Evaluation of Traffic Accidents No. through Variety of Weather Conditions According to Differences of Gender and Ages’ Categories of Drivers in Greece

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Evaluation of Traffic Accidents No. through Variety of Weather Conditions According to Differences of Gender and Ages Categories of Drivers in Greece

Evaluation of Traffic Accidents No. through Variety of Weather Conditions According to Differences of Gender and Ages Categories of Drivers in Greece

Jameel Al-Karablieh

Division of Transportation and Project Management Aristotle University of Thessaloniki

Thessaloniki, Greece

Prof. Fotini Kehagia

Division of Transportation and Project Management Aristotle University of Thessaloniki

Thessaloniki, Greece

Abstract – The research includes studying of impacts the diversity of weather conditions on the characteristics of drivers in terms of gender and ages categories of them by assessing No. of traffic accidents which resulted through each type of weather condition during period (2012-2016). The data of research obtained from Hellenic Statistical Authority (ELSTAT), which included No. of traffic accidents in Greece that distributed to 13 types of weather conditions and classified according to gender and ages categories of drivers who involved in these traffic accidents during period of study. The research comprised of studying the statistical data that collected about traffic accidents and evaluation that differences in their No. if they are affecting by the diversity of weather conditions and differences in gender and age of drivers. In addition, the study included assessment the relationship between the dependent variable, which includes of traffic accidents No. and the other independent variables that consists of weather conditions and drivers' characteristics through appropriate statistical tests. Depending on study of the statistical data and the results which obtained through the analysis, it was found that there is a relationship between the diversity of weather conditions and the differences in No. of traffic accidents during period of study. Also, it got that there is effects on No. differences of traffic accidents which resulted according to different characteristics of drivers during some weather conditions more than the other. In Greece, the study found that most of the traffic accidents happened during the clear sky of weather condition and the male drivers who have age category (18-35) have the bigger No. of accidents during the most of weather conditions especially in clear sky weather. Thus, the study recommended to increase the traffic awareness for all categories of drivers during the variety of weather conditions of your paper [title, text, heads, etc.] in its style sheet.

Keywords – Drivers characteristics, Drivers Gender, Drivers Age, Adverse Weather Conditions, Road weather safety, Weather effects, Driver behaviour, Road conditions, Traffic Accidents, Roads in Greece.

  1. INTRODUCTION

    The study evaluates the impact of various weather conditions on the different No. of traffic accidents depending on the variety of drivers' characteristics who involved in those accidents. There are many researches which related to assessment the effects of diversity weather conditions in terms of increasing or decreasing of accidents No. according to increase of bad weather condition through driving of vehicles on roads in several areas of the world. Some of researches have concluded that bad weather conditions may increase No. of traffic accidents and some others have concluded which No. of traffic accidents is bigger during good weather conditions.

    Adverse weather conditions, such as strong wind, heavy rain or snow, heavy fog and so on, have obvious impacts on roadway traffic operations, especially traffic safety. Also, Among adverse weather conditions, rainy weather may be one of the conditions which cause significant negative impacts on traffic safety. The combined impacts from roadway, vehicle, traffic control, and driver behavior under rainy weather conditions could increase the potential for safety problems and traffic crashes [1].

    Driving largely is a visual task, poor visibility conditions such as rain, fog, or snow create several additional demands on the driver and their ability to collect necessary visual information is drastically reduced. The driving task becomes more complex when weather-related conditions of reduced visibility are accompanied by wet surfaces [2]. Bad weather can raise the number of accidents significantly by 20% or more over the base rate [3].

    Adverse weather and road conditions, following e.g. rain, snowfall and temperature fluctuations, are a considerable cause of an elevated risk of traffic accidents and compromised traffic flow in northern Europe and northern America [4].

    Heavy rain weakens the visibility and wets the road surface, which causes drivers to pay more attention while driving, thus with the decrease of traffic flow the probability of severe accident decreases [5].

    Shifting weather patterns due to climate change, such as warmer temperatures, more rain, and less snow, will exacerbate road safety issues. For example, snowfall and rainfall are widely known to reduce visibility and make braking more difficult, and temperatures may influence the mode, frequency, and types of trips [6].

    An increase in maximum wind gust causes an increase in the number of crashes, Global radiation and sunshine duration both had a significant negative impact on road safety [7].

    Weather related crashes refer to crashes that occur during adverse weather conditions. Rainfall represents one of the most critical weather condition variables in traffic safety. Similarly, rain related crashes are those that occur during rainy conditions [8].

    Weather related crashes are those that occur in the presence of rain, sleet, snow, fog, wet pavement, snowy/slushy pavement, and/or icy pavement. Twenty-four percent of all crashes are weather related [9]. Weather conditions are considered to be a factor that affects the number of road accidents and casualties significantly, with different effects according to the type of road.

    Moreover, as the weather also affects mobility, it is to be expected that the effects of weather on the number of injury accidents and casualties are partly due to the changes in mobility occurring at the same time. Rainfall leads to a decrease of road accidents in the Athens urban area [10].

    According to results of the research in Athens is found that contrary to much previous research, increases in rainfall reduce the total number of accidents and fatalities as well as the pedestrian accidents and fatalities, a finding that may be attributed to the safety offset hypothesis resulting from more cautious and less speedy driver behaviour. Similarly, temperature increase was found to lead to increased accidents [11].

    This study will examine whether there is an impacts and relationship between the different characteristics of drivers in terms of gender and ages categories with various of weather conditions on different No. of traffic accidents.

  2. HYPOTHESES OF STUDY

    The main proposed hypothesizes of study as following:

    1. There is not influence of weather condition variation on differences of traffic accidents No. that involved of drivers.

    2. The No. of traffic accidents, which are resulting through adverse weather conditions are more than the accidents that are occurring during good weather conditions.

    3. There is not association between weather conditions and genders of drivers for influencing on different of traffic accidents No..

    4. There is not relationship between weather conditions and age categories of drivers for influencin on different of traffic accidents No..

    V. ANALYSIS OF DATA

    The stage of data analyzing included several of statistical tests for each type of data by using SPSS software. The study through the analysis process are depended on two types of variables which consisted of dependent variables which are included traffic accidents No. and independent variables which are included the weather conditions, gender and ages of drivers who involved in the accidents. The research is containing of four parts for data analysis as the following:

    1. Examination the influence of weather conditions variation on differences of total traffic accidents No. that involved of drivers during period (2012 2016):

    • Description Analysis:

      The data that got from ELSTAT are included No. of traffic accidents through variable weather conditions at accidents occurrence. The descriptive analysis is clarification that there are differences in traffic accidents No. depending on variety of weather conditions and different characteristics of drivers during the period of study (2012-2016) as shown in the statistical Table 1 and Figure 1.

      No. of Traffic Accidents

      Weather Conditions

      Year

      2012

      2013

      2014

      2015

      2016

      Clear sky

      11,316

      11,146

      10,650

      10,455

      10,181

      Strong wind

      34

      28

      20

      22

      28

      Frost

      124

      61

      46

      103

      103

      Fog / mist

      28

      12

      26

      16

      12

      Drizzle

      451

      423

      468

      379

      362

      Rain

      350

      309

      389

      335

      281

      Tempest

      10

      7

      4

      2

      4

      Storm

      10

      4

      11

      11

      4

      Hail

      1

      4

      2

      1

      1

      Snow

      21

      4

      5

      21

      14

      Smoke

      1

      3

      3

      3

      1

      Dust

      0

      1

      1

      2

      2

      Other

      52

      55

      44

      44

      78

      Total

      12,398

      12,057

      11,669

      11,394

      11,071

      Table 1: Statistical Description of Traffic Accidents No. According to Variation of Weather Conditions

  3. METHODOLOGY OF STUDY The study consists of main steps as the following:

    1. Collection the required statistical data from ELSTAT for traffic accidents No. according to variety of weather conditions and drivers information who involved in accidents.

    2. Analysis of data according to suitable statistical tests for each part by using SPSS software.

    3. Showing the results which got by data analysis and discussion of them.

    4. Viewing the conclusions depending on results that got from the study.

    5. Showing the recommendations to enhancement of drivers for contribution in satisfying of road safety.

  4. DATA COLLECTION

    The study depended on statistical data, which got from ELSTAT and they included traffic accidents No. according to weather conditions at accidents occurred and classified to age and gender groups of drivers during period (20122016). In addition, there are very few missing of traffic accidents information consideration to details in tables of data that received from ELSTAT and these missed information are not effecting on the analysis process.

    Consideration to values in Table 1 and as shown in the Figure 1; the clear sky of weather condition has the bigger No. of traffic accidents comparison to other weather conditions and the year of 2012 had the most amount of accidents during the period of study.

    12000

    10000

    8000

    6000

    4000

    2000

    0

    2012

    2013

    2014

    2015

    2016

    Weather Condition

    No. of Traffic Accidents

    Figure 1: Indication of Traffic Accidents No. Through Different Weather Conditions during period (2012-2016)

    This branch of analysis includes testing the significance of relationship between traffic accidents No. which involved of drivers and weather conditions. The hypothesis that assumed as the following:

    H0 = There are not relationship between traffic accidents No. which involved of drivers and different weather conditions at accidents occurrence.

    H1 = There are relationship between traffic accidents No. which involved of drivers and different weather conditions at accidents occurrence.

    For interpretation of the relationship between total traffic accidents and weather conditions used the statistical test "One Way ANOVA".

    Table 2: Descriptive for Data Analysis

    Then there is relationship between different of total traffic accidents and variation of weather conditions; also, there are significant between them at level (p < 0.05).

    1. Studying of association level between weather conditions and genders for effecting on traffic accidents No. during period (2012 2016):

      • Description Analysis:

      The data that collected from ELSTAT for traffic accidents No through variable weather conditions at accidents occurrence divided according gender of drivers who caused those accidents during the period of study (2012-2016) as shown in the Table 4 and Figure 2.

      Table 4: Traffic Accidents No. According to Variation of Weather Conditions Divided According to Gender of Drivers

      Weather Condition

      No. of Traffic Accidents No.

      Mean

      Std.

      Deviation

      Std. Error

      Clear sky

      53748

      1945.59

      1054.942

      4.55

      Strong wind

      132

      5.94

      3.457

      0.301

      Frost

      437

      20.25

      12.568

      0.601

      Fog / Mist

      94

      4.85

      2.462

      0.254

      Drizzle

      2083

      79.94

      40.438

      0.886

      Rain

      1664

      62.47

      31.349

      0.768

      Tempest

      27

      1.74

      1.059

      0.204

      Storm

      40

      2.8

      1.488

      0.235

      Hail

      9

      1

      0

      0

      Snow

      65

      4.11

      2.705

      0.335

      Smoke

      11

      1.55

      0.522

      0.157

      Dust

      6

      1

      0

      0

      Other

      273

      12.28

      6.604

      0.4

      Total/p>

      58589

      1789.69

      1136.202

      4.694

      Variables

      Weather Condition

      Total

      Clear sky

      Strong wind

      Frost

      Fog / Mist

      Drizzle

      Rain

      Tempest

      Storm

      Hail

      Snow

      Smoke

      Dust

      Other

      Male

      Year

      2012

      9434

      26

      102

      26

      379

      290

      9

      9

      1

      18

      1

      0

      43

      10338

      2013

      9125

      22

      50

      11

      344

      251

      4

      3

      2

      4

      3

      1

      49

      9869

      2014

      8647

      17

      39

      21

      394

      325

      3

      10

      1

      3

      3

      1

      43

      9507

      2015

      8561

      18

      90

      13

      331

      276

      1

      10

      1

      16

      1

      2

      40

      9360

      2016

      8332

      23

      86

      11

      292

      242

      3

      2

      0

      11

      1

      1

      68

      9072

      Total

      44099

      106

      367

      82

      1740

      1384

      20

      34

      5

      52

      9

      5

      243

      48146

      Female

      Year

      2012

      1882

      8

      22

      2

      72

      60

      1

      1

      0

      3

      0

      0

      9

      2060

      2013

      2021

      6

      11

      1

      79

      58

      3

      1

      2

      0

      0

      0

      6

      2188

      2014

      2003

      3

      7

      5

      74

      64

      1

      1

      1

      2

      0

      0

      1

      2162

      2015

      1894

      4

      13

      3

      48

      59

      1

      1

      0

      5

      2

      0

      4

      2034

      2016

      1849

      5

      17

      1

      70

      39

      1

      2

      1

      3

      0

      1

      10

      1999

      Total

      9649

      26

      70

      12

      343

      280

      7

      6

      4

      13

      2

      1

      30

      10443

      Table 3: Results of ANOVA Test

      Sum of Squares

      df

      Mean Square

      F

      Sig.

      Between Groups

      1.58E+10

      12

      1.32E+09

      1290.437

      0

      Within Groups

      5.98E+10

      58576

      1021242.304

      Total

      7.56E+10

      58588

      Consideration to the results in Table 2 and Table 3 of analysis; the values of calculated (df = 12), (F= 1290.437) and the statistical significant at level (p = 0.00 < 0.05). So that, we rejected the hypothesis (H0) and accepted the hypothesis (H1).

      Consideration to statistical data in Table 4 and as shown in Figure 2; the gender drivers has the bigger No. of traffic accidents at clear sky of weather condition comparison to others conditions during period of study.

      different of traffic accidents No. and there is significant at level (p < 0.05).

      40000

      Male

      30000

      Female

      Total No.pf Traffic Accidents

    2. Studying of association level between weather conditions and age categories for effecting on traffic accidents No. during period (2012 2016):

      – Description Analysis:

      20000

      10000

      The data that collected from ELSTAT for traffic accidents No through variable weather conditions at accidents occurrence divided according to six age categories of drivers who involved of those accidents during the period of study (2012-2016) as shown in the in Table 6 and Figure 3.

      0

      Weather Condition

      Figure 2: Indication of Total Traffic Accidents No. Through Different Weather Conditions according to Gender of Drivers during period (2012-2016)

      This type of analysis includes testing the significance of association between variation of weather conditions and genders of drivers for influencing on different traffic accidents No.. The hypothesis that assumed as the following:

      H0 = There are not association between variation weather conditions and genders of drivers for influencing on different traffic accidents No. .

      H1 = There are association between variation weather conditions and genders of drivers for influencing on different traffic accidents No. .

      For interpretation, the association between them used the statistical test "Univariate Analysis of Variance".

      Table 5: Univariate Analysis of Variance Test

      Total No. of Traffic Accidents

      20000

      18000

      16000

      0-17

      14000

      18-35

      12000

      10000

      8000

      6000

      4000

      2000

      0

      Weather Condition

      36-49

      50-64

      65+

      Unknown

      Source

      Type III Sum of Squares

      df

      Mean Square

      F

      Sig.

      Corrected Model

      3.79E+10

      25

      1.52E+09

      2356.471

      0

      Intercept

      3271990.34

      1

      3271990.34

      5.082

      0.024

      Weather Condition

      4.57E+09

      12

      3.81E+08

      591.215

      0

      Gender

      1136089.6

      1

      1136089.6

      1.765

      0.184

      Weather Condition

      * Gender

      1.61E+09

      12

      1.34E+08

      207.787

      0

      Error

      3.77E+10

      58563

      643835.914

      Total

      2.63E+11

      58589

      Corrected Total

      7.56E+10

      58588

      Figure 3: Indication of Total Traffic Accidents No. Through Different Weather Conditions according to Age Categories of Drivers during period (2012-2016)

      According to the statistical data in Table 6 and as shown in Figure 3; the age category (1835) of drivers has the bigger No. of traffic accidents in clear sky of weather condition comparison to others conditions during (2012 2016).

      This type of analysis includes testing the significance of association level between variation of weather conditions and age categories of drivers for influencing on different traffic accidents No.. The hypothesis that assumed as the following:

      H0 = There are not association between variation weather conditions and age categories of drivers for influencing on different traffic accidents No..

      Depending on the values in Table 5 of analysis; the value of (df- Gender * Weather Condition = 12), (F- Gender * Weather Condition = 207.787) and the value of significance (p= 0.000 < 0.05). So that, we reject the hypothesis (H0) and accept the hypothesis (H1). Then there is association between variations of Weather conditions and gender of drivers in influencing on

      Table 6: Traffic Accidents No. According to Variation of Weather Conditions Divided According to Age Categories of Drivers

      H1 = There are association between variation weather conditions and age categories of drivers for influencing on different traffic accidents No..

      For interpretation, the association between them used the statistical test "Univariate Analysis of Variance".

      Table 7: Univariate Analysis of Variance Test

      Source

      Type III Sum of Squares

      df

      Mean Square

      F

      Sig.

      Corrected Model

      4.584E10

      67

      6.842E8

      1344.068

      0.000

      Intercept

      1910737.892

      1

      1910737.892

      3.753

      0.053

      Weather Condition

      1.422E9

      12

      1.185E8

      232.814

      0.000

      Age

      2738009.562

      5

      547601.912

      1.076

      0.371

      Weather Condition * Age

      2.198E9

      50

      4.396E7

      86.361

      0.000

      Error

      2.979E10

      58521

      509070.987

      Total

      2.633E11

      58589

      Corrected Total

      7.563E10

      58588

      Year

      Age

      Weather Condition

      Total

      Clear sky

      Strong wind

      Frost

      Fog / Mist

      Drizzle

      Rain

      Tempest

      Storm

      Hail

      Snow

      Smoke

      Dust

      Other

      2012

      -17

      133

      0

      3

      1

      5

      2

      0

      0

      0

      0

      0

      0

      0

      144

      18-35

      4442

      12

      49

      10

      160

      130

      1

      4

      0

      8

      0

      0

      24

      4840

      36-49

      3008

      15

      34

      9

      128

      98

      5

      4

      0

      8

      0

      0

      15

      3324

      50-64

      1877

      5

      23

      5

      91

      64

      2

      0

      1

      3

      0

      0

      11

      2082

      65+

      1236

      1

      9

      3

      48

      37

      2

      1

      0

      1

      1

      0

      1

      1340

      Un known

      620

      1

      6

      0

      19

      19

      0

      1

      0

      1

      0

      0

      1

      668

      Total

      11316

      34

      124

      28

      451

      350

      10

      10

      1

      21

      1

      0

      1

      12398

      2013

      -17

      140

      1

      3

      0

      3

      2

      1

      0

      0

      0

      0

      0

      0

      150

      18-35

      4150

      7

      27

      3

      151

      123

      3

      3

      2

      0

      1

      1

      16

      4487

      36-49

      3045

      9

      15

      4

      123

      94

      2

      1

      2

      2

      2

      0

      19

      3318

      50-64

      2056

      5

      9

      4

      75

      57

      1

      0

      0

      1

      0

      0

      13

      2221

      65+

      1225

      6

      6

      0

      61

      28

      0

      0

      0

      0

      0

      0

      3

      1329

      Un known

      530

      0

      1

      1

      10

      5

      0

      0

      0

      1

      0

      0

      4

      552

      Total

      11146

      28

      61

      12

      423

      309

      7

      4

      4

      4

      3

      1

      55

      12057

      2014

      -17

      85

      0

      0

      0

      2

      3

      0

      0

      0

      0

      0

      0

      0

      90

      18-35

      3949

      7

      19

      11

      174

      120

      1

      3

      1

      1

      1

      0

      12

      4299

      36-49

      289

      10

      14

      8

      144

      130

      2

      1

      1

      3

      0

      1

      14

      3222

      50-64

      1999

      0

      8

      4

      81

      72

      1

      2

      0

      0

      0

      0

      9

      2176

      65+

      1184

      1

      5

      3

      46

      42

      0

      5

      0

      1

      2

      0

      5

      1294

      Un known

      539

      2

      0

      0

      21

      22

      0

      0

      0

      0

      0

      0

      4

      588

      Total

      10650

      20

      46

      26

      468

      389

      4

      11

      2

      5

      3

      1

      44

      11669

      2015

      -17

      100

      0

      1

      1

      2

      3

      0

      0

      0

      1

      0

      0

      1

      109

      18-35

      3717

      6

      44

      8

      138

      116

      1

      1

      0

      10

      0

      0

      15

      4056

      36-49

      2877

      8

      25

      3

      108

      104

      1

      4

      0

      6

      2

      1

      15

      3154

      50-64

      1913

      4

      21

      1

      64

      54

      0

      4

      0

      1

      0

      0

      5

      2067

      65+

      1297

      3

      2

      2

      56

      43

      0

      2

      1

      2

      1

      1

      3

      1413

      Un known

      551

      1

      10

      1

      11

      15

      0

      0

      0

      1

      0

      0

      5

      595

      Total

      10455

      22

      103

      16

      379

      335

      2

      11

      1

      21

      3

      2

      44

      11394

      2016

      -17

      89

      0

      0

      1

      1

      1

      0

      0

      0

      0

      0

      0

      2

      94

      18-35

      3515

      15

      41

      3

      124

      91

      1

      2

      1

      7

      0

      1

      27

      3828

      36-49

      2808

      3

      33

      3

      110

      76

      1

      0

      0

      1

      1

      1

      18

      3055

      50-64

      2063

      7

      18

      5

      72

      71

      2

      1

      0

      4

      0

      0

      15

      2258

      65+

      1290

      1

      6

      0

      48

      29

      0

      1

      0

      2

      0

      0

      10

      1387

      Un known

      416

      2

      5

      0

      7

      13

      0

      0

      0

      0

      0

      0

      6

      449

      Total

      10181

      28

      103

      12

      362

      281

      4

      4

      1

      14

      1

      2

      78

      11071

      Depending on the values in Table 7 of analysis; the value of (df-Weather Condition*Age= 50) (F-Weather Condition * Age= 86.361) and the value of significance (p = 0.000 < 0.05). So that, we rejected the hypothesis (H0) and accepted the hypothesis (H1). Then there is association between weather conditions and age categories of drivers in influencing on different of traffic accidents No. and there is significant at level (p < 0.05).

    3. Analysis of Model

    The model comprise of equation which identification No. of traffic accidents, which are resulting in Greece through variety of weather conditions and according to some characteristics of drivers. This branch of study considered the dependent variables which were No. of traffic accidents and the independent variables that were age categories and gender of drivers during period (2012 2016).

    The equation model resulted by Log Linear Regression Analysis and it considered female of gender variables, (36-49) of age categories variables of drivers and Strong wind of weather condition the references in the analysis as showing in the following:

    log (Y)=0 + 1X1 + 2X2 + 3X3 + ..+nXn (1)

    log (No. of Traffic Accidents) = 0 + 1 Gender1 (Male) + 2

    Age Category (1) + 3 Age Category (2) + 4 Age Category (4)

    + 5 Age Category (5) + 6 Age Category (6) + 7 W.C.* (1) +

    8 W.C. (3) + 9 W.C. (4) + 10 W.C. (5) + 11 W.C. (6) + 12

    W.C. (7) + 13 W.C. (8) + 14 W.C. (9) + 15 W.C. (10) + 16

    W.C. (11) + 17 W.C. (12) + 18 W.C. (13) (2)

    * W.C. is abbreviation for Weather Condition.

    Consideration to the calculated values, which produced in the following Table 8; the equation model of traffic accidents is resulted.

    Table 10: Coefficients a

    Model

    Unstandardized Coefficients

    Standardized Coefficients

    t

    Sig.

    Collinearity Statistics

    B

    Std. Error

    Beta

    Tolerance

    VIF

    (Constant)

    0.49

    0.013

    37.84

    0.000

    Gender1

    Male

    1.488

    0.002

    0.429

    927.245

    0.000

    0.989

    1.011

    Age1

    -17

    -3.205-

    0.006

    -0.240-

    -515.372-

    0.000

    0.973

    1.028

    Age2

    18-35

    0.289

    0.002

    /td>

    0.105

    187.363

    0.000

    0.674

    1.483

    Age4

    50-64

    -0.399-

    0.002

    -0.116-

    -216.535-

    0.000

    0.732

    1.367

    Age5

    65+

    -0.837-

    0.002

    -0.201-

    -388.593-

    0.000

    0.788

    1.268

    Age6

    Unknown

    -1.681-

    0.003

    -0.272-

    -558.444-

    0.000

    0.889

    1.124

    W.C. 1

    Clear sky

    5.81

    0.013

    1.204

    450.897

    0.000

    0.03

    33.729

    W.C. 3

    Frost

    1.137

    0.015

    0.074

    77.445

    0.000

    0.234

    4.279

    W.C. 4

    Fog / mist

    -0.195-

    0.02

    -0.006-

    -9.750-

    0.000

    0.585

    1.71

    W.C. 5

    Drizzle

    2.58

    0.013

    0.36

    194.372

    0.000

    0.062

    16.185

    W.C. 6

    Rain

    2.364

    0.013

    0.296

    176.835

    0.000

    0.076

    13.22

    W.C. 7

    Tempest

    -0.988-

    0.031

    -0.016-

    -31.628-

    0.000

    0.831

    1.204

    W.C. 8

    Storm

    -0.698-

    0.027

    -.014-

    -26.163-

    0.000

    0.768

    1.302

    W.C. 9

    Hail

    -1.308-

    0.051

    -0.012-

    -25.687-

    0.000

    0.936

    1.068

    W.C. 10

    Snow

    -0.392-

    0.022

    -0.010-

    -17.508-

    0.000

    0.671

    1.491

    W.C. 11

    Smoke

    -1.078-

    0.046

    -0.011-

    -23.239-

    0.000

    0.923

    1.083

    W.C. 12

    Dust

    -1.688-

    0.062

    -0.013-

    -27.343-

    0.000

    0.957

    1.045

    W.C. 13

    Other

    0.684

    0.016

    0.035

    43.644

    0.000

    0.327

    3.054

    Table 8: Model Summary

    Model

    R

    R Square

    Adjusted R Square

    Std. Error of the Estimate

    1

    dimension0

    0.994a

    0.988

    0.988

    0.14786

    1. Predictors: (Constant), Gender1, Age1, Age2, Age4, Age5, Age6, W.C.1, W.C.3,.., W.C.13

      As shown in Table 8; R value is (0.994) which indicates a high degree of correlation between the dependent variable of total No. of traffic accidents and the independent variables. In addition, the value of R2 indicates that almost (98.8%) of the total No. of traffic accidents variability is explained by the independent variables.

      Table 9: ANOVAb

      Model

      Sum of Squares

      df

      Mean Square

      F

      Sig.

      Regression

      102115.363

      18

      5673.076

      259504.207

      0.000

      Residual

      1280.411

      58570

      0.022

      Total

      103395.774

      58588

      1. Dependent Variable: log (Total No. of Traffic Accidents)

      2. Dependent Variable: log (Total No. of Traffic Accidents)

        Table 8 and Table 9 indicated that the regression model predicts the dependent variable significantly according the hypothesis

        H0: R2 = 0 H1: R2 0

        The calculated value is (p =0.00 < 0.05); Then the study rejected the null hypothesis (H0) and accepted the hypothesis H1. Thus, there is a relation between the dependent variables that were No. of traffic accidents and independents variables which were gender, age categories of drivers and weather conditions.

        Table 10 of coefficients which resulted providing the required values and information predict total No. of traffic accident from independent variables, as well as determine whether independent variables contributes statistically significantly to the model (by looking at the "Sig." column).

        According to values in Table 10; the log regression equation of this model as the following:

        log (No. of Traffic Accidents) = (0.490) + (1.488) Gender1 (Male) (3.205) Age Category (1) + (0.289) Age Category (2)

        (0.399) Age Category (4) (0.837) Age Category (5) (1.681)

        Age Category (6) + (5.810) W.C. (1) + (1.137) W.C. (3) – (0.195)

        W.C. (4) + (2.580) W.C. (5) + (2.364) W.C. (6) (0.988) W.C.

        (7) – (0.698) W.C. (8) (1.308) W.C. (9) (0.392) W.C. (10)

        (1.078) W.C. (11) (1.688) W.C. (12) + (0.684) W.C. (13)

        (3)

        • The value of (1.488) that resulted indicating for the total traffic accidents No., which resulted by male drivers that in average is bigger than value of total No. of traffic accident which resulted by female drivers.

        • The value of (3.205), which resulted indicating that the total No. of traffic accident for drivers who are in age category

          1. of (0-17), in average is less than the total No. of traffic accidents of the drivers who are in age category (3) of (36- 49).

        • The value of (0.289), which resulted indicating that the total No. of traffic accident for drivers who are in age category

          1. of (18-35), in average is bigger than the total No. of traffic accidents of the drivers who are in age category (3) of (36-49).

    • The value of (0.399), which resulted indicating that the total No. of traffic accident for drivers who are in age category

      1. of (18-35), in average is less than the total No. of traffic accidents of the drivers who are in age category (3) of (36- 49).

    • The value of (0.837), which resulted indicating that the total No. of traffic accident for drivers who are in age category

      1. of (18-35), in average is less than the total No. of traffic accidents of the drivers who are in age category (3) of (36- 49).

    • The value of (1.681), which resulted indicating that the total No. of traffic accident for drivers who are in age category

      1. of (unknown), in average is less than the total No. of traffic accidents of the drivers who are in age category (3) of (36-49).

    • The value of (5.810), which resulted are indicating about No. of traffic accidents which occurred in weather condition 1 (Clear sky), in average is bigger than the value of total traffic accidents No. which occurred in weather condition 2 (Strong wind).

    • The value of (1.137), which resulted are indicating about No. of traffic accidents which occurred in weather condition 3 (Frost), in averae is bigger than the value of total traffic accidents No. which occurred in weather condition 2 (Strong wind).

    • The value of (0.195), which resulted are indicating about No. of traffic accidents which occurred in weather condition 4 (Fog / mist), in average is less than the value of total traffic accidents No. which occurred in weather condition 2 (Strong wind).

    • The value of (2.580), which resulted are indicating about No. of traffic accidents which occurred in weather condition 5 (Drizzle), in average is bigger than the value of total traffic accidents No. which occurred in weather condition 2 (Strong wind).

    • The value of (2.364), which resulted are indicating about No. of traffic accidents which occurred in weather condition 6 (Rain), in average is bigger than the value of total traffic accidents No. which occurred in weather condition 2 (Strong wind).

    • The value of (0.988), which resulted are indicating about No. of traffic accidents which occurred in weather condition 7 (Tempest), in average is less than the value of total traffic accidents No. which occurred in weather condition 2 (Strong wind).

    • The value of (0.698), which resulted are indicating about No. of traffic accidents which occurred in weather condition 8 (Storm), in average is less than the value of total traffic

      accidents No. which occurred in weather condition 2 (Strong wind).

    • The value of (1.308), which resulted are indicating about No. of traffic accidents which occurred in weather condition 9 (Hail), in average is less than the value of total traffic accidents No. which occurred in weather condition 2 (Strong wind).

    • The value of (0.392), which resulted are indicating about No. of traffic accidents which occurred in weather condition 10 (Snow), in average is less than the value of total traffic accidents No. which occurred in weather condition 2 (Strong wind).

    • The value of (1.078), which resulted are indicating about No. of traffic accidents which occurred in weather condition 11 (Smoke), in average is less than the value of total traffic accidents No. which occurred in weather condition 2 (Strong wind).

    • The value of (1.688), which resulted are indicating about No. of traffic accidents which occurred in weather condition 12 (Dust), in average is less than the value of total traffic accidents No. which occurred in weather condition 2 (Strong wind).

    • The value of (0.684), which resulted are indicating about No. of traffic accidents which occurred in weather condition 13 (other), in average is bigger than the value of total traffic accidents No. which occurred in weather condition 2 (Strong wind).

    1. CONCLUSIONS

      Based on the data that collected and the results of the analysis which observed; there are main conclusions and facts that obtained as the following:

      1. The differences in the weather conditions at occurrence of traffic accidents lead to be differences of traffic accidents No.. In addition, during clear sky of weather condition, the drivers are more negatives impact on road safety comparison to other conditions depended on No. of traffic accidents.

      2. There is association between the gender of drivers and the variety of weather conditions which lead to be differences in No. of traffic accidents. However, male drivers are caused of traffic accidents in clear sky of weather condition.

      3. There is relationship between the diversity of weather conditions and age categories of drivers which is resulting to be differences in No. of traffic accidents. Also, the age category (18-35) of drivers are more caused to traffic accidents in the clear sky of weather condition.

      4. The weather condition of clear sky in Greece urging the drivers to do more traffic accidents comparison to other weather condition. Approximately at a rate (51% -68%) along a year, the weather is clear sky in most Greek cities, and this is providing to be bigger No. of traffic accidents during good weather conditions.

      5. The existence of bad weather conditions during driving of vehicles such as poor vision and glides may increase the attention and caution of drivers; Also, improving their commitment to traffic regulations. Thus, it result to decrease No. of traffic accidents.

    2. RECOMMENDATION

      Depended on the results and conclusions which obtained by the study; There are some recommendations that are contribution in preventing of traffic accidents occurring and encouraging the drivers for commitment to traffic instructions through driving; some of the recommendations as the following:

      1. Continuing of traffic awareness to all drivers categories and development the means of various types of visual and audio announcements.

      2. Explain the risk of non-compliance of traffic safety instructions that required on the roads by drivers and the results of traffic accidents that may lead to death, injury or at least cause damage to vehicles and economic losses.

      3. Development the means of traffic monitoring for drivers by using the modern intelligent systems.

      4. Conduct a field study to identify the reasons why young drivers of male and female are consider more likely to cause the traffic accidents comparison to other categories of drivers. In addition, studying the reasons that make the male gender have the bigger No. of traffic accidents comparison to female gender.

      5. Applying the means of laws to deter violations to drivers for decreasing the reasons, which lead to occurrence of traffic accidents.

      6. Urge the drivers to observe the necessary precautions and advices during driving in different weather conditions.

      7. Raising the awareness of drivers to increase their attention during bad weather when the vision is not clear, such as rain, fog, dew, etc.

    3. ACKNOWLEDGMENT

      We would like to thank the Division of Transportation and Project Management at Aristotle University of Thessaloniki for availability all the necessary supports and facilities to conduct the study and publishing through them. In addition, we are very grateful to ELSTAT for providing us the various statistical data, which required for doing the research.

    4. REFERENCES

[1] X. Cai, J. Lu, Y. Xing, C. Jiang and W. Lu, (2013), Analyzing Driving Risks of Roadway Traffic under Adverse Weather Conditions: In Case of Rain Day, 13th COTA International Conference of Transportation Professionals (CICTP 2013), Procedia – Social and Behavioral Sciences 96 ( 2013 ) 2563 2571.

[2] N. Chakrabartya and K. Guptab, (2013), Analysis of Driver Behaviour and Crash Characteristics during Adverse Weather Conditions, 2ndConference of Transportation Research Group of India (2nd CTRG), Procedia – Social and Behavioral Sciences 104 (2013) 1048 1057.

[3] A. Perrels, A. Votsis, V. Nurmi and K. Sihvola, (2015), Weather Conditions, Weather Information and Car Crashes, ISPRS International Journal of Geo-Information, ISPRS Int. J. Geo-Inf. 2015, 4, 2681-2703.

[4] M. Kilpelainen and H. Summala, (2007), Effects of weather and weather forecasts on driver behaviour, Transportation Research Part F 10 (2007) 288299.

[5] Y. Liu, (2013), Weather Impact on Road Accidents Severity In Maryland, University of Maryland, Civil and Environmental Engineering, 2013.

[6] B. Leard and K. Roth, (2015), How Climate Change Affects Traffic Accidents, Weather, Traffic Accidents, and Climate Change. Discussion paper 15-19. Washington, DC: RFF.

[7] E. Hermans and T. Brijs, T. Stiers and C. Offermans, (2007), The Impact of Weather Conditions on Road Safety Investigated on an Hourly Basis, Transportation Research Institute, Hasselt University, Belgium.

[8] T. Jackson and H. Sharif, (2016), Rainfall Impacts on Traffic Safety: Rain-related Fatal Crashes in Texas, Geomatics, Natural Hazards and Risk, 2016 Vol. 7, No. 2, 843-86.

[9] P. Pisano, L. Goodwin and M. Rossetti, (2008), U.S. highway crashes in adverse road weather conditions, U.S. DOT Federal Highway Administration; Washington, D.C., Noblis, Inc.; Washington, D.C. and

U.S. DOT Research and Innovative Technology Administration; Cambridge.

[10] R. Hayat, M. Debbarh, C. Antoniou and G. Yannis, (2013), Explaining the road accident risk: Weather effects, Weather effects. Accident Analysis and Prevention, Elsevier, 2013, 60, pp456-465.

[11] G. Yannis and M. Karlaftis, (2010), Weather Effects on Daily Traffic Accidents and Fatalities: A Time Series Count Data Approach, Proceedings of the 89th Annual meeting of the Transportation Research Board, Washington, January 2010.

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