## Development of Prediction Models for Motorcycle Crashes at Signalized Intersections on Urban Roads in Malaysia

## Development of Prediction Models for Motorcycle Crashes at Signalized Intersections on Urban Roads in Malaysia

**S. HARNEN**^{1*}**R.S.
RADIN UMAR**^{2}**S.V.
WONG**^{3}**W.I.
WAN HASHIM**^{4}

### ABSTRACT

Because more than half of the motor vehicles in Malaysia are motorcycles, the safety of this form of transportation is an important issue. As part of a motorcycle safety program, Malaysia became the first country to provide exclusive motorcycle lanes in the hopes of reducing motorcycle crashes along trunk roads. However, little work has been done to address intersection crashes involving motorcycles. This paper provides models for predicting motorcycle crashes at signalized intersections on urban roads in Malaysia. A generalized linear modeling technique with a quasi-likelihood approach was adopted to develop the models. Traffic entering the intersection, approach speed, lane width, number of lanes, shoulder width, and land use at the approach of the intersection were found to be significant in describing motorcycle crashes. These findings should enable engineers to draw up appropriate intersection treatment criteria specifically designed for motorcycle lane facilities in Malaysia and elsewhere.

KEYWORDS: Motorcycle crashes, generalized linear models, prediction model, intersection crashes, motorcycle crash model.

### INTRODUCTION

Motorcycle crashes continue to be a problem in both developing
and developed countries. Fatality rates (measured in deaths per
10,000 registered vehicles) in these crashes are much higher than in
nonmotorcycle^{1}
crashes. In the United States, the National Highway Traffic Safety
Administration (USDOT 2002) reported a fatality rate of 6.5 per 100
million vehicle-miles traveled, and motorcyclists were about 26.1
times as likely as passenger car occupants to die in a motor vehicle
traffic crash. The Canadian rate was 4.7 in 1999, which rose to 5.1
in 2000; the Canadian nonmotorcycle fatality rate in 2000 was 0.7
(Transport Canada 2001). Similarly large rates have been reported in
other developed countries: Australia's rate was 6.2 in 2001, an
increase of about 9% from 2000 and more than 4 times the fatality
rate of other road users (ATSB 2002); the United Kingdom's rate was
7.3 in 2000, decreasing to 6.6 in 2001, about 10 times the fatality
rate for passenger car occupants (DfT 2002); Swedish (SI 2000),
French, and German (OECD 2002) rates in 2000 were 4.1, 5.3, and 2.2,
respectively.

In developing countries, deaths and serious injuries from motorcycle accidents constitute a large portion of total road casualties especially in Asian countries, because motorized two-wheelers make up 40% to 95% of their vehicle fleets. As a result, more than half the road fatalities were riders or pillion passengers.

In Malaysia, motorcycles constitute more than half the total vehicle population and contribute more than 60% of the casualties (deaths and serious and slight injuries) in traffic crashes. In 2000, 79,816 crashes involved motorcycles, an increase of almost three-fold from 1990. Of these, almost 3,000 motorcyclists were killed every year during this period (figure 1). Moreover, motorcyclist casualties were much higher than those of occupants in other types of vehicles (figure 2).

In an attempt to reduce casualties, exclusive motorcycle lanes were constructed along major trunk roads in the country. Since the implementation of this initiative, a number of studies (Radin Umar 1996; Radin Umar et al. 1995, 2000) have been carried out to evaluate the impact of these lanes on motorcycle crashes on highway links. Results indicate the lanes had a significant effect (p <0.01), reducing motorcycle crashes by 39% following the opening of the lanes to traffic. However, little research has been done on motorcycle crashes at intersections. Indepth studies would allow traffic engineers to establish appropriate intersection treatment criteria specifically designed for motorcycle lane facilities.

Recent studies on traffic crash modeling have used the generalized linear modeling (GLM) approach (McCullagh and Nelder 1989) with Poisson or negative binomial error structure. This approach is widely accepted as more appropriate for the characteristics of crashes (i.e., discrete, rare, and independent) than the classical linear model based on normal error structure with a constant variance. Crashes can be characterized by their mean number per unit time and are simply represented by a Poisson random variable.

Many researchers have reported the usefulness of the GLM approach in developing predictive models for traffic crashes using either cross-sectional or time series analysis (Griebe and Nielsen 1996; Mountain et al. 1996, 1998; Tarko et al. 1999; Vogt and Bared 1998; Vogt 1999; Radin Umar et al. 1995, 2000; Radin Umar 1996; Bauer and Harwood 2000; Saied and Said 2001; Taylor et al. 2002). For example, an earlier study on crashes at intersections prepared for the Federal Highway Administration of the U.S. Department of Transportation in connection with the development of the Interactive Highway Safety Design Model (IHSDM) (Bauer and Harwood 2000) provided direct input into the Accident Analysis Module of the IHSDM.

The analysis included all collision types using three-year crash
frequencies (1990 to 1992) and geometric design, traffic control,
and traffic volume data from a database provided by the California
Department of Transportation. The analysis was performed using the
SAS GENMOD procedure. The models were developed using the GLM
approach with a log-normal regression model and a loglinear
regression model (a Poisson regression followed by a negative
binomial regression model). In this study, the 10% significance
level of the *t*-statistic of the parameter estimates was used
to assess the significance of the fitted model. The explanatory
variables (continuous and categorical) that follow were found to be
significant in explaining crashes at intersections:

- major road ADT (average daily traffic) and minor road ADT,
- average lane width on major roads,
- number of lanes on major and minor roads,
- design speed of major roads,
- major-road right-turn and left-turn channelizations,
- access control on major roads,
- functional class of major roads,
- outside shoulder width on major roads,
- terrain,
- road lighting,
- minor-road right-turn channelization,
- major-road left-turn prohibition, and
- median on major roads.

As an extension to our earlier analysis (Harnen et al. 2003a, 2003b), this paper presents the development of prediction models for motorcycle crashes at signalized intersections along both the exclusive and non-exclusive motorcycle lanes on urban roads in Malaysia. We used the GLM approach with Poisson error structure to develop our models. The parameter estimates and tests of their significance were carried out using GLIM 4 statistical software (NAG 1994), which is specifically designed for fitting generalized linear models.

### THE DATA

#### Selected Intersections

The intersections studied were located on urban roads in four districts of the state of Selangor, Malaysia. The data collected covered motorcycle crashes, traffic and pedestrian flow, approach speed, intersection geometry, number of legs, and land use. The intersections were selected based on the following conditions between 1997 and 2000: a) only marginal change in land use; b) no major modifications or upgrading; c) an equal number of lanes on the corresponding major and minor roads; d) only marginal change of signal characteristics, for example, signal timing and signal phasing; e) no access road within a 50-meter distance from the intersection stop lines; and f ) intersections must have had fatalities and/or serious and slight injuries in crashes. Please note that while data were collected on signal characteristics they are not analyzed here. However, they will be included in future work. Based on the intersection files (142 signalized intersections with motorcycle crashes in the period 1997 to 2000) extracted from the Microcomputer Accident Analysis Package (MAAP) database and visits to the sites to ensure that they met the requirements, 51 intersections were chosen. In this study, motorcycle crashes occurring within 50 meters of the corresponding stop lines of the intersection were classified as intersection crashes.

#### Motorcycle Crash Data

Four-year's worth of motorcycle crash data on the selected
intersections, from 1997 through 2000, were collected from the
police crash record form, POL 27 (Pin 1/91). The POL 27 is designed
for easy completion (Radin Umar et al. 1993) and is fully compatible
with the MAAP database developed by the Transport Research
Laboratory (Hills and Baguley 1993). Data were extracted from two
complementary sources: the MAAP database for fatal and serious
injury crashes, and the Computerized Accident Recording System (CARS
2000) database for slight injury crashes. Both databases are based
on the POL 27 record form.^{2}

#### Traffic Flow Data

In this study, the estimated annual average daily traffic (AADT) defines the traffic flow on each selected intersection. Hourly traffic volume (disaggregated by nonmotorcycles and motorcycles) was counted on major- and minor-road approaches and then converted to AADT by using hourly, daily, and monthly factors. These factors were determined based on 24-hour permanent traffic count station and traffic census data, available from the Highway Planning Unit, Ministry of Works in Malaysia (HPU 2001a, 2001b) and were developed using the method proposed by McShane et al. (1998). The AADT is expressed in terms of the number of nonmotorcycles per day and motorcycles per day.

#### Other Data Used

Approach speed and pedestrian flow were also considered in this study. However, while these data were not available in the database, they were collected onsite following criteria used by Golias (1997) in an earlier study. The 85th percentile approach speed on major and minor roads was used to represent the approach speed on each intersection. Arndt and Troutbeck (1998) also considered this characteristic in an earlier study on traffic crashes. The approach speeds were measured at a 50-meter distance upstream from the corresponding stop lines of the intersection and were counted for all vehicles moving during the time the signal was green.

Pedestrian flow at each intersection was defined as the total number of pedestrian crossings per hour counted on major- and minor-road approaches. It should be noted that pedestrians per hour rather than pedestrians per day was used because there was no supporting data to convert hourly pedestrian flow to annual average daily pedestrians (the AADT for pedestrians).

Intersection geometry, number of legs, and land use for each selected intersection were also observed onsite. Of the 51 selected intersections, 27 were three-legged while 24 were four-legged. The land use adjacent to the intersection was classified into two categories: commercial and noncommercial areas. A commercial area was defined as an area with a concentration of offices, shops, and railway and bus stations, while residential areas and unused land come under the category of noncommercial area. Of the 51 intersections, 33 were located in commercial areas and 18 were in noncommercial areas. Figure 3 shows a typical layout of intersection geometry considered in the study.

### MODEL DEVELOPMENT

Prior to carrying out the statistical modeling, we did some preliminary work to facilitate the modeling process. This included formulating the theoretical models, specifying the error structure and link function, identifying the model variables, and defining the goodness-of-fit and significance tests.

Using our earlier analysis of motorcycle crashes at intersections (Harnen et al. 2003a, 2003b) and studies of traffic crashes at intersections (Griebe and Nielsen 1996; Vogt and Bared 1998; Vogt 1999; Bauer and Harwood 2000; Saied and Said 2001), we defined the model structure and the variables included.

Two separate models (Models 1 and 2) were proposed. These models used the same data and structure but employed different explanatory variables. In Model 1, the response variable was the number of motorcycle crashes and the explanatory variables were traffic flow (disaggregated by nonmotorcycles and motorcycles both for major and minor roads), pedestrian flow, approach speed, lane width, number of lanes, number of legs, shoulder width, and land use. The continuous variables were identified as traffic flow, pedestrian flow, approach speed, lane width, and number of lanes, while the categorical variables were number of legs with two-factor levels, shoulder width with three-factor levels, and land-use with two-factor levels. In Model 2, the response variable was motorcycle crashes, while the explanatory variables were traffic flow and shoulder width. Both traffic flow and shoulder width were continuous variables.

The main differences in these two models are the explanatory variables included. Model 2, which has three continuous variables, is simpler than Model 1 and can be used further to establish major- and minor-road flow criteria for intersection treatment. This can be done by using the design curves relating major- and minor-road flows and shoulder widths developed based on Model 2.

Model 1, which has 13 variables (combination of continuous and categorical), was aimed at giving more room to engineers for analyzing the variables contributing to motorcycle crashes. Software that is specifically designed for Model 1 application could make it easier and faster to analyze the variables and estimate motorcycle crashes.

Taking the earlier studies on intersection crash modeling into consideration, the theoretical models containing all terms used in this study were formulated as follows:

#### Model 1

(1)

where

*z* = *β*_{1}*SPEED* +
*β*_{2}*LWm* + *β*_{3}*LWn* +
*β*_{4}*LNm*

+ *β*_{5}*LNn* + *β*_{6}*NL* +
*β*_{7}*SHDW* + *β*_{8}*LU* +
*e*

#### Model 2

(2)

where *MCA* is motorcycle crashes per year. Descriptions of
all the explanatory variables are presented in table
1. The *k*_{1}, *k*_{2},
*α*_{1}, *α*_{2}, *α*_{3},
*α*_{4}, *α*_{5}, *β*_{1},
*β*_{2}, *β*_{3}, *β*_{4},
*β*_{5}, *β*_{6}, *β*_{7},
*β*_{8}, *δ*_{1}, *δ*_{2},
and *λ*_{1} are the parameters to be estimated and the
(*e*) term is the error representing the residual difference
between the actual and predicted models.

Using a logarithmic transformation, the loglinear version of the model is:

#### Model 1

*Ln*(*MCA*) = *Ln*(*k*) +
*α*_{1}*Ln*(*QNMm*) +
*α*_{2}*Ln*(*QNMm*)

+
*α*_{3}*Ln*(*QNMm*) +
*α*_{4}*Ln*(*QNMn*) +
*α*_{5}*Ln*(*QPED*)

+
*β*_{1}(*SPEED*) +
*β*_{2}(*LWm*) + *β*_{3}(*LWn*)
+ *β*_{4}(*LNm*)

+
*β*_{5}(*LNn*) + *β*_{6}(*NL*) +
*β*_{7}(*SHDW*) + *β*_{8}(*LU*)
+ *e* (3)

#### Model 2

*Ln *(*MCA*) = *Ln*(*k*) +
*δ*_{1}*Ln*(*Q _{major}*)

+

*δ*

_{2}

*Ln*(

*Q*) +

_{minor}*λ*

_{1}(

*SHD*) +

*e*(4)

To allow direct interpretation of the parameter estimates
produced by GLIM 4, the flow functions in equations (3) and (4) need
to be transformed using a natural logarithmic (*Ln*), while the
others do not. It should be noted that the total four-year crash
frequencies were used to fit the models. However, by introducing an
offset variable in the fitting process, the final model would be
able to estimate the number of crashes per year. This approach has
also been implemented in earlier studies on traffic crashes at
intersections (Mountain et al. 1998) and motorcycle crashes at
intersections (Harnen et al. 2003a, 2003b).

We based the model on the Poisson error structure and used the quasi-likelihood approach (McCullagh and Nelder 1989) to overcome the dispersion problem. A loglinear cross-sectional model was employed with the link function specified as the log (NAG 1994). This approach has been used in earlier studies on motorcycle crashes on highway links (Radin 1996; Radin et al. 1995, 2000) and in our earlier analysis of motorcycle crashes at intersections (Harnen et al. 2003a, 2003b).

Using the quasi-likelihood approach, the dispersion parameter was
estimated from the mean deviance (scaled deviance over its degrees
of freedom). This may result in a model where the scaled deviance is
equal to its degrees of freedom. The final model was based on the
goodness-of-fit and significance tests carried out on the models
such as the change in scaled deviance from adding or removing the
terms, the ratio of scaled deviance to its degrees of freedom (mean
deviance), and the 5% significance level of *t*-statistics of
the parameter estimates.

Both multivariate and univariate analyses were conducted for Model 1, while only multivariate analysis was undertaken for Model 2. We used multivariate analysis to assess which of the variable(s) had the most effect on the probability of motorcycle crashes. The univariate analysis was employed to obtain a complete picture of the effect of all explanatory variables on motorcycle crashes. It should be noted that only those variables found significant at the 5% level in the univariate analysis were subsequently included in the multivariate analysis.

### RESULTS

#### Model 1

Table
2 presents the results of the univariate analysis for Model 1.
It can be seen that all terms, except *QPED*, *LNn*, and
*NL*, were significant at the 5% level. The respective scaled
deviance was equal to its corresponding degrees of freedom, as the
quasi-likelihood approach had been introduced in the fitting
process. Because the terms *QPED*, *LNn*, and *NL*
were not significant at the 5% level, they were then excluded from
any further analysis.

The multivariate analysis (table 3) shows that all explanatory variables were significant at the 5% level. The scaled deviance was equal to its degrees of freedom, changing from 15,022.0 to 39.0 with a loss of 11 degrees of freedom. The mean deviance changed from 300.4 to 1.0.

On the basis of the multivariate analysis, the final model is:

*MCA* = 0.002822 *QNMm *^{0.3241} . *QNMm
*^{0.0835} .

*QNMm
*^{0.0683} . *QNMm *^{0.1296} .
EXP* ^{z}* (5)

where

*z* = 0.02602 *SPEED* − 0.0727 *L Wm* − 0.0718
*L Wn*

−
0.01758 *L Nm* − *β*_{7}*SHDW* +
*β*_{8}*LU*

and where *MCA* is motorcycle crashes per year,
*β*_{7} = 0.0, 0.01755, and 0.02554 for *SHDW* =
1, 2, and 3, respectively, *β*_{8} = 0.0 and 0.01591
for *LU* = 1 and 2, respectively (table 1). Figure
4 shows the actual and predicted motorcycle crashes.

#### Model 2

Table 4 presents the results of the multivariate analysis of Model 2. All terms were found to be significant at the 5% level. The scaled deviance was equal to its degrees of freedom, because the quasi-likelihood approach had also been introduced in the fitting process. The scaled deviance changed from 854.8 to 47.0 with a loss of 3 degrees of freedom and the mean deviance changed from 17.1 to 1.0.

The final model developed in this analysis was:

*MCA* = 0.0004693 *Q _{major}*

^{0.5948}.

*Q*

_{minor}^{0.2411}.

EXP

^{−0.0589 SHD}(6)

Tables 2, 3, and 4 show that the variables have a consistent effect on motorcycle crashes. This is indicated by the sign (plus or minus) of the parameter estimates for each of the corresponding variables that are identical.

### DISCUSSION

#### Model 1

The final Model 1 reveals that the number of motorcycle crashes
per year is proportional to the traffic flow entering the
intersection. The estimates of *QNMm, QNMn, QMm,* and
*QMn* indicate that an increase in nonmotorcycle and motorcycle
flows on major and minor roads is associated with more motorcycle
crashes (figure
5). For instance, doubling nonmotorcycle flow on a major road
(*QNMm*) is expected to cause an increase of about 25% in
motorcycle crashes. If all traffic entering the intersection is
doubled, an increase of about 45% in motorcycle crashes would
result. We also found that nonmotorcycle flows on major roads
(*QNMm*) was the most important variable for the probability of
motorcycle crashes. The results support the findings of earlier
studies on traffic crashes at intersections (Summersgill 1991;
Mountain et al. 1998; Rodriguez and Sayed 1999; Vogt and Bared 1998;
Vogt 1999; Bauer and Harwood 2000).

The *SPEED* estimate shows that an increase in approach
speed is associated with a rise in motorcycle crashes. For instance,
if the approach speed goes up by 10 kilometers per hour, 30% more
motorcycle crashes can be expected. Our findings support earlier
studies on the relationship of traffic speed to crashes (Griebe and
Nielsen 1996; Vogt and Bared 1998; Bauer and Harwood 2000; Lynam et
al. 2001; USDOT 2002; Taylor et al. 2002).

The estimates of *LWm* and *LWn* imply that a wider
lane is associated with a reduction in motorcycle crashes. For
instance, widening the lane on major and minor roads by 0.50 meters
is expected to reduce motorcycle crashes by some 3.6% and 3.5%,
respectively. This result is in line with the finding reported in an
earlier study on traffic crashes at intersections (Bauer and Harwood
2000).

Meanwhile, the estimate of *LNm* indicates that an increase
in the number of lanes on a major road is associated with a
reduction in motorcycle crashes. However, the effect of this
variable is marginal (1.7%). The result seems to be in line with the
finding reported by Bauer and Harwood (2000). This reduction was
probably the result of the presence of an exclusive right turn lane
on the major road. Of the 51 intersections we studied, 48 had an
exclusive right turn lane on each major road approach. The presence
of such lanes may reduce rear-end crashes for motorcycles. It should
be mentioned that an exclusive turning lane was counted as a lane in
our measurements of *LNm*. Earlier studies confirmed the
benefit provided by such lanes for crash reduction at intersections
(Kulmala 1992; Vogt 1999; Bauer and Harwood 2000) and at links
(Tarko et al. 1999). However, for a better explanation, a separate
model should be developed to explain the effects of an exclusive
left, exclusive right, and short turning lanes on all types of
motorcycle crashes at intersections.

The *SHDW* estimates indicate that a wider paved shoulder is
associated with fewer motorcycle crashes. The result seems to be in
line with the finding reported by Bauer and Harwood (2000). For
instance, 25% more motorcycle crashes occur at intersections without
a shoulder than at intersections with a shoulder wider than 1.0
meters. When we compare motorcycle crashes at intersections without
a shoulder with crashes where the shoulder width is between 0.0
meters and ≤1.0 meters, the difference is smaller, only 1.7% more
crashes occur when there is no shoulder. This finding seems
reasonable because motorcyclists use the available shoulder width
when approaching an intersection, and the rates of rear-end and
sideswipe crash types between motorcycles on the shoulder and other
vehicles on the adjacent lane should be lower if the shoulder is
wider. This situation is common in countries like Malaysia with a
high population of motorcycles. However, a better explanation can be
provided, and a separate model was developed to explain the effect
of shoulder width on all types of motorcycle crashes at
intersections.

The estimate of *LU* shows that signalized intersections
located within commercial areas are associated with increased
motorcycle crashes. The result confirms the findings of an earlier
study on traffic crashes at four-legged signalized intersections
(Wang and Ieda 1997). However, the difference in the estimation of
motorcycle crashes between commercial and noncommercial areas is
marginal (1.6%). As explained earlier, this study includes only
those intersections located within commercial areas having no access
road to the adjacent land use within 50 meters of the intersection
stop lines. As such, the number of conflicts between vehicles
entering or leaving the intersection and vehicles turning into or
out of the adjacent land use may be reduced, hence fewer crashes.
The effect of access control or the number of accesses on traffic
crashes has also been reported in earlier studies (Vogt 1999; Bauer
and Harwood 2000).

#### Model 2

Model 2 results verify the contribution of traffic flow, both on
major roads (*Qmajor*) and minor roads (*Qminor*), to
motorcycle crashes. The estimates of the variables show that an
increase in traffic flow on major and minor roads is associated with
a greater number of motorcycle crashes, and an increase in shoulder
width (*SHD*) is associated with a reduction in these crashes.
For example, widening the shoulder by 1.0 meters is expected to
reduce the number of motorcycle crashes by about 6%. In this model,
the effect of shoulder width on motorcycle crashes can be directly
quantified when the width is changed, and this is one of the main
differences between Model 1 and Model 2.

As described earlier, design curves relating major- and minor-road flows for different shoulder widths can be developed based on Model 2 (figure 6). As discussed, wider shoulders at intersections offer higher levels of safety to motorcyclists approaching the junction. Based on the relationships among the variables developed based on Models 1 and 2, future work includes carrying out an indepth analysis of whether intersection treatments that have non-exclusive motorcycle lane facilities could reduce motorcycle crashes.

### CONCLUSIONS

This paper presents motorcycle crash prediction models for signalized intersections on urban roads in Malaysia. The models reveal that traffic flow, approach speed, intersection geometry, and land use are significant factors in explaining motorcycle crashes at signalized intersections. The number of crashes is proportional to the level of traffic entering the intersections. An increase in motorcycle crashes is associated with a larger total vehicle flow on major and minor roads. Nonmotorcycle flows on major roads had the most effect on the likelihood of motorcycle crashes.

An increase in approach speed is associated with more motorcycle crashes, while wider lanes, a greater number of lanes, and wider shoulders bring a reduction in these crashes. Furthermore, more motorcycle crashes occur at signalized intersections located within commercial areas than at intersections located outside of commercial areas.

The models developed in this study present information to aid traffic engineers in deciding the appropriate level of intervention for intersection treatment with respect to motorcycle crashes. Using our models, design parameters for intersections may be changed to achieve appropriate safety levels. Decisions on whether special treatment to minimize motorcycle conflicts is needed at intersections can be objectively carried out based on the models. However, the models might only be valid for a typical traffic environment in developing countries like Malaysia, where the proportion of motorcycles is 20% to 40% of all vehicles at signalized intersections.

For design options, further investigation of the role of parameters of traffic flow by time periods (hourly, peak hour, peak periods) and categorizing the models by time period(s) is suggested, and the need for further categorization of model structure by different intersection geometric configurations (e.g., intersections with and without exclusive motorcycle lanes) is also advised.

### ACKNOWLEDGMENTS

This paper reports findings of part of a study conducted for the
Intensified Research Priority Area (IRPA) project, *Development of
Design Criteria and Standards for Malaysian Motorcycle Lanes.* We
gratefully acknowledge the financial support from the Ministry of
Science, Technology and Environment Malaysia. The authors would like
to thank the Royal Malaysian Police and the Highway Planning Unit,
Ministry of Works, Malaysia, for providing the data.

### REFERENCES

Arndt, O.K. and R.J. Troutbeck. 1998. Relationship Between Roundabout Geometry and Accident Rates, presented at the International Symposium on Highway Geometric Design Practices, Boston, MA.

Australian Transport Safety Bureau (ATSB). 2002.
*Road Fatalities Australia: 2001 Statistical Summary.*
Canberra, Australia: Department of Transport and Regional
Services.

Bauer, K.M. and D.W. Harwood. 2000. *Statistical
Models of At-Grade Intersection Accidents-Addendum,* Publication
No. FHWA-RD-99-094. Washington, DC: U.S. Department of
Transportation, Federal Highway Administration.

Department for Transport (DfT). 2002. *Road
Accidents Great Britain: 2001, The Casualty Report.* London,
England.

Golias, J.C. 1997. Effects of Signalization on
Four-Arm Urban Junction Safety. *Accident Analysis and
Prevention* 29(2):181-190.

Griebe, P. and M.A. Nielsen. 1996. Safety at
Four-Armed Signalized Junctions Situated on Roads with Different
Speed Limits. *Proceedings of the Conference on Road Safety in
Europe,* VTI konferens No.7A, Part 2. Birmingham, England: VTI,
Swedish National Road and Transport Research Institute. 151-163.

Harnen, S., R.S. Radin Umar, S.V. Wong, and W.I. Wan
Hashim. 2003a. Motorcycle Crash Prediction Model for Non-Signalized
Intersections. *IATSS Research* 27(2):58-65.

____. 2003b. Predictive Models for Motorcycle
Accidents at Three-Legged Priority Junctions. *Traffic Injury
Prevention* 4:363-369.

Highway Planning Unit (HPU). 2001a. *Golden River
Permanent Count Station: Annual Report.* Kuala Lumpur, Malaysia:
Ministry of Works.

____. 2001b. *Traffic Volume Malaysia: Biannual
Report.* Kuala Lumpur, Malaysia: Ministry of Works.

Hills, B.L. and C.J. Baguley. 1993. Accident Data
Collection and Analysis: The Use of the Microcomputer Package MAAP
in Five Asian Countries. *Proceedings of the Conferences on Asian
Road Safety (CARS'93),* Kuala Lumpur, Malaysia, April 6-31.

Kulmala, R. 1992. Prediction Model for Accidents at
Highway Junctions. *ITE Compendium of Technical Papers,*
302-305.

Lynam, D., J. Broughton, R. Minton, and R.J.
Tumbridge. 2001. *An Analysis of Police Reports of Fatal Accidents
Involving Motorcycles,* Report TRL 492. Berkshire, England: TRL
Limited.

McCullagh, P. and J.A. Nelder. 1989. *Generalized
Linear Models,* 2nd edition. London, England: Chapman and Hall.

McShane, W.R., R.P. Roess, and E.S. Prasas. 1998.
*Traffic Engineering,* 2nd edition. Upper Saddle River, NJ:
Prentice Hall.

Mountain, L., B. Fawaz, and D. Jarret. 1996. Accident
Prediction Models for Roads with Minor Junctions. *Accident
Analysis and Prevention* 28(6):695-707.

Mountain, L., M. Maher, and B. Fawaz. 1998. The
Influence of Trends on Estimates of Accidents at Junctions.
*Accident Analysis and Prevention* 30(5):641-649.

Numerical Algorithm Group (NAG). 1994. *The GLIM
System: Release 4 Manual,* 2nd edition. Edited by B. Francis, M.
Green, and C. Payne. Oxford, England: Clarendon Press.

Organization for Economic Cooperation and Development
(OECD). 2002. *Fatalities by Traffic Participation, International
Road Traffic and Accident Database (IRTAD).* Paris, France.

Radin Umar, R.S. 1996. Accident Diagnostic System with Special Reference to Motorcycle Accidents in Malaysia, Ph.D thesis, University of Birmingham, England.

Radin Umar, RS., M. Ahmad Rodzi, and A. Aminuddin.
1993. Model Diagnosis dan Rawatan Kemalangan Jalan Raya di Malaysia.
*Pertanika Journal of Science and Technology* 1(1):125-151.

Radin Umar, R.S., G.M. Mackay, and B.L. Hills. 1995.
Preliminary Analysis of Exclusive Motorcycle Lanes Along the Federal
Highway F02 in Shah Alam, Malaysia. *IATSS Research
*19(2):93-98.

____. 2000. Multivariate Analysis of Motorcycle
Accidents and the Effect of Exclusive Motorcycle Lanes in Malaysia.
*Crash Prevention and Injury Control* 2(1):11-17.

Rodriguez, L.P. and T. Sayed. 1999. Accident Prediction Models for Urban Unsignalized Intersections in British Columbia, presented at the 78th Annual Meetings of the Transportation Research Board, Washington, DC, January.

Saied, A.M. and G.M. Said. 2001. A General Linear
Model Framework for Traffic Conflicts at Uncontrolled Intersections
in Greater Cairo. *Proceedings of the Conferences on Traffic
Safety on Three Continents, Moscow, Russia,* VTI konferens 18A
Part 3. Birmingham, England: VTI, Swedish National Road and
Transport Research Institute.

Summersgill, I. 1991. *What Determines Accident
Risk? Papers on Vehicle Safety, Traffic Safety and Road User Safety
Research, Safety 91.* Berkshire, England: TRL Limited.

Swedish Institute (SI). 2000. Road Safety in Sweden.
*Fact Sheets on Sweden.* Stockholm, Sweden.

Tarko, A.P., S. Eranky, K.C. Sinha, and R. Scinteie. 1999. An Attempt to Develop Crash Reduction Factors Using Regression Technique, presented at the 78th Annual Meetings of the Transportation Research Board, Washington, DC, January.

Taylor, M.C., A. Baruya, and J.V. Kennedy. 2002.*
The Relationship Between Speed and Accidents on Rural
Single-Carriageway Roads,* Report TRL 511. Berkshire, England:
TRL Limited.

Transport Canada. 2001. *Canadian Motor Vehicle
Traffic Collision Statistics.* Available at
www.tc.gc.ca/roadsafety/stats/menu.htm.

U.S. Department of Transportation (USDOT), National
Highway Traffic Safety Administration, National Center for
Statistics and Analysis. 2002. *Traffic Safety Facts 2001: A
Compilation of Motor Vehicle Crash Data from the Fatality Analysis
Reporting System and the General Estimates System,* Report No.
DOT HS 809 484. Washington, DC.

Vogt, A. 1999. *Crash Models for Intersections:
Four-Lane by Two-Lane Stop-Controlled and Two-Lane by Two-Lane
Signalized,* Report Number FHWA-RD-99-128. Washington, DC: U.S.
Department of Transportation, Federal Highway Administration.

Vogt, A. and J.G. Bared. 1998. *Accident Models for
Two-Lane Rural Road: Segments and Intersections,* Report Number
FHWA-RD-98-133. Washington, DC: U.S. Department of Transportation,
Federal Highway Administration.

Wang, Y. and H. Ieda. 1997. Effects of Drivers' Age, Flow Rate and Some Other Road Environment Related Factors on Traffic Accidents at Four-Legged Signalized Intersections, presented at the 2nd Conference of the Eastern Asia Society for Transportation Studies, Seoul, Korea.

### END NOTES

^{1} Nonmotorcycle refers to all
types of motorized vehicles excluding motorcycles.

^{2} The MAAP database is
located at the Road Safety Research Center, Universiti Putra
Malaysia, while the CARS 2000 database is located at the Traffic
Branch, Royal Malaysian Police Headquarters.

### ADDRESSES FOR CORRESPONDENCE

^{1} S. Harnen, Road Safety Research Center, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia; and Department of Civil Engineering, Universitas Brawijaya, Malang, East Java, Indonesia. E-mail: hsulistio@telkom.net

^{2} Corresponding author: R.S. Radin Umar, Road Safety Research Center and Department of Civil Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia. E-mail: radinumx@eng.upm.edu.my

^{3} S. V. Wong, Road Safety Research Center and Department of Mechanical and Manufacturing Engineering Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia. E-mail: wongsv@eng.upm.edu.my

^{4} W. I. Wan Hashim, School of Civil Engineering Universiti Sains Malaysia,14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang, Malaysia. E-mail: cewhwi@eng.usm.my