Average estimates of the non-captured data in Accident related

4.3.1 Average estimates of the non-captured data in Accident related

factors

The scores arrayed in the Table 12 demonstrate variations in the average estimates of non- captured data amongst the eight data fields in Accident related factors. The variations illustrate the degree at which data were mismanaged in the eight related variables/data fields during the road accident data collection process. The result shows that there are some abnormalities contributing to the mishandling of road accident data during the data collection activities. For more details on the simple mathematical approach to the average estimates of non-captured data refer to subsection 3.3.2.7.2.

However, under Accident related factors, a total average of 324 data is mismanaged due to poor data quality processing. From the set of scores grouped in the table below, with an in- depth understanding, it is observed that fewer number of variables/data fields in the Accident related factors lost a large number of road accident data, unlike other related factors discussed in the chapters 5 and 6. The results obtained from the analysis reveal that a huge average estimate of 156 [48.3%] data is mismanaged in Summary of persons involved as presented in the Figure 23 below, which is virtually up to the half value of the total average of data mismanaged in the Accident related factors. This huge amount is in excess of 47.3% of the least average estimate of 1.0% data that are considered missing in the Weather conditions and

visibility according to the results displayed in the table below.

Table 12: Estimated average numbers of non-captured accident data in Accident related factors

Average estimates of non-captured accident data in Accident related factors

Accident related factors Average estimates Percentage estimates

Accident date 4 1.2%

Day of week 15 4.6%

Time of accident 4 1.1%

Accident type 53 16.3%

Severity of injury 84 26.0%

Summary of persons involved 156 48.3%

Light condition 5 1.5%

Weather conditions and visibility 3 1.0%

Total average estimate 324

In addition, the results further reveal that Severity of injury and Accident type also contributed large amounts of non-captured data among other remaining five variables/data fields with average estimates of 84 [26.0%] and 53 [16.3%] respectively. Huge amount of data mishandled are lost to factors such as:

 Lack of consistency along the reporting system,

 Inability to acquire relevant information concerning the type of accident [which might be

due to the severity level of the accident], and

 Lack of commitment from the accident reporting officers.

This illustration reveals that little or no awareness is directed towards the significance of these three variables/ data fields by the form users, such as the reporting officers [police and traffic officers], the supervisory officer, and the DCO during the data collection protocols. Definitely, this may affect some important activities like decision-making in the transportation safety systems, effective management structure, and quality distribution of safety resources and tools, where quality road accident data is relevant in the improvement of processes. However,

this consequence maybe averted by necessitating regular supervision to deter incorrect representation and exclusion of relevant variables/data fields. If considered, therefore, the traffic management could advocate for necessary resources required to reduce the rates at which data are mismanaged during data collection process.

According to the results displayed in the chart below, it is observed that ‘the magnitude of the

registered/reported number of accidents does not determine the reduction rate in the amount of the data loss during the data collection activities.’ Clearly, under the Summary of persons involved, the average estimate calculated demonstrates higher scores of data loss per monthly

registered/reported number of road accidents, which fall within the range scores of 100 and above unlike other related data fields. This illustrates that less or no commitment was exercised by the reporting officers towards the understanding of this particular field, which could be ascribed to poor interpretation of information. This further insinuates that little or no supervision is implemented periodically to determine or check the competency of the reporting officers in the coverage of road accident data.

Figure 23: Total average estimates of non-captured accident data in Accident related factors

Quite the reverse, a minimal loss of data is demonstrated in few of the eight related variables/data fields grouped under the Accident related factors. From the chart above, it is understood that Weather conditions and visibility produces the least amount of missing data, with an average estimate of 3 [1.0%] non-captured data. And also, an equal average estimate of 4 non-captured data are calculated for both Time of accident and Accident date, with a close percentage estimates of 1.1% and 1.2% respectively. The magnitude of the average estimates

of non-captured data calculated indicates high reflection of a reliable interpretation of the three data fields mentioned above during road accident data collection.

More so, according to the analysis, it is understood that more awareness, commitments, and composures were directed towards a complete collection of data pertaining to the four variables/data fields with the lowest average estimates of non-captured data. It appears that many reporting officers find the information about the four data fields less difficult to acquire22_. Ultimately, this infers that a large number of the reporting officers understand the interpretation of these four data fields than any other data fields grouped in the Accident related factors.

Technically, ‘the lesser, the number of non-captured data that are acquired; the higher, the

number of captured data; and the better, the practicality of the usable data’. In other words,

‘the higher, the number of omitted data or errors discovered per field, the greater its

consequence on the practicality of the data captured per field’; although, it depends on the

degree of sufficiency of the quantity of data completed in the ARF. This illustration literally explains that, ‘the complete number of data captured correctly [usable data], determines the

extent of the practicality of a particular field towards the analysis of a real-world problem in the RTA’.

4.3.2 Histogram analysis of the non-captured data in Accident related