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Risk compensation—the case of road lighting
Accident Analysis and Prevention 31 (1999) 545 – 553 www.elsevier.com/locate/aap
Risk compensation—the case of road lighting Terje Assum *, Torkel Bjørnskau, Stein Fosser, Fridulv Sagberg Institute of Transport Economics, PO Box 6110 Etterstad, N-0602 Oslo, Norway Received 8 January 1999; accepted 28 January 1999
Abstract The hypothesis of this article is that drivers will not adjust their behavior, i.e. drivers are not expected to increase their speed, reduce their concentration or travel more when road lighting is installed. The hypothesis was based on previous research showing that road lighting reduces road accidents and that average driving speeds do not increase when road lighting is installed. Our results show that drivers do compensate for road lighting in terms of increased speed and reduced concentration. Consequently, the hypothesis is rejected. This means that road lighting could have a somewhat larger accident-reducing effect, if compensation could be avoided. The fact that previous research has found no change in average speed when road lighting is introduced, seems to be explained by increased driving speeds by some drivers being counterbalanced by a larger proportion of more slowly driving groups of drivers (elderly people and women), i.e. different subgroups of road users compensate in different ways. © 1999 Elsevier Science Ltd. All rights reserved. Keywords: Road lighting; Behavioral adaptation; Risk compensation; Speed; Concentration; Lateral position
1. Introduction
1.1. Risk compensation— the reason why countermeasures do not work? Risk compensation is usually defined as behavioral adaptation to a perceived lower risk situation, especially when the lower risk is brought about by an accident countermeasure. This kind of adaptation has been discussed in the road safety research literature for a long time (OECD, 1990), and theories of behavioral adaptation have been developed (Wilde, 1998). According to Wilde’s theory of risk homeostasis, risk will remain constant over time, even if risk-reducing measures are introduced, because unless the target level of risk is changed, people will adapt their behavior to the reduced risk situation. Although Wilde’s theory is controversial, there seems to be a general agreement in the literature that road users do adapt their behavior to certain risk-reducing measures.
This study was financed by the Norwegian Research Council, Oslo, Norway. * Corresponding author. Tel.: + 47-22573800; fax: + 47-22570290. E-mail address: [email protected] (T. Assum)
Several studies of the effects of road safety measures have produced unexpected results. Measures as different as driver training, (Wynne-Jones and Hurst, 1984; Lund et al., 1986; Glad, 1988), wider roads (Elvik, 1995a), pedestrian crossings (Yagar, 1986) and ABS brakes (Biehl et al., 1987) seem to produce no accident reduction. Is risk compensation or even risk homeostasis (Wilde, 1982) the reason why these measures do not reduce road accidents? If the kind of behavioral adaptation defined as risk compensation above, is the answer, why does it seem to happen only to some countermeasures? To try and answer this question at least partially, the Institute of Transport Economics carried out a research project on risk compensation in road traffic, studying drivers’ behavioral adaptation to: air bags, ABS brakes and road lighting. This article describes car drivers’ possible adaptation to road lighting. Adaptation to the other two measures is reported by Sagberg et al. (1997).
1.2. General hypotheses Why are some road safety measures compensated for to a greater degree than other road safety measures? The hypotheses below, if confirmed, may in part answer that question:
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1. Road users will compensate when they have adjusted to a risk factor, and this risk factor is later on reduced or removed, i.e. if road users have lowered their speed or increased their attention because the road is narrow, they will increase their speed or reduce their attention when the road is widened. 2. Drivers will not compensate for injury-reducing measures, because the risk of material damage to their vehicles is enough to refrain from compensation (Lund and O’Neill, 1986; Bjørnskau, 1995), e.g. drivers will adjust their behavior to antilock brakes, but not to airbags (Sagberg et al., 1997).
1.3. The case of road lighting Road lighting is known to reduce the number of road accidents as well as the risk of road accidents in darkness (OECD, 1979; Fridstrøm et al., 1993; Elvik, 1995b; Elvik et al., 1997). Whether road lighting increases speed, is more uncertain. On the one hand drivers could be expected to reduce speed and increase concentration during darkness, because visibility is reduced, and consequently drive faster or with less concentration when road lighting is introduced. On the other hand Cornwell (1972) has studied the effect of road lighting on two English roads, finding speed increase on the one and speed reduction on the other. Nevertheless, road lighting may be compensated for (at least to a certain degree). Do car drivers increase their speed, reduce their attention or travel more during the dark hours when road lighting is installed? The possibility remains that the accident-reducing effect of road lighting could have been higher than what is found, if car drivers did not compensate. If car drivers do not reduce their speed in darkness compared with daylight, they cannot be expected to increase their speed during the dark hours when road lighting is installed. Theoretically, it is possible that people drive faster in darkness with than without road lighting, even if they do not drive faster during daylight than during darkness. This implies, however, that people drive faster in darkness when road lighting is installed, than during daylight hours, which seems unreasonable.
1.4. Specific hypothesis Drivers do not seem to adjust their behavior to the risk factor darkness by reducing their speed. The hypothesis is therefore that drivers do not adjust their behavior when this risk factor is partly removed by road lighting, i.e. we do not expect drivers to increase their speed, reduce their concentration or travel more when road lighting is installed.
2. Method
2.1. Quasi experiment The possible risk compensation for road lighting was studied by a quasi experiment, i.e. before and after study with controls. Data on drivers’ behavior, including speed and concentration were collected during darkness hours on a section of the route E18 in southern Norway, before and after road lighting was installed on December 16, 1994. Behavioral and speed data for the same road section during daylight hours as well as data for darkness hours for an adjacent section of the same road without road lighting were used as controls. Road and traffic conditions are very much the same on the two sections of the route E18.
2.2. Data collected by radar, 6ideo and questionnaire Speed was measured by radar for three weeks before and four weeks after the installation of road lights, for a total of 273 780 vehicles. All cars passing during the preselected observation hours, a total of approximately 3100, were stopped and questionnaires were administered to the drivers, who filled them in on the spot. The drivers’ concentration was measured in two ways, by questions in the questionnaire as well as by video registration of the lateral position. The drivers were asked to score their concentration on the experimental section and on the control section on a scale from 1 to 7, indicating minimal and maximal concentration, respectively. Moreover, they were asked whether they changed their concentration when going from parts of the road with lighting to parts without or vice versa. Previous research (e.g. O’Hanlon et al., 1986; Brookhuis and deWaard, 1993) has shown that lateral vehicle control, as measured by the standard deviation of lateral position (SDLP), tends to deteriorate with prolonged driving, with increasing blood alcohol level, and after administration of certain sedative drugs. Consequently, it seems reasonable to use increased variability in lateral position as an indicator of decreased driver vigilance or concentration. This interpretation is also in accordance with the findings reported by MacDonald and Hoffmann (1980) that steering wheel reversal rate shifts to lower frequencies (implying increased variability of lateral position) either when the driver’s total effort is reduced or when the total task demand exceeds the driver’s performance capacity. Both these conditions are likely to be accompanied by reduced attention to the driving task. On straight road sections, drivers who are concentrated upon their driving can be assumed to continuously adjust their transverse position to avoid large deviations from a straight line. On the other hand, drivers who are less concentrated on the
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driving task will not notice as fast that they are moving towards the edge of the lane, and consequently they will be slower to adjust their position. Thus, small (and frequent) changes in lateral position — yielding a small SDLP—may indicate high concentration, whereas larger (and less frequent) changes — yielding a larger SDLP—may indicate low concentration. Lateral position was registered by video camera on a 200 m straight part of the experimental section before and after the installation of road lighting during daylight and darkness for a total of 515 cars. Ideally, the video recording of each car should have been transformed into a longitudinal by lateral position matrix, making possible the computation of SDLP. Since this was difficult to do on the basis of the video recording, a simpler scoring procedure was used. A transparent grid with longitudinal lines covering the picture of the roadway was placed on the video screen, the intervals between the lines corresponding to about 13 cm on the road (the resolution of the video picture did not permit smaller changes to be detected). The data set included only free-driving cars, which were not influenced by on-coming or leading traffic. During playback of the videotape, each incidence of a car crossing a line was recorded, using the right rear light to indicate the position of the car. Lateral shifts of one interval or more in one direction, followed by a shift in the opposite direction was defined as a deviation from the straight course, and the number of such deviations was used to indicate a lack of concentration. Although being a rather coarse measure, the number of large deviations is obviously correlated with SDLP. The number of such deviations turned out to be rather low —less than one per car on average — which seems to support our assumptions that this measure reflects only the low-frequent and large deviations associated with low concentration and not the small and frequent corrections during concentrated driving.
2.3. Data analysis and significance testing The speed data from the radars were averaged for each hour during the whole duration of the study. These average speeds were then the basic units for further analysis. The speed data shown in the results section are the means of the daily average values for darkness and daylight, respectively. The data were not weighted by traffic volume. The number of cars per section and day varied between 227 and 2611. The changes in speed from before to after installation of lighting were evaluated by two analysis-of-variance models. The first was a 2 ×2 analysis of variance with the repeated-measures factor daylight vs. darkness (measured on the same days), and the between-groups factor pre vs. post. The second model was also a 2 ×2 analysis with pre vs. post as between-groups factor, but
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in this case the repeated-measures factor was control vs. experimental road sections (measured on the same days). The straight and curved road sections were analyzed by both models. The first model was also used for analyzing the number of directional changes, based on lateral position measurements. Since lateral position was measured only on the experimental road section, the second model was not relevant. Unless otherwise stated in the results section, questionnaire data were analyzed by x 2 tests.
2.4. Compensation in terms of speed Because road lighting may influence speed differently in curves and straight parts of the road, speed was measured under both conditions. Compensation in terms of speed was operationalized in two ways. Firstly, speed in darkness (Spedar) was compared with speed during daylight hours (Speli) on the experimental part A of the road before (t1) and after (t2) road lighting was installed. Compensation for road lighting implies that the ratio of speed in darkness (Spedar) to speed in daylight is greater after (t2) than before (t1) the installation of road lighting, i.e.: (SpedarA/SpeliA)t2 \(SpedarA/SpeliA)t1
(1)
Secondly, speed in darkness (Spedar) on the experimental part A of the road was compared with speed in darkness on the control part B of the road, before (t1) and after (t2) the installation of road lighting on the experimental part. Compensation for road lighting implies that the ratio of speed on the experimental section A to speed on the control section B is greater after (t2) that before (t1) the installation for road lighting, i.e.: (SpedarA/SpedarB)t2 \ (SpedarA/SpedarB)t1
(2)
In addition to the speed measurements the drivers were asked a general question as to whether they would drive faster in darkness with road lighting or slower in darkness without road lighting.
3. Results
3.1. Speed 3.1.1. Inequality (1) — daylight speed as comparison Table 1 shows the average speeds during darkness and daylight for straight and curved parts of the experimental section of the road before and after road lighting was installed. Road traffic speed was measured in both directions. Using inequality (1) for the straight part, we get the following results: (SpedarA/SpeliA)t2 =81.4/79.0 = 1.03
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(SpedarA/SpeliA)t1 =77.8/78.0 = 1.00
are presented for straight and curved parts, respectively. For the straight parts the change from the before to the after period differed significantly (F= 40.86; PB 0.001) between the experimental and control sections, increase of 3.6 km/h and decrease of 0.2 km/h, respectively. Using inequality (2) for the straight parts, we get the following results:
For the straight section the change from the before to the after period differed significantly (F = 10.50; P= 0.003) between darkness and daylight. As shown in Table 1, speed in darkness increased by 3.6 km/h, whereas speed in daylight increased by only 1 km/h. Inequality (1) is fulfilled for the straight part of the road as 1.03\1.00. Based upon speed data for the straight part of the road the compensation effect is approximately 3.5%, i.e. the average speed increased by 3.5% more in darkness than in daylight after the installation of road lighting. Also for the curve the change from the before to the after period differed significantly (F = 11.40; P = 0.002) between darkness (0.5 km/h increase) and daylight (1.8 km/h decrease). Using the inequality (1) for the curved part of the road, we get:
(SpedarA/SpedarB)t2 = 81.4/79.0 = 1.03 (SpedarA/SpedarB)t1 = 77.8/79.2 = 0.98 Inequality (2) is fulfilled for the straight parts as 1.03\ 0.98. The compensation effect is approximately 5%; i.e. the speed is increased by 5% more on the experimental section than on the control section. For the curve the speed increased slightly on the experimental section (0.5 km/h), but on the control section the speed remained unchanged. The difference was, however, not significant (F= 1.76; P= 0.194). Applying inequality (2) for the curves, we find:
(SpedarA/SpeliA)t2 = 71.3/70.3 = 1.01 (SpedarA/SpeliA)t1 = 70.8/72.1 = 0.98
(SpedarA/SpedarB)t2 = 71.3/76.1 = 0.94
Inequality (1) is fulfilled also for the curved part of the road as 1.01 \ 0.98. The compensation effect is approximately 3.2%; i.e. the average speed is increased by 3.2% more in darkness than in daylight after the installation of the road lighting.
(SpedarA/SpedarB)t1 = 70.8/76.1 = 0.93 Inequality (2) is also fulfilled for the curves, as 0.94\ 0.93. But the difference is very small. The compensation effect is 0.7%; i.e. the average speed is increased by 0.7% more on the experimental than on the control section. In addition to the speed measurements the drivers were asked a general question on whether they would drive faster in darkness with road lighting or more
3.1.2. Inequality (2) —darkness speed of control section as comparison In Table 2 the average speeds during darkness for the experimental part A and the control part B of the road
Table 1 Average speed (km/h) during darkness (19:00–24:00 h) and daylight (09:00–15:00 h) for straight and curved parts of the experimental part of the road before and after the installation of road lighting Straight
Curve
Before
Darkness Daylight
After
Before
After
km/h
N
km/h
N
km/h
N
km/h
N
77.8 78.0
(20 348) (39 395)
81.4 79.0
(15 201) (29 905)
70.8 72.1
(14 288) (28 289)
71.3 70.3
(17 510) (35 078)
Table 2 Average speed (km/h) during darkness hours (19:00–24:00 h) for straight and curved parts of the experimental section A and the control section B before and after the installation of road lighting on section A Sections
Straight
Curve
Before
A: experimental B: control
After
Before
After
km/h
N
km/h
N
km/h
N
km/h
N
77.8 79.2
(20 348) (20 606)
81.4 79.0
(15 201) (13 972)
70.8 76.1
(14 288) (21 689)
71.3 76.1
(12 7510) (17 499)
T. Assum et al. / Accident Analysis and Pre6ention 31 (1999) 545–553 Table 3 Car drivers’ answers to the questions whether they would drive faster in darkness with than without road lighting or more slowly in darkness without road lighting (%)a
Yes No Don’t know Total N a
I would drive faster with road lighting
I would drive more slowly without road lighting
59 (56–62) 34 7 100 1440
75 (73–77) 20 4 99 1577
Confidence interval in brackets.
to concentration during daylight after (t2) the installation of lights on the experimental section, i.e.: (CondarA/ConliA)t2 B (CondarA/ConliA)t1
3.2. Compensation in terms of concentration Compensating in terms of reducing concentration is always possible instead of or in addition to increasing speed or other possible forms of compensation. However, concentration is much more difficult to measure than is speed, especially when drivers should not know about the measuring. As mentioned, in this study the drivers’ concentration was measured by video registration of lateral position and by questionnaire. Compensation in terms of concentration can be compared with two different controls, concentration on the experimental section during daylight, and concentration during darkness on the control section. Firstly, concentration during darkness (Condar) relative to concentration during daylight (Conli) before (t1) the installation of lights on the experimental road section is compared with concentration during darkness relative
(3)
Secondly, concentration during darkness on the experimental section (CondarA) relative to concentration during darkness on the control section (CondarB) before (t1) the installation of lights on the experimental section is compared with the concentration during darkness on the experimental section (CondarA) relative to the concentration during darkness on the control section (CondarB) after (t2) the installation of lights on the experimental section, i.e.: (CondarA/CondarB)t2 B (CondarA/CondarB)t1
slowly in darkness without road lighting. A split-half technique was applied, asking approximately 50% whether they would drive faster in darkness with road lighting and the other 50% whether they would drive more slowly in darkness without road lighting. The results in Table 3 clearly indicate that car drivers do adapt their behavior in terms of speed to road lighting. To both versions of the question a majority of the drivers answer that they adjust their behavior to road lighting. Even if speed could be expected to decrease a little if road lighting generates more road traffic in darkness, we find some compensation in terms of speed for all comparisons made. This result is corroborated by the answers in Table 3 to a general question of changes in speed with and without road lighting. The speed increase measured varies between approximately 1 and 5%. Though this increase is not considerable, our hypothesis about speed is rejected. Contrary to our hypothesis we find that drivers do compensate for road lighting, although to a modest degree.
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(4)
3.3. Concentration Table 4 shows that the differences in average concentration are small. But the differences between darkness and daylight on the experimental as well as the control section are significant on a 5% level (x 2 test). Applying inequality (3), we get the following results: (CondarA/ConliA)t2 = 4.9/4.9 =1.00 (CondarA/ConliA)t1 = 5.0/4.9 =1.02 The inequality (3) is fulfilled, as 1.00 B 1.02. The difference is quite small, however, and not significant. Applying inequality (4), we get the following results: (CondarA/CondarB)t2 = 4.9/5.1 = 0.96 (CondarA/CondarB)t1 = 5.0/4.8 = 1.04 The inequality (4) is fulfilled, as 0.96B 1.04, but the difference is not significant. The compensation effect of road lighting in terms of concentration seems, however, to be greater for inequality (4) than for inequality (3). This may be due to a possible co-variation between concentration and who drives in the darkness and daylight. If some people who would not drive during darkness without road lighting, start to drive after dark when road lighting is installed, and if these people are more concentrated than the average drivers, this co-variation may contribute to disguising the effect of road lighting on concentration. Table 5 shows car drivers by gender and age, driving during darkness and daylight before and after the installation of road lighting on the experimental section. The bottom line of Table 5 shows a slight tendency for men to drive relatively more during darkness than women. Table 5 also shows a tendency for elderly people to drive less during darkness. Moreover, women 45 years and older totaled 23% of all female drivers during darkness before the installation of road lights and 36% after. There is a similar, but smaller tendency for male drivers.
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Table 6 shows clearly that women score higher on concentration than men. This tendency is found in all age groups, during daylight and darkness, before and after road lighting. There is also a certain tendency for the concentration score to increase with age. Table 5 shows that driving during darkness co-
varies with age and gender, and that the installation of road lighting affects the age and gender distribution of drivers. Thus, we may conclude that the changes in age and gender distribution have disguised some of the concentration effect as measured by inequality (4).
Table 4 Car drivers’ assessment of concentration on the experimental and control sections during darkness and daylight before and after installation of road lighting on the experimental section (%) Concentration
Before
After
Experimental Daylight Minimum – – – – – Maximum N Average Standard deviation x2 Significance
1 2 3 4 5 6 7
2.4 3.1 7.6 26.8 22.9 16.7 16.7 939 4.9 1.4
Control Darkness
2.5 4.6 9.1 20.6 23.7 20.6 18.7 712 5.0 1.5 15.62 0.016
Experimental
Daylight 2.5 2.1 9.6 29.9 25.8 17.0 13.1 512 4.8 1.4
Darkness 2.2 5.4 10.5 23.5 22.9 18.1 17.5 371 4.8 1.5 13.73 0.033
Daylight 2.4 2.8 8.8 24.2 26.7 19.2 15.9 818 4.9 1.4
Control Darkness
2.8 3.2 8.2 24.1 24.8 18.9 18.0 634 4.9 1.5 1.94 0.925
Daylight 1.9 2.2 11.8 27.6 23.8 18.0 14.7 416 4.8 1.4
Darkness 1.1 2.5 7.9 21.3 25.3 21.6 20.2 356 5.1 1.4 11.80 0.067
Table 5 Car drivers by gender and age, driving during darkness and daylight hours before and after the installation of road lighting on the experimental section (%) Age (years)
Before
After
Daylight
18–44 45–64 65+ Total N %
Darkness
Daylight
Darkness
Male
Female
Male
Female
Male
Female
Male
Female
56 32 12 100 639 69
63 33 4 100 287 31
66 31 3 100 511 74
77 22 1 100 181 26
56 36 8 100 577 72
71 26 3 100 221 28
64 32 4 100 475 76
64 33 3 100 152 24
Table 6 Drivers by average concentration score, gender and age and by daylight and darkness before and after the installation of road lightinga Age (years)
Before
After
Daylight
18–44 45–64 65+ All N a
Darkness
Daylight
Darkness
Male
Female
Male
Female
Male
Female
Male
Female
4.53 4.85 5.06 4.70 635
5.25 5.43 5.55 5.31 286
4.79 4.95 4.50 4.83 509
5.45 5.00 – 5.33 181
4.67 4.84 5.19 4.76 577
5.30 5.21 6.50 5.32 220
4.77 4.96 4.83 4.82 471
5.42 5.13 6.20 5.35 150
Average concentration score: 1 is minimal and 7 maximal concentration.
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Table 7 Car drivers’ answers to questions on concentration when driving in darkness with and without road lighting (%)a
Yes No Don’t know Total N a
I would drive more concentrated without road lighting
I would drive less concentrated with road lighting
83 (81–85) 13 4 100 1575
29 (27–31) 64 7 100 1431
Confidence interval in brackets.
The drivers were also asked about concentration with or without road lighting. A split-half technique was applied, so that 50% of the drivers were asked whether they would be less concentrated when driving in darkness with than without road lighting. The other half was asked whether they would be more concentrated when driving in darkness without than with road lighting. The results are shown in Table 7. Theoretically, the two versions of the question should have yielded the same distribution of answers. The rather large difference between the two distributions is probably due to the fact that it seems less socially acceptable to admit a lower concentration with road lighting than a higher concentration without lighting. Anyway, the answers indicate that approximately between 30 and 80% of car drivers do compensate for road lighting in terms of changed concentration. But the question remains whether a questionnaire represents a valid measure of concentration. If similar changes in concentration are found when concentration is operationalized as lateral position, the validity of the questions in the questionnaire will increase.
Inequality (4) cannot be used, as lateral position was not recorded on the control section. The results of the video recordings of lateral position indicate, in the same way as the questionnaire data on concentration, that drivers do compensate for road lighting in terms of concentration. The difference in concentration is statistically significant and greater when measured by lateral position than by questionnaire. This fact may be due to the drivers being unwilling to admit that they are less concentrated at any time. The results on lateral position show that concentration is actually lower during darkness with road lighting than during daylight. We found a similar tendency for speed, being higher during darkness after the installation of road lighting than during daylight. A possible reason for these findings may be that the populations of drivers during daylight and darkness are different. Thus, it seems that drivers’ adjustment of behavior to risk factors and the reduction of risk factors are indeed quite complex.
3.4. Concentration measured as 6ariation in lateral position
Our data show that the car drivers compensate for road lighting both in terms of speed, and in terms of concentration. As mentioned above, road lighting is also known to reduce accidents. Compensation is thus not sufficient to make road lighting ineffective as a road accident countermeasure. Our main hypothesis is clearly rejected. This hypothesis was based upon the idea that as drivers do not seem to slow down during darkness hours, they should not increase their speed after the installation of road lighting. Our results indicate, however, that this idea is based upon the fallacy of assuming that results ob-
The number of deviations from a straight course was used as the operationalization of concentration, assuming that the fewer deviations, the more concentrated the driver. In inequality (3) a small number of changes in lateral position indicates high concentration, in contrast to the questionnaire data. The inequality (3) has to be turned in the computations below (Table 8). Using inequality (3), we get the following results: (CondarA/ConliA)t2 =0.94/0.77 =1.22 (CondarA/ConliA)t1 =0.59/0.69 =0.86 The inequality (3) ‘turned around’ is fulfilled as 1.22\ 0.86, i.e. the number of changes in lateral position is greater, and consequently the concentration is lower, after the installation of road lighting. The difference is considerable, 42%, and is statistically significant as shown by an interaction between the factors daylight vs. darkness and before vs. after (F =5.33; P= 0.021).
3.5. Interpretation of results
Table 8 Drivers by average number of changes in lateral position (\13 cm) on a 200 m road section during daylight and darkness hours before and after the installation of road lighting on the experimental section
Daylight Darkness
Before
After
0.69 (131) 0.59 (123)
0.77 (154) 0.94 (107)
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tained for the average driver is due to similar behavior for all drivers rather than different groups of drivers behaving in different ways. Table 5 indicates that people driving during darkness and daylight hours are not the same. Thus, the reason why previous research has shown that speeds during darkness and daylight are more or less the same, might be that people driving during darkness are a subgroup of drivers who generally drive faster than the average driver. When comparing average speed during darkness with daylight hours, we are thus comparing the speed of two different subgroups of drivers. Those who drive during darkness hours are likely to drive even faster during daylight hours, but the average daylight speed is reduced by the subgroup of drivers who do not drive during darkness hours, a subgroup, which is likely to drive more slowly during daylight hours than the average driver. The fact that our results show that the highest speed occurred during darkness after the installation of road lighting and the concentration, measured by changes in lateral position, was lowest under the same conditions, indicates that there is individual compensation for road lighting both in terms of speed and concentration. The reason why the opposite adjustment, i.e. reduction in speed during darkness, is not found generally, would then be that at an aggregate level, this tendency is counterbalanced by some drivers adjusting to darkness by not driving. These drivers are likely to be those who would also drive at a speed below average during daylight. Speed was measured both on curved and straight parts of the road, because risk compensation could be expected to be different under the two conditions. We found that the difference in speed change was 5% between the experimental and control sections on the straight part and 0.7% on the curved part of the road. A similar, but smaller difference is found when comparing speed in darkness with speed in daylight. The greater speed increase on the straight part may be explained by road lighting being more effective on a straight road, where the lights increase considerably the distance which can be seen. In curved parts, the visible distance is not increased very much by road lighting.
4. Conclusion The results presented in this article indicate that drivers do compensate for road lighting in terms of increased speed and reduced concentration. Our hypothesis about compensation for road lighting must then be rejected. The empirical basis for our hypothesis was the fact that previous research has shown road lighting to reduce road accidents and that drivers on the average do not increase their speed when road lighting is installed.
Our findings indicate that the latter fact can be explained by increased driving speeds by some drivers being counterbalanced by a larger proportion of more slowly driving groups of drivers (elderly people and women) after the installation of road lights, i.e. different subgroups of road users compensate in different ways. This means that risk compensation may occur even if the pertinent measure is known to reduce accidents and even for measures for which the road users on the average do not seem to adapt their behavior.
References Biehl, B., Aschenbrenner, M., Wurm, G., 1987. Einfluss der Risikokompensation auf die Wirkung von Verkehrssicherheitsmassnahmen am Beispiel ABS. Schriftreihe Unfall-under Sicherheitsforschung Strassenverkehr 63, 65 – 70. Bjørnskau, T., 1995. Hypotheses on Risk Compensation. Proceedings of the Conference Road Safety in Europe and Strategic Highway Research, Lille, France, 26 – 28 September 1994. VTI konsferens 2A, Part 4, 84 – 98, VTI — Swedish Road and Transport Research Institute, Linko¨ping. Brookhuis, K.A., deWaard, D., 1993. The use of psychophysiology to assess driver status. Ergonomics 36 (9), 1099 – 1110. Cornwell, P.R., 1972. Highway capacity, vehicle speeds and public lighting. Traffic Engineering and Control 13, 297 – 298. Elvik, R., 1995a. Vegutforming og vegutstyr (Road design and road equipment). Paper TST 0650/95. Institute of Transport Economics, Oslo. Elvik, R., 1995b. A meta-analysis of evaluations of public lighting as an accident countermeasure. Transportation Research Record 1485, 112 – 113. Elvik, R., Mysen, A.B., Vaa, T., 1997. Trafikksikkerhetsha˚ndbok (Traffic Safety Handbook). Institute of Transport Economics, Oslo. Fridstrøm, L., Ifver, J., Ingebrigtsen, S., Krogsga˚rd, T.L., 1993. Explaining the variation in road accident counts — a four-country generalized Poisson regression analysis. Nord 1993, 35. Nordic Council of Ministers, Copenhagen. Glad, A. 1988. Fase 2 i føreropplæringen. Effekt pa˚ ulykkesrisikoen (Phase 2 of driver training. Effect on accident risk). Institute of Transport Economics, Oslo. Lund, A.K., O’Neill, B., 1986. Perceived risks and driving behavior. Accident Analysis and Prevention 18, 367 – 370. Lund, A.K., Williams, A.F., Zador, P., 1986. High school driver education. Further evaluation of the DeKalb County study. Accident Analysis and Prevention 18, 349 – 357. MacDonald, W.A., Hoffmann, E.R., 1980. Review of relationships between steering wheel reversal rate and driving task demand. Human Factors 22 (6), 733 – 739. OECD-Road Research Group, 1979. Road Safety at Night. Paris. OECD-Road Transport Research, 1990. Behavioural Adaptations to Changes in the Road Transport System. Paris. O’Hanlon, J.F., Brookhuis, K.A., Louwerens, J.W., Volkerts, E.R., 1986. Performance testing as part of drug registration. In: O’Hanlon, J.F., deGier, J.J. (Eds.), Drugs and Driving. Taylor and Francis, London, pp. 311 – 330. Sagberg, F., Fosser, S., Sætermo, I.A., 1997. An investigation of behavioural adaptation to airbag and antilock brakes among taxi drivers. Accident Analysis and Prevention 29, 293 – 302. Wilde, G.J.S., 1982. The theory of risk homeostasis: implications for safety and health. Risk Analysis 2, 209 – 225.
T. Assum et al. / Accident Analysis and Pre6ention 31 (1999) 545–553 Wilde, G.J.S., 1998. Risk homeostasis theory: an overview. Paper presented at the Wilderness Risk Manager’s Conference, Salt Lake City, October 1997 (http://home.ican.net/%7E735769/ wilde.htm). Wynne-Jones, J.D., Hurst, P.M., 1984. The AA Driver Training
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Evaluation. Ministry of Transport, Wellington, New Zealand. Yagar, S., 1986. Safety Impacts of Installing Pedestrian Crosswalks. In: Carney, J.F. (Ed.), Proceeding of the conference Effectiveness of Highway Safety Improvements. American Society of Civil Engineers, New York, pp. 137 – 146.
Risk compensation—the case of road lighting Terje Assum *, Torkel Bjørnskau, Stein Fosser, Fridulv Sagberg Institute of Transport Economics, PO Box 6110 Etterstad, N-0602 Oslo, Norway Received 8 January 1999; accepted 28 January 1999
Abstract The hypothesis of this article is that drivers will not adjust their behavior, i.e. drivers are not expected to increase their speed, reduce their concentration or travel more when road lighting is installed. The hypothesis was based on previous research showing that road lighting reduces road accidents and that average driving speeds do not increase when road lighting is installed. Our results show that drivers do compensate for road lighting in terms of increased speed and reduced concentration. Consequently, the hypothesis is rejected. This means that road lighting could have a somewhat larger accident-reducing effect, if compensation could be avoided. The fact that previous research has found no change in average speed when road lighting is introduced, seems to be explained by increased driving speeds by some drivers being counterbalanced by a larger proportion of more slowly driving groups of drivers (elderly people and women), i.e. different subgroups of road users compensate in different ways. © 1999 Elsevier Science Ltd. All rights reserved. Keywords: Road lighting; Behavioral adaptation; Risk compensation; Speed; Concentration; Lateral position
1. Introduction
1.1. Risk compensation— the reason why countermeasures do not work? Risk compensation is usually defined as behavioral adaptation to a perceived lower risk situation, especially when the lower risk is brought about by an accident countermeasure. This kind of adaptation has been discussed in the road safety research literature for a long time (OECD, 1990), and theories of behavioral adaptation have been developed (Wilde, 1998). According to Wilde’s theory of risk homeostasis, risk will remain constant over time, even if risk-reducing measures are introduced, because unless the target level of risk is changed, people will adapt their behavior to the reduced risk situation. Although Wilde’s theory is controversial, there seems to be a general agreement in the literature that road users do adapt their behavior to certain risk-reducing measures.
This study was financed by the Norwegian Research Council, Oslo, Norway. * Corresponding author. Tel.: + 47-22573800; fax: + 47-22570290. E-mail address: [email protected] (T. Assum)
Several studies of the effects of road safety measures have produced unexpected results. Measures as different as driver training, (Wynne-Jones and Hurst, 1984; Lund et al., 1986; Glad, 1988), wider roads (Elvik, 1995a), pedestrian crossings (Yagar, 1986) and ABS brakes (Biehl et al., 1987) seem to produce no accident reduction. Is risk compensation or even risk homeostasis (Wilde, 1982) the reason why these measures do not reduce road accidents? If the kind of behavioral adaptation defined as risk compensation above, is the answer, why does it seem to happen only to some countermeasures? To try and answer this question at least partially, the Institute of Transport Economics carried out a research project on risk compensation in road traffic, studying drivers’ behavioral adaptation to: air bags, ABS brakes and road lighting. This article describes car drivers’ possible adaptation to road lighting. Adaptation to the other two measures is reported by Sagberg et al. (1997).
1.2. General hypotheses Why are some road safety measures compensated for to a greater degree than other road safety measures? The hypotheses below, if confirmed, may in part answer that question:
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1. Road users will compensate when they have adjusted to a risk factor, and this risk factor is later on reduced or removed, i.e. if road users have lowered their speed or increased their attention because the road is narrow, they will increase their speed or reduce their attention when the road is widened. 2. Drivers will not compensate for injury-reducing measures, because the risk of material damage to their vehicles is enough to refrain from compensation (Lund and O’Neill, 1986; Bjørnskau, 1995), e.g. drivers will adjust their behavior to antilock brakes, but not to airbags (Sagberg et al., 1997).
1.3. The case of road lighting Road lighting is known to reduce the number of road accidents as well as the risk of road accidents in darkness (OECD, 1979; Fridstrøm et al., 1993; Elvik, 1995b; Elvik et al., 1997). Whether road lighting increases speed, is more uncertain. On the one hand drivers could be expected to reduce speed and increase concentration during darkness, because visibility is reduced, and consequently drive faster or with less concentration when road lighting is introduced. On the other hand Cornwell (1972) has studied the effect of road lighting on two English roads, finding speed increase on the one and speed reduction on the other. Nevertheless, road lighting may be compensated for (at least to a certain degree). Do car drivers increase their speed, reduce their attention or travel more during the dark hours when road lighting is installed? The possibility remains that the accident-reducing effect of road lighting could have been higher than what is found, if car drivers did not compensate. If car drivers do not reduce their speed in darkness compared with daylight, they cannot be expected to increase their speed during the dark hours when road lighting is installed. Theoretically, it is possible that people drive faster in darkness with than without road lighting, even if they do not drive faster during daylight than during darkness. This implies, however, that people drive faster in darkness when road lighting is installed, than during daylight hours, which seems unreasonable.
1.4. Specific hypothesis Drivers do not seem to adjust their behavior to the risk factor darkness by reducing their speed. The hypothesis is therefore that drivers do not adjust their behavior when this risk factor is partly removed by road lighting, i.e. we do not expect drivers to increase their speed, reduce their concentration or travel more when road lighting is installed.
2. Method
2.1. Quasi experiment The possible risk compensation for road lighting was studied by a quasi experiment, i.e. before and after study with controls. Data on drivers’ behavior, including speed and concentration were collected during darkness hours on a section of the route E18 in southern Norway, before and after road lighting was installed on December 16, 1994. Behavioral and speed data for the same road section during daylight hours as well as data for darkness hours for an adjacent section of the same road without road lighting were used as controls. Road and traffic conditions are very much the same on the two sections of the route E18.
2.2. Data collected by radar, 6ideo and questionnaire Speed was measured by radar for three weeks before and four weeks after the installation of road lights, for a total of 273 780 vehicles. All cars passing during the preselected observation hours, a total of approximately 3100, were stopped and questionnaires were administered to the drivers, who filled them in on the spot. The drivers’ concentration was measured in two ways, by questions in the questionnaire as well as by video registration of the lateral position. The drivers were asked to score their concentration on the experimental section and on the control section on a scale from 1 to 7, indicating minimal and maximal concentration, respectively. Moreover, they were asked whether they changed their concentration when going from parts of the road with lighting to parts without or vice versa. Previous research (e.g. O’Hanlon et al., 1986; Brookhuis and deWaard, 1993) has shown that lateral vehicle control, as measured by the standard deviation of lateral position (SDLP), tends to deteriorate with prolonged driving, with increasing blood alcohol level, and after administration of certain sedative drugs. Consequently, it seems reasonable to use increased variability in lateral position as an indicator of decreased driver vigilance or concentration. This interpretation is also in accordance with the findings reported by MacDonald and Hoffmann (1980) that steering wheel reversal rate shifts to lower frequencies (implying increased variability of lateral position) either when the driver’s total effort is reduced or when the total task demand exceeds the driver’s performance capacity. Both these conditions are likely to be accompanied by reduced attention to the driving task. On straight road sections, drivers who are concentrated upon their driving can be assumed to continuously adjust their transverse position to avoid large deviations from a straight line. On the other hand, drivers who are less concentrated on the
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driving task will not notice as fast that they are moving towards the edge of the lane, and consequently they will be slower to adjust their position. Thus, small (and frequent) changes in lateral position — yielding a small SDLP—may indicate high concentration, whereas larger (and less frequent) changes — yielding a larger SDLP—may indicate low concentration. Lateral position was registered by video camera on a 200 m straight part of the experimental section before and after the installation of road lighting during daylight and darkness for a total of 515 cars. Ideally, the video recording of each car should have been transformed into a longitudinal by lateral position matrix, making possible the computation of SDLP. Since this was difficult to do on the basis of the video recording, a simpler scoring procedure was used. A transparent grid with longitudinal lines covering the picture of the roadway was placed on the video screen, the intervals between the lines corresponding to about 13 cm on the road (the resolution of the video picture did not permit smaller changes to be detected). The data set included only free-driving cars, which were not influenced by on-coming or leading traffic. During playback of the videotape, each incidence of a car crossing a line was recorded, using the right rear light to indicate the position of the car. Lateral shifts of one interval or more in one direction, followed by a shift in the opposite direction was defined as a deviation from the straight course, and the number of such deviations was used to indicate a lack of concentration. Although being a rather coarse measure, the number of large deviations is obviously correlated with SDLP. The number of such deviations turned out to be rather low —less than one per car on average — which seems to support our assumptions that this measure reflects only the low-frequent and large deviations associated with low concentration and not the small and frequent corrections during concentrated driving.
2.3. Data analysis and significance testing The speed data from the radars were averaged for each hour during the whole duration of the study. These average speeds were then the basic units for further analysis. The speed data shown in the results section are the means of the daily average values for darkness and daylight, respectively. The data were not weighted by traffic volume. The number of cars per section and day varied between 227 and 2611. The changes in speed from before to after installation of lighting were evaluated by two analysis-of-variance models. The first was a 2 ×2 analysis of variance with the repeated-measures factor daylight vs. darkness (measured on the same days), and the between-groups factor pre vs. post. The second model was also a 2 ×2 analysis with pre vs. post as between-groups factor, but
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in this case the repeated-measures factor was control vs. experimental road sections (measured on the same days). The straight and curved road sections were analyzed by both models. The first model was also used for analyzing the number of directional changes, based on lateral position measurements. Since lateral position was measured only on the experimental road section, the second model was not relevant. Unless otherwise stated in the results section, questionnaire data were analyzed by x 2 tests.
2.4. Compensation in terms of speed Because road lighting may influence speed differently in curves and straight parts of the road, speed was measured under both conditions. Compensation in terms of speed was operationalized in two ways. Firstly, speed in darkness (Spedar) was compared with speed during daylight hours (Speli) on the experimental part A of the road before (t1) and after (t2) road lighting was installed. Compensation for road lighting implies that the ratio of speed in darkness (Spedar) to speed in daylight is greater after (t2) than before (t1) the installation of road lighting, i.e.: (SpedarA/SpeliA)t2 \(SpedarA/SpeliA)t1
(1)
Secondly, speed in darkness (Spedar) on the experimental part A of the road was compared with speed in darkness on the control part B of the road, before (t1) and after (t2) the installation of road lighting on the experimental part. Compensation for road lighting implies that the ratio of speed on the experimental section A to speed on the control section B is greater after (t2) that before (t1) the installation for road lighting, i.e.: (SpedarA/SpedarB)t2 \ (SpedarA/SpedarB)t1
(2)
In addition to the speed measurements the drivers were asked a general question as to whether they would drive faster in darkness with road lighting or slower in darkness without road lighting.
3. Results
3.1. Speed 3.1.1. Inequality (1) — daylight speed as comparison Table 1 shows the average speeds during darkness and daylight for straight and curved parts of the experimental section of the road before and after road lighting was installed. Road traffic speed was measured in both directions. Using inequality (1) for the straight part, we get the following results: (SpedarA/SpeliA)t2 =81.4/79.0 = 1.03
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(SpedarA/SpeliA)t1 =77.8/78.0 = 1.00
are presented for straight and curved parts, respectively. For the straight parts the change from the before to the after period differed significantly (F= 40.86; PB 0.001) between the experimental and control sections, increase of 3.6 km/h and decrease of 0.2 km/h, respectively. Using inequality (2) for the straight parts, we get the following results:
For the straight section the change from the before to the after period differed significantly (F = 10.50; P= 0.003) between darkness and daylight. As shown in Table 1, speed in darkness increased by 3.6 km/h, whereas speed in daylight increased by only 1 km/h. Inequality (1) is fulfilled for the straight part of the road as 1.03\1.00. Based upon speed data for the straight part of the road the compensation effect is approximately 3.5%, i.e. the average speed increased by 3.5% more in darkness than in daylight after the installation of road lighting. Also for the curve the change from the before to the after period differed significantly (F = 11.40; P = 0.002) between darkness (0.5 km/h increase) and daylight (1.8 km/h decrease). Using the inequality (1) for the curved part of the road, we get:
(SpedarA/SpedarB)t2 = 81.4/79.0 = 1.03 (SpedarA/SpedarB)t1 = 77.8/79.2 = 0.98 Inequality (2) is fulfilled for the straight parts as 1.03\ 0.98. The compensation effect is approximately 5%; i.e. the speed is increased by 5% more on the experimental section than on the control section. For the curve the speed increased slightly on the experimental section (0.5 km/h), but on the control section the speed remained unchanged. The difference was, however, not significant (F= 1.76; P= 0.194). Applying inequality (2) for the curves, we find:
(SpedarA/SpeliA)t2 = 71.3/70.3 = 1.01 (SpedarA/SpeliA)t1 = 70.8/72.1 = 0.98
(SpedarA/SpedarB)t2 = 71.3/76.1 = 0.94
Inequality (1) is fulfilled also for the curved part of the road as 1.01 \ 0.98. The compensation effect is approximately 3.2%; i.e. the average speed is increased by 3.2% more in darkness than in daylight after the installation of the road lighting.
(SpedarA/SpedarB)t1 = 70.8/76.1 = 0.93 Inequality (2) is also fulfilled for the curves, as 0.94\ 0.93. But the difference is very small. The compensation effect is 0.7%; i.e. the average speed is increased by 0.7% more on the experimental than on the control section. In addition to the speed measurements the drivers were asked a general question on whether they would drive faster in darkness with road lighting or more
3.1.2. Inequality (2) —darkness speed of control section as comparison In Table 2 the average speeds during darkness for the experimental part A and the control part B of the road
Table 1 Average speed (km/h) during darkness (19:00–24:00 h) and daylight (09:00–15:00 h) for straight and curved parts of the experimental part of the road before and after the installation of road lighting Straight
Curve
Before
Darkness Daylight
After
Before
After
km/h
N
km/h
N
km/h
N
km/h
N
77.8 78.0
(20 348) (39 395)
81.4 79.0
(15 201) (29 905)
70.8 72.1
(14 288) (28 289)
71.3 70.3
(17 510) (35 078)
Table 2 Average speed (km/h) during darkness hours (19:00–24:00 h) for straight and curved parts of the experimental section A and the control section B before and after the installation of road lighting on section A Sections
Straight
Curve
Before
A: experimental B: control
After
Before
After
km/h
N
km/h
N
km/h
N
km/h
N
77.8 79.2
(20 348) (20 606)
81.4 79.0
(15 201) (13 972)
70.8 76.1
(14 288) (21 689)
71.3 76.1
(12 7510) (17 499)
T. Assum et al. / Accident Analysis and Pre6ention 31 (1999) 545–553 Table 3 Car drivers’ answers to the questions whether they would drive faster in darkness with than without road lighting or more slowly in darkness without road lighting (%)a
Yes No Don’t know Total N a
I would drive faster with road lighting
I would drive more slowly without road lighting
59 (56–62) 34 7 100 1440
75 (73–77) 20 4 99 1577
Confidence interval in brackets.
to concentration during daylight after (t2) the installation of lights on the experimental section, i.e.: (CondarA/ConliA)t2 B (CondarA/ConliA)t1
3.2. Compensation in terms of concentration Compensating in terms of reducing concentration is always possible instead of or in addition to increasing speed or other possible forms of compensation. However, concentration is much more difficult to measure than is speed, especially when drivers should not know about the measuring. As mentioned, in this study the drivers’ concentration was measured by video registration of lateral position and by questionnaire. Compensation in terms of concentration can be compared with two different controls, concentration on the experimental section during daylight, and concentration during darkness on the control section. Firstly, concentration during darkness (Condar) relative to concentration during daylight (Conli) before (t1) the installation of lights on the experimental road section is compared with concentration during darkness relative
(3)
Secondly, concentration during darkness on the experimental section (CondarA) relative to concentration during darkness on the control section (CondarB) before (t1) the installation of lights on the experimental section is compared with the concentration during darkness on the experimental section (CondarA) relative to the concentration during darkness on the control section (CondarB) after (t2) the installation of lights on the experimental section, i.e.: (CondarA/CondarB)t2 B (CondarA/CondarB)t1
slowly in darkness without road lighting. A split-half technique was applied, asking approximately 50% whether they would drive faster in darkness with road lighting and the other 50% whether they would drive more slowly in darkness without road lighting. The results in Table 3 clearly indicate that car drivers do adapt their behavior in terms of speed to road lighting. To both versions of the question a majority of the drivers answer that they adjust their behavior to road lighting. Even if speed could be expected to decrease a little if road lighting generates more road traffic in darkness, we find some compensation in terms of speed for all comparisons made. This result is corroborated by the answers in Table 3 to a general question of changes in speed with and without road lighting. The speed increase measured varies between approximately 1 and 5%. Though this increase is not considerable, our hypothesis about speed is rejected. Contrary to our hypothesis we find that drivers do compensate for road lighting, although to a modest degree.
549
(4)
3.3. Concentration Table 4 shows that the differences in average concentration are small. But the differences between darkness and daylight on the experimental as well as the control section are significant on a 5% level (x 2 test). Applying inequality (3), we get the following results: (CondarA/ConliA)t2 = 4.9/4.9 =1.00 (CondarA/ConliA)t1 = 5.0/4.9 =1.02 The inequality (3) is fulfilled, as 1.00 B 1.02. The difference is quite small, however, and not significant. Applying inequality (4), we get the following results: (CondarA/CondarB)t2 = 4.9/5.1 = 0.96 (CondarA/CondarB)t1 = 5.0/4.8 = 1.04 The inequality (4) is fulfilled, as 0.96B 1.04, but the difference is not significant. The compensation effect of road lighting in terms of concentration seems, however, to be greater for inequality (4) than for inequality (3). This may be due to a possible co-variation between concentration and who drives in the darkness and daylight. If some people who would not drive during darkness without road lighting, start to drive after dark when road lighting is installed, and if these people are more concentrated than the average drivers, this co-variation may contribute to disguising the effect of road lighting on concentration. Table 5 shows car drivers by gender and age, driving during darkness and daylight before and after the installation of road lighting on the experimental section. The bottom line of Table 5 shows a slight tendency for men to drive relatively more during darkness than women. Table 5 also shows a tendency for elderly people to drive less during darkness. Moreover, women 45 years and older totaled 23% of all female drivers during darkness before the installation of road lights and 36% after. There is a similar, but smaller tendency for male drivers.
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Table 6 shows clearly that women score higher on concentration than men. This tendency is found in all age groups, during daylight and darkness, before and after road lighting. There is also a certain tendency for the concentration score to increase with age. Table 5 shows that driving during darkness co-
varies with age and gender, and that the installation of road lighting affects the age and gender distribution of drivers. Thus, we may conclude that the changes in age and gender distribution have disguised some of the concentration effect as measured by inequality (4).
Table 4 Car drivers’ assessment of concentration on the experimental and control sections during darkness and daylight before and after installation of road lighting on the experimental section (%) Concentration
Before
After
Experimental Daylight Minimum – – – – – Maximum N Average Standard deviation x2 Significance
1 2 3 4 5 6 7
2.4 3.1 7.6 26.8 22.9 16.7 16.7 939 4.9 1.4
Control Darkness
2.5 4.6 9.1 20.6 23.7 20.6 18.7 712 5.0 1.5 15.62 0.016
Experimental
Daylight 2.5 2.1 9.6 29.9 25.8 17.0 13.1 512 4.8 1.4
Darkness 2.2 5.4 10.5 23.5 22.9 18.1 17.5 371 4.8 1.5 13.73 0.033
Daylight 2.4 2.8 8.8 24.2 26.7 19.2 15.9 818 4.9 1.4
Control Darkness
2.8 3.2 8.2 24.1 24.8 18.9 18.0 634 4.9 1.5 1.94 0.925
Daylight 1.9 2.2 11.8 27.6 23.8 18.0 14.7 416 4.8 1.4
Darkness 1.1 2.5 7.9 21.3 25.3 21.6 20.2 356 5.1 1.4 11.80 0.067
Table 5 Car drivers by gender and age, driving during darkness and daylight hours before and after the installation of road lighting on the experimental section (%) Age (years)
Before
After
Daylight
18–44 45–64 65+ Total N %
Darkness
Daylight
Darkness
Male
Female
Male
Female
Male
Female
Male
Female
56 32 12 100 639 69
63 33 4 100 287 31
66 31 3 100 511 74
77 22 1 100 181 26
56 36 8 100 577 72
71 26 3 100 221 28
64 32 4 100 475 76
64 33 3 100 152 24
Table 6 Drivers by average concentration score, gender and age and by daylight and darkness before and after the installation of road lightinga Age (years)
Before
After
Daylight
18–44 45–64 65+ All N a
Darkness
Daylight
Darkness
Male
Female
Male
Female
Male
Female
Male
Female
4.53 4.85 5.06 4.70 635
5.25 5.43 5.55 5.31 286
4.79 4.95 4.50 4.83 509
5.45 5.00 – 5.33 181
4.67 4.84 5.19 4.76 577
5.30 5.21 6.50 5.32 220
4.77 4.96 4.83 4.82 471
5.42 5.13 6.20 5.35 150
Average concentration score: 1 is minimal and 7 maximal concentration.
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Table 7 Car drivers’ answers to questions on concentration when driving in darkness with and without road lighting (%)a
Yes No Don’t know Total N a
I would drive more concentrated without road lighting
I would drive less concentrated with road lighting
83 (81–85) 13 4 100 1575
29 (27–31) 64 7 100 1431
Confidence interval in brackets.
The drivers were also asked about concentration with or without road lighting. A split-half technique was applied, so that 50% of the drivers were asked whether they would be less concentrated when driving in darkness with than without road lighting. The other half was asked whether they would be more concentrated when driving in darkness without than with road lighting. The results are shown in Table 7. Theoretically, the two versions of the question should have yielded the same distribution of answers. The rather large difference between the two distributions is probably due to the fact that it seems less socially acceptable to admit a lower concentration with road lighting than a higher concentration without lighting. Anyway, the answers indicate that approximately between 30 and 80% of car drivers do compensate for road lighting in terms of changed concentration. But the question remains whether a questionnaire represents a valid measure of concentration. If similar changes in concentration are found when concentration is operationalized as lateral position, the validity of the questions in the questionnaire will increase.
Inequality (4) cannot be used, as lateral position was not recorded on the control section. The results of the video recordings of lateral position indicate, in the same way as the questionnaire data on concentration, that drivers do compensate for road lighting in terms of concentration. The difference in concentration is statistically significant and greater when measured by lateral position than by questionnaire. This fact may be due to the drivers being unwilling to admit that they are less concentrated at any time. The results on lateral position show that concentration is actually lower during darkness with road lighting than during daylight. We found a similar tendency for speed, being higher during darkness after the installation of road lighting than during daylight. A possible reason for these findings may be that the populations of drivers during daylight and darkness are different. Thus, it seems that drivers’ adjustment of behavior to risk factors and the reduction of risk factors are indeed quite complex.
3.4. Concentration measured as 6ariation in lateral position
Our data show that the car drivers compensate for road lighting both in terms of speed, and in terms of concentration. As mentioned above, road lighting is also known to reduce accidents. Compensation is thus not sufficient to make road lighting ineffective as a road accident countermeasure. Our main hypothesis is clearly rejected. This hypothesis was based upon the idea that as drivers do not seem to slow down during darkness hours, they should not increase their speed after the installation of road lighting. Our results indicate, however, that this idea is based upon the fallacy of assuming that results ob-
The number of deviations from a straight course was used as the operationalization of concentration, assuming that the fewer deviations, the more concentrated the driver. In inequality (3) a small number of changes in lateral position indicates high concentration, in contrast to the questionnaire data. The inequality (3) has to be turned in the computations below (Table 8). Using inequality (3), we get the following results: (CondarA/ConliA)t2 =0.94/0.77 =1.22 (CondarA/ConliA)t1 =0.59/0.69 =0.86 The inequality (3) ‘turned around’ is fulfilled as 1.22\ 0.86, i.e. the number of changes in lateral position is greater, and consequently the concentration is lower, after the installation of road lighting. The difference is considerable, 42%, and is statistically significant as shown by an interaction between the factors daylight vs. darkness and before vs. after (F =5.33; P= 0.021).
3.5. Interpretation of results
Table 8 Drivers by average number of changes in lateral position (\13 cm) on a 200 m road section during daylight and darkness hours before and after the installation of road lighting on the experimental section
Daylight Darkness
Before
After
0.69 (131) 0.59 (123)
0.77 (154) 0.94 (107)
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tained for the average driver is due to similar behavior for all drivers rather than different groups of drivers behaving in different ways. Table 5 indicates that people driving during darkness and daylight hours are not the same. Thus, the reason why previous research has shown that speeds during darkness and daylight are more or less the same, might be that people driving during darkness are a subgroup of drivers who generally drive faster than the average driver. When comparing average speed during darkness with daylight hours, we are thus comparing the speed of two different subgroups of drivers. Those who drive during darkness hours are likely to drive even faster during daylight hours, but the average daylight speed is reduced by the subgroup of drivers who do not drive during darkness hours, a subgroup, which is likely to drive more slowly during daylight hours than the average driver. The fact that our results show that the highest speed occurred during darkness after the installation of road lighting and the concentration, measured by changes in lateral position, was lowest under the same conditions, indicates that there is individual compensation for road lighting both in terms of speed and concentration. The reason why the opposite adjustment, i.e. reduction in speed during darkness, is not found generally, would then be that at an aggregate level, this tendency is counterbalanced by some drivers adjusting to darkness by not driving. These drivers are likely to be those who would also drive at a speed below average during daylight. Speed was measured both on curved and straight parts of the road, because risk compensation could be expected to be different under the two conditions. We found that the difference in speed change was 5% between the experimental and control sections on the straight part and 0.7% on the curved part of the road. A similar, but smaller difference is found when comparing speed in darkness with speed in daylight. The greater speed increase on the straight part may be explained by road lighting being more effective on a straight road, where the lights increase considerably the distance which can be seen. In curved parts, the visible distance is not increased very much by road lighting.
4. Conclusion The results presented in this article indicate that drivers do compensate for road lighting in terms of increased speed and reduced concentration. Our hypothesis about compensation for road lighting must then be rejected. The empirical basis for our hypothesis was the fact that previous research has shown road lighting to reduce road accidents and that drivers on the average do not increase their speed when road lighting is installed.
Our findings indicate that the latter fact can be explained by increased driving speeds by some drivers being counterbalanced by a larger proportion of more slowly driving groups of drivers (elderly people and women) after the installation of road lights, i.e. different subgroups of road users compensate in different ways. This means that risk compensation may occur even if the pertinent measure is known to reduce accidents and even for measures for which the road users on the average do not seem to adapt their behavior.
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