Traffic risk

Safety + Safer roads + Smoother traffic + Risk-dependent ++ Weight and speed + Driver assistance... 

Figure 2.1 Motorisation, personal risk and traffic risk in different regions in 1995 (Al Haji, 2001)...

I have identified eight groups of indicators (see Figure 3.3) of which each corresponds to a special area of road safety. The groups listed are traffic risk, personal risk, socioeconomic indicators, road safety organisational structure, traffic police and enforcement, vehicle safety, roads situation and road user behaviour. The groups measure road safety development in terms of output or input classes. The output... 

Linking motorisation, traffic risk and personal risk... 

Regardless of whether one agrees or disagrees with Smeed s model, the fact remains that the model gave a simplified and fairly good representation between traffic risk and motorisation of different parts of the world during the earlier stages of road safety development. 

The correlation between traffic risk (fatahties per number of vehicle kilometres) and personal risk (fatalities per number of population) is shown in Figure 3.9. With a growing number of vehicles per population, countries move from the right to the left across the curve (Fred, 2001). An early level of motorisation, first leads to a growing... 

Figure 3.9 Traffic risk and personal risk in different countries where countries move from the right to the left across the curve (Adapted from -work by Fred, 2001)...

As we discussed earlier, the personal risk is a function of traffic risk and motorisation. Navin (1994) has converted this function into the following equation (see also Figure 3.10) ... 

Tf is the point where the exponential curve meets the T-axis, T is the traffic risk, fatahties per number of vehicles, and M is the motorisation, vehicles per population... 

Linking traffic risk, motorisation and personal risk with time... 

This chapter aims to determine -whether the varieties in road safety between ASEAN countries can be explained by differences in some indicators and socio-economic patterns. This chapter analyses the trends and levels of road safety among the region between 1994 till 2003. The indicators used here are traffic risk as number killed per vehicle, motorisation measured as vehicle per person, and personal risk as killed per person. Special cases of comparisons are presented when both indicators are considered together and when they interact with other indicators such as urbanisation and severity index (killed per casualties). This empirical study is supported by tables and graphs based on data gathered on our visits to the ASEAN region. This data will also be useful in testing the multidimensional index, ESDI, in the next chapter. 

There is no meaning to use the total number of fatalities or injuries to compare the safety problem in ASEAN countries, due to wide differences in population and motorisation in these countries. Measures of risk are essential in comparing different countries and studying the development of the road safety situation. Therefore, numbers of deaths have to be translated into measurable indicators that take road usage and exposure to accidents into account. The two general indicators of death rates relevant to our comparisons are traffic risk (fatalities per vehicles) and personal risk (fatalities per... 

Traffic risk is measured in terms of deaths per 10,000 registered motor vehicles or per 100 mUhon vehicle-kilometres travelled. The latter is the most relevant risk measure in road transport but unfortunately the total number of kilometres covered by vehicles is not normally available in most ASEAN countries. If the traffic risk is defined as the number of fatalities per 10,000 vehicles, several investigations show that this indicator is not enough to explain the real situation of traffic safety. This measure does not... 

Figure 4.1 Progression of the ASEAN countries according to the traffic risk indicator (fatalities per 10,000 vehicles) 1994-2003...

It would seem that ASEAN countries fall into three levels of traffic risks. Countries with the lowest levels, less than 5 killed per 10,000 vehicles, include Brunei, Singapore and Malaysia. Countries with a medium level, less than 20 and more than 5, include Thailand, the Plulippmes, Vietnam and Indonesia. The other ASEAN countries have a very high level of traffic risk, more than 20, including Myanmar, Cambodia and Laos. 

These results seem to correspond with the level of motorisation as already been discussed in the previous chapter (Figure 3.5). Most ASEAN countries are still at the lower level of the motorisation curve and a few are now starting to enter the phase of rapid motorisation (medium level) as Malaysia. The death figures rise until quite a high level of motorisation is reached and, subsequently, it falls. Perhaps this explains the low figure in traffic risk in Brunei, which is comparatively similar to that in HDCs. 

The HDCs have shown low figures in traffic risk despite the fact that they have a large growth in the number of vehicles, population and kilometres travelled by vehicles. For instance, the current traffic risk in Sweden and United Kingdom is less than 2. In countries like the Philippines and Vietnam the comparable rate is something like four to six times higher. 

In the process of clarifying the relationship between traffic risk and the level of motorisation in the same model. Figure 4.2 shows that as motorisation level (number of vehicles per population) increases, the traffic risk (fatalities per vehicle) decreases. This negative correlation can probably be explained by the fact that the more vehicles in a country led to more improvement in safety and the learning process by experiences in traffic (Jacobs and Hutchinson 1973), (Oppe, 1991b). 

Estimated regression line for ASEAN region Traffic Risk = 41,34 - 14,47 logten(Motorlsatlon)... 

Figure 4.2 Traffic risk and motorisation for ASEAN countries in 2003...

Concerning personal risk with motorisation. Figure 4.3 shows that there is no clear relationship between personal risk and motorisation. When motorisation increases, it leads to a decrease in the traffic risk but not necessarily to a decrease in personal risk. The quadratic curve indicates that the pattern is non-linear. The resulting tine of best-fit gives R-Sq=65.4 goodness of fit. This means 65% of the traffic risk has been explained by motorisation variables. 

The rapid increase of population in ASEAN countries has caused economic and social problems. The changes in urbanisation in recent decades have also led to increased traffic congestion in urban centres. This leads to an increase in traffic accidents on road networks that are not designed for the current volumes and types of traffic. The irregular increase of the population in urban areas with limited facilities has affected the balance between the supply and demand of transport in the ASEAN countries. Figure 4.4 shows that as urbanisation increases, the traffic risk (fatalities per vehicle) in most countries decreases. This can be probably explained by the fact that the increase in urbanisation has also led to improved road safety facihties in urban areas, better medical recovery, lower speeds, and better commuiucations. 

In principle, eight general themes have been selected in the construction of RSDI, which are traffic risk, personal risk, vehicle safety, roads situation, road user behaviour, socio-... 

The first chosen dimension is the level of traffic risk in a country, which deals with the fatalities rates. The fatality rate per vehicle is currently used, while fatality rate per vehicle-km or per person-km or per type of road user (pedestrians, cyclists, motorcyclists and drivers) are all other indicators of this dimension and they can be used on the long term of RSDI. 

The indicators included in RSDI measure road safety development in terms of direct (output or ends) and indhect (means or input). The direct indicators are derived measures e.g. fatalities rates (traffic risk and personal risk) and they are considered as good measures for explaining national road safety development. The indirect indicators are individual means in the way they can describe the development in a particular relevant theme to road safety. Many international indices include both terms in the same index. For example, the Composite Health Index includes infant mortality rates as an ou ut (dhect) indicator of health index. 

At the same time, it might be better if we state a target value for the minimum and maximum values that are identified, according to a special performance scale for each indicator. This can be estimated on the idea that there is potential progress ahead for all countries in road safety development. The indicators contribute differently to the RSDI. The low values of traffic risk, personal risk and severity index show a good safety level in a country, while in contrast the safety issue increases continuously with increase of the values of other indicators. 

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