Spatio-Temporal Modeling of the Impact of Climate Change on 

Road Accidents – A Case Study of New Brunswick

Md. Shohel Reza Amin

Research Assistant, Dept. of Building, Civil and Environmental Engineering, Concordia University

Alireza Zareie

PhD candidate, Dept. of Civil Engineering and Applied Mechanics, McGill University

Dr. Luis Amador-Jiménez

Assistant Professor, Dept. of Building, Civil and Environmental Engineering, Concordia University

ABSTRACT

The objective of this research is to study the impact of climate change on the hazardous weather-related road 

accidents. The New Brunswick province of Canada is considered as a case study. The study uses road accident data 

collected from police accident reports for the period of 1997-2007. The climate change modeling uses thirty-year 

weather records of seven climate zones of New Brunswick, National Centers for Environmental Prediction (NCEP) 

re-analysis dataset, and large-scale simulation data from the Canadian Global Circulation Model, General Climate

Model, and Coupled Global Climate Model (CGCM3). The large-scale simulation data from CGCM under SRESA2 scenario during 21st century are used to model the climate in the future. This study develops an Exposure to 

Weather-Accident Severity (EWAS) index and estimate the relationship between EWAS index and weather-related 

explanatory variables of road accidents by applying negative binomial regression and Poisson regression models. 

The regression models find out that surface-weather condition, weather-driver’s gender, weather-driver’s age, 

weather-driver’s experience and weather-vehicle’s age have strong positive correlation with EWAS index. The 

surface-road alignment and surface-road characteristics have negative relationship with EWAS index. The spatial 

pattern of EWAS index with respect to weather-related explanatory variables is examined for the fifteen census 

divisions of New Brunswick province, which derives similar relationships. The climate change modeling estimates

that the number of rainy days may increase in all climate zones and the number of snowy days and freezing days

may decrease or stay the same in most of the climate zones during three 30-year periods of 21st century (i.e. 2011-

2040, 2041-2070, 2071-2100). The findings of this study imply that more hazardous weather in future will result in 

increased accident severity. 

Keywords: Accident severity, climate change, statistical downscaling, Negative binomial regression, Poisson 

regression

INTRODUCTION

Canada is one of the highest ranked countries in terms of road accident fatalities among countries of the 

Organization for Economic Cooperation and Development (OECD). In 2008, Canada was ranked 4th in terms of 

fatalities (7.18 per billion vehicle kilometers traveled) among OECD countries (Transport Canada, 2011). In 2009, 

total fatalities and serious injuries were 2,209 and 11,451, respectively. Although these casualty statistics in Canada 

are declined by 25% compared to the period of 1996-2001, these casualty counts are still high. Transport Canada 

(2011) identifies impaired driving, speed and aggressive driving, and occupant protection as the key contributing 

factors of road accidents (Transport Canada, 2011). Meteorological conditions are also important contributing 

factors of road accidents. During the period of 1999-2008, a total of 1,479,691 road accidents were registered in 

Canada out of which approximately 30% accidents were occurred during hazardous weather conditions (11% during 

cloudy, 10% during rainfall, 6% during freezing rainfall, 1% visibility limitation and 1% during strong wind). The 

proportions of vehicle damages, human injuries and fatalities from road accidents during hazardous weather 

conditions are similar to that of total accidents. However, occurrence of road accidents, during different hazardous 

weather conditions, varies from season to season. During the winter season (December – February) of the period 

1999-2008, snowfall (not including drifting snow) is the most hazardous weather condition accounting for 15% - 

17% of the road accidents, 14%-16% of vehicle and property damages, 15% - 17% of injuries and 16% - 19% of 
fatalities (Figure 1). During the fall season (September – November) of the same period, rainfall was the most 

hazardous weather condition responsible for 12% - 17% of total road accidents, 12% - 17% vehicle and property 

damages, 12% - 17% injuries and 9% - 14% fatalities (Figure 1). During the summer season (June – August) of the 

same period, rainfall is also the most hazardous weather condition for 8% - 10% road accidents, 8% - 11% vehicle 

and property damages, 9% - 11% injuries and 6% - 8% fatalities (Figure 1). During the spring season (March – 

May) of the same period, rainfall and snowfall are the most hazardous weather conditions (Figure 1). For example, 

in the month of March, 13% road accidents, 12% associated vehicle and property damages, 13% injuries, and 13% 

fatalities were occurred during the snowfall. However, during the months of April and May, the most dangerous 

weather condition is rainfall (Figure 1). 

Figure 1: Road Accidents data during the period of 1999 – 2008 in Canada (Transport Canada, 2012)

Hazardous weather may not be the principal cause of road accidents, but it is an important contributing factor

because of reduced visibility and loss of vehicle control. Considering exiting road accident statistics, even a very 

small percentage of motor vehicle accidents is attributable to hazardous weather conditions, a significant effort 

should be made to discover how Canadian travellers are vulnerable to weather conditions and how such 

vulnerabilities may be overcome (Andrey, et al., 2003). 

The objective of this research is to study the impact of climate change on the hazardous weather-related road 

accidents. The New Brunswick province of Canada is taken as a case study. 

LITERATURE REVIEW 

Several studies (Cromley, 2007; Palutikof, 1991; Edwards, 1996) have examined the impact of hazardous weather 

conditions on road accidents. Edwards (1996) examined the spatial dimension of weather-related road accidents 

using accident data from UK Police Accident Report Forms. Edwards (1996) found a positive relationship between 

the incidences of weather hazards and road accidents. The analysis of road accident data at county level reveals a 

strong and consistent pattern of weather-accidents relationship, although the majority of the accidents in UK 

occurred during the non-hazardous and fine weather. The pattern of relationship between weather and accidents

changes over seasons and locations – from north to south and west to east. Edwards (1999) conducted another study 

on the panel longitudinal data of road accidents in hazardous weather conditions for England and Wales. Like the 

outcomes of previous study (Edwards, 1996), this study also found out a relationship between accident occurrence in 

adverse weather and actual weather patterns. The county-wide data revealed that the cyclic nature of road accidents 

recorded in the various adverse conditions showed remarkable similarity to the occurrence of these hazards. 

Andrey et al. (2003) conducted a study to identify the relationship between weather and accident risk in mid-sized 

Canadian cities (with different climates) by using a standardized method. The cities are Halifax-Dartmouth, Ottawa, 

Quebec, Hamilton, Waterloo region, and Regina. Andrey et al. (2003) estimated that precipitation is associated with 

a 75% increase in traffic accidents and a 45% increase in related injuries, as compared to ‘normal’ seasonal 

conditions. The snowfall effects were more pronounced than rainfall effects for accidents as a whole (Andrey et al., 
2003). This study also revealed that the sensitivity to hazardous weather varied from city to city and the probability 

of risk of injury was lower than that of risk of accidents. 

Fridström et al. (1995) applied generalized Poisson regression to estimate the contributions of various factors to 

monthly accident rates. This study was conducted at the county or provinces of Denmark, Finland, Norway and 

Sweden for the period of 1973-1987. The study identified that rainfall increased the accidents while snowfall 

decreased the accidents in the study areas. In case of fatal accidents, rainfall increased in Denmark but had no 

significance in Norway and Sweden. 

Keay and Simmonds (2006) examined the impact of rainfall on daily road accidents in the metropolitan area of 

Melbourne, Australia, over the period of 1987–2002. The analysis of accident data, standardized for variation of 

traffic volume, indicated a complex effect of rainfall. Similarly, Andreescu and Frost (1998) identified a significant 

positive correlation between daily precipitation and daily number of accidents at Montreal, Canada using accident 

data from the period of 1990-1992. 

Eisenberg (2004) found out that rainfall led to a stronger increase in the number of fatal accidents after a dry spell 

because the precipitation makes the roads slippery by clearing the oil accumulated on roads during dry periods. 

Eisenberg (2004) also observed that some states of US experienced greatly increased fatal accidents rates in wet 

conditions (e.g. Arizona and Maryland), while others were hardly affected at all (e.g. Connecticut and Indiana).

Similar results were estimated by Eisenberg and Warner (2005) considering the snowfall as the hazardous weather 

condition. Fridstrøm (1999) also got a similar result for Norway. 

 

Another important hazardous weather condition is fog or smog. Musk (1991) showed that accident rates and 

multiple accidents were increased during periods with thick fog. Drivers tend to maintain the speed or fail to reduce 

the speed under reduced visibility during hazardous weather conditions (White and Jeffrey, 1980; Musk, 1982).

Drivers also tend to become isolated from the road environment while driving in fog, and cannot perceive speed and 

actual distance from the preceding vehicle (Miller, 1967). Moore and Cooper (1972) estimated that the number of 

accident injuries increased in fog, although the traffic volume decreased by 20%. Rosenfeld (1996) estimated that 

more than twice the numbers of people were killed in fog-related road accidents comparing to the death in

hurricanes, lightning and tornadoes combined in US during the period of 1982-1991.

 

Most of the above studies have examined accident occurrences at fixed locations; very few have investigated 

temporal and spatial aspects of weather-related road accidents. Andersson and Chapman (2011) studied traffic 

accidents across the West Midlands during the winter months (December to February) with the aim of applying 

UKCIP (UK Climate Impacts Programme) climate change scenarios to determine how the number of days requiring 

winter road maintenance may change in the future and how this subsequently may affect road traffic accident 

statistics. Andersson and Chapman (2011) identified that under UKCIP climate-change-scenarios there would be a 

significant change to the winters experienced in the West Midlands. The study concluded that traffic accidents 

would be reduced because the low freezing temperatures would not be so frequent and the winter season would be 

shorter. This research studied the relationship between the climate change scenarios and road accidents only for the 

winter season; however did not consider other hazardous weather conditions. Moreover, there is a scope of work to 

establish the frequency and spatial extent of such adverse weather conditions to calculate accident risk during 

hazardous weather conditions.

METHODOLOGY

Data

The province of New Brunswick is located at the East coast in Canada and spans between 64 ̊ W- 69 ̊ W longitude 

and 45 ̊ N -48 ̊ N latitude (Figure 2). Data on road accidents, traffic flow, environmental conditions, and road 

geometry were collected for the road network of New Brunswick (Figure 2). The accident data, both single and 

multiple accidents, were based on police accident reports for the period of 1997-2007. The Accident Severity Index 

(ASI) was calculated summing the monetary values of Property Damage Only (PDO), injuries, and fatalities during

accidents (Equation 1). 

For this study, a 30-year continuous record (1961-1990) of daily rainfall, snowfall and mean temperature from 

different sources were used. The selected data for the 1961-1990 study period include: observed daily data for seven

climate zones of the province (Figure 2), NCEP re-analysis dataset, and large-scale simulation data from the 

CGCM3. Also, the large-scale simulation data from Canadian GCM under SRES-A2 scenario during 21st century 

were used to model the climate in the future.
Attributes of Road Accidents 

Accident severity, merely explained by total number of accidents, can misinterpret the randomness and severity of 

the accidents in a road network (Afghari, 2012). This study defines an Accident Severity Index (ASI) explaining 

severity of accidents (Equation 1). The ASI incorporates the proportional weights of injuries (I), fatalities (F) and 

PDO. The exact monetary implication of an injury, a fatality or a PDO depends on many other factors. Afghari 

(2012) identified the average monetary values of fatalities and injuries of the road accidents at the province of New 

Brunswick were 15 and 5 times higher than that for PDO. This study takes such monetary mean values and 

normalizes them by the mean cost of a PDO to obtain the coefficients used on the ASI (Equation 1).

ASI = PDO + 5I +15F (1)

Since the goal of this study is to determine the climate impact on the accident severity, an Exposure to WeatherAccident Severity (EWAS) Index was developed multiplying the severity of hazardous weather conditions for road 

accidents with ASI. The severity of hazardous weather condition is defined by 1-6 scale, where 1 defines ‘clear and 

cloudy’, 2 for ‘raining’, 3 for ‘snowing’, 4 for ‘sleet or hail or freezing rain’, 5 for ‘fog or smoke or smog’ and 6 for 

‘drifting snow’. 

Figure 2: Map of the study area – New Brunswick

The relationship between hazardous weather condition and road accidents is complex. Edwards (1996) 

recommended that emphasis should be given on the interaction of weather conditions with other factors of road 

accidents such as road surface condition, vehicle types, road categories, vehicle manoeuver, and driver’s age, sex 

and experience.

A wet road surface reduces the friction of the road surface in contact with a vehicle’s tires because a thin film of 

water builds up between the road surface and the tires (Brodsky and Hakkert, 1988; OECD, 1976). A greater 

stopping distance is required for this reduced friction. The reduction of friction is more apparent especially at the 

curves where vehicles attempt turning maneuvers (Brodsky and Hakkert, 1988). Road characteristics, such as 
divided or undivided multiple lanes of roads are important factors of road accidents. About two-thirds of fatalities

and 30% of injuries in road accidents occur on rural roads, typically undivided with two lanes (Transport Canada, 

2011). 

Poor light condition may aggravate the poor visibility during hazardous weather conditions. Technical advances 

have further complicated the picture (Edwards, 1996). Recent developments on safety features, such as anti-lock 

brakes, four-wheel drive and traction control, have improved the vehicles’ handling in poor weather conditions. This 

may reduce or increase the accident risk. On the other hand, drivers may feel more confident when driving vehicles 

equipped with safety features and may take greater risk than they may otherwise do (Edwards, 1996). 

Factors such as light condition, surface condition and road characteristics are major factors of road accidents;

however, most accidents can be attributed to human error (Evans, 1991; West et al., 1993). To evaluate the impact 

of weather on road accidents, factor of human judgment must be included (Edwards, 1996). Transport Canada 

(2011) statistics estimated that young drivers (15-34 years age) accounted for 40% of the fatalities and 45% of the 

serious injuries. Different studies (Abdel-Aty & Abdelwahab, 2004) identified that driver’s age and driver’s gender 

had significant impact on the road accidents. Driver’s age is associated with the road accidents because people have 

the Age-related macular degeneration (AMD) problem after a certain age (65 years and above) (RNIB, 2013). The 

AMD is an eye condition that affects a tiny part of the retina at the back of the eye and causes problems with the 

central vision (RNIB, 2013). Royal National Institute of Blind People (RNIB, 2013) also explains that more women 

have AMD problem than men. This study assumes that older drivers (age 65 years and above) and female drivers are 

more vulnerable to adverse weather condition during driving because of the visibility problem. 

 

Based on the literature review, this study identified several factors of road accidents that can be affected by the 

hazardous weather condition such as: light condition, surface condition, road alignment, roadway characteristics, 

driver’s gender, driver’s age, driver’s driving experience and vehicle’s age. The selected variables were integrated 

with the weather condition in order to determine the combined-impact of weather and these attributes on the 

weather-accident severity (EWAS index). The redefined explanatory variables of EWAS index, with the extent of 

severity (ranking), are given in the Table 1. 

Table 1: Explanatory variables of the EWAS Index Rank

Explanatory Variables

Lightweather Surfaceweather Surfaceroad 

alignment

Roadway - 

surface

Weather

-driver’s 

gender

Weather – 

driver’s 

age

Weatherdriving 

experience

Weather 

-vehicles 

age

1 Day lightclear & 

cloudy 

Dry-all 

weather 

condition

Dry-level 

& straight 

Dryundivided 

one-way 

& 

Maleclear & 

cloudy

Clear & 

cloudy-age 

<65yrs.

Clear & 

cloudyexp. ≥ 5yrs. 

Clear & 

cloudynew

2 Day lightrainingSnow-clear 

& cloudy

Dry-level 

& curve

Dry-divided 

with barrier/ 

median 

MalerainingRainingage < 65 

years

Rainingexp. ≥ 5 

years

Rainingnew 

3 Day lightsnowing SnowrainingDrystraight 

with grade

DryUndivided 

2/multiple 

& 

ln.

Malesnowing Snowingage < 65 

years

Snowingexp. ≥ 5 

years

Snowing

-new 

4 Day lightfreezing 

rain

Snowsnowing Dry-curve 

with grade

Snowundivided 

one-way

& 

Malefreezing 

rain

Freezing 

rain- age 

65 years

< 

Freezing 

rain- exp. 

5 years

≥ 

Freezing 

rain-new 

5 Day 

fog 

light- Snowfreezing rainDryroadhilly Snow-divided 

with barrier/ 

median

Male-fog Fog- age 

65 years

< Fog- exp. 

5 years

≥ Fog-new 

6 Day lightdrifting 

snow

Snow-fog Snow-level 

& straight

SnowUndivided 

2/multiple 

lanes

& 

Maledrifting 

snow

Drifting 

snow- age 

< 65 years

Drifting 

snow- exp. 

≥ 5 years

Drifting 

snownew 

7 Darkclear & 

cloudy 

Snowdrifting 

snow

Snow-level 

& curve

Ice-undivided 

& one-way

Femaleclear & 

cloudy

Clear & 

cloudy-age 

≥ 65 yrs. 

Clear & 

cloudyexp. 2-5yrs.

Clear & 

cloudymedium 

8 Darkraining Ice-clear 

cloudy

& Snowstraight 

with grade

Ice-divided 

with barrier/ 

median

FemalerainingRainingage ≥ 65 

years 

Rainingexp. 2-5 

years

Rainingmedium 
Rank

Explanatory Variables

Lightweather Surfaceweather Surfaceroad 

alignment

Roadway - 

surface

Weather

-driver’s 

gender

Weather – 

driver’s 

age

Weatherdriving 

experience

Weather 

-vehicles 

age

9 Darksnowing Ice-raining Snowcurve with 

grade

Ice-undivided 

& 2/multiple 

lanes

Femalesnowing Snowingage ≥ 65 

years

Snowingexp. 2-5 

years

Snowing

- medium 

10 Darkfreezing 

rain

Ice-snowing Snowhilly roadWetundivided & 

one-way

Femalefreezing 

rain

Freezing 

rain- age ≥ 

65 years 

Freezing 

rain- exp. 

2-5 years

Freezing 

rainmedium 

11 Dark- fog Ice-freezing 

rain

Ice-level & 

straight

Wet-divided 

with barrier/ 

median

Femalefog Fog- age ≥ 

65 years 

Fog- exp. 

2-5 years

Fogmedium 

12 Darkdrifting 

snow

Ice-fog Ice-level & 

curve

Wetundivided & 

2/multiple 

lanes 

Femaledrifting 

snow

Drifting 

snow- age 

≥ 65 years

Drifting 

snow- exp. 

2-5 years

Drifting 

snowmedium 

13 Duskclear & 

cloudy

Ice-drifting 

snow

Ice-straight 

with grade

Clear & 

cloudy- exp 

< 2 years

Clear & 

cloudyold 

14 DuskrainingWet-clear & 

cloudy

Ice-curve 

with grade

Rainingexp. < 2yrs.Rainingold 

15 Dusksnowing Wet-raining Ice- hilly 

road

Snowingexp. < 2yrs.Snowing

- old 

16 Duskfreezing 

rain

WetsnowingWet-level 

& straight 

Freezing 

rain-exp. < 

2 years

Freezing 

rain- old 

17 Dusk-fog Wet-freezing 

rain

Wet-level 

& curve

Fog- exp.< 

2 years

Fog- old 

18 Duskdrifting 

snow

Wet-fog Wetstraight 

with grade

Drifting 

snowexp.< 2 yrs.

Drifting 

snowold 

19 Dawnclear & 

cloudy

Wet-drifting 

snow 

Wet-curve 

with grade

20 DawnrainingWet-hilly 

road

21 Dawnsnowing

22 Dawnfreezing 

rain

23 Dawn-fog

24 Dawndrifting 

snow

Methods for accident analysis 

Conventional multiple linear regression analysis, assuming that the dependent variable is continuous and normally 

distributed with a constant variance, are not applicable to accident analysis. This analysis lacks the distributional 

property necessary to adequately describe random, discrete, and non-negative events (Miaou, et al., 1992; Miaou 

and Lum, 1993). Accident severity indices normally use frequency models or correlation models structured on the 

basis of multivariate Poisson regression analysis, negative binomial regression analysis, or multivariate Poissonlognormal regression analysis (El-Basyouny and Sayed, 2009). Wang et al. (1998) proposed a Poisson regression 

model (PR) to quantify the relationship between accident frequency and road geometry for Minnesota’s rural arterial 

highways. The previous models ignored to measure the ability of explanatory factors to explain the accident severity 

and frequency. Multivariate Poisson-lognormal regression analysis is often preferred for accident severity indices as 

this analysis accounts for over-dispersion (extra Poisson variation) that is often observed in accident data (El-
Basyouny and Sayed, 2009). Moreover, this analysis also allows a full general correlation structure (El-Basyouny 

and Sayed, 2009). 

Various researchers (Poch and Mannering, 1996; Abdel-Aty and Radwan, 2000; Sawalha and Sayed, 2001; Zang 

and Ivan, 2005) applied the Negative Binomial Regression (NBR) models in order to explain the relationship 

between the explanatory variables and road accident severity. Persaud and Musci (1995) investigated the 

relationship among hourly traffic volume, road geometries, and accident time (day light) on two- lane rural roads by 

applying the NBR model. The analysis revealed that accident potential was higher during the night for single 

accident vehicles. In case of multi-vehicle accidents, the accident potential was higher during daytime. Poch and 

Mannering (1996) analyzed the importance of geometric features and traffic aspects on the road accidents. AbdelAty and Radwan (2000) identified that traffic flow and road geometries are the major threats to road safety. 

Mountain et al. (1996) and Sawalha and Sayed (2001) conducted their research works in UK and Greater 

Vancouver, respectively. They identified that the length of road sections, traffic flow, intersections in corridors, and 

road alignment were the influential attributes of road accident severity. Pardillo and Llamas (2003) defined 

homogeneous sections with suitable lengths for two-lane rural roads in Spain. They derived that road accidents had 

significant correlations with vehicles’ speed, density of accessibility, average stopping sight distance and road 

geometry issues. Zang and Ivan (2005) found out vehicles’ speed and road alignment significantly influenced the 

road accidents. 

 

Eisenberg (2004) applied the NBR method to estimate the relationship between precipitation and traffic accidents in 

the US during the period of 1975-2000. The amount of precipitation, snowfall and snow depth were considered as 

the explanatory variables of accident counts. Eisenberg (2004) considered the location-month combination as a

dummy variable and included vehicle-mile traveled (VMT) for each location-year as the ‘offset’ term in the 

regression model.

Since both of the NBR and PR models were applied to estimate the relationship of accident severity with its 

explanatory variables, this study applies both models. The relationship between EWAS index for a particular 

accident incident (i) at a particular month (t) can be expressed by Equation 2. 

 (2) 

is intercept, and are regression coefficients with the assumption that the EWAS index follows a 

negative binomial distribution with parameters ( ) and . That is, the probability that the 

EWAS index is defined by a known of set of predictor variables, . The NBR function can be expressed 

by Equation 3. 

 (3) 

 

On the other hand, assuming the EWAS index follows a Poisson distribution, the relationship between EWAS index 

for a particular accident incident (i) at a particular month (t) can be expressed by Equation 4. 

 (4) 

Method for climate change modeling

To determine the impact of climate change on the road accident severity, this study needs to understand climate 

change scenarios. The Intergovernmental Panel on Climate Change (IPCC) presented a supplementary report in 

1992 and proposed six different climate change scenarios based on the world population and political, social, 

economical, technological, and environmental changes in the world by the end of 21st century. This report was 

assessed in 1995 and the IPCC published the Special Report on Emission Scenarios (SRES) in 2001. The IPCC 

proposed four different families of climate change scenarios as A1, A2, B1, and B2. These scenarios predict the 

GHGs emission in the atmosphere by the end of 21st century.

Earth-science scientists developed a series of Global Climate Models (GCMs) to predict the future changes in the 

world climate after getting strong evidence on climate change. These GCMs are coupled with emission scenarios to 

model the effects of human activities on the future climate and predict the atmospheric parameters by the end of 21st
century. GCMs have been recognized to be able to represent reasonably well the main features of the distribution of 

basic climate parameters at global scales, but outputs from these models are often characterized by coarse 

resolutions that limit their direct application for many impact studies. Hence, downscaling methods have been 

proposed for describing the linkage between the large-scale climate variables given by GCMs to the observed 

predictands at local sites (Nguyen et al., 2006). These downscaling methods are based on the assumption that largescale weather exhibits a strong influence on local-scale weather conditions (Fowler et al., 2007). In general, there are 

two broad downscaling methods: dynamical downscaling and statistical downscaling (Wilby & Dawson, 2007; Xu, 

1999; Yarnal et al., 2001). Dynamical downscaling methods or Regional Climate Models (RCM) are based on 

physical dynamics between synoptic variables (as predictors) and local-scale variables (as predictands) and it uses 

GCM variables to define time-varying atmospheric boundary conditions around a finite domain, while the statistical 

downscaling methods rely on the empirical relationship between regional scale predictors and local scale 

predictands (Wilby & Dawson, 2007).

This study applied Statistical DownScaling Method (SDSM) to evaluate the changes in the number of rainy, snowy, 

and freezing days in New Brunswick. The SDSMs are classified into three categories based on the nature of the 

chosen predictors: Perfect Prognosis (PP), Model Output Statistics (MOS) and Stochastic Weather Generators 

(SWGs). Each of these downscaling methods has their own strengths and weaknesses that have been summarized in 

several review papers (Hewitson & Crane, 1996; Maraun et al., 2010; Wilby & Wigley, 1997; Xu, 1999). The PP

method was applied in this study.

The PP method establishes a statistical relationship between observed large-scale predictors and observed local-scale 

predictands (i.e. precipitation, temperature). In the context of climate change, the main assumption of the PP method 

is the capability of the simulated large-scale predictors in representing a physically plausible realization of the future 

climate (Maraun, et al., 2010). This method divides downscaling scheme into two steps: first, the selection of 

informative large-scale predictors, and second the development of a statistical model for making a linkage between 

the large-scale predictors and the local-scale predictands. The ideal predictors should be informative, make physical 

sense, strongly correlated with the target variable, realistically represented by the GCMs while capturing multiyear 

variability, and finally collectively reflect the climate change signal (Wilby et al., 2004). 

In this study, the observed data in seven different climate zones of New Brunswick province (Figure 2) for 30 years

had been applied to select the large-scale predictors. The best predictors, with the highest explained variance, are

selected. The reanalysis data (NCEP) during calibration period (1961-1975) had been used to estimate a relationship 

between the selected predictors and predictands. The robustness of developed relation was validated using the 

reanalysis data at validation period (1976-1990). The calibration and validation procedure certifies the correctness 

and robustness of the selected predictors and if there were a good fit between observed and calibrated predictands 

during the validation period the estimated relationship had been applied to GCM predictors to generate scenarios 

during reference and future time period. Finally, the number of rainy and snowy days in each month were calculated 

assuming days with more than 1 mm of precipitation as the rainy days and days with mean temperature of -4oC to 0 

oC as the freezing days.

 

ANALYSIS AND DISCUSSION 

Spatial-temporal analysis of road accidents

The NBR and PR analysis were executed to estimate the relationship between the EWAS Index and the selected 

predictors. The fitness of the models was verified by testing the likelihood statistics, Wald chi-square test and 

likelihood ratio test of random effects. Log likelihood statistics estimated for determining whether convergence to 

stable estimates had been attained for the NBR and PR models. Since it uses maximum likelihood estimate, the 

maximum difference between the log likelihood of null and full model explains the fitness of the model.

The Wald statistic represents the square of the ratio between the regression coefficient and its standard error. This 

statistic follows a chi-square distribution with degree of freedom (df), which is equal to the standard normal 

distribution squared. The Chi-Square distribution is merely the distribution of the sum of the squares of a set of

normally distributed random variables. Its value stems from the fact that the sum of random variables from any 

distribution can be closely approximated by a normal distribution. Wald chi-square statistics (df = 8), with a p-value 

of 0.00, show the statistical significance of both NBR and PR models (Table 2). 

The likelihood-ratio tests define whether the data are better modeled using a panel structure or whether a pooled 

structure is preferred. The likelihood ratio value with a p-value of 0.00 justifies that the random effects (panel) 

parameterization with beta distribution is preferred over the pooled (constant dispersion) model (Table 2). 
The statistical significance of the explanatory variables of the EWAS index is displayed by the p-value, listed under 

the column P>│Z│. For both of the regression models (negative binomial and Poisson), the p-value is 0.000, below 

the standard threshold of 0.05, meaning that the coefficients of the explanatory variables are statistically significant 

(Table 3).

Table 2: Fitness of regression models for all accident data of New Brunswick

Fitness parameters Negative Binomial regression Poisson regression

Log likelihood (without model) -563460 -563460

Log likelihood (with model) -301937.26 -563234.19

Wald chi square (8)

Prob > chi square

8653.86

0.000

127227.43

0.0000

Likelihood-ratio test vs. pooled: chibar2(01)

Prob>=chibar2

77.58

0.000

431.31

0.000

The NBR and PR models explain the positive relationship between the explanatory variables and the EWAS index 

except in the cases of surface-road alignment and surface-road characteristics. The potentiality of weather-accident 

severity will be increased with the adverse light condition and adverse weather condition. For every one rank 

increases in light-weather condition, the log count of the EWAS index is expected to increase by 0.006 and 0.024 for 

NBR and PR models, respectively (Table 3). That means, a single unit degradation of light-weather condition would 

increase the weather-accident severity (EWAS) by 0.6% for NBR model and 2.4% for PR model (Table 3). 

Similarly, the weather-accident severity can be increased with the poor surface conditions and poor weather

conditions. A single unit degradation of surface-weather condition can contribute 7.1% (NBR model) and 7.6% (PR 

model) increase of weather-accident severity (Table 3). 

Female drivers are more vulnerable to weather-accident severity comparing to the male drivers. If the driver is 

female, the weather-accident severity (EWAS) can be increased by 2.4% (NBR model) and 3.7% (PR model) (Table 

3). Similarly, the older drivers are more vulnerable to weather-accident severity because of the visibility problems.

Senior drivers, with an age of 65 years and above, increase the probability of weather-accident severity by 3.1% 

(NBR model) and 5.6% (PR model) (Table 3). 

 

Table 3: Model summary for Negative Binomial regression and Poisson regression models

Variables Coefficient Std. error Z-value P>│Z│ 95% confidence internal

NBR PR NBR PR NBR PR NB

R 

PR NBR PR 

Light-weather 

condition

.006 .024 .0007 .0003 9.80 81.34 0.00 0.00 .006 .008 .006 .008

Surface-weather 

condition

.071 .076 .0024 .0010 29.47 73.86 0.00 0.00 .066 .075 .066 .075

Surface-road 

alignment 

-.011 .004 .0019 .0008 -5.40 4.67 0.00 0.00 -.014 -.007 -.014 -.007

Surface-road 

character

-.048 -.065 .0039 .0017 -12.3 -37.9 0.00 0.00 -.055 -.040 -.055 -.040

Weatherdriver’s gender.024 .037 .0009 .0004 27.63 97.06 0.00 0.00 .022 .025 .022 .025

Weatherdriver’s age.031 .056 .0011 .0005 27.07 113.0 0.00 0.00 .029 .034 .029 .034

Weatherdriver’s 

experience 

.013 .023 .0006 .0003 21.10 84.39 0.00 0.00 .012 .015 .012 .015

Weathervehicle’s age.014 .019 .0005 .0002 25.47 80.25 0.00 0.00 .013 .015 .013 .015

The less driving experience increases the probability of weather-accident severity because of less driving experience 

in the adverse weather and the lack of knowledge of what-to-do during the adverse weather conditions. Less driving 

experience increases the accident severity by 1.3% (NBR model) and 2.3% (PR model) during the adverse weather 

conditions (Table 3). In most of the driving cases on highway, the less-experienced drivers suddenly reduce the 

speed of the vehicles or overlap the lane demarcation because of the poor visibility and collided by the following 

and side vehicles, respectively. 
The old vehicles are more vulnerable to weather-accident severity comparing to the new vehicles probably because 

the brakes and traction control of new vehicles are obviously performing better than those of old vehicles. The older 

vehicles contribute 1.4% (NBR model) and 1.9% (PR model) increase of accident severity during the adverse 

weather conditions (Table 3). This result shows the opposite explanation of the findings of a study conducted by 

Edwards (1996). Edwards (1996) shows that drivers of new vehicles are taking more risk and vulnerable to road 

accidents as the vehicles are well-equipped with different safety features. The positive relation between weathervehicle’s age and weather-accident severity reveals that new vehicles with well-equipped safety features are less 

exposed to weather-accident severity. 

The surface-road characteristics attribute has inverse relationship with the EWAS index in both regression models, 

while surface-road alignment has inverse relationship with the EWAS index only in the NBR model. One-unit 

increase of surface-road character severity can contribute 4.8% (NBR model) and 6.5% (PR model) decrease of 

accident severity during the adverse weather conditions (Table 3). Similarly, the NBR model shows that one-unit 

increase of surface-road alignment severity can contribute to 1.1% decrease of accident severity during the adverse 

weather conditions (Table 3). The drivers are at their highest caution when driving at hilly and curves (vertical and 

horizontal) roads, especially during the poor surface-weather conditions. This is why; less weather-accident severity 

is observed for the poor-surface road alignment during the hazardous weather condition. Similarly, the drivers are 

more alert driving on an undivided road with two or multiple lanes especially when the surface and weather 

conditions are adverse to drive. 

The regression models were performed for each census division of New Brunswick in order to determine the spatiallongitudinal behavior of the EWAS index with respect to the predictor variables in each census division. The fitness 

tests verified the fitness of the regression models (Table 4 and 5) except in the case of NBR model for the Saint John 

census division (Table 4). 

Table 4: Fitness of NBR model for census divisions of New Brunswick

Census 

Division

Log likelihood 

(without model)

Log likelihood 

(with model)

Wald chi2 (prob> 

chi2)

Likelihood-ratio test vs. 

pooled chibar2 

(Prob>=chibar2)

Saint John -58476.95 -31676.423 1014.02 (0.000) 1.24 (0.132)

Charlotte -13882.19 -6898.908 280.66 (0.000) 14.38 (0.000)

Sunbury -13686.54 -6701.22 224.13 (0.000) 28.14 (0.000)

Queens -8468.78 -3955.98 119.64 (0.000) 28.93 (0.000)

Kings -38085.54 -20262.16 635.41 (0.000) 28.70 (0.000)

Albert -12250.65 -6569.62 207.85 (0.000) 15.40 (0.000)

Westmorland -131655.56 -78416.51 2263.90 (0.000) 50.23 (0.000)

Kent -19137.27 -9925.10 326.43 (0.000) 33.18 (0.000)

Northumberland -40210.328 -21530.925 522.42 (0.000) 12.25 (0.000)

York -76901.75 -43028.64 1113.55 (0.000) 14.41 (0.000)

Carleton -19417.75 -9071.76 324.16 (0.000) 25.13 (0.000)

Victoria -17720.7 -9057.26 263.73 (0.00) 2.55 (0.055)

Madawaska -22492.47 -11833.5 417.08 (0.000) 15.91 (0.0000

Restigouche -18931.2 -9477.52 323.8 (0.000) 44.01 (0.000)

Gloucester -66729.16 -32633.4 987.7 (0.000) 25.32 (0.000)

Table 5: Fitness of PR model for census divisions of New Brunswick

Census 

Division

Log likelihood 

(without model)

Log likelihood 

(with model)

Wald chi2 (prob> 

chi2)

Likelihood-ratio test vs. 

pooled chibar2 

(Prob>=chibar2)

Saint John -58476.95 -58406.812 14365.94(0.000) 138.08 (0.000)

Charlotte -13882.197 -13784.954 4794.70 (0.000) 186.45 (0.000)

Sunbury -13686.53 -13554.96 3220.54 (0.000) 262.41(0.000)

Queens -8468.78 -8273.09 1772.98 (0.000) 391.07 (0.000)

Kings -38085.55 -37887.67 7778.50 (0.000) 395.04 (0.000)

Albert -12250.65 -12145.96 3476.00 (0.000) 207.85 (0.000)

Westmorland -131655.56 -131455.25 27556.59 (0.00) 397.32 (0.000)

Kent -19137.27 -18965.96 4439.14 (0.000) 341.20 (0.000)
Census 

Division

Log likelihood 

(without model)

Log likelihood 

(with model)

Wald chi2 (prob> 

chi2)

Likelihood-ratio test vs. 

pooled chibar2 

(Prob>=chibar2)

Northumberland -40210.328 -40105.428 6617.60 (0.000) 209.67 (0.000)

York -76901.75 -76832.72 13341.94 (0.000) 137.38 (0.000)

Carleton -19417.75 -19161.23 8298.4 (0.000) 496.61 (0.000)

Victoria -17720.7 -17661.8 3828.87 (0.000) 117.00 (0.000)

Madawaska -22492.4 -22414.07 7586.3 (0.000) 151.68 (0.000)

Restigouche -18931.2 -18765.5 6788.8 (0.000) 326.3 (0.000)

Gloucester -66729.16 -66605.7 17462.2 (0.000) 241.00 (0.000)

The relationship between the explanatory variables and the EWAS index in each census division of New Brunswick 

is explained by NBR model (Table 6) and PR model (Table 7). The NBR and PR models estimated that the surfaceweather condition, weather-driver’s gender, weather-driver’s age, weather-driver’s experience and weather-vehicle’s 

age have strong positive relations with the EWAS index for each of census division of New Brunswick (Table 6 and 

7). These results (Table 6 and 7) echo the same outcomes from the NBR and PR analyses for the New Brunswick 

(Table 3). Similar to the province-wise (New Brunswick) scenario (Table 4), in most of the census divisions, the 

surface-road character has negative relationship with the weather-accident severity except in Sunbury (for NBR and 

PR models), Carleton (PR model) and Victoria (PR model) (Table 6 and 7). However, the positive contribution of 

weather-road character to weather-accident severity index (EWAS) is very low (Table 6 and 7). 

This study observes a mixed relationship between surface-road alignment variable and the EWAS index for different 

census division. The relationship is positive at Charlotte (0.6%), Queens (4.5%), Kings (0.7%), Albert (1.25%), 

Northumberland (0.04%), and Restigouche (1.5%) according to the estimation of NBR model (Table 6). 

Table 6: Regression coefficient of explanatory variables in NBR model

Census 

Division

 Explanatory variables

Lightweather 

condition

Surfaceweather 

condition

Surfaceroad 

alignment

Surfaceroad 

character

Weatherdriver’s 

gender

Weatherdriver’s 

age

Weatherdriver’s 

experience

Weathervehicle’s 

age

Saint John .017 .05 -.009 -.0228 .0226 .0332 .009 .014

Charlotte .0012 .078 .006 -.089 .0233 .051 .0169 .0189

Sunbury -.003 .055 -.0268 .008 .029 .041 .013 .017

Queens -.008 .0404 .045 -.094 .0217 .0414 .0176 .0126

Kings -.0015 .0708 .007 -.0726 .0232 .0288 .0121 .0142

Albert .010 .0964 .0125 -.121 .022 .0257 .01 .014

Westmorland .012 .08 -.015 -.054 .02 .0276 .0144 .009

Kent .003 .093 -.00009 -.0935 .0199 .0184 .02 .0169

Northumberland .0006 .059 .0004 -.052 .0259 .027 .0159 .0159

York .008 .052 -.006 -.027 .024 .032 .009 .009

Carleton .007 .093 -.002 -.102 .033 .044 .012 .02

Victoria -.002 .04 -.001 -.015 .016 .048 .018 .014

Madawaska .015 .078 -.041 -.017 .03 .035 .012 .018

Restigouche .024 .065 .015 -.093 .032 .038 .023 .013

Gloucester .006 .09 -.003 -.09 .027 .024 .014 .015

Table 7: Regression coefficient of explanatory variables in PR model

Census 

Division

Explanatory variables

Lightweather 

condition

Surfaceweather 

condition

Surfaceroad 

alignment

Surfaceroad 

character

Weatherdriver’s 

gender

Weatherdriver’s 

age

Weatherdriver’s 

experience

Weathervehicle’s 

age

Saint John .033 .052 -.009 -.0109 .035 .057 .017 .0207

Charlotte .0158 .056 .0124 -.048 .032 .0904 .029 .038

Sunbury .005 .0448 -.018 .053 .045 .0557 .018 .0157

Queens .0185 .0502 .0957 -.174 .0416 .069 .025 .013

Kings .01 .104 .039 -.164 .024 .053 .0197 .0164

Albert .023 .141 .052 -.237 .034 .0508 .0196 .0256

Westmorland .0259 .095 .0098 -.105 .0325 .0476 .0248 .013
Census 

Division

Explanatory variables

Lightweather 

condition

Surfaceweather 

condition

Surfaceroad 

alignment

Surfaceroad 

character

Weatherdriver’s 

gender

Weatherdriver’s 

age

Weatherdriver’s 

experience

Weathervehicle’s 

age

Kent .0197 .129 .005 -.135 .024 .02 .03 .02

Northumberland .0157 .0507 .004 -.033 .042 .048 .026 .021

York .029 .042 -.007 .0008 .034 .055 .018 .012

Carleton .016 .168 .049 -.27 .058 .068 .019 .032

Victoria .006 .049 -.008 .009 .024 .085 .024 .013

Madawaska .04 .109 -.049 -.037 .045 .055 .017 .027

Restigouche .05 .06 .038 -.099 .051 .075 .04 .013

Gloucester .024 .08 .02 -.096 .042 .05 .023 .02

Similarly, the PR model estimates the positive relationship between surface-road alignment variable and the EWAS 

index at Charlotte (1.24%), Queens (9.57%), Kings (3.9%), Albert (5.2%), Westmortland (0.9%), Kent (0.5%), 

Northumberland (0.4%), Carleton (4.9%), Restigouche (3.8%) and Gloucester (2%) (Table 7). These positive 

relationships explain that the combination of poor road alignment (e.g. hilly road, curved road etc.) and road surface 

condition (wet, ice, snow etc.) aggravates the weather-accident severity in these areas. 

Climate change modeling

The analysis of changes in rainy days shows that the number of rainy days will increase during all months in New 

Brunswick except in Aroostook zone in which the number of rainy days will decrease during summer and will 

increase during other months. Number of snowy days may decrease for all zones in the province except in Moncton 

zone in which the number of snowy days may increase during January and February. Also, there is an increasing 

trend in Aroostook zone during the months that snowfall occurs. Number of freezing days will have the same trend 

in all zones of New Brunswick and it will increase during winter and March and it will decrease in other months. 

Generally, the average temperature will increase in future and this increase will cause more freezing days (in the 

range of -4 o C to 0 o C) during winter and March. 

The average changes in the ratio of annual rainy, snowy, and freezing day were summarized in Table 8. The table 

shows that the number of rainy days may increase for all of the zones and the number of snowy days and freezing 

days may decrease or stay the same for most of the zones in the province. More specifically, the average number of 

rainy days may increase by 34, 31, 24, and 21 days per year during 2071-2100 at Doaktown, Fredericton, 

Aroostook, and Miramichi zone respectively and the average number of freezing days may decrease by 9, 8, 5, and 4 

days per year during the same period at Moncton, Saint John, Fredericton and Aroostook zone respectively and it 

may increase by 3 days per year in Doaktown.

 

Table 8: Annual average changes in the ratio of rainy, snowy and freezing days in New Brunswick

Zone

Rainy Days Snowy days Freezing days

2011 2041 2071 2011 2041 2071 2011 2041 2071

Aroostook 0.01 0.04 0.07 0.01 0.01 0.02 0.00 0.00 -0.01

Miramichi 0.02 0.04 0.06 -0.01 -0.01 -0.01 0.00 0.00 0.00

Doaktown 0.03 0.06 0.09 0.00 0.00 0.00 0.00 0.01 0.01

Fredericton 0.03 0.06 0.09 -0.02 -0.02 -0.02 0.00 0.00 -0.01

Kedgwick 0.00 0.03 0.02 0.01 0.00 0.00 0.00 0.00 0.00

Moncton 0.01 0.01 0.00 0.02 0.03 0.04 0.00 -0.01 -0.02

Saint John 0.02 0.03 0.03 0.00 -0.01 -0.02 0.00 -0.01 -0.02

Impact of climate change on accidents

The NBR and PR models estimates that the combined effects of hazardous weather conditions and different 

predictors of road accidents (which can be affected by hazardous weather conditions) are enormous not only in the 

province-wise scenario, but also in each census division. The increase of adverse weather conditions (e.g. raining, 

snow and freezing rain) should have an adverse impact on accident severity. The climate change scenario for three 

different periods (2011-2040, 2041-2070, 2071-2100) reveals that the ratio of hazardous weather days will be 
increased in most of the climate zones of New Brunswick during the 21st century. More hazardous weather will 

result in increased accident severity. 

CONCLUSION

The Road Safety Strategy 2015, developed by Transport Canada, identifies impaired driving, speed and aggressive 

driving, and occupant protection as the factors of road accidents. Uncontrollable meteorological conditions are also 

important contributing factors for road accidents. The objective of this research is to study the impact of climate 

change on the hazardous weather-related road accidents. The New Brunswick province of Canada is considered as a 

case study. 

The road accident data of New Brunswick were collected from the police accident reports. This study developed an 

Exposure to Weather-Accident Severity (EWAS) Index multiplying the severity of hazardous weather conditions 

for road accidents with the Accident Severity Index (ASI). The ASI incorporated proportional monetary 

implications of injuries, fatalities, and PDO. The road accident attributes, which could be affected by the hazardous 

weather conditions, were considered as the explanatory factors of the EWAS index. These are light-weather 

condition, road surface-weather condition, road surface-road alignment, roadway-surface condition, weatherdriver’s age, weather-driver’s gender, weather-driving experience, and weather-vehicle’s age.

 

For climate change modeling, this study used a 30-year continuous record (from 1961-1990) of daily rainfall, 

snowfall and mean temperature. The selected data included observed daily data for seven climate zones in the 

province, NCEP re-analysis dataset, and large-scale simulation data from the CGCM3. This study also used largescale simulation data from Canadian GCM under SRES-A2 scenario during 21st century.

 

The relationship between the EWAS index and predictors was examined by the negative binomial regression (NBR) 

and Poisson regression (PR) models. The NBR and PR models explain the positive relationship between the 

explanatory variables and the EWAS index except in the cases of surface-road alignment and surface-road 

characteristics. The drivers are at their highest caution when driving at hilly terrain and curves (vertical and 

horizontal), especially on poor surface-weather conditions. This is why; less weather-accident severity is observed 

for the poor-surface road alignment during the hazardous weather condition. Similarly, the drivers are more alert 

driving on an undivided road with two or multiple lanes especially when the surface and weather conditions are 

adverse to drive.

The spatial pattern of the EWAS index with respect to explanatory variables was examined for the fifteen census 

divisions of New Brunswick. The NBR and PR models estimated that the surface-weather condition, weatherdriver’s gender, weather-driver’s age, weather-driver’s experience and weather-vehicle’s age have strong positive 

relation with the EWAS index for each of the census divisions of New Brunswick. These echo the same outcomes 

from the NBR and PR analyses for the New Brunswick province. Similar to the province-wise scenario, in most of 

the census divisions, the surface-road character has negative relationship with the weather-accident severity except 

in Sunbury (for NBR and PR models), Carleton (PR model) and Victoria (PR model). The PR model estimates the 

positive relationship between surface-road alignment variable and EWAS index at Charlotte, Queens, Kings, Albert, 

Westmortland, Kent, Northumberland, Carleton, Restigouche, and Gloucester. These positive relationships explain 

that the combination of poor road alignment (e.g. hilly road, curved road etc.) and road surface condition (wet, ice, 

snow etc.) aggravates the weather-accident severity in these areas. 

The climate change modeling estimated that the number of rainy days may increase for all of the climate zones and 

the number of snowy days and freezing days may decrease or stay the same for most of the zones in the province. 

For example, the average number of rainy days may increase by 34, 31, 24, and 21 days per year during 2071-2100 

at Doaktown, Fredericton, Aroostook, and Miramichi zone respectively and the average number of freezing days 

may decrease by 9, 8, 5, and 4 days per year during the same period at Moncton, Saint John, Fredericton and 

Aroostook zone respectively. The freezing days may increase by 3 days per year in Doaktown. More hazardous 

weather will result in increased accident severity. This study suggests that the Road Safety Strategy 2015 of 

Transport Canada should not only adopt the holistic approaches based on the impaired driving, speed and aggressive 

driving, occupant protection, it should also take protective measures for the hazardous weather conditions to reduce

the accident severity.

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