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MRR 04/2012

Noradrenalina Isah

Sanizah Saleh

Maslina Musa

Kee Lai Sien

Anis Syakira Jailani

A Perception Study on the Efficacy of Traffic Enforcement from the Road Users’ Perspective

MRR_042012_Perception Study_30May2012.indd 1 5/30/12 10:27 AM

Research Report


Noradrenalina IsahSanizah SalehMaslina MusaKee Lai Sien Anis Syakira Jailani


MIROS © 2012 All Rights Reserved

Published by:

Malaysian Institute of Road Safety Research (MIROS)Lot 125-135, Jalan TKS 1, Taman Kajang Sentral,43000 Kajang, Selangor Darul Ehsan.

Perpustakaan Negara Malaysia Cataloguing-in-Publication Data

A perception study on the efficacy of traffic enforcement fromthe road users’ perspective / Noradrenalina Isah ... [et al.](Research report: MRR 04/2012)ISBN 978-967-5967-24-51. Traffic violations--Research--Malaysia.2. Traffic safety--Research--Malaysia.I. Noradrenalina Isah. II. Series.364.147072

For citation purposes

Noradrenalina I, Sanizah S, Maslina M, Kee LS, Anis Syakira J (2012), A Perception Study on the Efficacy of Traffic Enforcement from the Road Users’ Perspective, MRR 04/2012, Kuala Lumpur: Malaysian Institute of Road Safety Research.

Printed by: Publications Unit, MIROS

Font type : Myriad Pro LightSize : 11 pt / 15 pt

DISCLAIMERNone of the materials provided in this report may be used, reproduced or transmitted, in any form or by any means, electronic or mechanical, including recording or the use of any information storage and retrieval system, without written permission from MIROS. Any conclusion and opinions in this report may be subject to reevaluation in the event of any forthcoming additional information or investigations.


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A Perception Study on the Efficacy of Traffic Enforcement from The Road Users’ Perspective

Contents

List of Tables vi

List of Figures vii

Contributors ix

Acknowledgements xi

Abstract xiii

1.0 Overview 1

1.1 Introduction and Background 1

1.2 Objectives 2

1.3 Limitations 3

1.4 Significance of the Study 3

2.0 Literature Review 4

2.1 Rationale for Traffic Enforcement 4

2.2 Related Theories 5

2.3 Role of Perception of Being Caught 8

3.0 Method 11

3.1 Sample 11

3.2 Materials 12

3.3 Reliability 14

3.4 Procedure 14

4.0 Results and Discussion 16

4.1 Section A: Non-festival Days 16

4.1.1 Socio-demographic Profile of the Respondents during Non-festival Days 16

4.1.2 Perception Study Analysis 18

4.1.3 Factors Associated with the Perception of Being Caught 19

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4.1.4 Visibility of Enforcement Activities 21

4.1.5 Overt and Covert Enforcement 23

4.2 Section B: Festival Periods 25

4.2.1 Socio-demographic Profile of the Respondents during Festival Periods 25

4.2.2 Perception Study Analysis 28

4.2.3 Traffic Offences 29

4.2.4 Comparison of Road Users’ Perception of Being Caught between Ops Periods for Each Festive Season 32

4.2.5 Factors Associated with the Road Users’ Perception of Being Caught 34

4.2.6 Visibility of Enforcement Activities 36

4.2.7 Overt and Covert Enforcement Approaches 39

4.3 Comparison of Mean Perception of Being Caught between Road Users Who Have Been Fined and Those Who Have Never Been Fined for Traffic Offences 45

5.0 Conclusion 46

6.0 Recommendations 48

References 50

AppendicesAppendix 1 Perception study analysis on federal roads

throughout year 2008 56Appendix 2 Perception study analysis on expressways

throughout year 2008 68Appendix 3 Visibility of enforcement activities – Non-festival days 80Appendix 4 Comparison of road users’ perception of being

caught between Ops periods for Chinese New Year 81Appendix 5 Comparison of road users’ perception of being

caught between Ops periods for Hari Raya Aidilfitri 82Appendix 6 Comparison of road users’ perception of being

caught between Ops periods for Deepavali 83Appendix 7 Visibility of enforcement activities – Festival periods 85Appendix 8 Comparison of mean perception of being caught

between road users who have been fined and those who have never been fined for traffic offences – Non-festival days 86


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Appendix 9 Comparison of mean perception of being caught between road users who have been fined and those who have never been fined for traffic offences – Festival periods 87


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List of Tables

Table 1 Number of respondents gathered in 2008 11

Table 2 Reliability of study instruments 14

Table 3 Socio-demographic profiles of the respondents during non-festival days 17

Table 4 Socio-demographic profile of the respondents during festival periods 27

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List of Figures

Figure 1 Theoretical framework based on the theory of planned behaviour. The framework is expanded to include moral norm. 7

Figure 2 A schematic representation of the theory of planned behaviour 7

Figure 3 Percentage of road users’ perception of being caught at all locations during non-festival day 19

Figure 4 Factors associated with the perception of being caught according to the road users’ perception 21

Figure 5 Visibility of enforcement activities – Expressways and federal roads 22

Figure 6 Percentage of road users’ perception on effectiveness of traffic enforcement approaches – Expressways 24

Figure 7 Percentage of road users’ perception on effectiveness of traffic enforcement approaches – Federal roads 25

Figure 8 Percentage of road users’ perception of being caught for 2008 festival periods 29

Figure 9 Percentage of perception of being caught at all locations during the Chinese New Year period 30

Figure 10 Percentage of perception of being caught at all locations during the Hari Raya Aidilfitri period 31

Figure 11 Percentage of perception of being caught at all locations during Deepavali period 32

Figure 12 Factors associated with the perception of being caught according to the road users’ perception at all locations – All festival periods 34

Figure 13 Factors associated with the perception of being caught according to the road users’ perception at all locations – Chinese New Year 35

Figure 14 Factors associated with the perception of being caught according to the road users’ perception at all locations – Hari Raya Aidilfitri 35

Figure 15 Factors associated with the perception of being caught according to the road users’ perception – Deepavali 36

Figure 16 Percentage of visibility of traffic enforcement at all locations – All festival periods 37

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Figure 17 Percentage of visibility of traffic enforcement at all locations – Chinese New Year 37

Figure 18 Percentage of visibility of traffic enforcement at all locations – Hari Raya Aidilfitri 38

Figure 19 Percentage of visibility of traffic enforcement at all locations – Deepavali 38

Figure 20 Perception of enforcement approaches effectiveness throughout Chinese New Year period – Expressways 40

Figure 21 Perception of enforcement approaches effectiveness throughout Chinese New Year period – Federal roads 40

Figure 22 Perception of enforcement approaches effectiveness throughout Hari Raya Aidilfitri period – Expressways 42

Figure 23 Perception of enforcement approaches effectiveness throughout Hari Raya Aidilfitri period – Federal roads 42

Figure 24 Perception of enforcement approaches effectiveness throughout Deepavali period – Expressways 44

Figure 25 Perception of enforcement approaches effectiveness throughout Deepavali period – Federal roads 44


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Contributors

Malaysian Institute of Road Safety Research

Abdul Rashid YaacubAhmad Fakarudin RazaliAhmad Tarmimi AdnanChristina GimangFarah Anida MansorHanim Idzura Kalyubi Hasnor HassanHusaini SallehIsmail Rafian IbrahimKhairul Anuar IderisMohd Faiz FakhryMohd Hafis Mohd AtarMohd Norkhafiz BidinMohd Rahimi Nordin Mohd Rosli Mohd NoorMohd Syafiq Ibrahim Muhammad Afiq ZulkaplyMuhammad Azam DzulkfleyMuhammad Hafiz Mohd NoorNadhiya Zainal AbidinNavinder Singh a/l Jesbir SinghNorazila Edlin Zainal AbidinNorhayati Kamarul BaharinNuura Addina MohamadNur Syuhada ShukriSapinah SamsudinSiti Hajar IdrusSiti Nur Hidayah Osman Syed Muhaimin Syed HussainWan Kamal Wan MudaWan Norzuraini Ahmad Wan Ahmad


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Acknowledgements

We would like to express our deep appreciation to the former Director General of Malaysian Institute of Road Safety Research (MIROS) and the Director of Road User Behavioural Change Research Centre for extending full support in producing this report. Our deep appreciation also goes to Prof. Dr. Musa Hassan of Faculty of Modern Language and Communication, Universiti Putra Malaysia for editing the report.

This report involved the concurrence, cooperation and input of several government agencies, non-governmental agencies and the private sector. The agencies involved were the Royal Malaysian Police (RMP), IPK Selangor, Johor, Perak, Pahang, IPD Seremban, Sungai Petani, Road Transport Department (RTD), PLUS Expressways Berhad, MTD Infraperdana Sdn Bhd, Petroliam Nasional Berhad (PETRONAS), Shah Alam City Council, Sungai Petani Municipal Council, Batu Pahat Municipal Council, Muar Municipal Council, Klang Municipal Council and Kuala Selangor District Council.

We would like to express our gratitude to all these agencies and individuals for their invaluable time and the important role they played in shaping this comprehensive report. Last but not least thanks to the Research and Ethics Committee, Malaysian Institute of Road Safety Research for approving the design and conduct of this study and to all research officers, research assistants, enumerators and drivers for their role in data collection.


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Abstract

Malaysia has a high number of road crashes. In the year 2007 alone, there are 363,319 crashes and 6,282 fatalities for an average 17 deaths per day. Integrated traffic enforcement called Ops is conducted as a response but despite efforts, statistics remain high. The effectiveness of Ops is being evaluated and one of the ways is to measure road users’ perception of being caught (POBC). Effectiveness is measured by an increase and decrease in risk perception. The studies were conducted on four separate occasions, namely, Ops Chinese New Year, Ops Hari Raya Aidilfitri, Ops Deepavali and non-festival days throughout the year. On non-festival days, the perception of being caught was low—between 36% to 42%. During the festive periods, the perception of being caught was also low (41% and 35%) for before Ops Chinese New Year and Hari Raya Aidilfitri respectively, then increased to 52% and 42% during Ops Chinese New Year and Hari Raya Aidilfitri respectively, declining dramatically after the Ops on both occasions. For Deepavali, the percentages remain the same as it was under the three instances similar to the percentages for normal days. Speeding was the one offence majority of road users were wary of being summonsed while respondents agreed that the best form of traffic enforcement were overt traffic enforcement. Despite the concerted efforts, road users’ perception of being caught was low which was not even up to 50% of the total percentage. However, the studies measure the effects of enforcement for crash fatality reduction and not the effects of enforcement on traffic offences. It appears that Malaysian road users are not affected by enforcement and the root causes are due to road users’ perceived lack of enforcement by the authorities themselves. For future studies, it is recommended that the range of study to be increased to cover actual traffic enforcement in progress for example at road


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blocks rather than selected accident-prone areas. The tools for measuring should be simplified as well for less time consuming data collection. Future studies should take account of the multi cultural facets of Malaysian society for example the norms of various ethnicities that might be a factor on the road such as drinking habits and also the role of alcohol consumption on whether it affects perception of being caught.


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1.0 Overview

1.1 Introduction and Background

In Malaysia, during every festive season, lives will be lost in road crashes. The Malaysian tradition of ‘Balik Kampung’ which is supposed to be a joyful period of celebration would be marred by tragedies. Tragedies and road crashes seem to be the norm in every festive season namely Hari Raya Aidilfitri, Chinese New Year, Christmas and Deepavali. The number of deaths per day increase exponentially during this period as compared to normal days.

In 1996, when there were no integrated intervention strategies, 321 deaths were recorded during the 15 days of the festive season, which translates to approximately 21 deaths a day (Radin 1998). The Royal Malaysian Police reported that in 2005, there were 6,200 fatalities and 581,136 motor vehicles involved in crashes. In the year 2007, statistics showed that the number of crashes recorded was relatively high (363,319 cases) and fatalities (6,282 cases) with an average of 17 deaths per day (RMP 2007).

Furthermore, of all categories of traffic misdemeanours, license violation is the highest form of offence committed with 536,401 summonses issued in 2007. Road junction violations were second at 237,111 summonses. It was followed by crashes with 177,090 summonses issued. License plate violations generated the lowest number of summonses with 57,282 summonses being issued nationwide. Hence, Malaysia records generally high number of traffic crashes and traffic related fatalities every year. The highest incidence of traffic crashes and fatalities are during the festive seasons. To reduce these numbers law enforcement


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programmes called Ops Sikap (Operations) are conducted during these periods. In addition to law enforcement activities, extensive road safety campaigns are undertaken through various media.

Road casualty and fatality rates in Malaysia are still high despite increasing concerted traffic law enforcement and road safety campaigns. This must be taken seriously before it worsens. The effectiveness of traffic enforcement should be evaluated. The purpose of this study is to determine whether traffic enforcements are effective or not. One way to measure the effectiveness of traffic enforcement during the festive season and normal days are by evaluating the road users’ perception of being caught (POBC). By evaluating road users’ perceived probability of detection for traffic offences, implicitly it will indicate the current status of traffic enforcement effectiveness.

Therefore, this research seeks to answer the following questions.

• Is the perception of being caught affected by the implementation of Ops Sikap?

• What are the factors that associated to the perception of being caught?

• How much traffic enforcement is required in order to positively affect road users’ perception of being caught?

1.2 Objectives

The general objective of this study is to determine if there is an increase or decrease in road users’ perception of the risk of being caught for specific offences when there is or appears to be increased enforcement.

The specific objectives were to;

• determine road users’ perception of being caught during non-festive days;


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• analyse whether the perception of the risk of being caught increases or decreases before, during and after the implementation of Ops Sikap;

• compare road users’ perception of being caught for each festive season.

• identify factors associated with the perception of the risk of being caught (perceived risk of detection);

• determine the level of traffic enforcement visibility by the increase or decrease of road users’ perception;

• identify the effectiveness of enforcement approaches by studying road users’ perception.

1.3 Limitations

Although efforts were made to ensure that the sample for this study was representative of the country, data was collected only in Peninsular Malaysia.

In addition, the data is from self-reports and hence not free of bias. It is not possible to obtain external validation of the data through respondents’ actual summons history. We are also not able to compare the results to enforcement methods at specific locations as the enforcement authorities would not release their enforcement strategies and methods prior to the actual enforcement activity, fearing that it may negate their enforcement efforts.

1.4 Significance of the Study

This research is able to provide information that can be used to reduce the number of traffic crashes and fatalities. The study could determine how to positively influence the behaviour of motorists, in order to promote safer driving habits at all times.


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Evaluating the perception of being caught of motorists in Malaysia could help improve traffic law enforcement by adopting effective law enforcement methods which in turn can deter motorists from engaging in risky behaviours on the road.

Another contribution from this research is to develop better programmes to enhance road safety which ensures desired changes in motorist behaviour. With better information provided to motorists, they are at least warned in advance that traffic violations will be met with fitting repercussions.

2.0 Literature Review

2.1 Rationale for Traffic Enforcement

There are four essential components in changing road user behaviour, namely, legislation, enforcement, reinforcement, and education. All components are dependent on each other for them to be effective. Enforcement is one of the essential components in changing road user behaviour (Loner et al. 1995). Traffic enforcement seeks to generate a general deterrence effect on motorists. General deterrence is based on road user’s perception of being cited or arrested for a traffic violation. If enough potential violators believe there is a strong probability that they will be caught, some of them will avoid committing traffic offences, at least some of the time. The overall goal is to increase compliance by motorists to traffic laws, resulting in reduced vehicle crashes (National Highway Traffic Safety Administration 2005).

Traffic enforcement is predicted as a mechanism to achieve behaviour change. The underlying principle is that human behaviour can be modified by making people fearful of the consequences of their illegal actions (Gibbs 1975; Ross 1982; Homel 1988; Bjornskau & Elvik 1992, all as cited in Zaal 1994).


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Redelmeier et al. (2003) emphasised the well-known fact that extensive traffic-law enforcement activities reduce the frequency of fatal motor vehicle crashes in countries with high rates of motor-vehicle use.

A study in Norway by Assum and Ingebrigtsen (1990) indicated that the elimination of traffic offences could reduce crashes by 20% to 25%. Furthermore, Haglund and Aberg (2000) and Elliot et al. (2007) state that police surveillance and enforcement activities can have a significant effect on the compliance of traffic rules and regulations.

2.2 Related Theories

This study was conceived and developed on the basis of several theories which posit that the behaviour of road users reacts to external influences namely the perception of being caught for committing traffic offences.

a) Deterrence Model

Deterrence theory has been the dominant paradigm underpinning behavioural control in the field of road safety around the world (Elliot 2008). Deterrence, in general, is the control of behaviour that is affected because the potential offender does not consider the behaviour worth risking for fear of its consequences. Deterrence is only one of a number of compliance-gaining mechanisms.

It does not motivate solely by fear since society’s disapproval of a particular act and this disapproval may work in subtle ways to influence behaviour, thus deterrence has both a fear component and a moral (educative) component. Compulsive behaviours that are unlikely to be deterred can be classified as undeterred non-compliance. Impulsive behaviour can be described as undeterred non-compliance and would appear not to be influenced by deterrents. Under the Deterrence Model,


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only those who can be influenced through fear, i.e. are likely to offend and likely to be deterred by the threat of punishment. Three components in particular influence the deterrent effect of punishment: severity, certainty and swiftness. Of the three certainties is the most important, and swiftness the least important but all three interact and complement each other.

b) Theory of Reasoned Action

Fishbein and Ajzen (1975) developed a theory that defines the links between a person’s beliefs, attitudes, norms, intentions, and behaviours. Known as the Theory of Reasoned Action, it states that an individual’s behaviour is determined by its behavioural intention to perform it, which in turn is determined by the person’s attitudes and his subjective norms towards the behaviour. Subjective norms are “the person’s perception that most people who are important to him think he should or should not perform the behaviour in question”.

c) Theory of Planned Behaviour

According to the theory of planned behaviour (Ajzen 1991), people’s attitude towards the behaviour, their subjective norm, and their perceived behavioural control determine their behaviour indirectly via their intentions. People’s attitude towards a behaviour is determined by their beliefs about the likely consequences of the behaviour; their subjective norm is determined by their beliefs about the normative expectations of those who are important to them and their perceived behavioural control is determined by their beliefs about the presence of factors that may facilitate or obstruct the performance of the behaviour. Intention is defined as a willingness to try to perform the behaviour and the behaviour refers to a defined action.

Finally, given enough actual control over the behaviour, people are expected to carry out their intention as soon as an opportunity is present. Parker et al. (1995) expanded the theory


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with several new concepts, among them moral norm which was found to improve the predictive power of the model. Figure 1 shows a schematic representation of the theory expanded to include moral norm.

Attitude

Moral norm

Subjective norm

Intention Behaviour

Perceived behavioural

control

Figure 1 Theoretical framework based on the theory of planned behaviour (Ajzen 1991). The framework is expanded to include moral norm.

Figure 2 A schematic representation of the theory of planned behaviour (Ajzen 2006)

Normative beliefs

Normative beliefs

Attitude towards the behavioural

Subjective norm

Perceived behavioural

control

Behavioural beliefs

Normative beliefs

Control beliefs


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The conceptual framework of the Theory of Planned Behaviour, along with its predecessor, the theory of reasoned action by Fishbein and Ajzen (1975), has been applied to a wide variety of settings in order to account for and understand people’s behaviour, which also includes, driving violations as mentioned by Parker et al. (1992), speeding behaviour (De Pelsmacker and Janssens 2007; Warner and Aberg 2006; Letirand and Delhomme 2005), seatbelt use (Thuen and Rise 1994; Simsekoglu and Lajunen 2008), and drunk driving (Armitage et al. 2002; Sheeshan et al. 1996).

2.3 Role of Perception of Being Caught

It is well known that visible surveillance of driver behaviour decreases the rate of traffic violations (Amour 1984; Rothengatter 1982; Shinar and McKnight 1985). According to Summala (1985), it is probably the most effective measure in obtaining traffic law compliance. It is also generally accepted that risk of detection; rather than severity of punishment is the most important factor behind compliance with law. There is also a general agreement that increased enforcement leads to an increase in the perceived probability of detection and that this in turn affects driver behaviour. Empirical support for this statement concerning speed adjustment has been presented by Nilsson, Engdahl and Aberg (1983) and concerning drinking by Aberg, Enghdal and Nilsson (1986).

Traffic enforcement is “to implant a feeling of ever-present surveillance by highway patrol’s” (Irby and Jacobs 1960). The essential term being “the feeling of presence” rather than the actual presence of the police. The subjective factor, “feeling” in the concept of deterrence, was probably first introduced explicitly as subjective risk of detection in the mid 1960s (Brehmer 1966).

Brehmer (1966) explained that the subjective risk of detection develops as a function of the visibility of surveillance and the


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principles of learning psychology. Critical to creating a subjective risk of detection is that when the visibility of the surveillance location is restricted, the driver will learn two things: First, that there is a probability of given magnitude associated with his violation and an apprehension for that violation. Second, since the driver cannot precisely locate the surveillance location, he will gradually start observing the rules elsewhere rather than in the immediate vicinity of the surveillance source. Thus, traffic enforcement results in widespread deterrence in time and space on the condition that drivers cannot precisely anticipate the presence of surveillance.

Enforcement is especially effective in reducing speeding (Glendon 2003). Many studies have been undertaken on the perception of being caught for speeding (Mannering 2008; Blincoe 2006; Glendon 2003; Waard 1994). It is agreed that most of the drivers perceive that they are less likely to be caught for speeding. A study done by Mannering (2008) on the probability of being caught for speeding found that on the average the drivers believe that they will only be fined for speeding if they drive 10.88 km/h over the speed limit. He also found out that drivers associated safety measurements with the likelihood to be fined. They believe that they will only be fined by the police if safety is threatened.

Waard (1994) found that the perception of being caught plays an important role in speed choice which reduces the driver’s tendency to speed. Furthermore, with the use of marked police vehicles, the effect is more remarkable as it is believed that the presence of the vehicles is perceived as some kind of police enforcement by the drivers. The high perception of being caught reduces the driver’s tendency to speed. In the comparison between two methods of enforcement, it was found that stopping offenders on the road to give fines deter speeding much longer than mailing the fines. In addition, with the use of marked police vehicles, the effect is more remarkable as it is believed that the presence of the police car itself is perceived as some form of police enforcement by the drivers.


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It was also found that drivers who witness other drivers being stopped by the police are more likely to reduce their speed accordingly (Glendon 2003).

Furthermore, two studies identified in this review examined the act of speeding in relation to perceptions of risk. Adams-Guppy and Guppy (1995) investigated the role of the perceived probability of adverse events (perceived risk) together with utility measures (e.g. the importance of getting to a destination on time) in predicting self-reported behaviours. Drivers were asked to report on their frequency of speeding on motorways at 10 mph and 20 mph above the speed limit. Hierarchical multiple regressions indicated that, while perceptions of the risk of injury were not good predictors, time pressure was a good indicator. This finding implies that more frequently occurring positive factors are better predictors of behaviour than rare, but negative, events.

Brown and Cotton (2003) interviewed drivers about their perceptions of risk in relation to speeding, and risk-mitigating beliefs (the common-sense notions that a driver might employ to justify their speeding, e.g. that it is acceptable to speed when there are no cars around or when driving on a straight road). The findings suggests that those who adopt risk-mitigating beliefs report higher levels of speeding. Moreover, as estimates of risk partially mediated this effect, the implication is that these risk-mitigating beliefs may serve to reduce perceptions of the risk of speeding. The authors also identified that those drivers who reported speeding perceived higher levels of negative consequences. Gabany et al. (1997) found that the drivers’ perception and attitude toward speeding could be segmented into five parts, predisposing, enabling, and reinforcing constructs: ego gratification, risk-taking; time pressures; disdain of driving; and inattention.

Goerke (2003) studied about the implementation of efficient fines to traffic rule violators. He argues that the fines should depend on the severity of accidents the violation may cause.


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If it is proven that serious injuries are caused by the offences the fines should be higher than if there is no injury reported. However, the severity of fines should decrease if the level of detection is high. It was also found that the amount of fines should depend on seriousness of the offence. For example, if the driver is speeding just before an accident, the driver should be fined higher than those who are involved in an accident while not speeding.

3.0 Method

3.1 Sample

The population of this study comprised road users with a valid driving license. Quota sampling on the basis of ethnicity was used to select the sample, which comprised 60% Malays, 30% Chinese and 10% Indian and other ethnicity.

A total of 27,658 respondents were selected during the six weeks surrounding the three major festival periods of Chinese New Year, Hari Raya Aidilfitri and Deepavali and the non-festival periods referred to in this report as “non-festival days”. Table 1 shows the breakdown of sample sizes for each selection period.

Period Ops Chinese New Year

Non-festival days

Ops Hari Raya Aidilfitri

Ops Deepavali Total

Total 10,800 8,758 5,400 2,700 27,658

Table 1 Number of respondents gathered in 2008


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3.2 Materials

The Questionnaire

A questionnaire was developed for data collection. In keeping with the lingua franca of the country it was prepared in the Malay language. Consisting of 25 items, it was designed to measure three constructs namely:

1. road users’ perception of being caught for eight specified offences;

2. factors associated with the perception of being caught; and

3. road users’ perception of the effectiveness of overt and covert traffic enforcement methods.

Each construct is measured by eight questions with varied rating scales. Details are as follows.

1. Perception of the risk of being caught for committing the following offences:

a. speeding;

b. beating traffic lights;

c. queue jumping;

d. overtaking at a double lines;

e. illegal use of the emergency lane;

f. failure to use the seatbelt while driving;

g. failure to use a crash helmet while riding; and

h. dangerously cutting into traffic.

Respondents were asked how strongly they agreed with the statements for this construct on a scale of 0–10, with 0–3 indicating that their perception of the risk of being caught was low, 4–7 moderate and 8–10 high. 2. Factors that associated with the perception of being caught

are:

a. visibility of enforcement activities;


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b. media exposure and road safety campaign;

c. prior experience of being caught for committing traffic offences; and

d. the severity of traffic punishments for volating traffic laws.

Respondents were asked how strongly they agreed with the statements in respect of the above on a scale of 1–4, with 1 meaning that they strongly disagree, 2 – disagree, 3 – agree and 4 – strongly agree.

3. Perception of the effectiveness of overt and covert enforcement was measured in respect of:

a. police in observation towers;

b. plain-clothes (undercover) police personnel on road user transport;

c. police patrols in many areas;

d. placing police cut-outs (dummies) at crash-prone areas

e. roadblocks;

f. enforcement cameras in crash areas and dangerous areas;

g. using unmarked police vehicles for enforcement; and

h. signs warning of police presence along roads.

In this section, respondents were asked to rate how strongly they agreed with the statements on a scale from 1–5, where 1–2 indicated low, 3 indicated moderate and 4–5 indicated high.

An additional variable is the visibility of enforcement activities. For this, respondents were asked to rate how strongly they agreed with the statements on a scale from 0–10, where 0–3 indicated their POBC are low, 4–7 moderate and 8–10 high.


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3.3 Reliability

The reliability for each subscale, measured by Cronbach’s Alpha of between 0.810 to 0.924 is considered good to excellent. Details are in Table 2.

Subscales No. of items Cronbach’s Alpha

Perception of being caught for committing specific traffic offences

8 0.924

Factors associated with the perception of being caught

8 0.810

Perception on the effectiveness of traffic enforcement approaches

8 0.841

Table 2 Reliability of study instruments

3.4 Procedure

Festival Periods and Data Collection Areas

In the two weeks around the three major festivals in the country, the Royal Malaysian Police and the Road Transport Department initiated overt and covert enforcement methods in an attempt to reduce traffic violations and crashes. Examples of overt enforcement approaches are police in observation towers, police patrols, placing police cut-outs (dummies) and roadblocks while covert methods are plain clothes (undercover) police personnel on road user transport and enforcement cameras at crash areas. To gain insight into road user’s view on the effectiveness of these approaches, road users’ perception of being caught for traffic violations was evaluated. Effectiveness would be indicated by an increase or decrease in perception.

Current statistics show a marked difference in the number of crashes and offences on federal roads and expressways. Federal roads are any road maintained by the federal government of


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which route/road number begins with an integer or the letter F (Peninsular Malaysia only). While expressways are any road using tolled transit ticket of with its route/road number beginning with the letter E (RMP 2007). As such data for this study was collected from these two types of roads with collection from three locations along federal roads and three along expressways.

Ops Chinese New Year

A series of field surveys on alternate days over a period of six weeks (24 January to 23 February 2008) were carried out, before, during and after the launch of Ops Chinese New Year. Data was collected from 10,800 respondents from six locations: locations at Sg. Buloh, Genting Sempah, Karak and Seremban represented samples from expressways while Klang (Selangor), Muar (Johor) and Sungai Petani (Kedah) were data collection locations from federal roads.

Ops Hari Raya Aidilfitri and Deepavali

Similarly, data collection for Ops Hari Raya Aidilfitri was also undertaken at six locations, over six weeks, spanning periods before (9 September to 23 September 2008), during (24 September to 8 October 2008) and after (9 October to 23 October 2008) the Ops. Meanwhile for Ops Deepavali, data was collected at similar locations as Ops Hari Raya but over a shorter period of three weeks. Locations at Sg. Buloh, Genting Sempah, Karak and Seremban represented samples from expressways while Klang (Selangor), Batu Pahat (Johor) and Sungai Petani (Kedah) represented federal roads.

Non-festival days

Data collection was undertaken at three locations: two at federal roads and one from an expressway. Locations at Genting Sempah, Karak provided samples from expressways while Klang (Selangor) and Muar (Johor) represented federal roads. For expressways, collection was undertaken every two weeks and


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once a month for federal roads except for Klang (Selangor). The survey was conducted on every Thursday and Friday to represent week days and Saturday represented the weekend.

4.0 Results and Discussion

Results are presented in two sections namely Section A: Non-festival days throughout 2008, and Section B: Festival Periods of the Chinese New Year, Hari Raya Aidilfitri and Deepavali in 2008.

4.1 Section A: Non-festival Days

4.1.1 Socio-demographic Profile of the Respondents during Non-festival Days

A summary of the respondents’ socio-demographic profile appears in Table 3.

Age, Gender and Ethnicity

The age of the respondents range from 16 to 83 years with a mean of 32. The majority of respondents (69%) were aged 16–35 years while 63% were male and 37% female. The majority were Malays at 63%. 25% were Chinese and 12% were Indian and others.

Income, License Type and Vehicle Use Frequency

The majority of respondents came from the lower-income group in that 74% had monthly incomes of RM2000 and less. 12% had a monthly income of between RM2,001–RM2,500, 6% earn between RM2,501–RM3,000 and 8% earned more than RM3,000. Majority of the respondents (73%), held D licenses, and 63% had B license. The remaining 5% were E license holders. In terms of vehicle frequently used by respondents, majority of the


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respondents (59%) frequently used cars for travelling. The rest were followed by motorcycles and van/MPV’s (Multi-Purpose Vehicle)/Sport Utility Vehicles/Four Wheel Drives, 35% and 6% respectively.

Experience of being Fined

Based on Table 3, 64% of the respondents reported of not having experienced being fined or arrested for committing a traffic offence. 37% have been fined previously. Of those 92% reported received between 1–5 fines and 8% received between 6–10 fines. Only 1% of the respondent had received more than 10 fines. The numbers of fines received range from 1 to 20 with a mean of 3.

Items n %

Gender (n=8740)Male 5,536 63.3Female 3,204 36.7

Age group (n=8754)16–25 years old 3,009 34.426–35 years old 3,039 34.736–45 years old 1,598 18.346–55 years old 798 9.155 years old and above 310 3.5

Mean : 32

Range : 16–83

Ethnicity (n=8755)Malay 5,527 63.1Chinese 2,154 24.6Indian and others 1,074 12.3

Monthly income (n=8740)Less than RM1,500 4,366 50.0RM1,501–RM2,000 2,110 24.1RM2,001–RM2,500 1,026 11.7

Table 3 Socio-demographic profiles of the respondents during non-festival days

(Continue)


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Items n %RM2,501–RM3,000 534 6.1More than RM3,000 704 8.1

Type of license B/B1/B2 (n=8756) 5,529 63.1D (n=8756) 6,346 72.5E/E1/E2 (n=8756) 394 4.5

Vehicle frequently used (n=8158)Motorcycle 2,869 35.2Car 4,807 58.9Van 158 1.9MPV/SUV/4WD 291 3.6Others 33 0.4

Experience of being fined (n=8758)Yes 3,196 36.5No 5,562 63.5

No of fines received (n=3196)1–5 2,935 91.86–10 239 7.5More than 10 22 0.7

Mean : 3Range : 1–20

4.1.2 Perception Study Analysis

The respondents were required to answer based on the provided scale. The respondents were asked on their probability of being caught for specific traffic offences. In order to observe the pattern of perception of being caught, the collected data was measured as aggregate percentage of eight items which concentrate on eight specific traffic offences, each measured with an 11-point scale. The percentage collected is from 0% to 100% where 0% indicates perception of not being caught at all while a 100% indicates a sure perception of being caught. During non-festival days, road users feel that the probability of being caught is slightly higher for some offences than for others. This data was obtained by road users ranking the probability of being caught for eight traffic offences based on their own


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experience and knowledge. The percentages of the road users’ perception of being caught range from 36% to 42%. Based on Figure 3, the top five traffic offences that are perceived as posing the highest risk of detection during non-festival days are speeding (42%), dangerously cutting into traffic (40%), failure to use seatbelt while driving (39%), beating traffic light (37%) and failure to use a crash helmet while riding (37%). Hence, this study indicates that overall road users’ perception on probability of traffic offences detection is very low at below 50% as compared to the highest percentage of 100%. Moreover, respondents assumed that overtaking at double lines, queue jumping and illegal use of the emergency lane are not serious traffic offences and the probability of getting fined by the authorities when they commit those offences are low.

Figure 3 Percentage of road users’ perception of being caught at all locations during non-festival days

Failure to use seatbelt while

driving

Beating traffic lights

Failure to use a crash helmet

while riding

Overtaking at double lines

Queue-jumping Illegal use of the emergency lane

Speeding

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Dangerously cutting into

traffic

4.1.3 Factors Associated with the Perception of Being Caught

The evaluation of the perception of being caught is not merely based on the judgement of how much road users perceive their probability of being caught. Furthermore, the respondents were asked “what are the reasons that associated with their perception of being caught for specific traffic offences?” The data were categorised into two levels of perception according to the scores given by the respondents. The categories are ‘disagree’ (scores 1 and 2) and ‘agree’ (scores 3 and 4). The percentage of


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respondents giving high scores of 3 and 4 to the statements is analysed in this study. Mainly, for this study, four major factors contribute to the road users’ decision upon their perception of being caught. They are: 1) the visibility of enforcement activities, 2) media exposure and road safety campaigns, 3) the experience of being caught, and 4) the severity of traffic punishment for violating traffic laws. During the non-festival days, the two factors which respondents at all locations said influenced their perception of being caught the most were traffic punishment (69%) and media exposure and road safety campaign (64%). These were followed by the experience of being caught (58%) and visibility of enforcement activities (56%). Figure 4 shows the factors associated with the perception of being caught according to the road users’ perception.

This means, the authorities should emphasise more on imposing heavier summonses on traffic offenders. The punishment should be appropriate with the offence committed. Heavier penalty must be imposed on repeat offenders to deter them from repeating the same offences in the future. Additionally, the government should play a bigger role in terms of media exposure and road safety campaigns. This is to increase road safety awareness among the general public besides cultivating a safety-first culture alone.

However, during non-festival days, respondents believed that the visibility of enforcement activities is not a main factor that is associated with the perception of being caught.


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Figure 4 Factors associated with the perception of being caught according to the road users’ perception

Visibility of enforcement activities

Media exposure and road safety campaign

Experienced of being caught

The severity of traffic punishment for violating

traffic laws

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4.1.4 Visibility of Enforcement Activities

Enhancing the visibility of enforcement activities would increase the actual and perceived risk of detection (Fildes and Lee 1993). Respondents were asked about the extent of enforcement activities that they observed in certain areas. Responses were measured on an 11-point scale and categorised into three perception levels. The categories are low (scores 0–4), medium (scores 5–7) and high (scores 8–10). Findings summarised in Figure 5 show that the majority of the respondents (94%) reported low and medium visibility of enforcement activities during non-festival days on expressways throughout year 2008. Only 7% of the respondents reported high enforcement activities. The same pattern can be stated regarding federal roads, as shown in Figure 5, almost all respondents 92% reported low and medium visibility of enforcement activities during non-festival days throughout the year. The remainder (8%) of the respondents reported high level of enforcement activities. As a result, more effort should be taken by local authorities to enhance their visibility in order to increase road users’ perception of being caught.


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Figure 5 Visibility of enforcement activities – Expressways and federal roads

This shows that during non-festival days, respondents noticed that the authorities were rarely seen performing their enforcement duties. Respondents were of the opinion that the visibility of enforcement is low on the two types of roads; expressways and federal roads. This was in line with the results which respondents were in view that the probabilities of getting fined for committing traffic offences were low.

An independent-samples t-test was conducted to compare the visibility of enforcement activitiy scores for expressways and federal roads. There is a significant difference in scores for federal roads (M = 3.48, SD = 2.563) and expressways, M = 3.29, SD = 2.466; t (8125) = 3.414, p = .001. Thus, there is a higher scores visibility of enforcement activities on federal roads as compared to expressways. Enforcement visibility along federal roads is perceived to be higher as compared to that of expressways during non-festival days. See Appendix 3.

The authorities should seek for alternatives to overcome this problem. Among others, the forms of intervention that can be taken are to conduct more aggressive enforcement campaigns, frequent road patrols and roadblocks at accident prone areas.

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Low Medium High Low Medium High

Federal roads


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4.1.5 Overt and Covert Enforcement

Studies show that the method of traffic enforcement is an important factor in helping modify road user behaviour and reduce crash casualties and fatalities. Highly visible (i.e. overt) enforcement helps remind motorists that enforcement is present and potentially increases the actual and perceived risk of detection (Fildes and Lee 1993). Non-visible (i.e. covert) enforcement is said to heighten road users’ sense of uncertainty about detection risk and to prevent them adapting their traffic offence behaviour at specific times and locations when enforcement activities are clearly being carried out (Ostvik and Elvik 1990).

Greater reductions in casualty crashes have been reported with overt rather than covert enforcement (Diamantopoulou and Cameron 2002). Reductions were also evident when a mix of overt and covert radar enforcement activities were used (Diamantopoulou and Cameron 2002).

The survey also looked into the effectiveness of both overt and covert approaches in influencing road users’ perceived risk of detection. The percentage of respondents giving high scores of 4 or 5 to the statements was analysed in this study. Examples of overt enforcement activities in Malaysia are police personnel in observation towers, police patrols, placing police cut-outs (dummies) at crash-prone areas, roadblocks, enforcement cameras and warning signs of police presence along the roads. Covert enforcement would employ approaches such as plain-clothes (undercover) police personnel on public transport and using unmarked vehicles for enforcement.

Figure 6 shows that the majority of the respondents agreed that the installation of surveillance cameras (55%) is the most effective for overt approach of traffic enforcement. Police patrols and roadblocks come in second and third places with 53% and 50%, respectively. Meanwhile, for covert approaches, road users agreed that both approaches which consist of plain-


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Figure 6 Percentage of road users’ perception on effectiveness of traffic enforcement approaches – Expressways

clothes police personnel on public transport (44%) and using unmarked vehicles for enforcement (44%) are effective.

Figure 7 illustrates the percentage of road users’ perception on the effectiveness of traffic enforcement approaches for federal roads. For overt approaches, almost half (51%) of the respondents agreed that police patrols is more effective as compared to the use of surveillance cameras (50%). 48% of the respondents also agreed that roadblocks are an effective approach to be used on federal roads. Meanwhile, for covert approaches, respondents agreed that using unmarked vehicles for enforcement (44%) was more effective than plain-clothes police personnel on public transport (41%).

In conclusion, respondents perceive that overt enforcement, installation of surveillance cameras, police patrols and roadblocks were some of the more effective approaches in reducing traffic offenders. Therefore, it would be better for authorities to focus on launching Automated Enforcement Systems (AES) in Malaysia. Electronic enforcement via cameras can put more emphasis on major traffic offences such as speeding and beating traffic lights. It also plays a role in monitoring and detecting traffic offenders easily. With this approach, it can reduce the amount of officers required to regulate road users.


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Figure 7 Percentage of road users’ perception on effectiveness of traffic enforcement approaches – Federal roads

4.2 Section B: Festival Periods

4.2.1 Socio-demographic Profile of the Respondents during Festival Periods

Respondents’ Age, Gender and Ethnicity

Table 4 shows the summary of the respondents’ socio-demographic profiles for this study. A total of 18,900 respondents were involved in this study. The age of the respondents range from 16 to 81 years with a mean of 33. A majority of the respondents (64%) were 16–35 years old. 21% of the respondents were 36–45 years old. About 12% respondents were 46–55 years old. Only 3% of the respondents were older than 55 years. In regards to their gender, a majority of the respondents (63%) who participated in this study were males and the remaining percent (37%) were female respondents. Malays make up the majority of the respondents with 67%, Chinese (25%) and Indian and others (8%).


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Respondents’ Monthly Income, Type of License and Vehicle Frequently Used by Respondents’

A majority of the respondents were in the low and midle income level with those earning RM1,500 and less make up 35% of the respondents while those earning RM1,501–RM2,000 make up 35% of respondents. Followed by those with a monthly income of RM2,001–RM2,500 (11%), RM2,501–RM3,000 (8%), and more than RM3,000 (11%). All respondents surveyed were fully licensed drivers. D license holders form the majority with 80% of the respondents. The remainder of the respondents were B license holders (58%) and E license holders (12%). In regards to the vehicle frequently used by the respondents, more than half (64%) frequently use a car for travelling; followed by motorcycle and van/MPV (Multi-Purpose Vehicle)/SUV (Sport Utility Vehicle)/4WD (Four Wheel Drive), 27% and 9% respectively.

Respondents’ Experience of being Fined for Committing Traffic Offences

Based on Table 4, 58% of the respondents have reported of not having experienced being fined or arrested for committing a traffic offence. About 42% of the respondents have experienced being fined. Of those who reported of being fined, 88% respondents reported to having received between 1–5 fines and 10% received between 6–10 fines. Only 2% of the respondents have ever received more than 10 fines. The number of fines received ranged from one to 20 with a mean of three.


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Items n %

Gender (n=18,866)Male 13,533 63.3Female 5,333 36.7

Age group (n=18,843)16–25 years old 6,009 31.926–35 years old 6,120 32.536–45 years old 3,966 21.046–55 years old 2,192 11.655 years old and above 556 3.0


Ethnicity (n=18,888)Malay 12,658 67.0Chinese 4,699 24.9Indian and others 1,531 8.1

Monthly income (n=18,791)Less than RM1,500 6,510 34.6RM1,501–RM2,000 6,655 35.4RM2,001–RM2,500 2,128 11.3RM2,501–RM3,000 1,467 7.8More than RM3,000 2,031 10.8

Type of license B/B1/B2 (n=18,900) 10,903 57.7D (n=18,900) 15,049 79.6E/E1/E2 (n=18,896) 2,316 12.3

Vehicle frequently used (n=18,864)Motorcycle 5,138 27.2Car 12,145 64.4Van 536 2.8MPV/SUV/4WD 901 4.8Others 144 0.8

Experience of being fined (n=18,899)Yes 8,001 42.3No 10,898 57.7

No of fines received (n=8,001)1–5 7,000 87.56–10 814 10.2More than 10 187 2.3


Table 4 Socio-demographic profile of the respondents during festival periods


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4.2.2 Perception Study Analysis

In order to observe the pattern of perception of being caught, the collected data is categorised into three (3) main time periods, namely before the Ops, during the Ops and after the Ops. Road users’ perception of being caught is measured as aggregate percentage of eight items for specific traffic offences, each measured with an 11-point scale. The percentage of road users’ perception of being caught is determined for each Ops period of festive seasons. Figure 8 illustrates the summary of the percentage of road users’ perception of being caught (perceived risk of detection) according to Ops period for each festival period. Before the Ops, the percentages of road users’ perception of being caught were very low for Chinese New Year and Hari Raya Aidilfitri festival as compared to the other periods (during Ops and after Ops). Nevertheless, during the Ops, there were increments in road users’ perception of being caught for both festivals (Chinese New Year and Hari Raya Aidilfitri) which 52% and 42%, respectively. This might be due to road users’ awareness of the actual increase or the expectation of increased enforcement. Subsequently, the road users’ perceived risk of detection diminished dramatically after the Ops Chinese New Year and Ops Hari Raya Aidilfitri ended. In contrast with the Deepavali festival period, the percentage of road users’ perception of being caught before Ops were higher compared to during Ops, 34% and 33% respectively. There were slight differences in percentages of road users’ perceived risk of detection for Ops Deepavali periods (before Ops=34%, during Ops=33% and after Ops=34%).

Based on the following festival periods, the percentage of the overall perception of being caught has increased for the before Ops and during Ops period for Chinese New Year and Hari Raya Aidilfitri. The percentages decreased by the end of Ops. By contrast, the percentages were different during Deepavali. The percentages for the perception of being caught during Deepavali were approximately the same before and after the Ops. This could be because the authorities concentrated less


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on the Deepavali period as compared to other festival periods. Authorities do not conduct integrated enforcement methods during that time period. In fact, road users’ perception level of being caught remains the same as it is on non-festival days.

Figure 8 Percentage of road users’ perception of being caught for 2008 festival periods

4.2.3 Traffic Offences

The traffic offences data was obtained by the respondents ranking the probability of detection for eight specific traffic offences based on their experience and knowledge. They are: 1) speeding, 2) beating traffic lights, 3) queue jumping, 4) overtaking at double lines, 5) illegal use of the emergency lane, 6) failure to use the seatbelt while driving, 7) failure to use a crash helmet while riding, and 8) dangerously cutting into traffic. Specifically, the details of traffic offences for each festival period were discussed namely, Chinese New Year, Hari Raya Aidilfitri and Deepavali.

a) Chinese New Year

Although road users’ perception of being caught for specific traffic offences increased in overall during the Ops periods, the respondents felt that the probability of being caught was


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higher for some offences than for others. Overall, in descending order, the offences of speeding, beating traffic lights, overtaking at double lines, illegal use of the emergency lane and queue jumping were perceived as posing the five highest risk of detection. Meanwhile, dangerously cutting into traffic, failure to use a crash helmet while riding and failure to use the seatbelt while driving are perceived as posing the lowest risk of detection. Refer to Figure 9 for this. Even though dangerously cutting into traffic is one of the six main offences that could be punished during the Ops with a maximum compound of RM300, surprisingly, the respondents still assumed that their probability of being fined by the authorities for various traffic offences is low. In fact, they assumed that the failure to use the seatbelt while driving and a crash helmet while riding are not serious traffic offences.

This might be caused by misplaced focus by the authorities on traffic offences such as the failure to use seatbelts and helmets.

Figure 9 Percentage of perception of being caught at all locations during the Chinese New Year period

Illegal use of the emergency lane

Failure to use the seatbelt while driving

Failure to use a crash helmet while riding


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b) Hari Raya Aidilfitri

Figure 10 shows that for Hari Raya Aidilfitri, the result shows the same pattern as the previous Ops. Overall, in descending order, the offences of speeding, beating traffic lights and overtaking at double lines were perceived as posing the three highest risk of detection. It is followed by queue jumping and the failure to use the seatbelt. This means, a majority of the respondents agreed that all the three traffic offences were serious and offenders can be caught at any time upon committing those offences.

c) Deepavali

Overall, in descending order, the offences of queue jumping, speeding, overtaking at double lines, beating traffic lights and illegal use of the emergency lane were perceived as posing the five highest risk of detection. See Figure 11.

Figure 10 Percentage of perception of being caught at all locations during the Hari Raya Aidilfitri period





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4.2.4 Comparison of Road Users’ Perception of Being Caught between Ops Periods for Each Festive Season

a) Chinese New Year

A one-way between-group analysis of variance was conducted to explore Ops Chinese New Year period differences in mean scores of road users’ overall perception of being caught for committing traffic offences. The Chinese New Year were divided into three main periods (Group 1: Before Ops; Group 2: During Ops, Group 3: After Ops). There was a statistically significant difference at the p < .01 level in overall perception of being caught scores for three main periods: F (2, 10797) = 180.679, p = .0001. Post-hoc comparisons using the Tukey HSD test indicated that the mean score for perception of being caught during Ops (M = 5.2407, SD = 2.53278) was significantly different from before Ops (M = 4.0810, SD = 2.46086) and after Ops (M = 4.5723, SD = 2.78934). Thus, it shows that the road users’ overall perception of being caught scores during Ops was higher than before and after the Ops. It also indicates that road users’ overall perception of being caught scores before Ops was lower than after Ops. See Appendix 4.

Figure 11 Percentage of perception of being caught at all locations during Deepavali period





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The aim is to compare the mean scores of road users’ overall perception of being caught for traffic offences. A one-way between-group analysis of variance was performed to investigate whether there is a difference in mean scores of road users’ overall perception of being caught for traffic offences for Ops Hari Raya Aidilfitri periods. The ANOVA test revealed that there was a statistically significant difference in mean scores of overall perception of being caught for traffic offences for three main periods: F (2, 5397) = 42.002, p = .0001. Since there was a statistically significant different, it is necessary to conduct multiple comparison tests. The result was of the same pattern as Chinese New Year period. Post-hoc comparisons using the Tukey HSD test indicated that the mean score for perception of being caught during Ops (M = 4.2691, SD = 2.47047) was significantly different from before Ops (M = 3.5592, SD = 2.45844) and after Ops (M = 3.7540, SD = 2.26968). Therefore, it shows that the road users’ overall perception of being caught scores during Ops was higher than before and after the Ops. It also indicates that road users’ overall perception of being caught scores before Ops was lower than after Ops. See Appendix 5.

c) Deepavali

The aim is to compare the mean of overall perception of being caught for traffic offences. A one-way between-group analysis of variance was conducted to explore Ops Deepavali period differences in mean scores of road users’ overall perception of being caught for traffic offences. The ANOVA test revealed that there was no significant difference in mean scores of overall perception of being caught for traffic offences for three main periods: F (2, 2697) = 0.863, p = .422. See Appendix 6.


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4.2.5 Factors Associated with the Road Users’ Perception of Being Caught

As discussed earlier, the respondents were asked the reasons that are associated with their perception of being caught for specific traffic offences. Basically, four major factors contribute to the road users’ perception of being caught. They were: 1) the visibility of enforcement activities, 2) media exposure and road safety campaign, 3) the experience of being caught, and 4) the severity of traffic punishments for violating traffic laws. Figures 13, 14 and 15 demonstrate the factors associated with road users’ perception towards their probability of being caught throughout 2008 which emphasised on festival periods namely, Chinese New Year, Hari Raya Aidilfitri and Deepavali. In general, from the combination of the three festivities throughout the year 2008, the percentages for each factor range approximately from 54% to 64% at all locations. Media exposure and road safety campaign are the dominant factor (64%) being taken into consideration by the respondents. The severity of traffic punishments for violating traffic laws imposed upon traffic offenders (62%) as well as the visibility of traffic enforcement activities (56%) emerged as second and third related factors respectively. The experience of getting caught by the authority seems to influence respondents the least (54%). See Figure 12.

Visibility of enforcement

activities



The severity of traffic punishments for violating

traffic laws

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Figure 12 Factors associated with the perception of being caught according to the road users’ perception at all locations – All festival periods


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Figure 13 Factors associated with the perception of being caught according to the road users’ perception at all locations – Chinese New Year

Figure 14 Factors associated with the perception of being caught according to the road users’ perception at all locations – Hari Raya Aidilfitri


activities




traffic laws

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activities




traffic laws

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4.2.6 Visibility of Enforcement Activities

The findings are similar for the majority of respondents surveyed at both expressways and federal roads during all festival periods. As shown in Figure 16, 51% of respondents perceive that the visibility of enforcement activities were low along expressways while 48% of respondents perceived likewise for enforcement along federal roads. During Chinese New Year, 45% of respondents reported of medium visibility of traffic enforcement at both expressways and federal roads. The percentages were shown in Figure 17. However, the visibility of traffic enforcement was low during Hari Raya Aidilfitri with the majority of respondents from expressways and federal roads reporting visibility of 61% and 54% respectively (Please refer to Figure 18). Lastly, based on Figure 19, low traffic enforcement visibility was also reported during the Deepavali festive season. 59% of respondents reported of low visibility of enforcement activities along expressways and 65% responded similarly for federal roads.

An independent-samples t-test was conducted to compare the perception of the visibility of enforcement activities scores


activities




traffic laws

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Figure 15 Factors associated with the perception of being caught according to the road users’ perception – Deepavali


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for expressways and federal roads. There was a significant difference in scores for federal roads (M = 3.80, SD = 2.549) and expressways, M = 3.65, SD = 2.533; t (18761) = 4.107, p = .0001. Thus, there was a higher score visibility of enforcement activities on federal roads as compared to expressways. See Appendix 7.

Figure 16 Percentage of visibility of traffic enforcement at all locations – All festival periods

Figure 17 Percentage of visibility of traffic enforcement at all locations – Chinese New Year

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Expressways


Federal roads

Expressways

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Federal roads


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Figure 18 Percentage of visibility of traffic enforcement at all locations – Hari Raya Aidilfitri

Figure 19 Percentage of visibility of traffic enforcement at all locations – Deepavali

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Expressways


Federal roads

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Expressways


Federal roads


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4.2.7 Overt and Covert Enforcement Approaches

The survey also looked into the effectiveness of both the overt and covert approaches in influencing road users’ perceived risk of detection. Examples of overt enforcement in Malaysia are police-manned observation towers, police patrols, placing police cut-outs (dummies) in crash-prone areas, roadblocks, enforcement cameras and signs warning of police presence along the roads. Covert enforcement would employ approaches such as plain-clothes (undercover) police personnel on public transport and using unmarked vehicles for enforcement.

a) Chinese New Year

The survey also explored whether there was any relationship between the approach of enforcement and perception levels of detection. On expressways, the perception of being caught by covert enforcement approaches was slightly higher in all the periods surveyed [viz. before (51%), during (54%) and after (55%)] than the perception of being caught by overt enforcement activities [before (45%), during (53%) and after (51%)]. See Figure 20. While this greater perception has not yet been mapped against crash statistics during those time periods, this pattern of higher perception risk from covert approaches appears consistent with findings in other countries where covert enforcement efforts generally produce greater reduction in casualty crashes than overt methods (Diamantopoulou and Cameron 2002).

Results from federal roads show a different pattern as compared to expressways. From Figure 21, it is apparent that the perception of the risk of being caught was higher from overt enforcement [before (52%), during (53%) and after (46%) the Ops] compared with covert approaches during the same period of time [viz. (46%) before, during (49%) and (48%) after Ops]. In conclusion, for all locations, the perception of being caught by covert enforcement approaches was slightly higher for all the periods surveyed (48%) than the perception of being caught by overt enforcement activities (46%).


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In terms of the types of overt enforcement activities, respondents perceived that automated enforcement systems, traffic police patrols, and roadblocks would effectively enhance awareness of the risk of being caught if they infringe traffic rules.

Figure 20 Perception of enforcement approaches effectiveness throughout Chinese New Year period – Expressways

Figure 21 Perception of enforcement approaches effectiveness throughout Chinese New Year period – Federal roads


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The survey also explored whether there was any relationship between the approach of enforcement and perception levels of detection. In regards to expressways, the perception of being caught by overt enforcement approaches was slightly higher in all the periods surveyed [viz. before (50%), during (44%) and after (39%) than the perception of being caught by covert enforcement activities before (47%), during (44%) and after (36%)], as illustrated in Figure 22. While this greater perception has not yet been mapped against crash statistics during those time periods, this pattern of higher perception risk from overt approaches appears inconsistent with findings in other countries where covert enforcement efforts produce greater reduction in casualty crashes than overt methods could (Diamantopoulou and Cameron 2002).

Results concerning federal roads showed the same pattern as those of expressways. From Figure 23, it is apparent that the perception of the risk of being caught was higher in terms of overt enforcement [viz. before (49%), during (46%) and after (40%) the Ops as compared to covert approaches during the same period of time before (47%), during (42%) and after the Ops (38%)].

In respect of the types of overt enforcement activities, respondents perceived that traffic police patrol, automated enforcement systems and roadblocks would effectively enhance awareness of the risk of being caught if they infringe traffic rules. See Figures 22 and 23.


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Figure 22 Perception of enforcement approaches effectiveness throughout Hari Raya Aidilfitri period – Expressways

Figure 23 Perception of enforcement approaches effectiveness throughout Hari Raya Aidilfitri period – Federal roads


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c) Deepavali

As illustrated using Figures 24 and 25, the survey also explored whether there was any relationship between the approach of enforcement and the perceived levels of detection. On expressways, the perception of being caught by covert enforcement approaches was higher in all the periods surveyed [viz. before (49%), during (42%) and after (24%) than the perception of being caught by overt enforcement approaches before (39%), during (27%) and after (27%)]. See Figure 24. This pattern of higher perception risk from covert approaches appears consistent with findings in other countries where covert enforcement generally produces greater reduction in casualty crashes than overt methods do (Diamantopoulou and Cameron 2002). Note however, the differences are marginal.

Results concerning federal roads showed the same pattern as those of expressways. From Figure 25, it is apparent that the perception of the risk of being caught was higher from covert approaches [viz. before (39%), during (42%) and after (24%) the Ops compared with overt approaches during the same period of time before (49%), during (27%) and after (27%)]. Thus, for both locations, this pattern of higher perception risk due to covert approaches appears consistent.

In respect of the types of overt enforcement activities, respondents perceived automated enforcement systems, traffic police patrols and roadblocks would effectively enhance awareness of the risk of being caught if they infringe traffic rules.


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Figure 24 Perception of enforcement approaches effectiveness throughout Deepavali period – Expressways

Figure 25 Perception of enforcement approaches effectiveness throughout Deepavali period – Federal roads


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4.3 Comparison of Mean Perception of Being Caught between Road Users Who Have Been Fined and Those Who Have Never Been Fined for Traffic Offences

a) For non-festival days

The goal is to determine whether there is any difference in the mean perception of being caught among road users who have been fined and road users who have never been fined for traffic offences. An independent sample t-test was conducted to compare the mean perception of being caught among road users who have been fined and road users who have never been fined. A preliminary assumption testing was conducted to check for normality and equality of variance with no serious violation noted (Appendix 8). There was a significant difference in the mean perception of being caught for road users who have never been fined (M = 3.7081, SD = 2.39354) and road users who have been fined [M = 3.8341, SD = 2.41449; t (8755) = 2.363, p = .0001].

b) For festival periods

The goal is to determine whether there is any difference in the mean perception of being caught for road users who have been fined and road users who have never been fined for committing a traffic offence. An independent sample t-test was conducted to compare the mean perception of being caught for road users who have been fined and road users who have never been fined. A preliminary assumption testing was conducted to check for normality and equality of variance with no serious violation noted (Appendix 9). There was a significant difference in the mean perception of being caught for road users who have never been fined (M = 4.1478, SD = 2.54038) and road users who have been fined [M = 4.3622, SD = 2.49291; t (18897) = 5.778, p = .0001].


46


5.0 Conclusion

Despite the integrated efforts that have been undertaken by all stakeholders during the Ops, road users’ perception of being caught for traffic offences were still very low, not even at half (50%) of the total percentage.

The perception levels recorded were in the range of 35% to 52% for overall perception across all offences at all surveyed locations, for all of the Ops conducted in the year 2008. During non-festival days, road users’ perception of being caught was low at within the range of 36% to 42%. Thus, road users are of the opinion that despite committing traffic offences, the probability of them being fined by the authorities is low.

During festival periods, in which the survey was conducted to measure road users’ perception of being caught before, during and after the Ops, the road users’ perceived risk of detection for traffic offences were low for before Ops and showed an increase to 52% and 42% for during Ops for Chinese New Year and Hari Raya Aidilfitri respectively. However, the percentage of perception of being caught decreased dramatically after the Ops for both occasions.

However, for the Deepavali season, the statistics remained the same during the three instances as during non-festival days. The cause to this was attributed to the lack of integrated enforcement activities during that particular time period.

Speeding ranked as the one offence the most road users were wary of being fined for committing, followed by beating the traffic lights. This conclusion was based on the findings of the survey taken during festival periods and non-festival days.


47


Visibility of traffic enforcement was generally low to medium based on findings from both non-festival days and festival periods on expressways and federal roads. Therefore, authorities have to work on improving traffic enforcement visibility along them.

There were several factors that are associated with the road user’s perception of being caught for traffic offences. From findings concerning non-festival days, traffic punishment rated highly at 69% as the main factor for the perception of being caught. However, it ranks second behind media exposure whereby the latter was cited as the main factor at 64% for findings obtained during the festive seasons. The reason might be attributed to the barrage of road safety campaigns disseminated by the mainstream media during festive seasons. Without media campaigns, on non-festival days, the fear of paying a huge fine takes effect as road users were unsure when or where the next road block or enforcement activity is.

From the findings, a majority of respondents agreed that the best form of traffic enforcement was overt traffic enforcement. Hence, authorities should take heed by putting more patrols or making road enforcement activity as visible as possible, be it automated or by manpower.

The measure of these studies however was the effect of enforcement on crash fatality reduction on road users’ perception of being caught, not the effect of enforcement on the perception of being caught.

It appears that Malaysian road users are still unaffected by traffic enforcement activities in Malaysia. Road users are seemingly of the opinion that despite committing traffic offenses, the probability of them being fined by the authorities is low.

This means, road users are unafraid of committing traffic offenses as traffic enforcement activities in Malaysia are lax and loose. It appears that it is difficult to make Malaysian road users


48


abide to traffic rules. Even when they are fined, it is likely the authorities take a long time to follow up with the fine collection. The penalties may be too light; and the offenders are not worried about being fined at all.

6.0 Recommendations

There are several recommendations that would possibly increase the level of perceived risk of detection.

• Sustained media publicity and awareness campaigns on enforcement activities for a longer period.The use of the media to publicise enforcement activities can also be an effective means of increasing the perceived risk of detection. However, unless publicity is maintained over a long period its effect is usually only short (Elliot 1993). Road users adapt their behaviour according to their own experiences as well as those of their acquaintances. A meta-analysis carried out by Delhomme et al. (1999) shows that 8.5% of crashes could be reduced if traffic enforcement were carried out alongside the campaign. The figure can be increased to 15% if another campaign follows immediately after the earlier campaign. From time to time, drivers become aware of the real enforcement situation and modify their driving behaviour accordingly.

• Extension of traffic enforcement programmes duration.Harper (1991) stresses that only enforcement strategies which are highly intensive and sustained over a long period could ensure successful expected outcomes. Makinen (1988) and Aberg (1988) also highlights the importance of long duration of intensive periodic strategies aimed at increasing the perceived risk of detection.

• Increasing the visibility of traffic enforcement.According to Harper (1991), by making enforcement activities highly visible, it is possible to increase the level of


49


road users’ perceived risk of detection. Covert enforcement approaches should not be neglected since it also contributes in increasing the risk of being caught perception.

It is recommended that for further studies, the range of study should be increased to include actual areas in which traffic enforcement activities are conducted. The actual enforcement location (e.g. roadblock) would be obtained from the traffic police department and would not be limited to accident-prone areas as in this study. The current study only focused on general road users’ perception concerning all traffic offenses and were not based on actual enforcement activities.

The questionnaires can be improved and to be made simpler and as straightforward as possible. Additionally, questionnaires should be distributed at the site of roadblocks and road users are picked to answer questionnaires on the spot. This is to obtain true reflection of the enforcement activities in which respondents reactions are taken in real time.

Future studies should not neglect Deepavali, which is mostly celebrated by Indians, as a source of information to obtain a general perception of being caught. This is to obtain more accurate statistics on the perception of being caught to reflect Malaysia’s multi-ethnic background. Future studies should also consider the differences between various ethnic groups in Malaysia especially on cultural differences. It should not ignore the role of alcohol consumption and festivities as well as the effect of alcohol on the human physiology and whether it would affect the perception of being caught.


50


References

Aberg, L (1988), Driver behaviour and probability of detection on roads with temporary 30 km/h speed limit, In Road user behaviour: theory and research, Edited by Rothengatter, JA & de Bruin, RA, Assen: Van Gorcum.

Aberg, L, Engdahl, S & Nilsson, E (1986), Intensifirad overvankning med utandnigsprov, Swedish Transport Research Board, 12.

Adams-Guppy, JR & Guppy, A (1995), Speeding in relation to perceptions of risk, utility and driving style by British company car drivers, Ergonomics, 38: 2525–2535.

Ajzen, I (1991), The theory of planned behaviour, Organizational Behaviour and Human Decision Processes, 50: 179–211.

Ajzen, I (2006), Constructing a TpB questionnaire: conceptual and methodological considerations, Accessed on 31 January 2009, from http://people.umass.edu/aizen/tpb.html.

Armitage, CJ, Norman P & Conner, M (2002), Can the theory of planned behavior mediate the effects of age, gender and multidimensional health focus of control? Br. J. Health Psychol., 7: 299–316.

Armour, M (1984a), A review of the literature on police traffic law enforcement, Proceedings of the 14th Australian Road Research Board Conference, 14(1): 17–24.

Armour, M (1984b), The effect of police presence on urban traffic roads, Rosebery, NSW, Traffic Authority of New South Wales, III + 27 p , Research Note; RN 3/84.


51


Assum, T & Ingebrigtsen, S (1990), Trafikkulykker med alkoholpåvirkede forere 1987, Oslo, Transportokonomisk institutt, (TØI-notat 0915/1990).

Bjornskau, T & Elvik, R (1992), Can road traffic law enforcement permanently reduce the number of accidents? Accident Analysis and Prevention, 24(5): 507–520.

Blincoe, KM, Jones, AP, Sauerzapf, V & Haynes, R (2006), Speeding drivers’ attitudes and perceptions of speed cameras in rural England, Accident Analysis and Prevention, 38: 371–378.

Brehmer, B (1966), Polisovervekningens effecter pa trafikbeteendet, Stockholm, Statens hogertrafikkomission, Vetenskapliga arbetsgruppen, PM nr 56.

Brown, SL & Cotton, A (2003), Risk-mitigating beliefs, risk estimates, and selfreported speeding in a sample of Australian drivers, Journal of Safety Research, 34: 183–188.

De Pelsmacker, P & Janssens, W (2007), The effects of norm, attitudes and habits on speeding behavior: scale development and model building and estimation, Accident Analysis and Prevention, 39: 6–15.

de Waard, D & Rooijers, T (1994), An experimental study to evaluate the effectiveness of different methods and intensities of law enforcement on driving speed on motorways, Accident Analysis and Prevention, 26(6): 751–765.

Diamantopoulou, K & Cameron, M (2002), An evaluation of the effectiveness of overt and covert speed enforcement achieved through mobile radar operations, Report No. 187, Monash University Accident Research Centre, Melbourne: 13.

Delhomme, P et al. (1999), Evaluated road safety media campaigns: an overview of 265 evaluated media campaigns and some meta-analysis on accidents, GADGET-project (WP4), Contact No. RO-97-SC. 2235. Inrets-LPC, France.


52


Elliot, B (1993), Road safety mass media campaigns: a meta analysis, Report CR 118, Federal Office of Road Safety, Canberra.

Elliot, B (2008), Can we rely on deterrence theory to motivate safe road user behaviour? Joint ACRS-Travel safe National Conference: 216–234.

Elliot, MA, Armitage, CJ & Baughan, CJ (2007), Using the theory of planned behaviour to predict observed driving behaviour, Br. J. Soc. Psychol, 88: 964–972.

Fildes, BN & Lee, S (1993), The speed review: road environment, behaviour, speed limits, enforcement and crashes + Appendix of speed workshop papers, Canberra, ACT, Federal Office of Road Safety FORS/Rosebery, NSW, Road and Traffic Authority of New South Wales RTA, Road Safety Bureau RSB, 1993, XI + 147 + 150p., report No. CR 127/127A (FORS) Consultant Report; CR 3193/CR 3193A (RSB).

Fishbein, MA & Ajzen, I (1975), Belief, attitude, intention and behaviour: an introduction to theory and research, Reading, MA, Addison Wesley: 302.

Gabany, S, Plummer, P & Grigg, P (1997), Why drivers speed: the speed perception inventory, Journal of Safety Research, 28(1): 29–36.

Gibbs, JP (1975), Crime, punishment and deterrence, New York: Elsevier.

Glendon, AI & Cernecca, L (2003), Young drivers’ responses to anti-speeding and anti-drunk-driving messages, Transportation Research Part F, 6: 197–216.

Goerke, L (2003), Road traffic and efficient fines, European Journal of Law and Economics, 15: 65–84.


53


Haglund, M & Aberg, L (2000), Speed choice in relation to speed limit and influences from other drivers, Transp. Res. Part F: Psychol., 3: 39–51.

Harper, JG (1991), Traffic violation detections and deterrence: implications for automatic policing, Applied Ergonomics, 23 (3): 189–197.

Homel, R (1988), Policing and punishing the drinking driver: a study of general and specific deterrence, New York: Springer-Verlag, Research in Crimonology, XIX + 318p.

Irby, TS & Jacobs, HH (1960), An epidemiological approach to the control of automobile accidents: experimental patrol intensification at military base, Traffic Safety Research Review, 4: 4.

Letirand, F & Delhomme, P (2005), Speed behavior as a choice between observing and exceeding the speed limit, Transp. Res. Part F: Psychol., 8: 277–297.

Loner, LP, Clinton, K, Wilde, GJS, Roach, K, McKnight, AJ, MacLean, H, Guastello, SJ & Lamble, RW (1995), What works in changing road user behavior? Retrieved on 9 October 1995, from http://www.drivers.com/article/170.

Makinen, T (1988), Enforcement studies in Finland, In Road user behaviour: theory and research, Edited by Rothengatter, JA & de Bruin, RA, The Netherlands: Van Gorcum: 584–588.

Mannering, F (2009), An empirical analysis of driver perceptions of the relationship between speed limits and safety, Transportation Research Part F: 99–106.

National Highway Traffic Safety Administration (NHTSA) (1995), Statewide traffic enforcement plan, Washington, DC: National Center for Statistic and Analysis, Research and Development, US Department of Transportation.


54


Nilsson, E, Engdahl, S & Aberg, L, (1983), Trafikovervakningens langsiktiga effecter pa olyckor och beteenden, Swedish Transport Research Board, 13.

Ostvik E & Ekvik, R (1990), The effects of speed enforcement on individual road user behaviour and accidents, In Enforcement and rewarding: strategies and affects, Proceedings of the International Road Safety Symposium in Copenhagen, Denmark, 19–21 September: 56–59.

Parker, D, Manstead, ASR, Stradling, SG, Reason, JT & Baxter, JS (1992), Intention to commit driving violations: an application of the theory of planned behaviour, Journal of Applied Psychology, 77: 94–101.

Parker, D, Manstead, ASR, Stradling, SG (1995), Extending the theory of planned behaviour, The role of the personal norm, British Journal of Social Psychology, 34: 127–137.

Radin Umar, RS (1998), Analisis keberkesanan program keselamatan bersepadu semasa perayaan berkembar, RR2/1998, Universiti Putra Malaysia.

Redelmeier, D, Tibshirani, R & Evans, L (2003), Traffic-law enforcement and risk of death from motor-vehicle crashes: a case-crossover study, The Lancet, 361: 2177–2182.

Ross, HL (1982), Deterring the drinking driver: legal policy and social control, A Division of D.C. Health Co., Lexington, MA: Gower Publishing Co. Ltd.

Rothengatter, JA (1982), The effects of police surveillance and law enforcement on driver behaviour, Current Psychological Reviews, 2: 349–358.

Royal Malaysia Police, (2007), Statistical report road accident Malaysia, Traffic Department, Bukit Aman, Kuala lumpur.


55


Sheehan, M, Schonfeld, C, Ballard, R, Schofield, F, Najman, J & Siskind, V (1996), A three year out-come evaluation of a theory based drink driving education program, J. Drug Educ., 26: 295–312.

Shinar, D & McKnight, JA (1985), The effects of enforcement and public information on compliance, In Human behaviour and traffic safety, Edited by Evans, L & Schwing, RC, New York: Plenum: 385–415.

Simsekoglu, O & Lajunen, T (2008), Social psychology of seat belt use: a comparison of theory planned behavior and health belief model, Transp. Res. Part F: Psychol., 11: 181–191.

Summala, H (1985), Modeling driver behaviour: a pessimistic prediction? In Human behaviour and traffic safety, Edited by Evans, L & Schwing, RC, New York: Plenum Press: 43–65.

Thuen, F & Rise, J (1994), Young adolescents’ intention to use seat belts: the role of attitudinal normative beliefs, Health Educ. Res., 9: 215–223.

Warner, HW & Aberg, L (2006), Drivers’ decision to speed: a study inspired by the theory of planned behaviour, Transp. Res. Part F: Psychol., 2: 91–113.

Zaal, D (1994), Traffic law enforcement: a review of the literature, Report No. 53, Monash University Accident research Centre, Retrieved on 21 July 2008, from http://www.monash.edu.au/muarc/reports/muarc053.pd.


56


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36.1

37.5

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37.4

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39.5

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42.7

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63


Ove

rall

perc

eptio

nBe

atin

g tr

affic

light

sO

vert

akin

g at

dou

ble

lines

Failu

re to

use

the

seat

belt

whi

le d

rivin

gD

ange

rous

ly c

uttin

g in

to tr

affic

Spee

ding

Que

ue ju

mpi

ngIll

egal

use

of t

he e

mer

genc

y la

neFa

ilure

to u

se a

cra

sh h

elm

et w

hile

ridi

ng

Non

-fest

ival

day

sBe

fore

Ops

Ray

a


64



65


Ove

rall

perc

eptio

n

Beat

ing

traffi

c lig

hts

Ove

rtak

ing

at d

oubl

e lin

es

Failu

re to

use

the

seat

belt

whi

le d

rivin

g

Dan

gero

usly

cut

ting

into

traffi

c

Spee

ding

Que

ue ju

mpi

ng

Illeg

al u

se o

f the

em

erge

ncy

lane

Failu

re to

use

a c

rash

hel

met

whi

le ri

ding

Dur

ing

Ops

Ray

aA

fter

Ops

Ray

aBe

fore

Ops

Ray

a


66



67


Ove

rall

perc

eptio

nBe

atin

g tr

affic

light

sO

vert

akin

g at

dou

ble

lines

Failu

re to

use

the

seat

belt

whi

le d

rivin

gD

ange

rous

ly c

uttin

g in

to tr

affic

Spee

ding

Que

ue ju

mpi

ngIll

egal

use

of t

he e

mer

genc

y la

neFa

ilure

to u

se a

cra

sh h

elm

et w

hile

ridi

ng

Dur

ing

Ops

Dee

pava

liA

fter

Ops

Dee

pava

liBe

fore

Ops

Dee

pava

li


68


App

endi

x 2

Perc

eptio

n st

udy

anal

ysis

on

expr

essw

ays

thro

ugho

ut 2

008


69


Ove

rall

perc

eptio

nBe

atin

g tr

affic

light

sO

vert

akin

g at

dou

ble

lines

Failu

re to

use

the

seat

belt

whi

le d

rivin

gD

ange

rous

ly c

uttin

g in

to tr

affic

Spee

ding

Que

ue ju

mpi

ng

Illeg

al u

se o

f the

em

erge

ncy

lane

Failu

re to

use

a c

rash

hel

met

whi

le ri

ding

Dur

ing

Ops

CN

YA

fter

Ops

CN

YBe

fore

Ops

CN

YN

on-

fest

ival

da

ys


70



71


Ove

rall

perc

eptio

nBe

atin

g tr

affic

light

sO

vert

akin

g at

dou

ble

lines

Failu

re to

use

the

seat

belt

whi

le d

rivin

gD

ange

rous

ly c

uttin

g in

to tr

affic

Spee

ding

Que

ue ju

mpi

ng

Illeg

al u

se o

f the

em

erge

ncy

lane

Failu

re to

use

a c

rash

hel

met

whi

le ri

ding

Aft

er O

ps C

NY

Non

-fest

ival

day

s


72



73


Ove

rall

perc

eptio

nBe

atin

g tr

affic

light

sO

vert

akin

g at

dou

ble

lines

Failu

re to

use

the

seat

belt

whi

le d

rivin

gD

ange

rous

ly c

uttin

g in

to tr

affic

Spee

ding

Que

ue ju

mpi

ng

Illeg

al u

se o

f the

em

erge

ncy

lane

Failu

re to

use

a c

rash

hel

met

whi

le ri

ding

Non

-fest

ival

day

s


74



75


Ove

rall

perc

eptio

nBe

atin

g tr

affic

light

sO

vert

akin

g at

dou

ble

lines

Failu

re to

use

the

seat

belt

whi

le d

rivin

gD

ange

rous

ly c

uttin

g in

to tr

affic

Spee

ding

Que

ue ju

mpi

ngIll

egal

use

of t

he e

mer

genc

y la

ne

Failu

re to

use

a c

rash

hel

met

whi

le ri

ding

Non

-fest

ival

day

sBe

fore

Ops

Ray

a


76



77


Ove

rall

perc

eptio

nBe

atin

g tr

affic

light

sO

vert

akin

g at

dou

ble

lines

Failu

re to

use

the

seat

belt

whi

le d

rivin

gD

ange

rous

ly c

uttin

g in

to tr

affic

Spee

ding

Que

ue ju

mpi

ngIll

egal

use

of t

he e

mer

genc

y la

ne

Failu

re to

use

a c

rash

hel

met

whi

le ri

dingBe

fore

Ops

Dee

pava

liD

urin

g O

ps R

aya

Aft

er O

ps R

aya


78



79


Ove

rall

perc

eptio

nBe

atin

g tr

affic

light

sO

vert

akin

g at

dou

ble

lines

Failu

re to

use

the

seat

belt

whi

le d

rivin

gD

ange

rous

ly c

uttin

g in

to tr

affic

Spee

ding

Que

ue ju

mpi

ngIll

egal

use

of t

he e

mer

genc

y la

ne

Failu

re to

use

a c

rash

hel

met

whi

le ri

ding

Dur

ing

Ops

Dee

pava

liA

fter

Ops

Dee

pava

li


80


Appendix 3 Visibility of enforcement activities – Non-festival days

Appendix 3.1 Preliminary assumption testing

Appendix 3.2 Output of independent t-test


81


Appendix 4 Comparison of road users’ perception of being caught between Ops periods for Chinese New Year


82


Appendix 5 Comparison of road users’ perception of being caught between Ops periods for Hari Raya Aidilfitri


83


Appendix 6 Comparison of road users’ perception of being caught between Ops periods for Deepavali


84



85


Appendix 7 Visibility of enforcement activities – Festival periods




86


Appendix 8 Comparison of mean perception of being caught between road users who have been fined and those who have never been fined for traffic offences – Non-festival days



87


Appendix 9 Comparison of mean perception of being caught between road users who have been fined and those who have never been fined for traffic offences – Festival periods




Malaysian Institute of Road Safety ResearchLot 125-135, Jalan TKS 1, Taman Kajang Sentral43000 Kajang, Selangor Darul EhsanTel +603 8924 9200 Fax + 603 8733 2005Website www.miros.gov.my Email [email protected]

Designed by: Publications Unit, MIROS



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