A qualitative assessment methodology for road safety policy strategies

Accident Analysis and Prevention 36 (2004) 281–293

A qualitative assessment methodology for road safety policy strategies S.C. Wong a,∗ , B.S.Y. Leung a , Becky P.Y. Loo b , W.T. Hung c , Hong K. Lo d a

d

Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, PR China b Department of Geography, The University of Hong Kong, Pokfulam Road, Hong Kong, PR China c Department of Civil and Structural Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, PR China Department of Civil Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, PR China Received 3 July 2002; received in revised form 17 November 2002; accepted 16 December 2002

Abstract This paper proposes a qualitative assessment methodology that is comprised of a cluster analysis and an autoregression analysis that assess the effects of various road safety strategies implemented in Hong Kong over the last 10 years. The cluster analysis is first used to group over a hundred road safety projects and programs into a smaller set of meaningful road safety policy strategy clusters. These strategies, together with the trend factor, seasonal pattern, car crashworthiness and meteorological data are then used in the autoregression analysis to relate to the fatality and casualty rates of drivers, passengers, motorcyclists, and pedestrians. This method allows the evaluation of the overall effects of the road safety strategies, and the effects and relative significance of each individual strategy. The evaluation method is described, and the main findings of the study are discussed. © 2003 Elsevier Science Ltd. All rights reserved. Keywords: Road safety strategy; Qualitative assessment; Cluster analysis; Time series analysis; Autoregression analysis

1. Introduction The Hong Kong Government organizes numerous road safety projects and programs every year, which cover the whole territory and cost millions of dollars. These programs range from the dissemination of road safety messages, road safety towns, campaigns for the wearing of seat belts and against drink driving, the improvement of road signs, to the maintenance of good road surfaces. The road safety strategies that they feature all aim to improve three major factors: the behavior of road users including drivers and pedestrians, vehicles, and the road environment. As much effort has been spent on improving road safety in the implementation of these strategies (Allsop, 2002), it is of great importance that policy-makers know whether they have actually reduced traffic accidents. Road safety authorities elsewhere have evaluated the effectiveness of such strategies. For example, Dee (1998) reported the effects of seat belt laws and their enforcement status in the US, Tofflemire and Whitehead (1997) evaluated the influence of daytime light running on traffic safety in Canada, and Chen et al. (2000) conducted an evaluation of the photo-radar program in British Columbia. ∗ Corresponding author. Tel.: +86-852-2859-2668; fax: +86-852-2559-5337. E-mail address: [email protected] (S.C. Wong).

These evaluation exercises had two basic characteristics. Firstly, most studied the effectiveness of individual road safety strategies, for example, a photo-radar program (Chen et al., 2000), blood alcohol limits (Mann et al., 2001), the daytime running of lights (Tofflemire and Whitehead, 1997), seat belt laws (Dee, 1998), a commercial driver’s license program (Hagge and Romanowicz, 1996), media advertising and police enforcement (Tarawneh et al., 1999), photo-radar and speed display boards (Bloch, 1998), the police enforcement of speed limits (Sisiopiku and Patel, 1999), automated speed limit enforcement (Elvik, 1997), automated speed monitoring cameras (Ali et al., 1997), a roadside inspection selection system (Lantz et al., 1997), a motorcyclist safety training program (Billheimer, 1998) and speed limits (Agent et al., 1998; Binkowski et al., 1998). When some looked at the combined effects of integrated safety programs (Lindqvist et al., 2001; Ytterstad and Wasmuth, 1995), they could not differentiate the relative effects of individual component strategies. Secondly, all of the above evaluations attempted to quantify the effects of these road safety strategies. The techniques that were commonly employed in conducting these evaluations were: (a) simple pre–post comparisons, (b) pre–post comparisons with similar control areas, (c) time series analyses, (d) multiple time series analyses with comparison areas, and (e) weighted least-squares regressions.

0001-4575/$ – see front matter © 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0001-4575(03)00006-X

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The current study proposes a qualitative assessment methodology that is comprised of a cluster analysis and an autoregression analysis that assess the effects of various road safety strategies that have been implemented in Hong Kong over the last 10 years. This method allows the evaluation of the overall effects of the road safety strategies and the effects and relative significance of each individual strategy. The evaluation method is described, and the main findings of the study are discussed.

2. Study approach The data for this study were mainly obtained from the publications of various departments of the Government of the Hong Kong Special Administrative Region (HKSAR), and include annual observations from 1990 to 1999. The quarterly casualty data were collected from the “Traffic Accident Report”, which was published by the Hong Kong Police Force (1990–1998), and for the year 1999 were obtained from the Traffic Branch Headquarters of the Hong Kong Police Force. The motor vehicle licensing data, meteorological data, and annual population data were extracted from the “Hong Kong Monthly Digest of Statistics”, which was published by the Census and Statistics Department (1990–1999). Car crashworthiness test data were obtained from the New Car Assessment Program (NCAP) of the US (National Highway Traffic Safety Administration, 2001). Due to the limited time series available, the average star ratings for frontal collision tests (including both driver and passenger seats) for one of the most popular car models in Hong Kong, Toyota Corolla, are included to reflect the impact of technological advancement in vehicle manufacturing on road safety. Data regarding the road safety projects and programs were compiled from the “Road Safety Strategy Papers” and the “Annual Reports” that were issued by the Road Safety Council (1990–1999). In the last 10 years, the government implemented over a hundred road safety projects and programs (Loo et al., 2002). However, it was difficult to extract any systematic schedules and developments from the individual projects and programs. Nevertheless, there might have been certain policy strategies, explicit or implicit, behind the scenes. Hence, this study is divided into two stages. The first stage extracts distinct clusters of policy strategies that are characterized by the nature of road safety measures as well as their implementation schedules. Cluster analysis is employed for this purpose, and the results portray the systematic policy strategies that the government adopted and developed, explicitly or implicitly, to improve road safety in Hong Kong in the last decade. The second stage relates these policy strategies to the accident figures by means of an autoregression analysis. The resultant models help to identify a set of significant policy strategies that were qualitatively effective in improving road safety in Hong Kong.

3. Cluster analysis Cluster analysis is used to categorize the road safety projects and programs into groups according to their similarities. This analysis will help to produce distinct sets of independent variables for the autoregression model analysis in the second stage of study (to be discussed in Section 4). A group of highly correlated independent variables in an autoregression model will affect the reliability of the model equation, and therefore distort the overall model validity. When this condition of multicollinearity exists, the regression coefficients may be of the incorrect signs and magnitudes, and be unreliable (Bowerman and O’Connell, 1993, 1997; Hair et al., 1995). In this study, a cluster analysis is first used to group the highly correlated variables into physically descriptive road safety policy strategies. This grouping allows us to better conceptualize the numerous road safety measures as distinct policy clusters before analyzing their effectiveness in improving road safety in Hong Kong over the last decade. 3.1. Cluster analysis input The inputs for the cluster analysis are dummy variables that represent the implementation schedules of the various road safety projects and programs. The study period is divided into equal intervals, each of 3 months (or a quarter): January–March, April–June, July–September, and October–December, from the first quarter of 1990 to the fourth quarter of 1999. Hence, there are a total of 40 intervals in the study. Let δit be a variable that represents a particular road safety project or program that was initiated and implemented within the study period. The variable takes a value of 1 if the road safety project or program i was active in a particular interval (quarter) t, and a value of 0 otherwise. Over a hundred road safety projects and programs are included in the cluster analysis. 3.2. Method used for cluster analysis Ward’s method is used to determine which cases or clusters should be combined at each step (Jarrell, 1994). Each road safety project or program is considered as a case in the analysis. In Ward’s method, the Euclidean distance measure is used to calculate the similarity between cases in each cluster. The Euclidean distance between Case i and Case j is determined by
 dij = (δit − δjt )2 (1) t

where the summation is applied to all time intervals in the analysis. Ward’s method adopts an agglomerative hierarchical clustering procedure that groups cases together according to their similarity as measured by the squared Euclidean distance. Initially, each case is regarded as a separate cluster. All of the cases are then grouped into clusters in a stepwise

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Table 1 The road safety policy strategy clusters that were identified in the cluster analysis Strategy cluster

Category

Strategy descriptor

1 2 3 4 5 6 7 8 9 10A 10B 11 12 13 14A 14B 15

Publicity Publicity Publicity Publicity Education Publicity Publicity Publicity Publicity Education Legislation Education Enforcement Enforcement Legislation Enforcement Enforcement

Publicizing of road safety messages through souvenir items Road safety campaigns targeted at drivers of different vehicle classes Publicizing of road safety issues by non-government bodies Dissemination of pedestrian safety messages through printed items Road safety programs organized by the Hong Kong Police Force Publicizing of road safety legislation through printed items Dissemination of road safety legislation messages through electronic media Broadcasting of road safety messages through various electronic media Encouragement of safe driving attitudes through various media Raising public awareness of road safety through local publicity campaigns Drink driving legislation Introduction of road safety knowledge to the general public through formal educational programs Enforcement of vehicle maintenance laws Toughening of the penalty for tailgating Rear seat belt legislation Installation of speed cameras Deterrence of red light jumping

procedure using the statistical software SPSS (Norusis, 1993; Coakes and Steed, 1999). 3.3. Results of cluster analysis The results of the cluster analysis indicated that the road safety projects and programs could be clustered into sets of distinct road safety policy strategies according to their implementation schedules. Together with careful considerations of their strategic goals, measure characteristics and target populations, a total of 17 policy strategy clusters were identified, and are summarized in Table 1. The policy strategy clusters can be categorized according to the approaches that were employed, namely: publicity, education, legislation, and enforcement. Publicity and education aim at instilling knowledge of road traffic safety rules, encouraging the correct behavior and attitudes of road users in various traffic situations, and developing public awareness of the importance and usefulness of road safety measures. Continuous publicity in various media constantly reminds road users of the importance of correct attitudes and behavior in a road system, which helps to reinforce the effectiveness of road safety education. Publicity and education are needed to encourage self-discipline. There are occasions whereby people fail to restrain themselves, and legislation and enforcement are required to regulate the safe use of road space. Detailed descriptions of these policy strategy clusters can be found in Wong et al. (2002). The discussions of various policy clusters and their relative effectiveness in reducing the casualty rates of road users will be given in Section 4.3. 4. Autoregression analysis 4.1. Dependent variables In the autoregression analysis, the dependent variables include the casualty rates for the four classes of road users—

driver, passenger, motorcyclist, and pedestrian—at three levels of accident severity—fatal, serious, and slight. Hence, there are a total of 12 dependent variables. For drivers, passengers, and motorcyclists, the casualty rate is defined as the number of casualties in a quarter of a year divided by the average vehicle fleet size in that quarter; whereas for pedestrians, the casualty rate is defined as the number of casualties in a quarter divided by the average population in that quarter. There are a total of 40 sample points, one for each quarter in the 1990–1999 period. The casualty rates for drivers, passengers, motorcyclists and pedestrians in the study period are plotted in Figs. 1–4, respectively. The casualty rates are in log-scale to allow temporal variations of the fatal and serious series to be properly shown. All of the casualty rates show a declining trend in the last decade, with the exception of the fatality rate of drivers, which does not have any obvious trend. In view of the noticeable declining trends in the time series, autocorrelation (serial correlation) needs to be taken into account in the regression analysis. As a consequence, we computed the Durban–Watson statistics and the Box–Ljung statistics on the residuals of the 12 linear multiple regression equations (DeLurgio, 1998; Wei, 1990). The results confirm that autocorrelation is statistically significant and it is more appropriate for us to conduct autoregression at the second stage of analysis. Moreover, Figs. 1–4 suggest that there are seasonal fluctuations in the accident data. Hence, the trend and seasonal patterns are modeled as two components in the autoregression analysis. Lastly, it is considered that a multiplicative autoregression model based on the rate of change of the dependent variables over time (which allows variable slopes over the value of the dependent variable and hence constant elasticity) is more appropriate than an additive model based on particular values (points) of the dependent variables (which leads to constant slopes over the value of the dependent variable and hence variable elasticity) (DeLurgio, 1998; Greene, 2003).

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Fig. 1. The casualty rates of drivers.

Fig. 2. The casualty rates of passengers.

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Fig. 3. The casualty rates of motorcyclists.

Fig. 4. The casualty rates of pedestrians.

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4.2. Independent variables

confounding the underlying relationships between the policy clusters and the casualty rates. Based on the bivariate correlation matrix, clusters 5, 6, 9, 10A, 10B, 13, 14A and 14B are excluded. In other words, this qualitative analysis requires manual intervention to eliminate the least significant influencing strategy cluster. Such care in handling the independent variables helps to minimize the problems of multicollinearity. However, as the bivariate correlation matrix only measures simple correlation between two variables, the problem of multicollinearity (multiple correlation among the independent variables) may still exist in the regression model (Hair et al., 1995). We further assess multicollinearity in Section 4.3. Apart from the regular seasonal variations, such as the Chinese new year and Christmas period, atypical meteorological conditions, such as an exceptionally wet and windy summer, could well have influenced road safety because they affect road conditions and the driving environment. Therefore, a set of meteorological variables is used to capture the effects of atypical weather conditions on road safety. The following meteorological data were collected for the study period: average air temperature (◦ C), average relative humidity (%), total rainfall (mm), total bright sunshine hours (h) and average wind speed (km/h). After the transformation, temperature and wind are highly correlated with rain (this reflects the tropical monsoon climate in Hong Kong). Thus, temperature and wind are excluded from the autoregression analysis. In the next section, we also test whether such exclusion is justifiable statistically.

Based on the cluster analysis results, the strategy variables are formed as follows. Let there be a set of strategy clusters C. Each strategy cluster c contains a set of road safety projects and programs, Ωc . The strategy clusters within the set are mutually exclusive, i.e. Ωc ∩ Ωc = φ, an empty set, for all c and c in C; and ∪c∈C = Ω, the set of all road safety projects and programs. For each strategy cluster c, the policy variable is defined as  i∈Ω δit +1 (2) yct =   c t i∈Ωc δit where yct is a strategy index that represents a normalized strategy profile for each strategy cluster c. The constant 1 is included to enable us to conduct natural log transformation and build the multiplicative model. In examining the strategy profiles for publicity, education, legislation, and enforcement in the last decade, it is interesting to note that publicity and education received much attention after 1995, which reflects a deliberate shift in government policy. Moreover, new road safety enforcement measures were continuously developed and implemented over the period, which indicates the government’s determination to reduce traffic accidents. To study the interrelationships between the strategy clusters, a bivariate correlation analysis was performed on the transformed variables, and the correlation matrix is shown as Table 2. Some strategy clusters were found to be highly correlated, and should not be included together in the autoregression analysis. In Table 2, high bivariate correlation coefficients (with r > 0.7) are highlighted. In the subsequent analysis, these highly correlated clusters are not simultaneously included into the autocorrelation model to avoid

4.3. Method used for autoregression analysis The multiple regression equation takes the following form:

Table 2 The bivariate correlation table for the policy strategy clusters Strategy 1

2

1 2 3 4 5 6 7 8 9 10A 10B 11 12 13 14A 14B 15

1.00 −0.24 1.00 −0.31 −0.30 1.00 −0.28 0.30 0.81∗∗ 1.00 −0.20 −0.16 −0.15 0.04 −0.29 0.32∗∗ 0.07 0.20 −0.23 0.49∗∗ 0.59∗∗ 0.48∗∗ 0.23 −0.18 0.59∗∗ 0.24 −0.20 0.39∗∗ 0.50∗∗ 0.71∗∗ −0.25 0.44∗∗ 0.57∗∗ 0.75∗∗ −0.03 −0.05 −0.13 −0.09 −0.23 0.12 0.16 0.02 −0.31 0.75∗∗ 0.33∗∗ 0.31 −0.40∗∗ 0.59∗∗ 0.78∗∗ 0.78∗∗ −0.40∗∗ 0.59∗∗ 0.78∗∗ 0.78∗∗ −0.19 0.28 0.37∗∗ 0.44∗∗

1.00 −0.25 0.16 0.27 −0.28 0.30 0.36∗∗ 0.16 0.13 0.50∗∗ 0.54∗∗ 0.67∗∗ 0.41∗∗ 0.41∗∗ 0.45∗∗ 0.45∗∗ 0.75∗∗

3

4

5

6

7

8

9

1.00 0.66∗∗ 1.00 −0.15 0.52∗∗ 1.00 −0.12 −0.17 −0.13 1.00 0.40∗∗ 0.53∗∗ 0.38∗∗ 0.30 0.36∗∗ 0.54∗∗ 0.43∗∗ 0.34∗∗ 0.38∗∗ 0.26 −0.12 −0.01 0.10 0.15 0.12 0.10 0.01 0.49∗∗ 0.45∗∗ 0.59∗∗ −0.07 0.33∗∗ 0.58∗∗ 0.46∗∗ −0.07 0.33∗∗ 0.58∗∗ 0.46∗∗ 0.24 0.35∗∗ 0.28 0.22

Correlation coefficients greater than 0.7 or smaller than −0.7 are typed in italics. ∗∗ Significant at 0.05 level.

10A

10B

11

12

13

14A

14B 15

1.00 0.99∗∗ 0.43∗∗ 0.29 0.48∗∗ 0.62∗∗ 0.62∗∗ 0.64∗∗

1.00 0.40∗∗ 0.28 0.58∗∗ 0.73∗∗ 0.73∗∗ 0.65∗∗

1.00 0.56∗∗ 0.13 0.01 0.01 0.63∗∗

1.00 0.16 0.21 0.21 0.43∗∗

1.00 0.79∗∗ 1.00 0.79∗∗ 1.00 1.00 0.38∗∗ 0.48∗∗ 0.48 1.00

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ln(z) = γ0 +



αc ln(yc ) +



βj ln(mj )

+ γ1 Q1 + γ2 Q2 + γ3 Q3 + ρet−1 + et

(3)

where z is a particular casualty rate; yc the strategy variable; mj the meteorological variable; Q1 , Q2 and Q3 the dummy seasonal variables; et the error term for time t; ρ the first-order autoregression coefficient (denoting autocorrelation of errors); γ0 the constant (capturing the mean influence of other variables not included in the model); and αc , βj , γ 1 , γ 2 and γ 3 are the partial regression coefficients. To avoid the dummy variable trap, the fourth quarter of the year is dropped and is part of the constant term γ0 . The optimal value of ρ is estimated by the Cochrane–Orcutt iterative least squares (COILS) method (DeLurgio, 1998; Greene, 2003; Isard et al., 1998). The F-statistics, significant F-statistics and variance inflation factor (VIF) are not computed because the assumptions of ordinary least-squares regression analysis do not hold in time series analysis. Instead, we conduct Partial F-tests to determine whether the exclusion of the seven policy cluster regressors and two meteorological regressors, as explained in Section 4.2, is justified statistically. The Partial F is given by the equation: Fcalculated =

(µR /µU )/m µU /(n − k)

(4)

where µU is the unrestricted sum of square (SSE) with all variables in the model, µR the restricted SSE with m variables excluded, k the total number of estimated coefficients and m the number of dropped independent variables. The Partial F-test “is an important test because with multicollinearity problems, individual regression coefficient t-values are not reliable measures for determining whether or not to include a variable or group of variables” (DeLurgio, 1998, p. 414). For autoregression, this test is even more important because the estimation of ρ makes use of the full information of all independent variables and does not allow the stepwise procedure to be followed. Based on the degree of freedom of the numerator (m) and denominator (n − k), and the significant level (α), the critical F, Ftable , can be obtained from a statistical table. The null hypothesis is that there is no significant additional explained variance from the unrestricted model (with all variables included). It implies that the exclusion of the m independent variables from the autoregression is justified statistically and they do not account for additional explained variance. In other words, the inclusion of these excluded variables will result in multicollinearity. The null hypothesis is rejected when Fcalculated > Ftable Results of the Partial F-tests with the exclusion of the seven policy clusters and two meteorological variables show that the problem of multicollinearity has been quite successfully dealt with in most cases. In 9 out of 12 autoregression models, the values of Fcalculated are smaller than the critical F (Ftable = 2.65). In other words, the exclusion of selected variables is justified statistically and the excluded

287

variables do not contribute to additional explained variance. The three exceptions are serious casualty of drivers (Fcalculated = 4.935), serious casualty of motorcyclists (Fcalculated = 2.744) and serious casualty of pedestrians (Fcalculated = 4.014). As a result, we further examine the unrestricted model and refine the first restrictive model in search for a better model to fit individual autoregression equations. To recall, the first restrictive model excludes policy clusters 5, 6, 9, 10A, 10B, 13, 14A and 14B, and the meteorological variables of temperature and rainfall. Built upon the first restrictive model and the results of the unrestricted model, more than five additional restrictive models (with different combinations of excluded variables) have been tried in identifying the best models that have the highest explanatory power but do not suffer from multicollinearity. For each of these models, the Partial F-test is conducted for all 12 autoregression equations. When the results of the Partial F-tests, the COILS R2 of the model and the partial regression coefficients are considered, the second restrictive model is built by including policy clusters 9 and 13 in the first restrictive model. This second restrictive model provides a better fit for 7 out of the 12 autoregression equations. They are the casualty rates for motorcyclists at all levels of severity, the fatal and serious casualty rates of drivers and the serious and slight casualty rates of pedestrians. For the other equations, the second restrictive model represents a poorer fit and the first restrictive model is adopted. These findings are intuitively logical and powerful because different combinations of policy strategies are likely to be the most effective in reducing casualty for different classes of road users. Statistically, the underlying relationships among the variability of the dependent and independent variables vary for different sets of dependent variables. 4.4. Results of autoregression analysis: effectiveness of measures The autoregression results for different classes of road users and severity levels are summarized in Table 3. The partial regression coefficients significant at 0.05 level and the summary results of COILS are presented. Under the multiplicative model, a partial regression coefficient can be interpreted as the elasticity of the regressor, indicating its relative impact on the dependent variable (Greene, 2003). In other words, a highly effective policy cluster in reducing a particular casualty rate should be a negative partial regression coefficient higher than 1. Before we examine the results of individual autoregression models, it is worthwhile to highlight some general observations. First, the values of the COILS R2 of the fatality models are generally much lower than those of the serious and slight models for all classes of road users. In particular, the explanatory power of the models for the fatality of drivers (R2 = 0.540) and motorcyclists (R2 = 0.582) is low. These results suggest that the road safety measures in

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Table 3 Summary of the autoregression results Category/unit

Driver casualty rate Fatal

Constant

–

Strategy 1 2 3 4 5 6 7 8 9 10A 10B 11 12 13 14A 14B 15

Publicity Publicity Publicity Publicity Education Publicity Publicity Publicity Publicity Education Legislation Education Enforcement Enforcement Legislation Enforcement Enforcement

Crashworthiness Sunshine Wind Speed Humidity Rain

Average star rating h km/h % mm

Quarter 1 2 3

Dummy Dummy Dummy

COILS estimates R2 Number of iteration ρ

–

Strategy 1 2 3 4 5 6 7 8 9 10A 10B 11 12 13 14A 14B 15

Publicity Publicity Publicity Publicity Education Publicity Publicity Publicity Publicity Education Legislation Education Enforcement Enforcement Legislation Enforcement Enforcement

Crashworthiness Sunshine Wind Speed Humidity Rain

Average star rating h km/h % mm

Quarter 1 2 3

Dummy Dummy Dummy

COILS estimates R2 Number of iteration ρ

Slight

Fatal

−2.280 (0.030)

2.487 (0.000) −1.697 (0.013)

7.439 (0.006)

−2.811 (0.000)

−1.589 (0.002)

−5.998 (0.024)

Serious

Slight

4.340 (0.027)

4.266 (0.001)

1.123 (0.003) −1.652 (0.021)

0.532 (0.024)

−1.637 (0.001) 1.670 (0.002)

−0.728 (0.023)

−5.523 (0.020)

−4.421 (0.008)

−1.484 (0.011)

−9.710 (0.000)

−7.114 (0.008)

−0.835 (0.045)

−0.970 (0.017)

14.764 (0.041)

33.799 (0.045) −4.189 (0.033)

7.871 (0.030)

−2.798 (0.005) 0.151 (0.001)

−1.267 (0.004)

−1.100 (0.000)

0.097 (0.013)

0.079 (0.002)

0.293 (0.036)

0.939 6 −0.578

0.945 10 −0.781

Motorcyclist casualty rate Fatal

Constant

Serious

−10.270 (0.022)

0.540 10 −0.634 Category/unit

Passenger casualty rate

Serious

0.708 10 −0.335

0.923 5 −0.619

0.921 6 −0.369

Pedestrian casualty rate Slight

Fatal

Serious

Slight

−6.532 (0.042)

0.555 (0.042) −1.146 (0.016)

−2.486 (0.014) −3.813 (0.048)

1.055 (0.007)

−4.078 (0.004)

−1.739 (0.000)

−1.709 (0.002)

−2.046 (0.001)

1.262 (0.010) −1.271 (0.004)

−4.032 (0.000)

−1.478 (0.026)

−0.760 (0.028)

−0.803 (0.012)

−1.774 (0.022)

−1.939 (0.018)

0.230 (0.023)

0.582 6 −0.442

The values in parenthesis indicate the level of significance for t-statistics.

0.890 5 −0.474

0.936 8 −0.334

0.863 10 −0.178

0.975 5 −0.424

0.897 5 −0.144

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Hong Kong over the last decade has in general been more effective in reducing serious and slight injuries than fatality. For serious injuries, the explanatory power of the models also differs among different classes of road users. Generally, the road safety measures adopted in Hong Kong have been more powerful in explaining for changes in the serious casualty rates of pedestrians (R2 = 0.975), followed by drivers (R2 = 0.939), passengers (R2 = 0.923), and finally motorcyclists (R2 = 0.890). For slight injuries, the road safety measures adopted in Hong Kong have been more powerful in accounting for changes in the slight casualty rates of drivers (R2 = 0.945). The explanatory power is weaker in accounting for slight injuries of pedestrians (R2 = 0.897). Generally, the government’s road safety strategy in the 1990–1999 period has been the most effective in explaining for the fall in serious injuries of pedestrians (R2 = 0.975). As in other social sciences research, the model does not claim to take into account all factors affecting road accidents in Hong Kong and some regression coefficients seem to be of the “wrong” signs. These other factors (in a sense reflected in the constant term of the autoregression model) and unexpected relationships are important subjects for further research. For instance, we treat the fatal, serious, slight casualty rates of different classes of road users as 12 independent variables but these casualty rates can be related in some ways. (To illustrate, the seat belt legislation may reduce fatality and thereby increase the serious and slight injury rates.) Nonetheless, most autoregression models do possess satisfactory explanatory power—the R2 of the models are reasonably high and most regression coefficients turn out to have the “correct” signs. More importantly, the analysis provides useful information to policy-makers and citizens alike in understanding and evaluating the effectiveness of different road safety measures adopted in Hong Kong over the last decade. It is against this background that we interpret the results of the autoregression analysis. 4.4.1. Drivers The fatality rate of drivers was not well explained by the autoregression model (R2 = 0.540). Moreover, only two regression coefficients are having the expected signs. They are car crashworthiness (−4.189) and policy strategy cluster 8 (−5.998). The improvement of car crashworthiness over the last decade contributed significantly to the reduction of drivers’ fatality. In fact, the multiplicative model suggests that an improvement in the car crashworthiness of 1% is expected to be associated with 4.189% reduction in drivers’ fatality rate. Across different types of car users and casualty rates, the vehicle crashworthiness improvement has been the most effective in reducing drivers’ fatality. Furthermore, an examination of strategy cluster 8 shows that it mainly encompassed the broadcasting of road safety messages through various electronic media. In particular, there was a series of radio segments to broadcast safety tips. The use of the radio may be quite effective in instilling road safety awareness among drivers.

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Table 3 also shows that publicity and enforcement were significant contributing factors in reducing the serious casualty rate of drivers. In particular, policy cluster 12 (−9.710) on enforcing the vehicle maintenance laws to ensure that all vehicles are maintained properly and were roadworthy has also been highly effective in reducing the serious casualty rate of drivers. Moreover, the publicity-related strategy clusters of 7 (−2.811) and 9 (−1.484) were significant in reducing the serious casualty rate of drivers over the last decade. Strategy cluster 7, which includes measures of disseminating important messages about upcoming road safety legislations and the implementation dates of these legislations, was effective in raising the alertness of drivers about road safety. In fact, strategy cluster 7 may better be conceptualized as “publicity/legislation”; and it highlights the fact that road safety legislations alone may not be effective unless when they were accompanied by vigorous publicity efforts. Strategy cluster 9 targeting at the seriousness of careless driving and behavior such as speeding, reckless lane changing, and tailgating has also been playing a significant role in accounting for the declining serious casualty rate of drivers. In contrast, high rainfall (0.151) and the spring season (0.293) were significant contributory factors to serious casualty rate of drivers. Rainfall can lead to slippery road and lower tire friction, hence, higher accident rates. In Hong Kong, spring is often associated with low visibility. Due the topological and other geographical factors, the smog problem is often serious in the first quarter of the year. Lastly, there are negative relationships of the serious driver casualty rate with relative humidity (−2.798) and the crashworthiness of cars (−0.835). With better vehicle design and manufacturing technology, drivers have been better protected in traffic accidents. When the effects of rainfall and the first quarter of the year have been filtered, relative humidity has been negatively associated with the serious casualty rate. This may be due to the fact that low relative humidity is often associated with stronger and more dazzling sunlight in Hong Kong. For slight injury of drivers, strategy cluster 12 has been the most powerful (−7.114) in accounting for the falling casualty rate. In fact, the promotion of regular vehicle maintenance also helped to ensure that vehicles functioned safely on the roads. The slight casualty rate of drivers was also negatively related to the publicity measures. In particular, strategy clusters 4 (−1.697) and 7 (−1.589). In a sense, the above findings suggest that publicity has been quite effective in improving the safety of drivers. Apart from government efforts, the improvement in the crashworthiness of vehicles over the past decade has also been a significant contributory factor in reducing the slight casualty rate of drivers (−0.970). 4.4.2. Passengers Many passengers are killed or seriously injured in traffic accidents that are caused by carelessness or reckless driving attitudes. A passenger in a vehicle can do very little to avoid an accident. Therefore, to reduce the passenger casualty rate, measures that target drivers are the most effective, such as

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encouraging drivers to have correct driving behavior and to educate them in road safety awareness. It is important to note that the fatality rate was negatively correlated with strategy cluster 3 (−2.280), which represents the general realization among the driving population of the importance of good driving habits and skills. This created a market for the promotion of safe driving by private sectors through publications and magazines, such as the Hong Kong Automobile Association’s “Motorist”, which carried useful road safety messages to highlight the dangerous driving behavior, for example, tailgating, speeding, and careless lane changing. Again, results on the autoregression analysis suggest that the serious casualty rate of passengers has been related to a much wider spectrum of variables, including car crashworthiness and the seasonal variables. The results also show that the publicity strategy clusters of 4 (−1.652) and 7 (−1.637) and the enforcement on vehicle maintenance, i.e. strategy cluster 12 (−5.523), have been highly effective in reducing the serious casualty rate of passengers. Moreover, the improvement of car crashworthiness (−1.267) also significantly accounts for the fall in serious injuries of passengers. Conversely, rainfall (0.097) has been positively associated with the serious casualty rate for passengers. Findings about the slight casualty rate of passengers are highly consistent with the situation of serious injuries. Strategy cluster 12 on enforcement (−4.421) and strategy cluster 7 on publicity (−0.728) are important in accounting for the drop in the slight casualty rate of passengers. Moreover, higher rainfall (0.079) was associated with higher slight casualty rate. Once again, the improvement in car crashworthiness (−1.100) has proven to be a statistically important factor in protecting passengers from both serious and slight casualties. 4.4.3. Motorcyclists Our autoregression model was the least powerful in explaining for the fatality rate of motorcyclists over the last decade (R2 = 0.52). None of the policy clusters were significant in explaining for the fall in the fatality of motorcyclists. This may have reflected the predominant attention of the government in targeting private vehicle drivers and passengers, as evidenced from the seat belt legislations and other publicity materials about drink driving and speeding, over the 1990–1999 period. This has changed recently with the introduction of the probationary driving license system of motorcyclists on 1 December 2000 (Transport Department, 2002). Yet, the first-order autoregression coefficient (ρ) was negative (−0.442), highlighting that the general trend has been for the fatality rate of motorcyclists to fall. In other words, the general road safety publicity, education, legislation and enforcement measures may still be important in raising the general road safety awareness of the general public, including motorcyclists. However, more focused efforts targeted at motorcyclists may be required to bring about significant reduction in the motorcyclist fatality rate.

For serious motorcyclist casualty rate, it is noteworthy to highlight that strategy cluster 13 (−4.032) on toughening of the penalty for tailgating was particular powerful in accounting for the declining rate. In fact, tailgating is one of the most common contributory factors to traffic accidents that involve motorcycles. Motorcyclists enjoy easy maneuverability and can easily move through the small gaps between cars to avoid traffic congestion. However, this often leads to tailgating. To tackle this problem, anti-tailgating operations were mounted by the Traffic Police on major expressways. Vehicles, including motorcycles, were required to maintain a gap of at least 2 s between themselves and the vehicles ahead. This reduced the chance of front–rear collisions between vehicles in cases of emergency or sudden braking. Although for motorcars front–rear collisions may not be as serious as other kinds of traffic accidents, such as head-on collisions, they usually cause serious injuries to unprotected motorcyclists. Other than the anti-tailgating operation of strategy cluster 13 (−1.478), one other publicity-related strategy cluster 4 (−1.146) was statistically significantly in accounting for the drop in the slight casualty rate of motorcyclists. Moreover, an important observation is that car crashworthiness (−0.760) was again negatively associated with the slight casualty rate of motorcyclists. Although the car crashworthiness rating in this study does not directly measure crashworthiness of motorcycles, this independent variable may be seen as indicative of the better technology in vehicle manufacturing, such as the improvement of the ABS brake system, that helps to reduce casualties not only for drivers and passengers inside the cars but all other parties on the road, such as the motorcyclists and pedestrians. Lastly, we note that the slight casualty rate of motorcyclists was also negatively associated with relative humidity (−1.774) but it was higher in the second quarter of the year (0.230). This might have reflected the higher incentives of motorcyclists to drive during the warm weather of the second quarter in Hong Kong and, hence, the higher slight casualty rate. Another plausible explanation is the higher tendency for drivers and motorcyclists alike to drive faster and lower their guard during clear days. 4.4.4. Pedestrians The publicity campaigns, including strategy clusters 3 (−2.486), 4 (−3.813) and 7 (−4.078), played a crucial role in reducing the fatality rate of pedestrians, as they targeted the whole population rather than any sub-population, such as a particular driver community. Publicity campaigns under strategy cluster 7 made use of various electronic media, such as television, radio and the Internet, to disseminate road safety legislation messages. They proved to be very effective in disseminating pedestrian safety messages. Moreover, strategy cluster 4 that mainly encompassed dissemination of pedestrian safety messages through printed items was also highly effective in reducing the pedestrian fatality rate. Important pedestrian safety messages such as “observe the traffic rules”, “traffic rules protect you”, and “green light—all right” were printed on giveaway items that

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were distributed to the public. Under this strategy cluster, colorful posters with road safety messages were displayed in public places and different forms of public transport. These items reminded people of the importance of road safety, and could have helped to reduce the number of fatal accidents that involved pedestrians. Moreover, the efforts of non-governmental organizations in promoting road safety (strategy cluster 3) have also been playing a major role in contributing to the fall in the fatality rate of passengers over the last decade. For the serious casualty rate of pedestrians, it is important to note that, apart from publicity policy cluster 7 (−1.739), strategy cluster 9 (−1.271) has also been important in reducing the casualty rate. The use of television commercials, radio announcements, web sites and eye-catching posters on public transportation has been instrumental in raising the road safety awareness of the general public. Moreover, car crashworthiness index (−0.803) has also been negative associated with the serious casualty rate of pedestrians. As

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mentioned earlier, this index reflects the general technological advancement in vehicle manufacturing, including the brake system, that helps to improve the safety of all road users. Lastly, publicity-related strategies 4 (−1.709) and 7 (−2.046) have been important in explaining the declining slight casualty rate of pedestrians. The widespread dissemination of pedestrian safety messages through the display of road safety posters in public places and various forms of public transportation, and the distribution of leaflets, has been effective. Again, relative humidity (−1.939) has been negatively associated with the slight casualty rate of pedestrians.

5. Validation tests As in most time series analysis, we are interested in validating the results of our model. In order to test the sensitivity of our results to the particularities of the sampling, the empirical validation approach is adopted. One way is to

Table 4 Predictive power of the full and validation models Driver casualty rate

Passenger casualty rate

Fatal

Serious

Slight

Fatal

Serious

Slight

Observed rates (z) Mean Standard deviation

0.000024 8.37E−06

0.000364 6.74E−05

0.002270 4.90E−04

0.000023 6.81E−06

0.000495 8.66E−05

0.003657 4.04E−04

Full sample model (EZRM ) Mean Standard deviation COILS R2 COILS standard error Average percentage of absolute difference with z (%)

0.000023 6.68E−06 0.53978162 0.41600327 24.86

0.000364 6.70E−05 0.93901965 0.08694897 4.96

0.002268 4.81E−04 0.94473351 0.07027991 3.87

0.000023 5.55E−06 0.70793896 0.23377713 18.89

0.000495 8.13E−05 0.92330352 0.09021057 5.45

0.003655 3.86E−04 0.92097684 0.05309911 3.05

Validation model (EZVM ) Mean Standard deviation COILS R2 COILS standard error Average percentage of absolute difference with z (%)

0.000048 7.33E−5 0.75401501 0.34071616 128.06

0.000352 7.28E−05 0.96563597 0.06622404 6.98

0.002262 4.80E−04 0.96664509 0.06245882 4.07

0.000022 7.74E−06 0.71884343 0.2358295 24.9

0.000473 9.88E−05 0.96998003 0.06239546 7.00

0.003643 3.92E−04 0.9434613 0.0483615 3.16

Motorcyclist casualty rate Fatal Observed rates (z) Mean Standard deviation

0.000228 1.02E−04

Pedestrian casualty rate

Serious

Slight

0.005590 9.81E−04

0.020635 3.03E−03

Fatal 0.000007 2.42E−06

Serious

Slight

0.000063 1.31E−05

0.000156 2.23E−05

Full sample model (EZRM ) Mean Standard deviation COILS R2 COILS standard error Average percentage of absolute difference with z (%)

0.000219 8.01E−05 0.58224381 0.48591366 24.47

0.005582 8.99E−04 0.88968861 0.09962083 5.67

0.020626 2.85E−03 0.935509 0.06562521 3.54

0.000007 2.26E−06 0.86259042 0.20847078 12.43

0.000063 1.28E−05 0.97543765 0.06169425 3.43

0.000156 2.01E−05 0.89699589 0.06800102 3.62

Validation model (EZVM ) Mean Standard deviation COILS R2 COILS standard error Average percentage of absolute difference with z (%)

0.000208 8.96E−05 0.39092243 0.58545153 29.86

0.005699 9.32E−04 0.87653297 0.08971619 7.16

0.019904 3.99E−03 0.88228582 0.06408398 6.73

0.000007 2.35E−06 0.85938022 0.21418364 14.77

0.000065 1.07E−05 0.95789678 0.06893747 8.00

0.000159 1.80E−05 0.83557038 0.07713527 5.46

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Fig. 5. Predictive power of the autoregression model for the slight casualty rate of drivers.

test our model on a new sample drawn from the general population. However, this is not feasible because of limited data availability for a sufficiently long period. Thus, we take the split sample approach by dividing the sample into the estimation and validation sub-samples (Hair et al., 1995). For the sake of simplicity, we take the last eight observations, i.e. the eight quarters from 1998 to 1999, to be the validation sub-sample and run the autoregression analysis again. For this validation model, the estimation period is from the first quarter of 1990 to the last quarter of 1997 and the prediction period extends to the end of the study period, i.e. the last quarter of 1999. In fact, “the need for continued validation efforts and model refinements remind us that no regression model, unless estimated from the entire population, is the final and absolute model” (Hair et al., 1995, page 128). Based on the fit of the results of the validation autoregression models, we save the expected values of the casualty rate, EZVM , and then compare this to the expected values of the casualty rate of the full period, EZRM , and the actual observed value of the casualty rate, z. The predictive power of the full and validation models is summarized in Table 4. The additional sample points for 1998–1999 do contribute to a better degree of estimation. Nonetheless, differences of the validation model are reasonably good with the average percentage of error (under- or over-estimation) to be much lower than 10% in all but the fatality models. Again, these observations are reasonable because of the much lower explanatory power of these autoregression models in the first place. Their predictive power is, therefore, also particularly sensitive to sampling particularities. Caution is needed when applying the autoregression model for estimating the fatal-

ity rates. In contrast, the predictive power is the strongest and most stable for slight casualty rates. The average degree of difference of the validation autoregression models is only 3.16% for passengers, 4.07% for drivers, 5.46% for pedestrians and 6.73% for motorcyclists. In comparison, the average degree of difference of the full sample autoregression models is 3.05, 3.87, 3.62 and 3.54% for the passengers, drivers, pedestrians and motorcyclists, respectively. Fig. 5 displays the observed slight casualty rate of passengers over the study period, and the expected casualty rates estimated from the validation and the full sample models for drivers. It can be seen that the autoregression model quite closely approximates the observed slight casualty rates and the taking out of some sample points in estimating the model does not lead to substantial deterioration in the predictive power.

6. Conclusion The objective of this paper was to formulate a qualitative assessment methodology for the road safety strategies that have been carried out during the last decade in Hong Kong. Based on a cluster analysis, over a hundred road safety projects and programs were grouped into a smaller set of meaningful road safety policy strategy clusters that helped to qualify the various road safety strategies and their implementation profiles. These strategies, together with the trend factor, seasonal pattern, car crashworthiness and meteorological data were used in an autoregression analysis to relate to the fatality and casualty rates of drivers, passengers, motorcyclists, and pedestrians.

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It must be emphasized that the method of analysis that is proposed in this paper only attempts to qualitatively assess whether certain road safety policy strategies might be effective in reducing traffic accidents and casualties. This is by no means a complete analysis, because it can only identify certain possible candidate measures that are likely to be beneficial, and which therefore deserve in-depth quantitative study. For those explanatory variables that are not significant in the autoregression models, there is merely no strong statistical evidence to extract their effectiveness on road safety from the qualitative data sets, and this does not imply that they might not be locally effective. Lastly, this paper does not incorporate the delay effect of policy strategies, which will be an interesting topic for further research. Moreover, the further disaggregation of accident data in terms of vehicle classes and age groups will extend this method of analysis. Acknowledgements The research was supported by an Outstanding Young Researcher Award 2000 from The University of Hong Kong. The authors wish to thank Prof. Richard Allsop, and two anonymous referees for their helpful suggestions and critical and constructive comments on an earlier version of the paper. References Agent, K.R., Pigman, J.G., Weber, J.M., 1998. Evaluation of speed limits in Kentucky. Transportation Res. Rec. 1640, 57–64. Ali, S.Y., Al-saleh, O., Koushki, P.A., 1997. Effectiveness of automated speed-monitoring cameras in Kuwait. Transportation Res. Rec. 1595, 20–26. Allsop, R.E., 2002. Road safety work—implementation and monitoring now and pragmatism about the longer term. In: Wong, S.C., Hung, W.T., Lo, H.K. (Eds.), Road Safety—Strategy and Implementation. China Public Security Publisher, Shenzhen, China (Chapter 2). Billheimer, J.W., 1998. Evaluation of California motorcyclist safety program. Transportation Res. Rec. 1640, 100–109. Binkowski, S.E., Maleck, T.L., Taylor, W.C., Czewski, T.S., 1998. Evaluation of Michigan 70-mph speed limit. Transportation Res. Rec. 1640, 37–46. Bloch, S.A., 1998. Comparative study of speed reduction effects of photoradar and speed display boards. Transportation Res. Rec. 1640, 27–36. Bowerman, B.L., O’Connell, R.T., 1993. Forecasting and Time Series: An Applied Approach. Duxbury Press, USA. Bowerman, B.L., O’Connell, R.T., 1997. Applied Statistics: Improving Business Processes. Times Mirror Higher Education Group, Inc., USA. Census and Statistics Department, 1990–1999. Hong Kong Digest of Statistics. Hong Kong Government, Hong Kong. Chen, G., Wilson, J., Meckle, W., Cooper, P., 2000. Evaluation of photo radar program in British Columbia. Accid. Anal. Prev. 32, 517–526. Coakes, S.J., Steed, L.G., 1999. SPSS: Analysis without Anguish (Versions 7.0, 7.5, 8.0 for Windows). Wiley, Australia. Dee, T.S., 1998. Reconsidering the effects of seat belt laws and their enforcement status. Accid. Anal. Prev. 30, 1–10. DeLurgio, S.A., 1998. Forecasting Principles and Applications. McGrawHill, USA. Elvik, R., 1997. Effects on accidents of automatic speed enforcement in Norway. Transportation Res. Rec. 1595, 14–19. Greene, W.H., 2003. Econometric Analysis. Pearson Education, Inc., USA.

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