a study on motorcycle rider characteristic and behavior in

Journal of the Eastern Asia Society for Transportation Studies, Vol.9, 2011

A Study on Motorcycle Rider Characteristic and Behavior in Metro Manila

Francis Aldrine A. UYProfessorSchool of Civil Engineering and Environmental and Sanitary EngineeringMapua Institute of Technology2nd Flr. South Building Mapua Institute of Technology, Muralla Street, Intramuros, Manila, PhilippinesFax: +63-2-527-8456E-mail: [email protected]

Jose Regin F. REGIDORAssociate ProfessorInstitute of Civil EngineeringCollege of EngineeringUniversity of the Philippines, Diliman, Quezon City, PhilippinesFax: +63-2-929-0495E-mail: [email protected]

Abstract: This study presents essential information on motorcycle rider characteristic and behavior resulting from the questionnaire survey for riders and non-riders that was conducted along the top 5 roads in Metro Manila with the most number of recorded motorcycle road crashes in the past 5 years. The questionnaire has eight (8) sections that focus on rider’s

personal details, driving experience and training, riding habit, opinion on road safety, road crash experience, motorcycle preference and anger and aggression test. The results of the rider and non-rider survey were analyzed to yield significant variables influencing motorcycle road crash experience and frequency. A total of 2,000 motorcycle rider participated in the survey and a total of 600 other road users.

Key Words: motorcycle, road crash, behavior, characteristic

1. MOTORCYCLE ROAD CRASHES IN METRO MANILA

The continuous increase in fuel cost and worsening effect of road congestion has influenced the increase in motorcycle registration. The result of this rapid mode shift is the alarming frequency of motorcycle road crashes. Fig 1 presents the trends in gasoline price, motorcycle registration and road crash count.

Figure 1 Trends in motorcycle registration, road crash and gasoline price

In 2007, a study on the impact of the increasing number of motorcycle in Metro Manila was completed by the researcher, Uy et al. (2007). Incidence of motorcycle road crashes based on severity, road type, time, weather condition, collision type, junction type, traffic control, road crash factors and classification of people involved were presented. The study concluded that

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there is a rapid increase in motorcycle registration and as a result significant increase in

motorcycle road crashes was reported. The average road crash rate per 10,000 vehicles from

2002 to 2005 was reported to double each year. The increase on motorcycle registration was

reported to be 57% each year. The city with most number of motorcycle road crashes was

Quezon City. The top three roads with the most number of motorcycle road crashes were Real

Street, EDSA and Commonwealth Avenue. In most cases, about 56%, the motorcycle rider is

injured and only 1.5% case when riders are killed. About 20.2% cases reported involved killed

pedestrian. The top human factor that caused road crash was too fast driving followed by

inattentiveness and being too close to other vehicles. Majority of the road crashes happened

along major roads at night time. Motorcycle road crashes frequently happen along straight

roads and at no junction control. The most frequent collision type was side swipe and this is

consistent to too fast riding, inattentiveness and being too close to other vehicles on the road.

In Table 1 below, the statistics on motorcycle registration and crash is related to the total

population of people in Metro Manila from 2002 to 2009.

Table 1 Population, motorcycle registration and crash statistics in Metro Manila

The total motorcycle road crash recorded in Metro Manila Accident Reporting and Analysis

System (MMARAS) from 2005 to 2009 is about 55,169.00. There were about 512 fatal,

27,059 non-fatal and 27,598 damage only road crash type based on severity. A crash is

classified as fatal when there is at least one casualty who died due to the crash. Crash where

there are casualties that are slightly or seriously injured and no death is classified as non-fatal

crash. Road crashes that resulted only to vehicle or road facility or structure damage is

classifies as damage only to property crash. Quezon City has the highest recorded motorcycle

road crash at 14,368 followed by Marikina at 4,525 and Pasig City at 4,465. In terms of

severity most motorcycle road crashes are classified as non fatal and damage only. In Quezon

City there is almost equal number of damage only and non fatal road crashes at 6,315 and

7,904 respectively. The numbers of damage only and non fatal road crashes in most cities are

almost equal. It can be noted that average fatal road crash in each city is about 0.88% of the

sum of damage only and non fatal road crash. Fatal road crashes can also be noted to be

approximately 2% of damage only or non fatal road crashes. MMARAS records confirmed

that motorcycle road crashes mostly involve cars. Car road crash has the most recorded road

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crash of the total cases in the last five years. The top road crash factor identified as caused of

road crash is too fast driving. Inattentiveness and bad overtaking are other major road crash

factors that can be related to training and experience of the rider. Motorycles also are

frequently involved in road crashes because of too close manuever through other road user.

Motorycle riders can usually go through between other vehicles and this increase the risk of

road crash involvement. The third top factor is bad turning. The total estimated cost of

motorcycle related road crash in Metro Manila from 2005 to 2009 is estimated to have reached

12.15 Billion Pesos. There has been extensive research effort devoted to improve motorcycle

safety considering the vehicle and traffic flow but the relationship between rider characteristic,

behavior and crash is still not well understood. It is the objective of this study to present a

comprehensive report on motorcycle rider characteristic and behavior and analyze significant

variables influencing road crash experience and frequency.

2. METHODOLOGY

The questionnaire survey was conducted at motorcycle parking areas and gasoline stations

along the top 5 roads with the most number of motorcycle road crashes namely

Commonwealth Avenue, Alabang-Zapote Road, EDSA, Quirino Highway and J.P. Rizal Street.

The survey was conducted between June to November 2010.

Figure 2 Location of top 5 roads with most number of motorcycle road crashes

Table 2 Sample size along each top 5 roads

Rank Road Required

Respondents

Adjusted No.

of

Respondents

(+5%)

Motorcycle

AADT

1st Commonwealth

Avenue 381 400 25,593

2nd Alabang –

Zapote Road 381 400 7,880 (obs.)

3rd EDSA 381 400 35,091

4th Quirino Highway 381 400 14,832 (obs.)

5th J.P. Rizal Street

(Marikina) 381 400 6,368 (obs.)

TOTAL 2000

The location of these roads is shown in Fig 2. The questionnaire has eight (8) sections that

focus on rider’s personal details, driving experience and training, riding habit, opinion on road

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safety, road crash experience, motorcycle preference and anger and aggression test. The rider

questionnaire survey was conducted at commercial establishment motorcycle parking spaces

and gasoline’s stations along the top 5 roads. The results of the questionnaire survey were

analyzed to generate and present pertinent information on rider characteristics and behavior of

motorcycle riders. Furthermore, analysis on the significance of variables influencing motorcycle

road crash experience, frequency and severity was undertaken. The non-rider questionnaire

survey was conducted for pedestrians or commuters, car, bus, taxi and jeepney drivers. A total

of 2,000 respondents participated for the rider questionnaire survey and 600 respondents for

non-rider. The acquired information from the accomplished survey forms was encoded in a

spreadsheet to generate a tally sheet where descriptive statistics and significant variables were

derived and analyzed.

3. MOTORYCLE RIDER QUESTIONNAIRE SURVEY RESULT

3.1 Personal Details of Motorcycle Riders

Out of the 2,000 respondents 91% were male and 9% were female. The age distribution of the

respondents is shown in Fig 4. Majority of the respondents are within the age range of 21 to 35

years old (73%). The average age of the respondents is 30 years old. It can also be noted that

there is about 1% of respondents who are below 18 years old.

Pie Chart of Gender

MC Master Database-Statistica 201v*2001c

Gender

Female, 9%

Male, 91%

Female, 9%

Male, 91%

Histogram of Rider's Age

MC Master Database-Statistica 201v*2001c

0% 0%

6%

23%

28%

22%

10%

7%

2%1%

1% 1%

-5 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75

Rider's Age

0

100

200

300

400

500

600

No o

f obs

0% 0%

6%

23%

28%

22%

10%

7%

2%1%

1% 1%

Figure 3 Respondents based on gender Figure 4 Age distribution of respondents

In Fig 5 it is shown that most of the respondent went to college (60%) and this was followed

by high school (35%). On livelihood in Fig 6, majority of the riders are employed (74%) with

an average monthly income of PHP 15,530. Histogram of Education Level

35%

60%

3%1% 0%

High School College Masteral Elementary Doctoral

Education Level

0

200

400

600

800

1000

1200

1400

No o

f obs

35%

60%

3%1% 0%

Histogram of Livelihood

74%

19%

7%

Employed Self-Employed Others

Livelihood

0

200

400

600

800

1000

1200

1400

1600

No o

f obs

74%

19%

7%

Figure 5 Respondents level of education Figure 6 Respondents livelihood

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Rider’s income and experience is presented in Fig 7 and 8. Majority of the respondent’s

monthly income is between PHP 8,000 to 15,000. Most of the respondents have only one

motorcycle. Majority of the respondents have 3 to 5 years experience in riding.

Histogram of Monthly Income

38%

46%

10%

3%

1% 0% 0% 0% 0% 0% 1%

0 10000 20000 30000 40000 50000 60000 70000 80000 90000 1E5

Monthly Income in Pesos

0

100

200

300

400

500

600

700

800

900

1000

No o

f obs

38%

46%

10%

3%

1% 0% 0% 0% 0% 0% 1%

Histogram of Riding Years Experience

63%

27%

5%3%

1% 1% 0% 0% 0%

5 10 15 20 25 30 35 40 45 50

Riding Years Experience

0

200

400

600

800

1000

1200

1400

No o

f obs

63%

27%

5%3%

1% 1% 0% 0% 0%

Figure 7 Respondents monthly income Figure 8 Respondents riding experience

The type of motorcycle mostly used by the respondents is a learner motorcycle (125cc or

below) at 61% followed by Scooters at 24%. In terms of the make, it is mostly Honda (41%)

and was purchased between years 2006 to 2010. The engine size is mostly 110cc and the

odometer reading is mostly below 20,000 km. Histogram of MC Type

61%

3%

24%

4%5%

1% 1% 1% 0%

Learner

Commuter

Scooter

Trike

Sports

Others

Touring

Custom

Offroad

MC Type

0

200

400

600

800

1000

1200

1400

No o

f obs

61%

3%

24%

4%5%

1% 1% 1% 0%

Histogram of Engine Size (cc)

95%

4%1%

0 150 300

Engine Size (cc)

0

200

400

600

800

1000

1200

1400

1600

1800

2000

No o

f obs

95%

4%1%

Figure 9 Motorcycle type distribution Figure 10 Motorcycle engine size

Histogram of MC Make

0%0%0% 0% 0%0%

41%

7%

4%

0% 0%

3%

8%

0%1% 0%0%

18%

1% 0%0% 1%

16%

0%

110 R

US

I

AS

IA S

TA

R

BLA

ZE

BR

AV

O

BR

FA

VO

FU

RY

HO

ND

A

KA

WA

SA

KI

KA

WA

ZA

KI

KE

MC

O

ME

C

MO

TO

RS

TA

R

OT

HE

RS

RA

LA

L

RU

SI

SH

OG

UN

R

SK

Y60

SU

ZU

KI

SY

M

X1 Y

AM

AH

A

XR

M

XY

M

YA

MA

HA

YU

RO

X

MC Make

0

100

200

300

400

500

600

700

800

900

No o

f obs

0%0%0% 0% 0%0%

41%

7%

4%

0% 0%

3%

8%

0%1% 0%0%

18%

1% 0%0% 1%

16%

0%

Histogram of Odometer Rdg.

33%

23%

11%

9%

4%

2%1% 1% 1%

0%

13%

0 10000 20000 30000 40000 50000 60000 70000 80000 90000 1E5

Odometer Rdg.

0

100

200

300

400

500

600

700

No o

f obs

33%

23%

11%

9%

4%

2%1% 1% 1%

0%

13%

Figure 11 Motorcycle make distribution Figure 12 Motorcycle odometer reading

1 USD = 43.58 PHP

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Summary of details related to purchase of motorcycle is presented in Fig 13 to 14. The price

range of motorcycle when purchased is between PHP 40,000.00 to 70,000.00 and was paid by

majority on installment terms (65%).

Pie Chart of Payment Type

Payment Type

Cash, 36%

Mortgaged, 65%

Cash, 36%

Mortgaged, 65%

Histogram of MC Price

1%2%

5%

8%

20%21%

23%

11%

5%

2%

0%1%

0% 0% 0% 1%0% 0%

1%

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

1E5

1.1E5

1.2E5

1.3E5

1.4E5

1.5E5

1.6E5

1.7E5

1.8E5

1.9E5

2E5

MC Price in Pesos

0

50

100

150

200

250

300

350

400

450

500

No o

f obs

1%2%

5%

8%

20%21%

23%

11%

5%

2%

0%1%

0% 0% 0% 1%0% 0%

1%

Figure 13 Motorcycle payment terms Figure 14 Motorcycle purchase price

3.2 Training and Experience of Motorcycle Riders

Summary of details related to training and experience is presented in Fig 15 to 17. Most of the

respondents have acquired their driving license 3 to 5 years ago. About 80% rode their

motorcycle upon receiving their driver’s license. It can be noted that 90% of the respondents

confirms they have taken actual examination and 68% was required to pass an actual test ride.

The majority of the respondents confirmed that they have taken and passed written

examination and test ride for license application but it should also be noted that the percentage

of respondents who admitted that they were not required for a test ride is significantly high at

32%. It can be also noted that 62% confirmed that they have not received any formal training.

A total of 72% is allowed to use motorcycle of any engine size. It was also determined that

87% is allowed to drive other types of vehicle. Most of the respondents (70%) believe that

they ride their motorcycles within 20 kilometers everyday which suggest that the use of

motorcycle is used for short distance trips. Most of the respondents confirm that their purpose

of using motorcycle is for work or business (49%) followed by personal need (24%) and

leisure trips (15%). Bar/Column Plot of multiple variables

80%

90%

74%

72%

38%

20%

10%

26%

28%

62%

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Did you ride motorcycle as soon as you aquired your license?

Did you take and pass a true licensure (LTO) exam?

Is your Licensure exam with actual test ride?

Can you ride any size of motorcycle?

Did you have formal training in riding?

80%

YES

NO

Figure 15 Summary of rider’s response on license exam and training related questions

1 USD = 43.58 PHP

1463


Histogram of MC Usage (km/day)

27%

43%

12%

6% 5%

2%1% 2%

0%1%

0% 0% 0% 0%0%

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190

MC Usage (km/day)

0

100

200

300

400

500

600

700

800

900

No o

f obs

27%

43%

12%

6% 5%

2%1% 2%

0%1%

0% 0% 0% 0%

Rider's Purpose of Using MC

33%

11%

24%

16%15%

1%

Commuting to work

To visit relatives

Personal Errands

As part of job

Leisure trips

Others

0%

5%

10%

15%

20%

25%

30%

35%33%

11%

24%

16%15%

1%

Figure 16 Motorcycle daily usage Figure 17 Riders reason for using motorcycle

3.3 Motorcycle Riding Habits On riding habits it was determined that only 38% of the respondent claim that they use bright and reflective clothing at least 50% of the time. About 53% confirms that they use their headlights even during day time 50% of the time. This two items focus on motorcycle visibility on the road. A positive 78% of the respondent confirms they always wear their helmet when riding their motorcycle. A significant percentage of 77% confirms they often change lane at least less than 50% of the time on the road and 75% uses their signal light when they have intention to change lane 50% of the time.

Figure 18 Change lane manuever Figure 19 Use signal light

An alarming 73% rides their motorcycle even they are tired and 48% confirms they ride with the influence of alcohol or drugs at least less than 50% of the time.

Figure 20 riding when tired Figure 21 Riding in the influence of alcohol

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About 73.80% of the respondents usually go through small spaces between other vehicles on the road and 62% misjudge the speed needed to negotiate bends in the road. 52% overtakes other vehicle on the road that is over the allowed speed limit and usually (71.32%) overtake more than two vehicles.

3.4 Opinion on Road Safety The top 3 most important safety measures for motorcyclist are regular maintenance (15.60%), wearing of helmet and other protection (15.48%) and no riding when in the influence of alcohol (15.37%). The top 3 least important safety measures for motorcyclist are driving when tired (16.12%), prohibit motorcycle along major roads (14.40%) and exclusive lanes for motorcycle (13.91%). On the question of the most likely category of road user to cause road crash with motorcycle rider’s a surprising 44.63% believes it’s also motorcycle riders this was

followed by bus or truck (28.60%) and cars (14.03%). The perceived most frequent road crash type is bad overtaking (31.91%) followed by side-swipe (18.65%) and misjudging bends (13.68%). Majority of the respondent’s (97.67%) use motorcycle helmet. The most frequent helmet colors used are black (33.45%), red (23.88%) and blue (21.75%). Majority of respondents helmet cost between 500 to 1,500 Pesos (52.26%) and 72.89% of the respondents stated that they were offered by the motorcycle supplier a helmet. Majority of the respondents (76%) believes they use standard and good quality helmets. About 24% of the respondents admit they do not use standard helmet and most of them confirms that the cost of helmet is the main reason for this.

3.5 Road crash Experience As shown in Fig 22, a surprising 29% of the respondents admit they have experience motorcycle road crash. In terms of severity there are about 6% fatal, 49% non fatal and 45%damage only. Majority happened at night time but there is only small difference in count and can be considered statistically even.

Accident Experience

YES

NO

29%

71%

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

29%

71%

Figure 22 Percentage with accident experience Figure 23 Accident based on severity

The weather condition during the accident was usually rainy (46%) and sunny (44%). The location and junction type was frequently at straight sections (52.39%) and intersections (31.39%). Top 3 cause of collision are alone and no collision (45.40%), fallen back rider or things (18.42%) and side-swipe (7.28%). Top 3 human factors that cause road crash are too fast riding (18.81%), out of control (8.65%) and too close riding (8.35%).

3.6 Anger and Aggression Test The anger examination was made using the Deffenbacher Driving Anger Scale (Deffenbacher et al., 1994). The respondents were instructed to imagine that each of the enumerated situations described in the survey instrument was actually happening to them and they were required to rate the amount of anger that would be provoked. The anger assessment tool makes use of a 5

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point scale. To determine the anger score, the sum the ratings for the 14 road situations stated

in the survey questionnaire was computed. The highest possible score is 70 points. The

summary of the result is presented in Fig 24 below. The aggression examination was made

using the Aggression Questionnaire of Buss & Perry (1992). Using a 5 point scale, the

respondents were required to indicate how uncharacteristic or characteristic each of the

enumerated statements is describing them. The Aggression scale consists of 4 factors, Physical

Aggression (PA), Verbal Aggression (VA), Anger (A) and Hostility (H). The total score for

Aggression is the sum of the factor scores. The highest possible score is 135 points. The

summary of result is presented in Figure 25 below. One of the aims of the research is to

compare the anger and aggression score of motorcycle riders with other road users. The

summary of results are shown if Fig 24 and 25. It can be noted that using the anger scale test,

results showed that other road users except bus drivers has higher anger score than motorcycle

riders. These results would suggest that given a situation on the road, the other road users will

feel and express anger more rapidly than motorcycle riders. In terms of agression, motorycle

riders average total score is the lowest among considered road users. This result would suggest

that motorcycle riders are exposed to more aggressive road users. The aggression test results

would somehow provide us information on determining the most possible initiator of a road

crash. Bar/Column Plot of Anger Score

Anger Score

41%

41%

38%

31%

37%

33%

0% 5% 10% 15% 20% 25% 30% 35% 40% 45%

Car

Jeepney

Taxi

Bus

Pedestrian

MC Rider (Ave.)

Figure 24 Average score for Driving Anger Scale for all respondents

Bar/Column Plot of Aggression Score

Aggression Score

55%

53%

55%

53%

61%

52%

0% 10% 20% 30% 40% 50% 60% 70%

Car

Jeepney

Taxi

Bus

Pedestrian

MC Rider (Ave.)

Figure 25 Average score for aggression test for all respondents

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4. RIDER CHARACTERISTIC AND BEHAVIOR INFLUENCING MOTORCYCLE

ROAD CRASH EXPERIENCE AND FREQUENCY

4.1 Significant Variables Influencing Rider Road Crash Experience

Using the result of the motorcycle rider questionnaire survey the incidence of road crash

experience based on rider characteristic and behaviour was analyzed. Variables derived from

the result of the survey were screened to eliminate insignificant factors. T-test revealed

significant difference in the mean of rider’s age with and without road crash experience. This

suggests that younger riders at age 22 to 37 experience road crash. Figure 26 presents the

histogram of rider’s age and relevant statistical results. Histogram of multiple variables

MC Riders Profile (with vs without crash exp) 94v*1458c

Rider's Age = 1453*5*normal(x, 30.8032, 8.2639)

Rider's Age (w/crash exp) = 547*5*normal(x, 29.4461, 7.5829)

Rider's Age: D = 0.106, p < 0.0100, Lilliefors-p < 0.01;

N = 1453, Mean = 30.8032, StdDv = 8.2639, Max = 64, Min = 3;

SW-W = 0.9489, p = 0.0000

Rider's Age (w/crash exp): D = 0.0813, p < 0.0100, Lilliefors-p < 0.01;


SW-W = 0.9242, p = 0.0000

Rider's Age

Rider's Age (w/crash exp)-5 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75

0

50

100

150

200

250

300

350

400

450

No o

f obs

Figure 26 Histogram of rider’s age (with and without crash experience) Histogram of multiple variables


Riding Yrs. = 1453*5*expon(x, 0.1804)

Riding Yrs. (w/crash exp) = 547*5*expon(x, 0.1636)

Riding Yrs.: N = 1453, Mean = 5.5437, StdDv = 4.9817, Max = 42, Min = 0.083

Riding Yrs. (w/crash exp): N = 547, Mean = 6.1106, StdDv = 5.6275, Max = 40, Min = 0.417

Riding Yrs.

Riding Yrs. (w/crash exp)-5 0 5 10 15 20 25 30 35 40 45 50

0

200

400

600

800

1000

1200

1400

No o

f obs

Figure 27 Histogram of rider’s experience (with and without crash experience)

The observation on the age range of riders with crash experienced is confirmed by the

MMARAS record. Out of almost ten thousand cases wherein the investigator was able to

include the age of riders involved in the crash the age range of 22 to 37 is confirmed. T-test

result presented confirmed that there is no significant difference between the mean of the

rider’s age with crash experience from the questionnaire survey result and MMARAS age data.

Figure 27 presents the histogram of rider’s experience in years. The mean of experience in

1467


years for rider’s with and without road crash experience were revealed to be significantly

different. The result suggests that a chance of road crash experience is negatively influenced by

more years of riding. The result of the aggression assessment between riders with and without

road crash experience is revealed to be significant in the variable screening process. The

aggression score data for rider’s with and without road crash experience is presented in Figure

28. The data passed the normality test. The result suggests that riders with road crash

experience have higher aggression scores. Histogram of multiple variables


Aggression Score = 1453*10*normal(x, 51.2767, 13.5262)

Aggression Score (w/crash exp) = 547*10*normal(x, 54.4717, 14.9942)

Aggression Score: D = 0.061, p < 0.0100, Lilliefors-p < 0.01;


SW-W = 0.9892, p = 0.00000

Aggression Score (w/crash exp): D = 0.0985, p < 0.0100, Lilliefors-p < 0.01;


SW-W = 0.9779, p = 0.00000

Aggression Score

Aggression Score (w/crash exp)10 20 30 40 50 60 70 80 90 100 110 120

0

50

100

150

200

250

300

350

400

450

500

No o

f obs

Figure 28 Histogram of aggression score (with and without crash experience)

Histogram of multiple variables


Verbal Aggression Score = 1453*5*normal(x, 11.2663, 3.3972)

Verbal Aggression Score (w/crash exp) = 547*5*normal(x, 11.6929, 3.4275)

Verbal Aggression Score: D = 0.1081, p < 0.0100, Lilliefors-p < 0.01;


SW-W = 0.9587, p = 0.0000

Verbal Aggression Score (w/crash exp): D = 0.086, p < 0.0100, Lilliefors-p < 0.01;


SW-W = 0.9817, p = 0.00000

Verbal Aggression Score

Verbal Aggression Score (w/crash exp)0 5 10 15 20 25 30 35 400

100

200

300

400

500

600

700

800

No

of o

bs

Figure 29 Histogram of verbal aggression score (with and without crash experience)

Figure 29 to Figure 31 presents the detail of the aggression score through the examination of

the four areas of aggression. Analysis revealed verbal, anger and hostility as significant

aggression factors. Verbal aggression score is a measure of rider’s tendency to be verbally

argumentative. Figure 29 presents the data and statistics on verbal aggression for riders with

and without road crash experience. Anger score measures rider’s anger-related arousal and

sense of control. Figure 30 presents the histogram of anger scores for riders with and without

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crash experience. There is approximately one point difference between the means. Hostility

score is a measure of rider’s feelings of resentment, suspicion and alienation. These feelings

seriously undermine physical and psychological health. Figure 31 presents the histogram of

hostility scores for riders with and without road crash experience. There is an observed 2 point

difference between the means. Histogram of multiple variables


Anger Score = 1453*2*normal(x, 9.3021, 3.8977)

Anger Score (w/crash exp) = 547*2*normal(x, 9.8501, 4.3521)

Anger Score: D = 0.0897, p < 0.0100, Lilliefors-p < 0.01;


SW-W = 0.9847, p = 0.0000

Anger Score (w/crash exp): D = 0.1078, p < 0.0100, Lilliefors-p < 0.01;


SW-W = 0.9728, p = 0.00000

Anger Score

Anger Score (w/crash exp)-2 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28

0

50

100

150

200

250

300

350

400

No o

f obs

Figure 30 Histogram of anger aggression score (with and without crash experience)

Histogram of multiple variables


Hostility Score = 1453*5*normal(x, 16.0977, 4.9568)

Hostility Score (w/crash exp) = 547*5*normal(x, 18.1079, 5.1428)

Hostility Score: D = 0.0948, p < 0.0100, Lilliefors-p < 0.01;


SW-W = 0.9641, p = 0.0000

Hostility Score (w/crash exp): D = 0.0818, p < 0.0100, Lilliefors-p < 0.01;


SW-W = 0.9841, p = 0.00001

Hostility Score

Hostility Score (w/crash exp)0 5 10 15 20 25 30 35 40 45

0

100

200

300

400

500

600

No o

f obs

Figure 31 Histogram of hostility score (with and without crash experience)

4.2 Significant Variables Influencing Rider Road Crash Frequency

The riding habit score derived from the questionnaire survey passed the variable screening

requirement. The riding habit score range is +20 to -20. Positive value denotes good riding

habit and vice versa. It can be noted that roads with more positive riding habit score has lesser

motorcycle road crash count. This is presented in Figure 32 below. Aggression level is

observed to have very large influence on motorcycle road crash count along roads. Figure 33

presents the linear relationship between aggression score and road crash count with correlation

coefficient R of 0.6691. It can be stated that aggression score has a very large effect on road

crash count. Aggressive drivers have the tendency to be easily provoked under various

negative traffic and road conditions. This can be related to the top human factors that caused

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road crash that is presented in chapter III. Over speeding and riding too close to other vehicles

can be considered as rider’s response to negative traffic or road conditions. Larger aggression

score corresponds to higher road crash count because of negative responses of riders that

contributes to occurrence of road crash.

Figure 32 Scatter plots & statistics of average riding habit score & crash count

Scatterplot of Road Crash Count against Av e. Anggression

Summary of Video Data-FINAL 96v *7c

Av e. Anggression:Road Crash Count: y = -9273.3676 + 192.277*x;

r = 0.6691, p = 0.2168; r2 = 0.4477

Road Crash Count = -9273.3676+192.277*x; 0.95 Conf .Int.

50 51 52 53 54 55 56

Av e. Anggression

0

200

400

600

800

1000

1200

1400

1600

Ro

ad

Cra

sh C

ou

nt

Figure 33 Scatter plots & statistics of average aggression scores & crash count

Among the four areas of aggression measure, anger and hostility can be noted to have

large influence on road crash count. The higher anger score corresponds to quicker anger

development resulting to negative responses that contributes to road crash occurrence. Figure

34 and 35 presents a good model on the influence of anger and hostility aggression score with

a coefficient of correlation R of 0.6413 and 0.7447 respectively.

Scatterplot of Ave. Riding Habit against Road Crash Count

Questionnaire-Operation-MMARAS Data-FINAL 105v*24c

Ave. Riding Habit = 3.8747-0.0006*x

Road Crash Count:Ave. Riding Habit: y = 3.8747 - 0.0006*x;

r = -0.5628, p = 0.0362; r2 = 0.3167

0 200 400 600 800 1000 1200 1400 1600

Road Crash Count

2.2

2.4

2.6

2.8

3.0

3.2

3.4

3.6

3.8

4.0

4.2

Ave

. R

idin

g H

ab

it

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Scatterplot of Road Crash Count against Ave. Anger Aggression Score

Summary of Video Data-FINAL 96v*7c

Ave. Anger Aggression Score:Road Crash Count: y = -4567.6319 + 563.038*x;

r = 0.6413, p = 0.2435; r2 = 0.4113

Road Crash Count = -4567.6319+563.038*x; 0.95 Conf.Int.

8.6 8.8 9.0 9.2 9.4 9.6 9.8 10.0 10.2 10.4 10.6

Ave. Anger Aggression Score

0

200

400

600

800

1000

1200

1400

1600

Ro

ad

Cra

sh

Co

un

t

Figure 34 Scatter plots & statistics of average anger aggression scores & crash count

Scatterplot of Road Crash Count against Ave. Hostility Score

Summary of Video Data-FINAL 96v*7c

Ave. Hostility Score:Road Crash Count: y = -5534.7667 + 377.7681*x;

r = 0.7447, p = 0.1487; r2 = 0.5546

Road Crash Count = -5534.7667+377.7681*x; 0.95 Conf.Int.

15.2 15.4 15.6 15.8 16.0 16.2 16.4 16.6 16.8 17.0 17.2 17.4 17.6 17.8 18.0 18.2

Ave. Hostility Score

0

200

400

600

800

1000

1200

1400

1600

Ro

ad

Cra

sh

Co

unt

Figure 35 Scatter plots & statistics of average hostility aggression scores & crash count

5. CONCLUSION & RECOMMENDATIONS

Since, motorcycle is considered as the most vulnerable and unstable vehicle on our roads, the

Land Transportation Office should require all motorcycle rider license applicants to attend and

complete a mandatory training program on road safety and proper riding habits prior to filling

of application. It should be noted that based on the results, there is 62% of respondents of the

questionnaire survey who admitted that they had no formal training but passed the licensure

examination. The training program should also include knowledge and behavioural assessment

like the anger and aggression test. The result of aggression test will aid in determining proper

training to be given to riders. The adoption of aggression score requires the development of a

scale to further differentiate level of aggression and recommend appropriate training that will

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be required to minimize probability of road crash experience. The riding habit score have

shown significant influence on crash frequency. The developed riding habit assessment tool

adopted in the study as part of the questionnaire may be further enhanced and adopted for rider

licensure examination. Researches that would allow collection of actual blood pressure or heart

beat during drive test should be conducted for a more in-depth analysis and understanding on

rider’s behaviour and its influence to road safety. Rider’s age was found to be significant in

motorcycle crash experience and severity. It is recommended that accident insurance should be

promoted or if possible required. It should also be noted that majority of the respondents used

motorcycle for work or business. Thus, the cost of insurance should be shouldered by the

employer or company. Business that require their employees the use of motorcycle should

ensure that fatigue, stress and exposure is considered and addressed. Further study on this area

may be considered by researchers to determine the implication of motorcycle riding as required

by an individual’s work. Since there is an alarming percentage of a rider who claimed that they

ride even under the influence of alcohol, initiatives should be implemented to address this

problem. Concerned agencies should support laws prohibiting driving under the influence of

alcohol or drugs and work for its immediate approval and enforcement. Amendment on the

helmet law should be done in order to make standard helmet a required protective gear when

purchasing a motorcycle. All motorcycle suppliers should automatically include the purchase of

standard helmet. The cost of standard helmet should be included in the total purchase price or

package. This recommendation is supported by the result of the questionnaire survey wherein

significant number of respondents considered cost as the major reason why they do not use

standard helmet. In our current state, riders have the option not to buy standard helmet

because it is not required during purchase of the motorcycle. Concerned government agencies

and motorcycle associations should promote motorcycle visibility and protection on the road

through various initiatives like trainings and seminars. Reflective or bright clothing should be

promoted among motorcycle riders and the use of signal and head light. Initiatives like the

“Helmet and Headlight ON” (H20) is an excellent example. To further improve motorcycle

visibility, motorcycle manufacturers and suppliers in the Philippines should be required to

design the motorcycle to automatically turn on its head light when engine is on and signal light

during left and right manoeuvres. This specification will significantly improve motorcycle

visibility and avoid motorcycle road crash along narrow and congested roads. The most

significant factors that was found to influence road crash experience are rider’s age, experience

in years and aggression scores. It should be emphasized that the aggression scores significantly

differentiates riders with and without crash experience. The result of analysis concludes that

younger and aggressive riders are most likely to experience road crash. The most significant

factors that influence motorcycle road crash frequency are riding habit score and aggression

scores.

ACKNOWLEDGEMENT

This is to acknowledge the Road Safety Unit of the Metro Manila Development Authority and

the Department of Public Works and Highways for sharing essential data for the completion of

the study. The researcher acknowledges the NCTS-IBS Research program and the Mapua

Institute of Technology for the financial support that was provided for the success of the study.

Lastly, special recognition is given to Shell Philippines, Trinoma parking and SM Southmall

parking administration for accommodating our survey team and providing survey stations for

the study.

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