Synthesis of identified relations – detailed causalities

The previous paragraphs presented the results of a focused literature review aiming to determine specific detailed causalities and equations linking the priority indicators to fatalities and injuries. Over 200 references were examined and a total of 125 detailed causalities were identified from approximately 95 different studies, journal articles, reports, etc.

Table 4.1 presents the total number of identified detailed causalities per indicator, further categorized according to country type (high-income vs. medium- and low-income), and according to type of study. The table also includes studies that attempted to develop a causal relationship but finally concluded that such a relationship was not statistically significant.

From a detailed review of the literature, several quantitative relations that link the model’s priority indicators to road safety outcomes (fatalities and injuries) were identified, as shown in table 4.2.

Table 4.1

Total number of identified detailed causalities per indicator

No. Indicator

I02 Country has a national road safety

strategic plan 2 1 1 1 0 1

I03 Country has time-based, quantified

national road safety targets 7 7 0 6 0 1

I04 Country has a clearly empowered

agency leading road safety 1 1 0 0 0 1

I05 Country has a defined allocation of expenditure for dedicated road safety

programmes 1 1 0 0 0 1

I06 Share of trips / traffic per mode 4 3 1 2 0 2

I07 Country has a target to eliminate

high-risk roads 0 0 0 0 0 0

I08 Number of passenger cars per 1,000

inhabitants 2 1 1 0 0 2

No. Indicator

I16 Percentage of rural road network not

satisfying design standards 1 1 0 0 1 0

I17 Daytime helmet wearing rates for

motorcycles 12 11 1 12 0 0

I18 Seat belt wearing rates on front seats

of cars 13 13 0 7 6 0

I19 Mean EMS response time 3 3 0 3 0 0

TOTAL ^{125 118 7 74 28 23}

Table 4.2

Preliminary list of detailed causalities with increased usefulness for SafeFITS

Model priority indicator  Detailed causalities with increased usefulness for SafeFITS 

No. Description Layer Pillar Study Type High-income countries

Low- and

Model priority indicator  Detailed causalities with increased usefulness for SafeFITS 

No. Description Layer Pillar Study Type High-income countries

Low- and

minimum yes => -0.77% in fatalities per year

Indiv. effects studies - n/a

maximum yes => -0.92% in fatalities per year

Indiv. effects studies -

best estimate yes => -0.80% in fatalities per year

Model priority indicator  Detailed causalities with increased usefulness for SafeFITS 

No. Description Layer Pillar Study Type High-income countries

Low- and

minimum yes => -10% in PTW fatalities per 10,000 registered PTWs Indiv. effects studies - n/a

maximum yes => -43% in PTW fatalities per 10,000 registered PTWs Indiv. effects studies -

best estimate yes => -20% in PTW fatalities per 10,000 registered PTWs

Meta-analysis studies yes => -26% in PTW fatalities n/a Statistical - Mathematical

Model priority indicator  Detailed causalities with increased usefulness for SafeFITS 

No. Description Layer Pillar Study Type High-income countries

Low- and

yes => -13% in total accidents in affected road sections (Australia

Meta-analysis studies stationary visible enforcement =>

-17% in total accidents in affected

Model priority indicator  Detailed causalities with increased usefulness for SafeFITS 

No. Description Layer Pillar Study Type High-income countries

Low- and

However, the following limitations should be pointed out:

1. Although literature on accident causalities is extensive, there is very limited available information originating from studies in middle and low-income countries. Many of the studies from middle and low-income countries suffer from methodological weaknesses or lack of high quality road safety data, and thus the identified causalities have limited reliability.

2. In some cases, a quantitative relation to estimate an overall (local or nationwide) accident reduction that can be attributed to the specific indicator is not available, although there is an obvious influence in road safety outcomes.

Examples of such cases are indicators I11 (“Country has systematic policies and practices in place for Road Safety Audits of new road projects”) and I15 (“Share of High Risk Sites treated”). In both cases, there are several studies that estimate accident reduction ratios that refer to specific implementation of road safety audits/inspections or specific high-risk sites treatments programmes. However, these accident reduction ratios cannot be related to the above indicators on a more generalized scale.

3. Some indicators – mainly from the economy and management layer – are characterized by complex and sometimes conflicting objectives. Improving road safety is often not the only objective, and in many cases, not the most important.

Furthermore, the measures themselves are often complex and several variations may exist. Since the effects largely depend on the way these measures are designed, implemented and used, it is often very difficult to generalize about their effects.

4. In some cases (e.g. indicators I13 and I14), in order to produce comparable results between different studies, the detailed causalities identified above have been based on logical assumptions (e.g. the average number of speed or alcohol controls that can be performed during a police officer’s shift), which could possibly influence the estimated effects.

5. Finally, attention should be paid to the geographic origin of detailed causalities. The implementation of a quantitative relation developed using data from a specific geographical area in a different context should be done with caution and with proper verification of the results.

The detailed review of selected causalities largely confirmed the findings on the literature review of all causalities, presented in chapter 2. Because of these limitations, a dedicated analysis methodology was developed in order to meet the SafeFITS objectives. Thus, new causalities were estimated from original statistical data analyses, which took into account as many dimensions of the problem as possible. The methodology developed and the respective results are presented in the following chapters.