data associated with traffic, roads and accidents. (Hong, et al., 2005) 3. Traffic Accident Causes in Sultanate of Oman The main objective of the study is to develop traffic accident prediction models that are related to the total accidents, fatalities and injuries as a dependent variable, with possible causes of accidents that are related to driver behavior, as independent variables. The independent variables are shown in Table 1. Traffic accident data was collected from the Ministry of Health and Royal Oman Police for the last 11 years from 2003 to 2013. Table 1: Traffic Accident Causes (Independent Variables) No. 1 2 3 4 5 6 7 8 9 10 11 Independent Variable Speed Neglect Fatigue Overtaking Sudden Stopping Improper Act Drunk Driving Weather Condition Safety Distance Vehicle Defects Road Defects The following are some definitions of accident causes that are related to driver behaviors. Some causes are clear enough to be defined and others are not related to the driver behaviors. 1. Overtaking Overtaking is one of the highest risk maneuvers for both drivers and riders because it can put the overtaking vehicle into the path of oncoming traffic, often at high speeds. If there is a head-on collision, the speed of both vehicles combines to create a much more severe impact (ROSPA, 2009, www.rospa.com). 2. Neglect When a person is negligent, it means that he or she has behaved in a thoughtless or careless manner, which has caused harm or injury to another person. A person can be negligent by doing something that he or she should not have done (for example, running a red light ), or by failing to do something that he or she should have done (for example, failing to yield, stop for a pedestrian, or turn on lights when driving at night). 4|Page Seventh Traffic Safety Conference, 12-13 May 2015, Amman Jordan Negligence is a legal theory often used in car accident cases. A driver must use care to avoid injuring other motorists, passengers, or pedestrians -- basically, anyone that he or she encounters on the road. If a driver is not reasonably careful and injures someone as a result, the driver is liable for injuring the accident victim (www.nolo.com). 3. Fatigue Fatigue driving is the driver, after prolonged periods of continuous driving, experiences mental and physical functional disorder. If the driver does not have a good sleep at the night, even a short period of driving can still cause fatigue driving. Driving Fatigue will affect the driver's attention, feeling, perception, thinking, judging, decision making and other aspects. Driving fatigue is not a morbid state, but a physiological self-protective response. The physical strength and capabilities could be fully restored after a proper rest. Excessive fatigue driving are the result of lack of rest compensation and may suddenly show up in some morbid. It will probably lead to an accident. Fatigue driving cannot be ignored according to the nature of fatigue, Fatigue can be divided into two Catalogues, one is physical fatigue, and the other is mental fatigue (www.caredrive.com). 4. Safety Distance It is the safe distance between the vehicle and the vehicle in front, so the vehicle can have enough time to stop if need to stop suddenly. Drivers must care about their speed when they want to stop because the faster they are going, the longer it will take to stop. 4. Data Collection and Model Formulation 4.1. Statistical Data Traffic accident data was collected from the Ministry of Health and Royal Oman Police for the last 11 years from 2003 to 2013. SPSS (Statistical Package Computer Program) will be used to determine the prediction accident models for the last 11 years according to the accident report data. The following tables and figures show the statistical data of the dependent and independent variables. Table 2: The Eleven Independent Variables Variable x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 5|Page Independent Variable Speed Neglect Fatigue Overtaking Sudden Stopping Improper Act Drunk Driving Weather Condition Safety Distance Vehicle Defects Road Defects Seventh Traffic Safety Conference, 12-13 May 2015, Amman Jordan Table 3: Total Number of Accidents, Fatalities and Injuries (2003-2013) Year Total Accidents Fatalities Injuries 2003 10197 578 6735 2004 9460 637 6636 2005 9247 689 6658 2006 9869 681 7548 2007 8816 798 8531 2008 7982 951 10558 2009 7253 953 9783 2010 7571 820 10066 2011 7719 1056 11437 2012 8209 1139 11618 2013 7829 913 10802 TOTAL 94152 9215 100372 Table 4: Total Number of Accidents with the Accident' Causes (2003-2013) Cause Year 2003 Speed 2831 Neglect 5805 Fatigue 2 Drunk Driving 111 Overtaking 240 Weather Condition 9 Sudden Stopping 23 Safety Distance 446 Improper Act 630 Vehicle Defects 91 Road Defects 9 TOTAL 10197 6|Page 2004 2089 5850 9 118 265 2 18 617 355 128 9 9460 2005 2933 5354 11 79 158 13 4 414 179 91 11 9247 2006 5236 3649 3 76 165 25 11 350 263 80 11 9869 2007 5344 2098 13 93 194 51 36 269 574 128 16 8816 2008 4734 922 13 167 419 36 70 378 1064 154 25 7982 2009 3744 615 18 172 374 130 85 240 1639 178 58 7253 2010 4032 494 18 203 248 66 47 252 1967 188 56 7571 2011 3861 569 27 167 397 22 60 566 1726 232 92 7719 2012 4328 721 25 134 384 27 74 542 1609 260 105 8209 2013 3952 845 13 136 314 51 25 553 1579 243 118 7829 Seventh Traffic Safety Conference, 12-13 May 2015, Amman Jordan Table 5: Number of Fatalities with the Accident' Causes (2003-2013) Cause Year Speed Neglect Fatigue Drunk Driving Overtaking Weather Condition Sudden Stopping Safety Distance Improper Act Vehicle Defects Road Defects TOTAL 2003 281 139 1 6 83 6 4 1 24 32 1 578 2004 348 143 22 3 91 0 0 1 8 21 0 637 2005 358 193 6 4 75 3 0 1 11 30 8 689 2006 386 58 4 7 88 4 0 3 82 35 14 681 2007 458 78 9 6 122 6 0 2 55 58 4 798 2008 2009 2010 2011 2012 2013 566 499 487 593 595 471 74 49 44 70 94 95 4 22 6 20 37 12 7 9 5 15 6 4 158 159 97 165 175 125 2 6 5 5 8 2 1 2 0 0 0 0 9 9 12 22 16 22 80 121 106 92 141 105 40 61 47 57 55 61 10 16 11 17 12 16 951 953 820 1056 1139 913 Table 6: Number of Injuries with the Accident' Causes (2003-2013) Cause Year Speed Neglect Fatigue Drunk Driving Overtaking Weather Condition Sudden Stopping Safety Distance Improper Act Vehicle Defects Road Defects TOTAL 7|Page 2003 2533 3092 1 58 361 31 8 92 364 185 10 6735 2004 2012 3363 43 40 548 6 12 134 247 222 9 6636 2005 2644 3183 4 27 404 17 2 108 110 149 10 6658 2006 4454 2119 4 50 357 39 11 77 273 144 20 7548 2007 2008 5038 5395 1650 1266 10 21 77 94 445 941 70 84 20 82 152 576 770 1824 289 243 10 32 8531 10558 2009 2010 2011 4074 4340 4932 642 457 573 30 24 27 97 110 99 913 600 975 198 85 34 142 68 106 398 400 1084 2925 3560 3118 266 333 343 98 89 146 9783 10066 11437 2012 5424 672 33 91 908 51 100 941 2849 413 136 11618 2013 6923 845 13 136 314 57 21 553 1579 243 118 10802 Seventh Traffic Safety Conference, 12-13 May 2015, Amman Jordan Figure 2: Total Number of Accidents, Fatalities and Injuries from 2003 to 2013 4.2 Statistical Model Formulation SPSS software was used in formulating the regression models by using the method of least squares. The method of least squares is a procedure to determine the best fit line to date to find linear relationships between dependent and independent variables. The least square method (LSM) is probably the most popular technique in statistics. This is due to several factors. First, most common estimators can be casted within this framework. For example, the mean of a distribution is the value that minimizes the sum of squared deviations of the scores. Second, using squares makes LSM mathematically very tractable because the Pythagorean Theorem indicates that, when the error is independent of an estimated quantity, one can add the squared error and the squared estimated quantity. Third, the mathematical tools and algorithms involved in LSM (derivatives, Eigen decomposition and singular value decomposition) have been well studied for a relatively long time (srmo.sagepub.com). In this study, the dependent variables are: total number of accidents, fatalities, injuries and property damage. Otherwise, the independent variables are speed, neglect, fatigue, drunk driving, overtaking, weather condition, sudden stopping, safety distance, improper act, vehicle defects and road defects. The relationship between variables can be written using the following equation: = + + + …. + Where: = Dependent variable = Value of the nth independent variable. = The value of Y when X is equal to zero. This is also called the “Y Intercept”. = Regression coefficient of the nth independent variable 8|Page Seventh Traffic Safety Conference, 12-13 May 2015, Amman Jordan This relationship is known as a multiple linear regression model. The multiple linear regression models are an extension of a simple linear regression model to incorporate two or more explanatory variable in a prediction equation for a response variable. The stepwise method was used to form the multiple linear regression models. Stepwise regression is designed to find the most parsimonious set of predictors that are most effective in predicting the dependent variable. Variables are added to the regression equation one at a time, using the statistical criterion of maximizing the R² of the included variables. Several models were formed and the best models were selected. The selected model has to have not less than two variables and not more than three or four variables. The multiple linear regression analysis makes several key assumptions (www.statisticssolutions.com):      Linear relationship Multivariate normality No or little multicollinearity No autocorrelation Homoscedasticity Correlation quantifies the extent to which two quantitative variables, X and Y. The correlation coefficient is scaled so that it is always between -1 and +1. When r is close to 0 this means that there is little relationship between the variables and the farther away from 0 r is, in either the positive or negative direction, the greater the relationship between the two variables. The sign of the correlation coefficient determines whether the correlation is positive or negative. The magnitude of the correlation coefficient determines the strength of the correlation. The correlation strength can be described as: (www.sjsu.edu) 0 < |r| < 0.3 weak correlation 0.3 < |r| < 0.7 moderate correlation |r| > 0.7 strong correlation 4.3 Prediction Regression Models For the traffic accident prediction models, the independent variables were presented in table 2 and the dependent variables are shown in table 7 below: Table 7: The Dependent Variables for each Model Dependent Variable Definition Total Accidents Total Fatalities Total Injuries 9|Page Seventh Traffic Safety Conference, 12-13 May 2015, Amman Jordan 5. Results and Analysis 5.1 Development of total traffic accident model (Y1) Using the SPSS software, the input data are shown in table 4 and the output of the software program are presented below: Table 8: Model Summary of the Total Number of Accidents Model Summaryh Change Statistics Std. Error Mod R Adjusted R of the R Square F Square Square Estimate Change Change Sig. F el R 1 .903a .815 .794 456.205 .815 39.654 1 9 .000 2 b .940 .925 275.068 .125 16.756 1 8 .003 3 c .986 .972 .961 199.620 .032 8.190 1 7 .024 4 .998d .996 .993 85.072 .023 32.542 1 6 .001 5 e .999 .999 33.893 .004 32.800 1 5 .002 f 1.000 1.000 17.313 .000 15.163 1 4 .018 g 1.000 1.000 3.205 .000 113.744 1 3 .002 6 7 .970 1.000 1.000 1.000 df1 df2 Change a. Predictors: (Constant), x2 b. Predictors: (Constant), x2, x1 c. Predictors: (Constant), x2, x1, x9 d. Predictors: (Constant), x2, x1, x9, x8 e. Predictors: (Constant), x2, x1, x9, x8, x7 f. Predictors: (Constant), x2, x1, x9, x8, x7, x3 g. Predictors: (Constant), x2, x1, x9, x8, x7, x3, x4 h. Dependent Variable: Total_Accidents The selected model is: = 1364.124 + 0.868 + 0.883 + 1.006 + 1.306 The model shows a strong correlation (R2 = 0.996) between dependent and independent variables. The traffic accident model has a strong correlation with neglect, speed, improper act and safety distance. 10 | P a g e Seventh Traffic Safety Conference, 12-13 May 2015, Amman Jordan 5.2 Development of total fatality model (Y2) The statistical input data are shown in table 5 and the output of SPSS program are shown as follows: Table 9: Model Summary of the Total Number of Fatalities Model Summaryc Mod R el R Adjusted R Std. Error Square Square of the Estimate Change Statistics R Square F df1 Change Change df2 Sig. F Change 1 a .962 .926 .918 51.686 .926 113.224 1 9 .000 2 .978b .957 .946 42.088 .030 1 8 .046 5.573 a. Predictors: (Constant), x1 b. Predictors: (Constant), x1, x3 c. Dependent Variable: Total_Fatalities Table 10: Coefficients of the Total Number of Fatalities Coefficientsa Unstandardized Standardized Coefficients Coefficients Collinearity Correlations Statistics ZeroModel 1 2 B Std. Error (Constant) 77.512 73.124 x1 1.659 .156 (Constant) 116.030 61.740 x1 1.480 .148 x3 3.327 1.409 a. Dependent Variable: Total_Fatilities 11 | P a g e Beta T Sig. 1.060 .317 10.641 .000 1.879 .097 .859 10.020 .202 2.361 .962 Toler order Partial Part ance VIF .962 .962 .962 1.000 1.000 .000 .962 .962 .738 .738 1.354 .046 .642 .641 .174 .738 1.354 Seventh Traffic Safety Conference, 12-13 May 2015, Amman Jordan The selected model is: = 116.030 + 1.480 + 3.327 The model shows a strong correlation (R2 = 0.957) between dependent and independent variables. The total traffic accident fatality model has a strong correlation with the speed and neglect. 5.3 Development of total injury model (Y3) The statistical input data are shown in table 6 and the output of SPSS program are as follows: Table 11: Model Summary of the Total Number of Injuries Model Summaryc Change Statistics Mode l R Square R a 1 2 .902 .976b Adjusted R Std. Error of Square the Estimate .813 .953 .792 .942 R Square Change 894.868 473.647 .813 .140 F Change df1 39.175 24.126 Sig. F Change df2 1 1 9 8 .000 .001 a. Predictors: (Constant), x8 b. Predictors: (Constant), x8, x4 c. Dependent Variable: Total_Injuries Table 12: Coefficients of the Total Number of Injuries Coefficientsa Model 1 2 Unstandardized Standardized Coefficients Coefficients B Std. Error (Constant) 7052.218 427.134 x8 5.049 .807 (Constant) 5444.379 397.826 x8 3.323 .553 x4 28.989 5.902 a. Dependent Variable: Total_Injuries 12 | P a g e Beta Collinearity Correlations t Sig. 16.511 .000 6.259 .000 13.685 .000 .593 6.008 .485 4.912 .902 Statistics Zero-order Partial Part Tolerance VIF .902 .902 .902 1.000 1.000 .000 .902 .905 .458 .596 1.678 .001 .862 .867 .375 .596 1.678 Seventh Traffic Safety Conference, 12-13 May 2015, Amman Jordan The selected model is: = 5444.379 + 3.323 + 28.989 The model shows a strong correlation (R2 = 0.953) between dependent and independent variables. The total traffic accident injury model has a strong correlation with the safety distance and drunk driving. 6. Discussion of Results The multiple linear regression analysis was used to find the relationships between the dependent and independent variables. The eleven independent variables were shown in table 2. For this data, four models were created and the summary of model results is shown below in table 15. Table 15: Summary of the Resulted Regression Models Model No. 1 R2 Regression Model = 1364.124 + 0.868 + 0.883 + 1.006 + 1.306 0.996 2 = 116.030 + 1.480 + 3.327 0.957 3 = 5444.379 + 3.323 + 28.989 0.954 6.1 Validation of Model (1) “Total Traffic Accident Number”: The model is: = 1364.124 + 0.868 + 0.883 + 1.006 + 1.306 The statistical SPSS output were presented in tables 3.7, 3.8 and 3.9. All independent variables had negative correlation with the total accidents except x2 and x8 which had a positive correlation. The variable x2 (neglect) had the highest correlation with accident number. Results show a strong correlation coefficient between dependent and independent variables (R2 = 0.996). Which means the proportion of variance in the dependent variable explained by all of the independent variables is 99.6%. So, variables like speed, neglect, safety distance and improper act had a 99.6% of predictability about the model. By looking to the P value of the model, it was found to be less than 0.05 which showed the model significant. The t statistics for the b coefficients are less than the level of significance of 0.05. That conducted there is a statistically significant relationship between the number of accident and speed, neglect, improper act and safety distance. The value of the constant ( ) is equal to 1364.124, which is the intercept or the predicted value of X if Y is 0. So, if scores are 0 the reading score is 1364.124. The values of ( ) are 0.868, 0.883, 1.006 and 1.306 respectively, values that Y will change by if X changes by 13 | P a g e Seventh Traffic Safety Conference, 12-13 May 2015, Amman Jordan 1 unit. All ( ) values are positive, indicating a direct relationships. For example, as the variable neglect by one increase, the total number of accidents increases by 0.868. By looking closely at standardized coefficients to compare effect of independent variables, the variable neglect x2 (1.958) had much more influence than the other three variables x1, x9 and x8 which their beta equal to 0.884, 0.671 and 0.176, respectively. It is necessary that prediction accident cases from the analysis of data should be verified for having a confidence with real accident data. The validation of the model can be tested by comparing between the observed number of accidents and the predicted one. There is a very slight difference between them. Percentage difference equals the absolute value of the change in value, divided by the average of the 2 numbers, all multiplied by 100. The percentage difference can be finding by: Percentage difference= (| V1 - V2 | / ((V1 + V2)/2)) * 100 The difference was found not more than 1% for each year. For example, the observed number of total accident in 2003 is 10197 accidents and the predicted number is found to be 10119 accidents. So, the predicted one is 0.8% less than the observed number. 6.2 Validation of Model (2) “Total Fatalities”: The model is: = 116.030 + 1.480 + 3.327 The statistical SPSS output were shown in tables 3.10, 3.11 and 3.12. All independent variables had positive correlation with the total number of fatalities except x2 and x7 which had a negative correlation. The variable x1 (speed) had the highest correlation with fatality number. Results show a strong correlation coefficient between dependent and independent variables (R2 = 0.957). Which means the proportion of variance in the dependent variable explained by all of the independent variables is 95.7%. So, variables like speed and fatigue had a 95.7% of predictability about the model. By looking to the P value of the model, it was found to be less than 0.05 which showed the model significant. The constant value ( ) is equal to 116.030; this is the predicted value of total number of fatalities when all other variables are zero. The coefficient ( ) of the speed is about 1.5. So for every unit increase in speed, expect an approximately 1.5 point increase in the number of fatalities, holding all other variables constant. On the other hand, an increase in fatigue will make a change in the number of fatalities about 3.3 point. By looking closely at standardized coefficients to compare effect of independent variables, the variable speed x1 (0.859) had much more influence than the variable fatigue x3 (0.202) on the number of fatalities. The model validity was tested and the difference was found not more than 10% for each year. For example, the observed number of fatalities in 2003 was 578 fatalities and the predicted number was found to be 535 fatalities. So, the predicted one is 8% less than the observed number. 14 | P a g e Seventh Traffic Safety Conference, 12-13 May 2015, Amman Jordan 6.3 Validation of Model (3) “Total Injuries”: The model is: = 5444.379 + 3.323 + 28.989 The statistical SPSS output were shown in tables 3.13, 3.14 and 3.15. All independent variables had positive correlation with the total number of injuries except x2 which had a negative correlation. The variable x2 (neglect) had the highest correlation with the injury number. Results show a strong correlation coefficient between dependent and independent variables (R2 = 0.953). Which means the proportion of variance in the dependent variable explained by all of the independent variables is 95.3%. So, variables like safety distance and drunk driving had a 95.3% of predictability about the model. By looking to the P value of the model, it was found to be less than 0.05 which showed the model significant. The constant value ( ) is equal to 5444.379; this is the predicted value of total number of injuries when all other variables are zero. The coefficient ( ) of the safety distance is about 3.3. So for every unit increase in safety distance, expect an approximately 3.3 point increase in the number of injuries, holding all other variables constant. On the other hand, an increase in drunk driving will make a change in the number of injuries about 29 point. By looking closely at standardized coefficients to compare effect of independent variables, the variable safety distance x8 (0.593) had more influence than the variable drunk driving x4 (0.485) on the number of injuries. The model validity was tested and the difference was found not more than 10% for each year. For example, the observed number of injuries in 2003 was 6735 injuries and the predicted number was found to be 7431 injuries. So, the predicted one is 10% higher than the observed number. 7. Conclusions The following conclusions can be concluded from this research study: 1) The predicted models of the statistical traffic accident data that resulted from SPSS computer program indicate a strong correlation coefficient between dependent and independent variables. 2) The statistical results show the models are statically significant and statically highly significant, where the P-values are less than 0.05 and 0.001. 3) According to the analysis of the traffic accident data and weather condition, vehicle defects and road defects were not considered to be main causes of traffic accidents in Oman. 4) Speed appears as the main cause of traffic accidents in the total number of traffic accidents, fatalities and injuries. 5) Speed, neglect, safety distance between vehicles and improper act are the major causes of traffic accidents which are related to the driver behaviors. By managing these accident causes, the number of accidents should be decreased. 15 | P a g e Seventh Traffic Safety Conference, 12-13 May 2015, Amman Jordan 6) The driver behaviors, speed and fatigue are the leading causes of the total number of fatalities during the eleven years. So, fatality number can be reduced in the future by managing a countermeasure program especially for the speed and fatigue. 7) The main causes of the accident injuries are the safety distance and drunk driving. Those causes are related to the driver behaviors. The number of injuries can be reduced by applying a countermeasure program especially for the safety distance and drunk driving. 8) The validation of the predicted models indicates a small error (less than 10%) for the first four models. This was found by comparing the predicted results with the actual numbers. 8. Recommendations According to the previous results the following recommendations can be listed to improve traffic safety: 1) Traffic accident reduction requires countermeasure programs. The mechanism by which independent variables (driver behaviors) are selected for application of a countermeasure should be included as a part of the government highway safety program. 2) A management program (Black spots program) based on before and after study for a special location in order to determine the accident reduction factor for each countermeasure. 3) Provide an educational traffic safety programs in schools and universities. 4) Increase low enforcements for speeding, neglecting roles, improper acting overtaking and drinking of alcohol. 5) More researches are needed with more traffic accident causes due to driver behaviors. 6) A comprehensive traffic safety management program is required for the traffic accident numbers, fatalities, injuries and property damages. 16 | P a g e Seventh Traffic Safety Conference, 12-13 May 2015, Amman Jordan REFERENCES Abojaradeh M., (2013), “Traffic Accidents Prediction Models to Improve Traffic Safety in Greater Amman Area". Journal of Civil and Environment Research issued from IISTE USA. Volume 3, No. 2, 2013. pp 87-101. Abojaradeh M. A., Jrew B., and D. Abojaradeh, (2009). “Traffic Accidents Prediction Models in Amman Area in Amman Jordan”. The Forth Conference on Scientific Research in Jordan, Jordan Society for Scientific Research, 7 November 2009, Amman Jordan. Highway and Traffic in Jordan, pp 59-80. Abojaradeh, M., Jrew B. (2012), "The Effect of Driver Behavior Mistakes on Traffic Safety in Jordan" published and presented in the 6th Traffic Safety in Jordan by the Jordan Traffic Institute, Jordan 19-20 November 2012. Hong, D., LEE, Y., YANG, H., KIM, J., W., (2005), “Development of Traffic Accident Prediction Models by Traffic and Road Characteristics in Urban Areas”, Proceeding of the Eastern Asia Society for Transportation Studies, Vol. 5, pp. 2046-2061, 2005. Jaddan, K., Al-Hyari, I., Naghawi, H., Ammourah, R., AlNabulsi, Z., (2013), “ Traffic Safety in Jordan: Magnitude, Cost and Potential Countermeasures”, Journal of Traffic and Logistics Engineering, Vol. 1, No. 1, June 2013. Mazharul, M., Islam and Al Hadhrami A.Y.S (2012), “Increased Motorization and Road Traffic Accidents in Oman”, Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 3(6): 907-914 Montgomery D., Runger G. (2010). Applied Statistics and Probability for Engineers Fifth Edition, USA. National Highway Traffic Safety Administration (NHTSA) (2010), USA. Nicholas J. Garber, Lester A. Hoel , (2009)."Traffic and Highway Engineering", Fourth edition, University of Virginia. ROSPA, 2009, www.rospa.com Royal Oman Police (2013), “Facts and Figures 2013” World Health Organization (2010) , "Global Plan for the Decade of Action for Road Safety 2011-2020", Geneva. World Health Organization (2013), "Global Status Report on Road Safety: Supporting a Decade of Action", World Health Organization, Geneva. srmo.sagepub.com www.care-driver.com www.nolo.com www.sjsu.edu www.statisticssolutions.com 17 | P a g e
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