The Effect of Police Enforcement on Road Traffic Accidents Andrew Scott

238 Pages·2011·4.45 MB·English

by  Andy Scott

The Effect of Police Enforcement on
Road Traffic Accidents

Andrew Scott

Submitted in partial fulfilment of the requirements for the degree of

Doctor of Philosophy

Awarded by Edinburgh Napier University

April 2010
Abstract

The primary goal of this thesis is to investigate the effectiveness of police
enforcement on Road Traffic Accidents; specifically, ‘Does police enforcement
activity have any real effect on levels of Killed and Seriously Injured road traffic
accidents?’

Data relating to forty one Police Force Areas in England and Wales was
analysed by means of Zero Truncated Poisson regression, Cluster Analysis
and Multilevel Modelling. Enforcement measures available to the police, for
which data is available in this report, range from Prosecutions and Fixed
Penalty Notices to Written Warnings and Vehicle Defect Rectification Notices.

Results from the Zero Truncated Poisson regression models have significant
effects (P < .05), in relation to both contemporary and lagged Annual data and
contemporary Quarterly data, for all proxy variables except Prosecutions.
Significant effects (P < .05) are also found for Fixed Penalty Notices lagged by
two quarters, Vehicle Defect Rectification Notices and speeding related Fixed
Penalty Notices lagged by one quarter.

Results from Cluster Analysis verify the trend linking increased police
enforcement with decreasing KSI rates. Clusters derived from population
based KSI rates are more clearly defined than those using Vehicle kilometres
travelled based KSI rates.

Multilevel modelling found significant fixed effects (P < .05) for Fixed Penalty
Notices and speeding related Fixed Penalty Notices in relation to both derived
and regional clusters, linking an increase in enforcement to a decrease in the
overall KSI rate.

There would seem to be little doubt, based on the findings of this report, that
higher levels of police enforcement, as measured here, lead to decreasing
numbers of KSI accidents.

ii
Acknowledgements
I am extremely grateful to all the people who have helped me with this thesis.

In particular I would like to thank my supervisors Professor Robert Raeside
and Phil Darby for their support, encouragement and boundless enthusiasm.

Special thanks to my partner Hayley, always there, always supportive. Thank
you, it meant so much. Also, to my daughter Amy, the sunshine in my life, just
for being Amy.

iii
Author’s Declaration

I declare that no material contained in this thesis has been used in any other
submission for an academic award and that the work presented is my own.

This thesis is copyright material and no quotation or excerpt from it may be
published without proper acknowledgement

Andrew Scott

iv
Contents
List of Acronyms and Abbreviations ix
List of Variables Descriptors ix

Chapter 1: Introduction 1
1.2 Aims 3
1.3 Research Approach 3
1.4 Data 4
1.5 Analysis Plan 5
1.6 Structure of Thesis 7

Chapter 2: Literature Review 8
2.1 Introduction 8
2.2 Enforcement 9
2.3 Speed Cameras 13
2.4 Traffic Light Cameras 16
2.5 Seat Belt Use 17
2.6 Drink Driving 20
2.7 Attitudes and Behaviour 26
2.8 Road Safety Media Campaigns 30

Chapter 3: Research Methods 32
3.1 Introduction to Research Methods 32
3.2 Data Collection 34
3.3 Data Preparation 38

3.4 Exploratory Data Analysis 40
3.4.1 Introduction 40
3.4.2 Trends in Data 43
3.4.3 Annual Trends in Data 44
3.4.4 Quarterly Trends in Data 50
3.5 Proposed Methods of Analysis 51

v
Chapter 4: Poisson Regression Analysis 54
4.1 Introduction to Poisson Regression Analysis 54
4.2 Data Analysis using Poisson Regression 57
4.3 Development of Poisson Regression Models 59
4.3.1 Modelling Annual Accident Data 59
4.3.2 Analysis of Annual Data 60
4.3.3 Model Fitting 61
4.4 Modelling Quarterly Accident Data 75

Chapter 5: Cluster Analysis 87
5.1 Introduction to Cluster Analysis 87
5.2 Ward Method Cluster Analyses on KSI Rate by
Population and FPN_1000’s 88
5.2.1 Analysis of Variance 92
5.3 Ward Method Cluster Analysis on KSI Rate by Vkm and
FPN_1000’s 93
5.3.1 Analysis of Variance for KSI RATE by Vkm and
FPN_1000‟s 94
5.4 Fuzzy C-means Cluster Analysis 96

Chapter 6: Multilevel Modelling 99
6.1 Introduction to Multilevel Modelling 99
6.2 Multilevel Modelling of Accident Data 100
6.2.1 Multilevel Models using FPN‟s 100
6.2.2 Multilevel Models using ZFPN_G16_1000‟s 103
6.3 Multilevel Models based on Regional Clusters 105
6.3.1 Multilevel Models using ZFPN_1000‟s on
Regional Clusters 106
6.3.2 Multilevel Models using ZFPN_G16_1000‟s 108
6.4 Discussion of Results relating to Annual Data 111
6.5 Multilevel Modelling of Quarterly Data 114
6.5.1 Quarter 3 Multilevel Models using ZFPN_1000‟s 115

vi
6.5.2. Quarter 3 Multilevel Models using
ZLag1FPN_1000‟s and ZLag1_FPN_1000‟s 117
6.5.3 Quarter 3 Multilevel Models using
ZFPN_G16_1000‟s 120
6.5.3.1 Quarter 3 Multilevel Models using
ZLag1_FPN_G16_1000‟s and
ZLag2_FPN_G16_1000‟s 120
6.5.4 Quarter 4 Multilevel Models using ZFPN_1000‟s 121
6.5.4.1 Quarter 4 Multilevel Models using
ZLag1FPN_1000‟s and ZLag1_FPN_1000‟s 123
6.5.5 Quarter 4 Multilevel Models using ZFPN_G16_1000‟s 123
6.5.5.1 Quarter 4 Multilevel Models using
ZLag1_FPN_G16_1000‟s and
ZLag2_FPN_G16_1000‟s 124
6.6 Multilevel Models on Regional Clusters 124
6.6.1 Quarter 3 Multilevel Models using ZFPN_1000‟s
on Regional Clusters 125
6.6.2. Quarter 3 Multilevel Models using ZLag1_FPN_1000‟s
and ZLag1_FPN_1000‟s on Regional Clusters 127
6.6.3 Quarter 3 Multilevel Models using ZFPN_G16_1000‟s
on Regional Clusters 128
6.6.3.1 Quarter 3 Multilevel Models using
ZLag1_FPN_G16_1000‟s and ZLag2_FPN_G16
_1000‟s on Regional Clusters 129
6.6.4 Quarter 4 Multilevel Models using ZFPN_1000‟s
on Regional Clusters 129
6.6.4.1 Quarter 4 Multilevel Models using
ZLag1FPN_1000‟s and ZLag1_FPN_1000‟s
on Regional Clusters 131
6.6.5 Quarter 4 Multilevel Models using ZFPN_G16_1000‟s
on Regional Clusters 131
6.6.5.1 Quarter 4 Multilevel Models using
ZLag1_FPN_G16_1000‟s and
ZLag2_FPN_G16_1000‟s 132
vii
6.7 Discussion of Results relating to Quarterly Data 133
6.7.1 Results from Analysis of Derived Clusters 133
6.7.2 Results from Analysis of Regional Clusters 133

Chapter 7: Discussion and Conclusions 135
7.1 Introduction 135
7.2 Discussion of Results 136
7.2.1 Results from Zero Truncated Poisson Regression 136
7.2.2 Discussion of Results from Cluster Analysis 143
7.3 Multilevel Modelling 146
7.3.1 Discussion of Multilevel Modelling Results 146
7.4 Limitations 148
7.5 Contribution to Knowledge 149
7.6 Recommendations 150
7.6.1 Recommendations for Practice and Policy 150
7.6.2 Recommendations for Further Research 150
7.7 Conclusions 151

Appendices 152
Appendix 3 152
Appendix 4 159
Appendix 5 189
Appendix 6a 192
Appendix 6b 198
Appendix 6c 208

References 217

viii
Acronyms and Abbreviations

DV Dependent Variable
FCM Fuzzy C-means Clustering
FPN Fixed Penalty Notices
FPN G16 Speeding Related Fixed Penalty Notices
GA Geographical Area
IMD Index of Mean Deprivation
KSI Killed and Serious Injury/Killed and Seriously Injured
NB Negative Binomial
PFA Police Force Area
Pop Population
Pros Prosecutions
QTR Quarter
RTA Road Traffic Accident
Std Standard
VDRN Vehicle Defect Rectification Notices
Vkm Vehicle Kilometres travelled
WW Written Warnings
ZTP Zero Truncated Poisson

Variable Descriptors
IMD Index of Multiple Deprivation
Geographical Area sqkm Geographical area of each PFA
Percent Motorway Percentage of total motorway in each PFA
lnpop Log of Population
lnvkm Log of Vehicle kilometres
All Penalties Sum of all Penalties
Lag1 All Penalties Annual one Year Lag
FPN Fixed Penalty Notices
Lag FPN Annual one Year Lag
Lag 1 FPN One quarter, 3 months, Lag
ix
Lag 2 FPN Two quarter, 6 months, Lag
FPN_G16 Speeding related FPN‟s
Lag FPN 16 Annual one Year Lag
Lag 1 FPN G16 One quarter, 3 months, Lag
Lag 2 FPN G16 Two quarter, 6 months, Lag
PROSECUTIONS Successful Prosecutions
Lag Prosecutions Annual one Year Lag
Lag 1 Prosecutions One quarter, 3 months, Lag
Lag 2 Prosecutions Two quarter, 6 months, Lag
VDRN Vehicle Defect Rectification Notices
Lag VDRN Annual one Year Lag
Lag 1 VDRN One quarter, 3 months, Lag
Lag 2 VDRN Two quarter, 6 months, Lag
WW Written Warnings
Lag WW Annual one Year Lag
Lag 1 WW One quarter, 3 months, Lag
Lag 2 WW Two quarter, 6 months, Lag
x