DRAG-ALZ-1, a first model of monthly total road demand, accident frequency, severity and victims, by category and of mean speed on highways, Algeria 1970-2007

by

Marc Gaudry1 and Slimane Himouri2

1 Agora Jules Dupuit (AJD), www.e-ajd.net Université de Montréal, Montréal, Canada

[email protected] 2 Département d’architecture Université Abdelhamid Ibn Badis, Mostaganem, Algérie [email protected]

This paper benefited from research visits by Marc Gaudry at the Algerian Centre national de prévention et de sécurité routières (CNPSR, Alger) and the Abdelhamid Ibn Badis University of Mostaganem in 2008, and from research stays by Slimane Himouri at the French Institut national de recherche sur les transports et leur sécurité (INRETS, Arcueil) between 2002 and 2008. Since its inception in 2002, the project has received direct support from the University of Mostaganem and indirect support from the National Sciences and Engineering Research Council of Canada (NSERCC) and the Fonds québécois de la recherche sur la nature et les technologies (FQRNT) which both contributed over time to improvements of the LEVEL-1.4 algorithm implemented in the TRIO Version 2 software used here but also downloadable in its current sixth version (L-1.6) from the Agora Jules Dupuit website. We are very grateful to Cong-Liem Tran of the Agora Jules Dupuit for significant research assistance and to Akli Berri of IFSTTAR for very helpful comments. The paper is forthcoming in 2012 in Research in Transportation (RETREC), Elsevier, Oxford.

Version 5, July 24, 2011; updated December 18, 2012

Publication AJD-140 Agora Jules Dupuit, Université de Montréal

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Abstract

We construct a first country-wide model of demand for road use and of road safety outcomes for Algeria making use of the DRAG-type framework and of flexible regression estimation methods that make a demonstrable difference to the quality of our results. We imply that the availability of high quality Algerian data could make it worthwhile not only to consider updates of the model with longer data series but also to study variants of the model, notably with disaggregation of freight activities across industrial sectors.

Key words: road accidents, Algeria, Box-Cox transformations, autocorrelation, heteroskedasticity.

Table of contents 1. Introduction...... 3 2. Model purpose and structure...... 4 3. Maximum likelihood estimation: stationarity, homoskedasticity, form and signs ...... 7 4. Tables of selected results by regressand for all regressors ...... 9 5. The role of demand and driving behaviour...... 10 6. The role of automotive fuel price, motorization, weather and safety laws...... 11 7. The role of economic activities and of inactivity...... 14 8. The attitude of drivers: unique events and special religious activities ...... 17 9. Conclusion ...... 19 10. References...... 19 11. Appendix. A Tablex edition of selected model results, all variables (TRIO, Version 2)...... 21

List of Figures

Figure 1. Lightly injured road victims by month in Algeria, 1970-2007 ...... 4 Figure 2. Severely injured road victims by month in Algeria, 1970-2007 ...... 5 Figure 3. Killed road victims by month in Algeria, 1970-2007...... 5 Figure 4. Total monthly vehicle-kilometers with gasoline and diesel components, 1970-2007 ...... 10 Figure 5. Average monthly speed (km/h) on highways, 1970-2007...... 10 Figure 6. Highway share (%) of total monthly traffic, 1970-2007 ...... 11 Figure 7. Indices of diesel and gasoline fuel prices per litre-km, 1970-2008 (DZD of December 2007)...... 12 Figure 8. Monthly rainfall (mm), 1970-2007...... 13 Figure 9. Employee monthly vacation index, 1970-2007...... 14 Figure 10. Monthly consumption expenditures (constant DZD of December 2007)...... 15 Figure 11. Monthly tonnage transported by road (tons lifted), 1970-2007 ...... 15

List of tables

Table 1. Percentage of under-reporting in police reports, wilaya 22 (Sidi Bel Abbes File)...... 4 Table 2. Recursive and subsidiary model equations...... 6 Table 3. Optimal form and sign changes for the 10 stationary homoskedastic models...... 8 Table 4. Elasticities and t-statistics of demand and driving behaviour variables ...... 11 Table 5. Elasticities and t-statistics of price, motorization, weather and road safety law variables...... 13 Table 6. Elasticities and t-statistics of economic activity, including unemployment, variables ...... 16 Table 7. Elasticities and t-statistics of unique event and special religious activity variables...... 18

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DRAG-ALZ-1, a first model of monthly total road demand, accident frequency, severity and victims by category, and of mean speed on highways, Algeria 1970-2007

by

Marc Gaudry and Slimane Himouri1

1. Introduction It has been noted in another part of this journal issue (Gaudry & Lapparent, 2012) that the prevailing modelling approach used to explain numbers of road accident victims was renewed in 1984 by the introduction of the DRAG strategy which: (i) formulated the problem as a simultaneous equation system ―of road Demand, Risk taking and Damages― typically estimated in recursive manner unless disaggregate data were at hand; (ii) decomposed damage outcomes (victims) by category within and among implicit exposure, frequency and severity risk dimensions, thereby allowing for the explicit detection of substitutions among outcome subcategories as in any proper demand system; (iii) made extensive use of Box-Cox transformations (BCT), first as best-fit devices to measure substitutions with precision if and when optimal forms of equations by subcategory were not logarithmic in their variables, and second as fundamental statistical devices generalizing fixed form regression; (iv) produced correlation signs, coefficients and associated elasticities that avoided both fixed form regression limitations and researchers’ pervasive wilful sign and form selection2 malpractices.

The time series Algerian case presented here is fully consistent with this strategy, as previously applied to six aggregate or count data models specified for national or regional areas of North America or Western Europe (Gaudry & Lassarre, 2000)3 and to aggregate intercity Spanish road flows (Aparicio et al., 2009; 2012). It is conceived as a first-cut model in the sense that many of the aggregated explanatory variables found in some 10 principal equations (e.g. variables pertaining to vehicle types or to the intermediate and final activity structure of the Algerian economy by industrial sector) could at some future stage be disaggregated for use in a second-generation model that would make fuller use of available high-quality component series, described at some length elsewhere (Himouri & Gaudry, 2008; Gaudry, 2008).

More importantly for our purposes, the data on road accidents and victims are certainly of a quality comparable to that found in most economically advanced countries, as established by Himouri (2008) in a detailed comparison of the exhaustive list of hospital victims by category found in the Sidi Bel Abbes File4 for wilaya5 22 with that of the police and gendarmerie reports used here for modelling purposes6. The

1 This paper benefited from research visits by Marc Gaudry at the Algerian Centre national de prévention et de sécurité routières (CNPSR, Alger) and the Abdelhamid Ibn Badis University of Mostaganem in 2008, and from research stays by Slimane Himouri at the French Institut national de recherche sur les transports et leur sécurité (INRETS, Arcueil) between 2002 and 2008. Since its inception in 2002, the project has received direct support from the University of Mostaganem and indirect support from the National Sciences and Engineering Research Council of Canada (NSERCC) and the Fonds québecois de la recherche sur la nature et les technologies (FQRNT) which both contributed over time to improvements of the LEVEL-1.4 algorithm implemented in the TRIO Version 2 software used here but also downloadable in its current sixth version (L-1.6) from the Agora Jules Dupuit website. We are very grateful to Cong-Liem Tran of the Agora Jules Dupuit for significant research assistance and to Akli Berri of IFSTTAR for very helpful comments. 2 It is frequent for researchers to maximize the number of expected signs in regression results by adjusting the functional form or by intentionally refusing to let the data decide on the form, lest wished for fragile regression signs and t-statistics be rejected under data-determined Box-Cox tests. For a typical example of borderline linear results that, stunningly, remained decidedly untested in form over successive paper revisions, see Dionne et alii (1992, 1997) who explore air safety outcomes in Canada with count data; and contrast this practice with Fridstrøm’s (1997, 1999, 2000) singlehanded provision of honest BCT form-tested results with comparable count data on road accidents. Here the form tests are summarized in Table 3. 3 The book also contains a discrete cross-sectional simultaneous equations model linking accidents and road infrastructure characteristics, as well as a fully documented description of the LEVEL-1.4 maximum likelihood estimation algorithm used here. 4 Professor Suleiman’s work to build this file parallels work done in France on the Registre du Rhône (Amoros, 2007). 5 One of the 48 Algerian administrative regions similar to a French département, and with a comparable average population. 3

results, summarized in Table 1, interestingly indicate that, contrary to what happens in many countries, the number of slightly injured persons is overestimated by Algerian police and gendarmerie, an error that carries to the total number of bodily injured victims despite their under-reporting of severely injured or killed victims. The reasons for such over-reporting are unclear but have apparently little to do with moral hazard “after the fact” no doubt present for instance in the Quebec no-fault road victim compensation system which opened the door to false declarations by “injured” road users (Gaudry, 1992).

Table 1. Percentage of under-reporting in police reports, wilaya 22 (Sidi Bel Abbes File) Under-reported (%) 1996 1997 1998 1999 2000 2001 2002 All bodily injury accidents -0,44 -0,98 -0,81 -0,34 -0,99 -0,56 -0,44 Lightly injured victims (blegers in Table 2) on the day of the accident -10,28 -13,84 -14,47 -12,11 -12,79 -14,46 -11,76 Severely injured victims (bgraves in Table 2) on the day of the accident 14,81 13,79 13,89 10,42 11,59 9,09 10,11 Killed victims7 (tues in Table 2) 5,66 5,88 6,38 4,08 n.a. 6,67 6,98

Systematic over-reporting of lightly injured and under-reporting of other categories of victims always have to be kept in mind for the interpretation of model coefficients, and to some extent for that of elasticities, but have only a modest effect on general results if percentage variations over time are deemed correct: in particular, they have no effect at all on BCT form estimates if an intercept is used in the regression, an unavoidable requirement of regression with BCT (Schlesselman, 1971) in any case.

2. Model purpose and structure The purpose of the model is to explain the 456 observations on the monthly number of victims lightly injured (Figure 1), severely injured (Figure 2) and killed (Figure 3) from January 1970 until December 2007. As shown in these figures, and distinguished between urban and rural environments elsewhere (Gaudry & Lapparent, 2012, Figures 18 and 19), the national totals by damage outcome category exhibit a number of visible differences over and above the normal seasonality of such data.

Figure 1. Lightly injured road victims by month in Algeria, 1970-2007

6 The data shown above (e.g. Gaudry & Lapparent, 2012, Figure 18) are naturally derived from the police and gendarmerie files. 7 In Algeria, road deaths are counted for the first six days after the accident, as was done in France until 2005. The implementation of the new accident report, identical for police and gendarmerie, will be accompanied by a shift to international practice (30 days). 4

Figure 2. Severely injured road victims by month in Algeria, 1970-2007

Figure 3. Killed road victims by month in Algeria, 1970-2007

In the model structure found in Table 2, the endogenous variables are the demand for road use DR (kmt), accident frequency by category A (acorp, amort), accident severity/gravity by category G (gl, gg, gmm), victims by category VI (blegers, bgraves, tues) and some subsidiary variables Y pertaining to average speed on national highways (vitrn) and average belt-wearing (csec). We will not report on trials made with the last of these (therefore unnumbered in Table 2), or with other variables, but solely on the 10 numbered variables.

In view of the fact that VI is the product of A and G vectors of elements, the 3 equations estimated for victims are not strictly “necessary”: sufficient information to derive their substantive results (principally elasticities) is contained in the results of frequency and severity component equations. But we still report on equations for victims to document the difference between explaining them directly (as often done in the literature) and explaining VI by a decomposition between products of A and G terms (as done here), and as a check on the validity of results for A and G elements by category. We will also report on the speed equation despite the fact that it is not a total network indicator but only an indicator of highway speed unsuited as explanatory variable of the nation-wide indicators A, G or VI of and, in terms of observational error, is not a proper harmonic mean of speeds on highways but a sort of arithmetic average. As such errors on a dependent variable go into the regression residuals, its results are still of interest here, in passing as it were.

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In Table 2, none of the Y variables that could have been jointly determined appear as explanatory variables: the only endogenous variable to appear as an explanatory variable is the quantity of driving DR (kmt) that belongs to the set D: the {Demand-Performance} system of interest {[DR] [A·G] = [VI]} is clearly recursive but we will neglect any potential contemporaneous correlation among the final random terms of the system equations. It is not really surprising, as found a number of times since this was first tested and reported (Gaudry, 1984), that the current level of safety outcomes should play no detectable role in the explanation of demand for road use with aggregate data, in contrast with what is found with discrete data where perceived risk and driving care more easily show up as jointly determined (e.g. Gaudry & Vernier, 1999, 2000, 2002).

Table 2. Recursive and subsidiary model equations

Col. Code Definition of dependent Explanatory variables Xk drawn from 12 sets o Initial structure N name variables y D Y P M W L Ap Af EAs RC Recursive system equations Demand (DR) dr 1 kmt Total vehicle-kilometres DR<-- ( ,Y ,X ) PM W Ap Af E RC Accident frequency (A) acorp Injury crashes A A 3 ag p f A <-- (DR ,Y ,X ) D Y M W L EAs RC 4 amort Fatal crashes DR DR Accident severity (G) 5 gl Lightly injured/injury crash A A ag p f 6 gg Severely injured/injury crash D Y M W L EAs RC G <-- (DR ,Y ,X ) DR DR 7 gmm Fatalities/fatal crash Victims (VI) 8 blegers Lightly injured victims A A ag p f 9 bgraves Severely injured victims VI D Y M W L EAs RC <-- (DR ,Y ,X ) DR DR 10 tues Fatally injured victims Subsidiary equations

2 vitrn Average highway speed y D Y M W L E As RC Y <-- ( ,X ) -- csec Safety belt compliance rate Y L As RC where the sets of variables denote, respectively D: demand related, such as DR (vehicle-km) and the share of such traffic on highways Y: driving care, such as belt wearing and speed P: prices Ap: activity levels of persons (p) As: index of special activities (days/month) M: motorisation Af: activity levels of freight (f) R: index of religious activities (days/month) X: W: weather Ai/DR: level of activity i (p or f) per total vehicle-km C: month composition (days/month) L: driving laws E: socio-economic standing (unemployment…) and equation intercept.

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3. Maximum likelihood estimation: stationarity, homoskedasticity, form and signs Form and sphericalness of the distribution of residuals. The econometric specification for all 10 equations, estimated with the LEVEL L-1.4 algorithm (Liem et alii, 1993) implemented in Version 2.0 of the TRIO software (Gaudry & alii., 2005) combines functional form extensions of classical regression with the joint establishment of spherical distributions of residuals corrected for both heteroskedasticity and serial correlation in accordance with the following specification:

()λ ()λ y K X (1) yXu=⋅∑ β k +, tkktt k = 1 with 12/ ⎡ ⎛ ()λ ⎞⎤ ∑ zm (2) uZvtmmt= ⎢exp⎜ δ ⎟⎥ • t , ⎣ ⎝ m ⎠⎦ and vvw= ∑ ρ + , (3) tltl− t l where the Zm variables that cause heteroskedasticity, specified as in Gaudry & Dagenais (1979), may also be Xk variables and the multiple serial autoregressive scheme is assumed to be stationary. The use of a common autoregressive scheme for all variables of an equation, as implied by (3), steers a middle course between ignoring the problem and whitewashing all series, an extremely dangerous exercise with economic series all constructed with various degrees of error and in accordance with administrative rules: having purged the systematic levels that cause the real dependence explored by the relationship, whitewashing individual series obviously risks leaving us with only the error component of the series (Dagenais, 1994).

The detection of colinearity. Here colinearity has been reduced to an acceptable level in all equations through decisions made on the basis of variance decomposition indices (Belsley & alii, 1980) which have the enormous advantage of incorporating the contribution of the constant, taking due account of the dependence of these indices on regressor scale (Erkel-Rousse, 1995). In order to fine tune the selected specifications of each of the 10 equations, our analysis was carried out both on untransformed regressors and ―more relevantly― on optimally transformed values as well.

Some pitfalls of Box-Cox transformations. Principal maximum likelihood results are found in Table 3. In all equations, the Box-Jenkins analysis of residuals wt has shown that, after taking first and twelfth orders of autocorrelation into account, they are stationary white noises. This finding is in line with the expectation that residuals of reasonably well specified models (missing few relevant explanatory variables) tend to be stationary. A careful analysis has also shown, after specifying various trials with (2), that no significant heteroskedasticity is present and that homoskedasticity holds.

This means that the resulting t-statistics of explanatory variables, all computed conditionally upon optimal form values so that they do not depend on units of measurement of the explanatory variables (Spitzer, 1984), are reliable. Other checks on the potential presence of calculated expected values of dependent variables too close to 0 for comfort after hypothesizing a normal distribution of residuals were also satisfactory8, thereby providing an ex post justification of the use of the Rosett & Nelson (1975) two-limit Tobit model without limit observations in the maximum likelihood procedure formulated in Liem et alii (1993).

The impact of form on regressor signs. In such conditions, it is interesting to comment on the functional form estimates. First, we note that only Models 2 and 8 would have been acceptable in logarithmic form: all other models are neither linear nor logarithmic. Second, the number of sign changes occurring between the Linear and the optimal BCT forms demonstrates the importance of duly testing for functional form and of

8 For each equation run, the program automatically computes the average probability that yt be a limit observation. 7

practicing due scientific skepticism with respect to results of models where the untested form has been assumed to be linear (or logarithmic) a priori.

This matters enormously for the credibility of results. As one goes from Linear to optimal BCT form in Model 1 for instance, the employment variable (emp) changes from an elasticity of -0,48 with a t-statistic of (-2,66) to an elasticity of 0,18 with a t-statistic of (1,21) and that the number of vehicles per employee (parcpe) changes from an elasticity of -0,28 with a t-statistic of (1,50) to an elasticity of 0,37 with a t- statistic of (2,58). Clearly, borderline linear results referred to in Footnote 2 above pose a scientific credibility problem because results obtained from forms assumed a priori are conditional on that form and therefore often untenable because linearity is in fact extremely rare in nature and in utility analysis. We now turn to economic results.

Table 3. Optimal form and sign changes for the 10 stationary homoskedastic models Demand Speed Accident frequency Accident severity Victims Vehicle- Average Bodily Lightly Heavily Dependent Fatal Killed/ Lightly Heavily kilo- highway injury injured/ injured/ Killed variable acc. amort injured injured meters speed acc. acorp acorp Code name kmt vitrn acorp amort gl gg gmm blegers bgraves tues Column 1 2 3 4 5 6 7 8 9 10 Log-likelihood and form (underlined if optimal) Linear 778.445 -701.519 -2772.74 -1803.92 735.967 734.299 1606.466 -2900.71 -1820.92 -1827.44 Logarithmic 846.868 -694.455 -2732.98 -1748.03 798.198 473.089 1607.397 -2808.96 -1769.15 -1700.00 1 Box-Cox 846.868 -694.314 -2730.87 -1737.81 837.912 1102.688 1606.626 -2807.82 -1755.66 -1761.28 2 Box-Cox 851.716 -691.428 -2728.98 -1737.01 848.156 1102.689 1615.368 -2807.80 -1753.27 -1760.15 Optimal Box-Cox form parameters λ 0.34 1.23 0.25 -2.32 -5.18 -0.90 y -0.51 -2.26 0.04 -0.50 λx 0.24 0.11 0.04 0.03 1.69 -0.49 Autocorrelation coefficients (and their t-statistics)

ρ1 .80 (25.36) .74 (29.86) .70 (22.38) .19 (5.85) .82 (27.54) .01 (0.24) .83 (22.52) .58 (19.80) .16 (5.19) .21 (6.43) ρ12 .16 (5.51) .24 (9.32) .28 (9.69) .72 (25.69) .15 (5.12) .69 (26.74) .06 (1.21) .39 (14.17) .75 (27.59) .70 (24.68) Number of sign changes between Linear and optimal Box-Cox cases / number of coefficients Number 3/20 1/20 5/26 5/27 10/27 11/27 4/27 9/27 6/27 4/27

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4. Tables of selected results by regressand for all regressors On elasticities and t-statistics. Forthcoming tables of results have columns presenting, for each explanatory variable, the elasticity of each dependent variable and the t-statistics of the underlying βk coefficient. The latter are computed conditionally upon the of the BCT and the former are calculated at sample means in accordance with the following expressions, respectively for continuous and dummy variables:

∂yX Xλk (4-A) ηβ(,yX )≡=kk , k ≠, for any continuous and strictly positive X , kkλy yX,, X l k k l ∂Xyk y

with the Box-Cox transformation from (1) defined for any strictly positive variable y or X k as ⎧ X λ −1 X ()λ =≠⎪ k if λ 0, k ⎨ λ ⎪ ⎩lnX k if λ → 0;

Nk (4-B) ηη(,yXkk )+ = (, yX ) + , for any dummy variable X k , X k yX,, X k l Nk

where η(,yXk ) + is simply the elasticity of y with respect to X k evaluated at the sample mean of X k 9 + X k corrected over Nk positive observations of the dummy X k .

In our conventional use of t-statistics of regression coefficients, each system equation is considered independently and the prime interest is whether each coefficient differs from zero (βk = 0 ) in it. In fact, when substitution between frequency and severity, or among their subcategories, matters, it would be of interest to perform formal inequality restriction tests to find out if an effect is significantly say positive in one equation (βik ≥ 0 ) and negative in the other ( β jk ≤0 ), or even proportionately greater in one accident equation than in another: variables ordered by severity level do not pertain to independent trials.

But appropriate tests, on lines suggested for instance by Dufour (1989), are complicated to apply across systems where each equation has its own specification (1)-(2)-(3) of Box-Cox powers as well as autocorrelated and heteroskedastic errors: for any given variable, we will consequently limit our cross- equation remarks to informal notices of the existence of apparent patterns of estimated individual coefficients (and of their t-statistics) without formally testing for the bounds of such patterns.

Of columns and rows. In all sub-tables of results below, the sequence of columns used in Table 3 is maintained: Columns 8-10 pertain to the 3 categories of victims, Columns 3-4 and 5-7 to the frequency and severity of accidents10 and Columns 1-2 relate to the explanation of vehicle-km and of average speed on highways (routes nationales) only. Code numbers of regression runs at the top of columns guarantee that the authors can reproduce and supply to interested readers the full regression results (coefficients, partial derivatives of the Log-likelihood, etc.), should those be requested in addition to those shown (elasticities, t- statistics). On the rows, a starred definition of a variable means that no Box-Cox transformation is applied to it and an underlined definition flags a dummy variable.

Formally, the 10 equations make use of 35 distinct explanatory variables (constants included) belonging to the twelve sets presented in Table 2. But we neglect for brevity results for group C and consider results for the variables found in all the remaining groups, collecting such results within 4 sub-tables.

9 The appropriateness of this approximation of the discrete effect of the presence of the dummy variable on the dependent variable, expressed in percentage, is discussed at length in Dagenais et alii (1987). It makes the construction of tables of “elasticities” of all explanatory variables, including dummies, feasible and avoids having to make direct sense of coefficients that have little intuitive meaning in BCT models. 10 Elasticities of the number of victims by category derivable from the sum of frequency and severity elasticity estimates, readily calculable by the reader, are not shown because those obtained from regression runs on victims by category are shown instead. 9

5. The role of demand and driving behaviour Traffic. In Table 4, the (underlined) elasticities of crash frequencies and victims with respect to total traffic and its highway share decrease with the severity level. The traffic variable measuring vehicle-km is derived from automotive vehicle fuel sales by taking into account the evolving fuel efficiency of the stock of vehicles by category: the total, composed of diesel (for 1/3 on average) and gasoline (for 2/3 on average) parts, is shown in Figure 4 where it is also clear that diesel powered vehicles have over time greatly increased their share of total vehicle-km.

Some use is made of this disaggregate information in Table 5 where the fuel price is obtained by using those average shares as weights of the monthly retail diesel and gasoline prices of Figure 7 expressed in constant Algerian dinars (DZD) per kilometre of 2007 by taking the energy efficiency of the fleet into account.

Figure 4. Total monthly vehicle-kilometers with gasoline and diesel components, 1970-2007

Speed. We note in passing that the average speed observed on highways, shown in Figure 5 and explained in Column 2, trends upwards but has a clear seasonal component somewhat resembling that of the share of traffic using such highways found in Figure 6. In both cases, the civil war causes a dip after 1992, with a rupture visible in 1993-1995.

Figure 5. Average monthly speed (km/h) on highways, 1970-2007

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Figure 6. Highway share (%) of total monthly traffic, 1970-2007

Safety belts. The rate of belt use, measured since 1979, has increased from 50% to about 85%, a level comparable to that found on average in the United States of America. It is associated with (underlined) patterns of greater frequency and severity, but the t-statistics of this variable are individually quite weak despite exhibiting some relative strength when they pertain to the frequency, severity and outcome of fatal events: overall, some risk compensation behaviour seems present because higher rates of belt wearing imply a shift towards more damaging outcomes.

Table 4. Elasticities and t-statistics of demand and driving behaviour variables System Demand Speed Accident frequency Accident severity Victims Vehicle- Average Bodily Lightly Heavily Dependent Fatal Killed/ Lightly Heavily kilo- highway injury injured/ injured/ Killed variable acc. amort injured injured meters speed acc. acorp acorp Run code kmt:7 vitrn:6 acorp:8 amort:6 gl:7 gg:6 gmm:9 blegers:6 bgraves:7 tues:6 Column 1 2 3 4 5 6 7 8 9 10 Demand (D) Total 1.80 0,30 0.37 0.44 -0.00 1.94 0.24 0.32 vehicle-km (9.98) (3.49) (1.36) (1.21) (-0.16) (11.34) (2.80) (3.55) Traffic share 0.81 0.60 0.13 4.69 -0.01 0.98 0.60 0.60 of highways (10.16) (4.66) (1.75) (0.70) (-0.93) (9.86) (4.63) (4.65) Traffic on 0.11 highways (7.21) Driving behaviour (Y) % safety belt -0.02 0.04 0.01 -0.12 -0.00 0.03 -0.03 0.00 0.02 compliance (-1.02) (0.80) (1.14) (-1.65) (-0.49) (1.30) (-0.59) (0.27) (2.32)

6. The role of automotive fuel price, motorization, weather and safety laws Fuel prices. The (fuel efficiency weighted) average price of automotive fuel is extremely low in Algeria, as can be seen on Figure 7: at the official exchange rate of about 100 dinars per Euro, the resulting kilometric price index of 0,15 €/km-li. in 2008, comprising distribution costs and excise taxes, basically implied a subsidy equal to the cost of raw materials11.

This cheapness possibly explains why, in contrast to findings in many other countries such as France (Jaeger, 1997), Quebec (Fournier & Simard, 2000) or the United States (Grabowski & Morrisey, 2004, 2006), the price of fuel was utterly insignificant in our model as an exploratory variable of highway speed or

11 As each barrel contains about 160 litres, a price of $ 160 per barrel would translate into a raw material cost of about $1 per litre. Such foregone earnings are interpretable as a subsidy. 11 of accidents, their severity and victims: it consequently only appears in the equation explaining total use of vehicles in Column 1 of Table 5, where the resulting low short run12 price elasticity of demand equal to -0,22 is also compatible with a lack of sensitivity to price.

Figure 7. Indices of diesel and gasoline fuel prices per litre-km, 1970-2008 (DZD of December 2007)

Vehicle mix. The vehicle mix variables (share of cars and motorcycles, share of trucks) are constructed from interpolated yearly measures of stocks of registered vehicles and therefore constructed with significant monthly error. Despite this, the results contain some weak indications, highlighted in Table 5 by underlined elasticity values, that the more frequent accidents involving heavy trucks may be somewhat less severe than the rest, presumably involving predominantly cars with relatively higher passenger occupancy rates and relatively lower protection.

This result is consistent with that originally found in Quebec (Gaudry, 1984 or 2002, Table 5) where diesel truck-kilometres increased all accident frequencies but reduced their severity, an effect that disappeared from the second version of the model (Fournier & Simard, 2000) because total road use was not disaggregated between gasoline and diesel powered vehicles in order to shift the model focus to congestion.

Weather. Climactic factors play a small role. Higher temperatures increase speeds and the relative frequency of fatal accidents and of fatalities, as found in other places such as France (Jaeger, 1997) or West Germany (Blum & Gaudry, 2000). More daylight increases driving, as in Norway (Fridstrøm, 2000), as well as speed. It is particularly interesting that rainfall, which typically has some detectable effect in most national models, has absolutely none here despite its significant variability evidenced in Figure 8.

Laws. The interesting question concerning new road safety laws, regulations and fines, is whether they produce permanent shifts in safety outcomes, a dynamic issue addressed only partially by their standard specification as standard dummy variables, the preferred formulation due to the very time consuming alternative consisting in formulating flexible time effect profiles (sometimes called quasi-dummy variables) and in testing them systematically, ceteris paribus, to determine best fit. But even if it cannot handle turning points, “intervention analysis” still yields useful information about shifts. Among interventions considered in Table 5, lowering the blood alcohol concentration limit (since 1985) and removing driving permits13 after

12 We do not compute the longer term elasticities derivable from the autocorrelation schemes found in Table 3. 13 Combined with much heavier fines since March 2005. Vehicles may now also be seized, especially in rural areas where the gendarmerie has specialized road safety teams (Escadrons de Sécurité Routière), until fines are paid. Although the police (concentrated in urban areas) and the gendarmerie (found mostly in rural areas) each give about 1,5 million fines per year, the police, which has no specialized road safety units, is reported to be the less severe of the two bodies. 12 some offences (since 2004) yielded some statistically significant results. As underlined in the table, they both lowered highway speed and reduced fatal accident frequency and all categories of victims. Permit removal also modified the severity pattern in a shift away from heavy and towards light severity rates.

Figure 8. Monthly rainfall (mm), 1970-2007

Table 5. Elasticities and t-statistics of price, motorization, weather and road safety law variables Demand Speed Accident frequency Accident severity Victims Vehicle- Average Bodily Lightly Heavily Dependent Fatal Killed/ Lightly Heavily kilo- highway injury injured/ injured/ Killed variable acc. amort injured injured meters speed acc. acorp acorp Run code kmt:7 vitrn:6 acorp:8 amort:6 gl:7 gg:6 gmm:9 blegers:6 bgraves:7 tues:6 Column 1 2 3 4 5 6 7 8 9 10 Price (P) Fuel price per -0.22 litre-km (-2.60) Motorization (M) Vehicles per 0.37 employee (2.58) % of cars and 0.97 0.63

motorcycles (1.44) (2.05) % of heavy 0.41 0.11 -0.03 0.36 -0.10 0.31 -0.05 -0.11

trucks (1.09) (0.61) (-0.08) (0.07) (-1.97) (0.84) (-0.32) (-0.61) Weather (W) Temperature 0.06 0.33 -0.01 -0.00 0.01 0.64 -0.00 -0.04 -0.01 0.00 (Celsius) (5.05) (4.87) (-0.38) (-0.01) (0.49) (0.87) (-0.44) (-1.65) (-0.47) (0.13) *Rainfall 0.00 0.00 0.00 0.00 0.00 -0.02 -0.00 -0.00 0.00 0.00 (mm) (1.21) (0.87) (0.03) (0.08) (0.63) (-0.09) (-0.30) (-0.34) (0.32) (0.14) Sunlight 0.02 0.02 0.02 0.26 -0.00 -0.55 0.00 0.00 0.02 0.02 (h) (2.16) (3.35) (1.13) (1.65) (-0.08) (-0.80) (0.34) (0.16) (1.63) (1.47) Laws (L) -0.03 0.01 0.01 0.01 -0.57 -0.01 0.02 0.01 -0.00 Belt law ’78 (-1.14) (0.19) (0.12) (0.03) (-0.42) (-0.30) (0.38) (0.15) (-0.02) Highway ’85 -0.00 0.02 0.00 -0.00 -0.41 0.01 -0.01 0.00 0.00

speed 110/90 (-0.04) (0.54) (0.07) (-0.00) (-0.52) (0.83) (-0.15) (0.21) (0.13) BAC & permit -0.02 -0.03 -0.02 -0.00 0.85 0.01 -0.04 -0.01 -0.13

removal ’87 (-1.34) (-0.55) (-0.75) (-0.02) (0.41) (1.85) (-0.89) (-0.48) (-0.45) Higher -0.01 0.03 0.01 0.01 -0.28 0.00 0.03 0.00 0.01

fines ’91 (-0.68) (0.45) (0.13) (0.04) (-0.13) (0.03) (0.57) (0.06) (0.15) Permit -0.05 -0.16 0.06 -1.79 -0.02 -0.41 -0.08 -0.17

removal 2004 (-3.03) (-12.55) (1.30) (-1.96) (-2.56) (-1.95) (-6.57) -13.18) A starred variable is not transformed and an underline flags a dummy variable.

13

7. The role of economic activities and of unemployment inactivity The double role of activity indicators. As expected from Table 3, activity variables for passengers and freight have two distinct roles: in Table 6, they consequently appear firstly (in Column 1) on their own as generators of derived demand for road use (vehicle-km) and secondly (in Columns 3-10), for given total demand, as traffic mix indicators (activity/vehicle-km) influencing accident frequency, severity and victims. In both roles, changes in the level of any activity effectively imply changes in vehicle-km but our current model provides no way of determining the type of vehicle, or the implied occupancy rate or loading, associated with the activity modification considered.

In their second role as numerators within ratios, activity variables effectively mimic trip purpose shares (the proportions of vehicle-km by trip purpose). In fact, we implicitly assume that they behave very much like trip shares14, ignoring the fact that their denominator is really, in all cases (for passengers and freight trip purposes), a current endogenous variable of the system. Previous corrections of this potential simultaneity bias effected within the DRAG-1 model for Quebec (Gaudry, 1984 or 2002) showed that corrections would make very little difference to the results, probably because of the recursive nature of the system.

Activities of persons. The availability of excellent data on employment and on reasons for absence from work made it possible to construct employee “at work” and “on vacation” indices that informatively track real trip purpose levels, as the vacation index shown in Figure 9 illustrates: it reflects both employment levels and vacation rates over the whole economy. The shopping index of Figure 10 is constructed directly from household consumption expenditure components measured in constant Algerian dinars (DZD): it appears that expenditures are increasingly variable across months, a favourable situation for our purposes. We also constructed schooling indices based on student registrations and detailed vacation calendars since 1970 but, although extremely variable, the resulting construct was never significant, as one would expect from a country where few study trips are made by motorized vehicles.

The results in Table 6 indicate that all activities of persons generate travel and that their trip purpose shares all increase both accident frequency and victims (relative to the residual shares of non modeled activities), but the share of vacation trip was the only trip purpose for which the severity of accidents (in bold and underlined) also increased (relative to that of non modeled activities), no doubt due to relatively high occupancy rate of vehicles for such outings.

Figure 9. Employee monthly vacation index, 1970-2007

14 As the list of activities is incomplete, the ratios are implicitly defined with reference to « other trip purposes ». 14

Figure 10. Monthly household consumption expenditures (constant DZD of December 2007)

Activities of freight. Algeria maintains a number of high quality statistical series pertaining to the transport and to the export and the import of goods, and notably the series on total tons transported (lifted) by road, shown in Figure 11.

In future work, these indices could be linked to otherwise available measures of manufacturing, construction and industrial outputs and some idea of the composition of tons transported by road (with potentially different road safety implications) established, as was done for the DRAG-2 model in Quebec (Fournier & Simard, 2000) where such freight activity components exist for seven sectors (forestry, residential construction, etc.), and where the sum of road demand elasticities with respect to activities of freight and persons equals about 1,00.

It is easy to compute a comparable statistic from Column 1 of Table 6 where the available elasticities sum to 0,94, which implies that some marginal precision could be gained, probably from the disaggregation of freight activities, because the doubling of all activities should, ceteris paribus and with elasticities evaluated at sample means, imply a doubling of road flows. But the current model is acceptable from this perspective.

Figure 11. Monthly tonnage transported by road (tons lifted), 1970-2007

15

In safety terms, we see in Table 6 that the freight “trip purpose” indicator (the ratio of total tons transported by road to total road vehicle-km ―admittedly with a numerator composed of light and heavy goods carried by very different kinds of utility and heavy freight trucks), involves a relative decrease in average injury scores because the elasticities (in bold and underlined) of frequency, severity and number of victims heavily injured and killed decrease relatively to those for lightly injured victims. Again, this is consistent with the relatively lower occupancy rates and higher protectiveness of freight vehicles relative to passenger vehicles.

Economic standing: unemployment inactivity. In many modeling contexts, it is never clear whether being unemployed should be considered an economic activity (in Algeria, as elsewhere, some financial remuneration has for some time (since 1994) been associated with this status), or should be classified purely as a status that can modify the attitude of drivers ―and for which results should then be better discussed in forthcoming Table 7. We chose to include the results in Table 6 to facilitate the comparison of results with those associated with more conventional continuous economic activities.

Household expenditures of the unemployed are included in retail sales, and therefore generate trips. As unemployment inactivity does not per se generate a proper derived trip demand15, the remaining question in our model is whether the attitude of unemployed drivers might differ from that of employed drivers, an issue indirectly and imperfectly approached by an unemployment rate variable. What is then noteworthy in Table 6 beyond a general sign pattern amazingly matching perfectly that of increased truck (freight) presence?

First, higher unemployment rates lower speed on highways (Column 2). Second, as indicated by elasticities in bold and underlined, the frequency of fatal accidents, their severity and the number of victims killed also all decrease. This triplet of results is exactly the same found in models for France, Quebec and Norway. The general implication, even in these 3 models where the impact of unemployment on speeds is not directly observed, is that unemployed drivers, having lower values of time than employed drivers, drive more slowly, an interpretation also supported here by elasticities listed in Columns 8-9 where the number of heavily injured victims also decreases at the same time as the number of lightly injured victims increases.

Table 6. Elasticities and t-statistics of economic activity, including unemployment, variables Demand Speed Accident frequency Accident severity Victims Vehicle- Average Bodily Lightly Heavily Dependent Fatal Killed/ Lightly Heavily kilo- highway injury injured/ injured/ Killed variable acc. amort injured injured meters speed acc. acorp acorp Run code kmt:7 vitrn:6 acorp:8 amort:6 gl:7 gg:6 gmm:9 blegers:6 bgraves:7 tues:6 Column 1 2 3 4 5 6 7 8 9 10 Activities of persons (Ap) 0.18 Employed (1.21) Employed per 1.15 0.18 0.18 -6.56 0.02 1.25 0.17 0.18 vehicle-km (8.07) (2.86) (0.74) (-3.09) (1.73) (10.20) (3.17) (2.83) 0.57 Shopping (32.09) Shopping per 0.14 0.28 -0.20 0.27 0.00 0.02 0.22 0.25

vehicle-km (2.74) (4.16) (-5.38) (1.40) (0.42) (0.30) (3.26) (3.82) 0.02 Vacation (7.89) Vacation per 0.01 0.00 0.00 0.02 0.00 0.02 0.00 0.01

vehicle-km (2.86) (0.89) (0.87) (0.40) (0.75) (2.28) (0.94) (1.29) Activities of freight (Af) Tons 0.17 transported (4.10) Tons per 0.38 -0.15 0.17 1.42 -0.01 0.44 -0.16 -0.15

vehicle-km (4.21) (-2.20) (2.45) (1.17) (-2.57) (4.40) (-2.47) (-2.14) Economic standing (E) Rate (%) of -0.08 0.22 -0.11 0.12 0.15 -0.02 0.24 -0.07 -0.11 unemployed (-2.36) (2.32) (-2.52) (0.83) (0.08) -0,74 (2.72) (-1.92) (-2.58)

15 Although it might generate some personal trips or tours, unobserved in this model. 16

8. The attitude of drivers: unique events and special religious activities One-time events. Special events that occur within the limits of a month, such as major strikes (October 1988 and June 1991), floods (in Algiers in November 2001) or large earthquakes (at Boumerdes in May 2003), are not very interesting for us because dummy variables created for them basically annul the observation, capturing the model error for that month. It is more interesting to isolate the effect of events occurring over more than one month by specifying variables consisting in relative activity levels during the months in question, specifications that produce so-called « quasi-dummy » variables. For instance, once might use monthly stadium attendance (or television ratings) for the Pan Arab Games if they were available: such variables could well capture the overall impact of national TV watching, and lower driving, in the Demand equation as well as of attitudinal change, perhaps due to atypical driver mix during celebrations, in the Accident, Severity and Victim equations.

In the absence of detailed monthly information incorporating activity level variations, one is left with simpler specifications consisting in the number or proportions of days in the month for which the activity can be defined. This was the approach used here for the First Gulf War (in Kuwait, from January 16 until February 17, 1991) and for the Pan Arab Games of 2004 (from September 24 until October 10). In the absence of good information on the time distribution of security measures (army road controls, for instance) during the civil war, we assumed a constant level per complete month for the « intervention » dummy variable (from December 20, 1991, until March 31, 1995), obviously a very coarse approximation. Such simplifications could eventually be refined with more detailed information, although experience with quasi- dummy variables typically demonstrates the difficulty of fitting the shape of the distribution over time: to curvature design and exact length of effects rapidly set in, often leaving analysts only with robust first-order constant effects.

The conditionality of some effects estimated by dummy variables. A further problem in the interpretation of results arises if an event of interest has modified the activities present in the model. For instance, if the Pan Arab Games reduced shopping nationally because of television watching, the coefficient of the game variable in the Demand equation measures a first effect (actually positive, in this case, perhaps due to visitors) on driving beyond that already taken into account in the shopping variable itself (conditional on it), and coefficients of the Safety equations further measure second conditional residual effects (the effects on activities and driving having already been accounted for) on Safety. As our model contains no subsidiary equation to explain the level of shopping, it is impossible to calculate the full effect of the games but possible to calculate only their residual impact on driving (conditional on reduced shopping) and on safety (conditional on the amount of driving).

The same naturally holds for other intervention variables, as it does for religious activity indicators such as the number of days of Ramadan per month. For instance, if Ramadan fasting impacts the level of activities, as many claim, this effect is already accounted for in the activity levels (for which no subsidiary equations are developed in our model) and we detect here only (conditional) residual impacts on Demand and Safety. In an extension of the model, one could envisage testing the impact of fasting on industrial, manufacturing and agricultural output levels (and on total tonnage transported by road) as was done in the DRAG-1 model for the impact of legal alcohol driving limits on alcohol consumption and for the impact of a major change in vehicle insurance regime in 1978 on vehicle ownership and on the number of driving permit holders, all three variables (alcohol consumption, vehicle ownership and the number of permits) appearing in the Safety equations and detecting residual effects there. But the development of subsidiary equations is quite a job.

Linearity of quasi-dummy variables and multicolinearity. All of the quasi-dummy variables listed in Table 7 are assumed to intervene linearly in all equations. As stated in Equation (4), we have limited use of BCT to strictly positive variables, but this need not be the case with quasi-dummy regressors. They can the transformed if one of two procedures is applied. In one case, null values are replaced by very small values, but this approximation only makes sense if the variable contains relatively few zero observations; in the

17 other ―proper― procedure, actually applied in the TRIO software we used, a dummy variable is created to compensate for the shift at zero implied by the application of the transformation to a quasi-dummy variable.

But creating as many compensating dummy variables as there are quasi-dummy variables poses problems of its own, in two directions: the required numerical compensation dummy variables can interfere with pre- existing substantive dummy variables, such as the 5 variables representing legal interventions discussed above; also, they easily create colinearity and often require due pruning. Overall, it was felt that linearity was the best choice for the 6 quasi-dummy variables, the only ones not transformed16 in the model.

Little to write home about. Interestingly, none of the results for the 6 variables considered in Table 7 are individually very significant, but this does not mean that some results do not make sense or that patterns of sign results are without interest.

Taking the Kuwait war first, the results showing reduced numbers of all road victims are clearly in line with what was found elsewhere. In Israel, the number of road fatalities for the two months of the war were 10% lower17 than their normal expected value; in the model for France (op. cit.), the results closely match those found in Table 6 because the number of all categories of victims falls during the war, albeit not much or very significantly, an effect also found for Stockholm (Tegnér et alii, 2000) where road victims decrease indirectly through a 5% drop in vehicle-km, a change not cancelled by mixed residual effects on Safety.

The most significant of the residual effects associated with religious practice occur on special holidays (El Kebir and El Seghir) that both imply similar increases in the number of severely injured and killed victims, perhaps due to the especially high occupancy rate of vehicles travelling on these days (on normal holidays, the elasticities of heavily injured and killed victims are only 0.01 and less statistically significant, with t-values of 3.31 and 3.91, respectively). But residual effects linked to the Ramadan are extremely weak and, were they more significant, would apparently have an impact on road victims in a direction opposite to that of the two religious holidays. All considered, such residual effects are small.

Table 7. Elasticities and t-statistics of unique event and special religious activity variables Demand Speed Accident frequency Accident severity Victims Vehicle- Average Bodily Lightly Heavily Dependent Fatal Killed/ Lightly Heavily kilo- highway injury injured/ injured/ Killed variable acc. amort injured injured meters speed acc. acorp acorp Run code kmt:7 vitrn:6 acorp:8 amort:6 gl:7 gg:6 gmm:9 blegers:6 bgraves:7 tues:6 Column 1 2 3 4 5 6 7 8 9 10 Unique events (As) *Kuwait war 0.04 -0.03 -0.04 -0.00 0.47 0.00 -0.03 -0.03 -0.02 1991 (1.49) (-0.94) (-0.58) (0.00) (0.11) (0.31) (-0.85) (-0.60) (-0.33) *Security 0.01 0.01 0.02 0.03 -0.01 -0.66 -0.01 0.02 0.03 0.02 1991-95 (0.73) (0.78) (0.86) (1.62) (-018) (-0.32) (-1.92) (1.16) (1.45) (0.99) *Pan Arab 0.13 -0.00 0.02 0.05 -0.65 0.01 0.03 0.04 0.04

Games 1994 (0.20) (-0.07) (0.51) (0.96) (-0.19) (4.19) (1.24) (1.63) (1.29) Special religious days (R) *Ramadan -0.00 0.00 -0.00 -0.01 -0.00 -0.22 -0.00 0.00 -0.01 -0.01 fasting (-1.20) (0.19) (-0.31) (-0.67) (-0.04) (-0.29) (-0.54) (0.09) (-0.70) (-0.93) *Aïd el adha 0.00 -0.01 0.00 0.02 -0.01 -0.54 0.00 -0.01 0.02 0.03 (El Kebir) (1.36) (-2.86) (0.22) (2.34) (-1.22) (-072) (0.04) (0.58) (2.48) (2.35) *Aïd el fitr 0.01 -0.00 -0.00 0.06 -0.01 0.77 0.00 -0.01 0.05 0.06 (El Seghir) (1.70) (-1.16) (-0.58) (5.58) (-1.66) (0.85) (0.11) (-1.54) (5.43) (5.39) A starred variable is not transformed.

16 Even the aggregation variables describing the composition of months (work days, holidays, week-end days) but, like the constant, not reported on for brevity, are transformed. The full results are found in the Appendix. 17 A gain larger than the number of inhabitants killed by rockets fired on Israel. Similarly the reduction in road fatalities in countries belonging to the coalition was certainly larger than the number (about 350) of coalition soldiers killed during this war. 18

9. Conclusion We have constructed a first country-wide model of demand for road use and of road safety outcomes for Algeria making use of the DRAG-type framework and of flexible regression estimation methods that have made a demonstrable difference to the quality of our results. We have implied that the availability of high quality Algerian data could make it worthwhile not only to consider updates of the model with longer data series but also to study variants of the model, notably with disaggregation of freight activities across industrial sectors. We have not addressed the difficult question of forecasting the moment at which road fatalities might start falling in Algeria, as they have around 1972-1973 in a number of OECD countries (Gaudry & Gelgoot, 2009): there is more work to be done.

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Gaudry, M. and M. Dagenais (1979). Heteroscedasticity and the Use of Box-Cox Transformations. Economics Letters 2, 3, 225-229. Gaudry, M. and S. Gelgoot (2009). An analysis of simultaneous 1972-73 national road fatality maxima using turning forms nesting monotonic Smeed and DRAG specifications applied to aggregate multinational and multiprovincial data. (DRAFT Version 7), 50 pages, November 2001, June 2002, October. www.e-ajd.net. Gaudry, M. and M. de Lapparent (2012). Multivariate road safety models: future research orientations and current use to forecast performance. Forthcoming in Research in Transportation Economics. Gaudry, M. and S. Lassarre (dir.), (2000). Structural Road Accident Model: The International DRAG Family, Elsevier Science, Oxford. Gaudry, M. and K. Vernier (1999). Effets du tracé et des états de chaussée sur le comportement des usagers vis-à-vis de la vitesse et de la sécurité: une analyse économétrique. In Peeters, M. and Heuchenne, D., eds, Evaluation Methods of Road Safety/Méthodes d’évaluation des mesures de sécurité routière, World Road Association/Association Mondiale de la Route, 82-105. Gaudry, M. and K. Vernier (2000). The Road, Risk, Uncertainty and Speed. In Gaudry, M. and S. Lassarre, eds. Structural Road Accident Models: The International DRAG Family, Pergamon, Elsevier Science, Oxford, Ch. 9, 225-236. Gaudry, M. and K. Vernier (2002). Effects of Road Geometry and Surface on Speed and Safety: a first simultaneous non linear equation analysis distinguishing between risk and uncertainty. Publication AJD-18, Agora Jules Dupuit, Université de Montréal, 55 p., June. www.e-ajd.net. Grabowski, D. C. and M. A. Morrisey (2004). Gasoline Prices and Motor Vehicle Fatalities. Journal of Policy Analysis and Management 23, 3, 575–593. Grabowski, D. C. and M. A. Morrisey (2006). Do higher gasoline taxes save lives? Economics Letters 90, 51–55. Himouri, S. (2008). Note sur la sous déclaration et la qualité des données de la sécurité routière en Algérie, 13 p., 7 juin. Himouri, S. and M. Gaudry (2008). DRAG-Algérie : Évolution de la base de données. Publication AJD-122, Agora Jules Dupuit, Université de Montréal. Unpublished manuscript, 16 p., July. Jaeger, L. (1997). L’évaluation du risque dans le système des transports routiers par le développement du modèle TAG. Thèse de Doctorat de Sciences Économiques, Faculté des Sciences Économiques et de Gestion, Université Louis Pasteur, Strasbourg. Liem, T.C., Dagenais, M. and M. Gaudry (1993). LEVEL: The L-1.4 program for BC-GAUHESEQ regression — Box-Cox Generalized AUtoregressive HEteroskedastic Single EQuation models. Publication CRT-510, Centre de recherche sur les transports, Université de Montréal, 41 p., 1987, 1990, 1993. www.e-ajd.net. Rosett, R. N. and F. D. Nelson (1975). Estimation of the two-limit Probit regression model. Econometrica 43, 141- 146. Schlesselman, J. (1971). Power families: a note on the Box and Cox transformation. Journal of the Royal Statistical Society Series B 33, 307-31. Spitzer, J. J. (1984). Variance estimates in models with the Box-Cox transformations: Implications for estimation and hypothesis testing. Review of Economics and Statistics 66, 645-652. Tegnér, G., Holmberg, I., Loncar-Lucassi, V. and C.Nilsson (2000). The DRAG-Stockholm-2 model. In Gaudry, M. and S. Lassarre, eds. Structural Road Accident Models: The International DRAG Family, Pergamon, Elsevier Science, Oxford, Ch. 5, 127-156.

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11. Appendix. A Tablex edition of selected model results, all variables (TRIO, Version 2) ======I. ELASTICITY S(y) (EP) TYPE =LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 VARIANT = kmt vitrn acorp amort gl gg gmm blegers bgraves tues (COND. T-STATISTIC) VERSION = 7 6 8 6 7 6 9 6 7 6 DEP.VAR. = kmt vitrn acorp amort gl gg gmm blegers bgraves tues ======------Demand ------Total vehicle-km kmt 1.804 .304 .374 .439 -.001 1.937 .240 .316 (9.98) (3.49) (1.36) (1.21) (-.16) (11.34) (2.80) (3.55) LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1

Traffic share ptrn .806 .596 .125 4.690 -.013 .977 .595 .600 national roads (10.16) (4.66) (1.75) (.70) (-.93) (9.86) (4.63) (4.65) LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1

Traffic on kmtrn .105 national roads (7.21) LAM 1

------Driving behavior ------Safety belt csec -.020 .041 .008 -.115 -.000 .025 -.029 .002 .016 compliance (%) (-1.02) (.80) (1.14) (-1.65) (-.49) (1.30) (-.59) (.27) (2.32) LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1

----- Price ----- Km price 35% prixkmp -.218 diesel share (DN 07) (-2.60) LAM 1

------Motorization ------Vehicles per employee parcpe .374 (2.58) LAM 1

Car and propvpm .971 .630 motorcycle share (1.44) (2.05) LAM 1 LAM 1

Heavy truck share proppl .406 .109 -.034 .360 -.102 .311 -.054 -.108 (1.09) (.61) (-.08) (.07) (-1.97) (.84) (-.32) (-.61) LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1

------Network Infrastructure Weather ------Temperature (Celsius) tc .057 .033 -.009 -.000 .011 .644 -.002 -.041 -.009 .003 (5.05) (4.87) (-.38) (-.01) (.49) (.87) (-.44) (-1.65) (-.47) (.13) LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1

Rainfall (mm) pm .002 .001 .000 .000 .001 -.018 -.000 -.001 .001 .000 -- (1.21) (.87) (.03) (.08) (.63) (-.09) (-.30) (-.34) (.32) (.14)

Sunlight (h) ih .017 .017 .018 .026 -.001 -.552 .001 .003 .024 .024 (2.16) (3.35) (1.13) (1.65) (-.08) (-.80) (.34) (.16) (1.63) (1.47) LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1

------Network Laws ------Belt law dummy 1978 l78 -.026 .012 .006 .005 -.565 -.005 .023 .007 -.001 === (-1.14) (.19) (.12) (.03) (-.42) (-.30) (.38) (.15) (-.02)

Highway speed l85 -.001 .020 .001 -.001 -.412 .005 -.005 .004 .003 110/90 dummy 1985 === (-.04) (.54) (.07) (-.00) (-.52) (.83) (-.15) (.21) (.13)

BAC & permit l87 -.019 -.029 -.021 -.004 .851 .010 -.040 -.011 -.013 removal dummy 1987 === (-1.34) (-.55) (-.75) (-.02) (.41) (1.85) (-.89) (-.48) (-.45)

Higher l91 -.014 .031 .005 .005 -.275 .000 .033 .002 .006 fines dummy 1991 === (-.68) (.45) (.13) (.04) (-.13) (.03) (.57) (.06) (.15)

Driving permit l04 -.045 -.156 .063 -1.786 -.023 -.041 -.075 -.169 removal dummy 2004 === (-3.03) (-12.55) (1.30) (-1.96) (-2.56) (-1.95) (-6.57) (-13.18)

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======I. ELASTICITY S(y) (EP) TYPE =LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 VARIANT = kmt vitrn acorp amort gl gg gmm blegers bgraves tues (COND. T-STATISTIC) VERSION = 7 6 8 6 7 6 9 6 7 6 DEP.VAR. = kmt vitrn acorp amort gl gg gmm blegers bgraves tues ======------Activities Persons ------Employees emp .175 (1.21) LAM 1

Employment ratio empr 1.148 .179 .178 -6.557 .015 1.254 .172 .184 (8.07) (2.86) (.74) (-3.09) (1.73) (10.20) (3.17) (2.83) LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1

Shopping trips deac .574 (32.09) LAM 1

Shopping trip raac .141 .283 -.195 .265 .003 .016 .224 .254 purpose ratio (2.74) (4.16) (-5.38) (1.40) (.42) (.30) (3.26) (3.82) LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1

Vacation trips deva .022 (7.89) LAM 1

Vacation trip rava .014 .004 .004 .024 .000 .015 .004 .006 purpose ratio (2.86) (.89) (.87) (.40) (.75) (2.28) (.94) (1.29) LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1

------Activities Freight ------Total tons tontr .173 transported by road (4.10) LAM 1

Total freight raton .382 -.153 .174 1.421 -.008 .437 -.158 -.152 purpose ratio (4.21) (-2.20) (2.45) (1.17) (-2.57) (4.40) (-2.47) (-2.14) LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1

------Activities Special ------First Gulf gg91 .041 -.028 -.039 -.000 .466 .001 -.033 -.034 -.023 War (Kuwait) 1991 ---- (1.49) (-.94) (-.58) (-.00) (.11) (.31) (-.85) (-.60) (-.33)

Maximum security es92 .011 .008 .020 .034 -.012 -.655 -.007 .023 .026 .022 measures 1991-95 ---- (.73) (.78) (.86) (1.62) (-.18) (-.32) (-1.92) (1.16) (1.45) (.99)

Pan Arab Games 2004 jp2004 .013 -.002 .019 .050 -.648 .011 .033 .039 .035 ------(.20) (-.07) (.51) (.96) (-.19) (4.19) (1.24) (1.63) (1.29)

------Drivers: general characteristics ------Unemployed rate (%) tchom -.076 .224 -.109 .124 .148 -.015 .242 -.073 -.111 (-2.36) (2.32) (-2.52) (.83) (.08) (-.74) (2.72) (-1.92) (-2.58) LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1

------Drivers: fasting or festive ------Fasting days ramadan jram -.003 .000 -.002 -.005 -.000 -.222 -.001 .001 -.005 -.007 ---- (-1.20) (.19) (-.31) (-.67) (-.04) (-.29) (-.54) (.09) (-.70) (-.93)

Holy days aïd el jfaa .004 -.006 .002 .023 -.006 -.541 .000 -.005 .024 .025 adha (El Kebir) ---- (1.36) (-2.26) (.22) (2.34) (-1.22) (-.72) (.04) (-.58) (2.48) (2.35)

Holy days aïd el jfaf .007 -.003 -.004 .055 -.010 .767 .000 -.014 .052 .056 fitr (El Seqhir) ---- (1.70) (-1.16) (-.58) (5.58) (-1.66) (.85) (.11) (-1.54) (5.43) (5.39)

------Et cetera: aggregation, trends ------Week-ends days jwe .024 -.021 -.023 .116 -.045 1.869 -.006 -.067 .110 .111 (2.11) (-2.73) (-.82) (3.05) (-2.09) (.83) (-1.27) (-2.10) (2.94) (2.82) LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1

Holidays jf .002 -.002 .001 .013 -.003 .231 .000 -.003 .011 .013 (2.16) (-2.98) (.54) (3.81) (-1.52) (.75) (.53) (-.87) (3.31) (3.91)

Work days jouv .048 -.050 -.103 .290 -.101 2.678 -.001 -.196 .269 .294 (1.79) (-3.23) (-1.72) (3.31) (-2.13) (.50) (-.06) (-2.71) (3.18) (3.09) LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 LAM 1 ------REGRESSION CONSTANT CONSTANT ------(-2.44) (5.91) (-2.66) (18.25) (.08) (-.84) (-1.88) (1.10) (145.60) (18.36)

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======II. PARAMETERS TYPE =LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 VARIANT = kmt vitrn acorp amort gl gg gmm blegers bgraves tues (COND. T-STATISTIC) VERSION = 7 6 8 6 7 6 9 6 7 6 DEP.VAR. = kmt vitrn acorp amort gl gg gmm blegers bgraves tues ======------BOX-COX TRANSFORMATIONS: UNCOND: [T-STATISTIC=0] / [T-STATISTIC=1] ------LAMBDA(Y) kmt .337 [6.67] [-13.09]

LAMBDA(Y) vitrn 1.234 [2.25] [.43]

LAMBDA(Y) acorp .250 [2.66] [-7.99]

LAMBDA(Y) gl -2.318 [-18.08] [-25.88]

LAMBDA(Y) gmm -5.117 [-5.80] [-6.94]

LAMBDA(Y) bgraves -.896 [-5.91] [-12.50]

LAMBDA(Y) - GROUP 1 LAM 1 -.509 -2.260 .035 -.503 [-4.16] [-17.76] [1.18] [-4.22] [-12.33] [-25.62] [-32.75] [-12.60]

LAMBDA(X) - GROUP 1 LAM 1 .237 .105 .040 -.509 .028 -2.260 1.687 .035 -.491 -.503 [5.68] [.67] [1.20] [-4.16] [.36] [-17.76] [1.49] [1.18] [-2.35] [-4.22] [-18.31] [-5.71] [-28.95] [-12.33] [-12.42] [-25.62] [.61] [-32.75] [-7.13] [-12.60]

------AUTOCORRELATION ------ORDER 1 RHO 1 .798 .735 .695 .189 .824 .011 .830 .578 .159 .209 (25.36) (29.86) (22.38) (5.85) (27.54) (.24) (22.52) (19.80) (5.19) (6.43)

ORDER 12 RHO 12 .155 .237 .283 .716 .147 .690 .057 .393 .753 .701 (5.51) (9.32) (9.69) (25.69) (5.12) (26.74) (1.21) (14.17) (27.59) (24.68)

======III.GENERAL STATISTICS TYPE =LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 LEVEL-1 VARIANT = kmt vitrn acorp amort gl gg gmm blegers bgraves tues VERSION = 7 6 8 6 7 6 9 6 7 6 DEP.VAR. = kmt vitrn acorp amort gl gg gmm blegers bgraves tues ======LOG-LIKELIHOOD 851.716 -691.428 -2728.98 -1737.81 848.156 1102.688 1615.368 -2807.82 -1753.27 -1761.28

PSEUDO-R2 : - (E) .998 .946 .960 .958 .940 -.031 .830 .976 .955 .955 - (L) .999 .946 .963 .969 .965 1.000 .837 .981 .967 .967 - (E) ADJUSTED FOR D.F. .998 .943 .957 .955 .935 -.103 .818 .974 .951 .952 - (L) ADJUSTED FOR D.F. .999 .943 .960 .967 .963 1.000 .825 .980 .964 .965

AVERAGE PROBABILITY (Y=LIMIT OBSERV.) .000 .000 .000 .000 .000 .021 .000 .000 .000 .000

SAMPLE : - NUMBER OF OBSERVATIONS 444 444 444 444 444 444 444 444 444 444 - FIRST OBSERVATION 13 13 13 13 13 13 13 13 13 13 - LAST OBSERVATION 456 456 456 456 456 456 456 456 456 456

NUMBER OF ESTIMATED PARAMETERS : - FIXED PART : . BETAS 20 20 26 27 27 27 27 27 27 27 . BOX-COX 2 2 2 1 2 1 2 1 2 1 . ASSOCIATED DUMMIES 0 0 0 0 0 0 0 0 0 0 - AUTOCORRELATION 2 2 2 2 2 2 2 2 2 2 - HETEROSKEDASTICITY : . DELTAS 0 0 0 0 0 0 0 0 0 0 . BOX-COX 0 0 0 0 0 0 0 0 0 0 . ASSOCIATED DUMMIES 0 0 0 0 0 0 0 0 0 0 ======

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