Guidance for the analysis of STATS19 Contributory Factors

Transcription

1 PPR488 Guidance for the analysis of STATS19 Contributory Factors J Broughton, M Keigan, J Knowles and L Smith

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3 Transport Research Laboratory PUBLISHED PROJECT REPORT PPR488 Guidance for the analysis of STATS19 Contributory Factors by J Broughton, M Keigan, J Knowles and L Smith (TRL) Prepared for: Project Record: Client: Develop methods and guidance for analysing STATS19 contributory factors TRL Academy, (Neil Paulley) Copyright Transport Research Laboratory May 2010 This Published Report has been prepared for the TRL Academy. Published Project Reports are written primarily for the Client rather than for a general audience and are published with the Client s approval. The views expressed are those of the authors and not necessarily those of TRL Academy. Name Date Approved Project Manager M Keigan 05/05/2010 Technical Referee B Sexton 05/05/2010

4 When purchased in hard copy, this publication is printed on paper that is FSC (Forest Stewardship Council) registered and TCF (Totally Chlorine Free) registered. TRL PPR488

5 Contents Executive summary v Abstract 1 1 Introduction A brief history Recording contributory factors STATS19 at TRL 5 2 Literature Review Introduction UK analysis of STATS19 Contributory Factors International experience 9 3 Review of the contributory factors Consistency of factors Recording of confidence Did a police officer attend the accident? By accident severity Police force 17 4 Review of individual contributory factors Review of Stats20 guidance Checks between selected contributory factors and other STATS19 variables CF 101 Poor or defective road surface CF Deposit on road CF 104 Inadequate or masked signs or road markings CF 105 Defective traffic signals CF 107 Temporary road layout CF 109 Animal or object in carriageway CF301 - Disobeyed automatic traffic signal CF302 - Disobeyed - Give Way - or - Stop - sign or markings CF303 - Disobeyed double white lines CF304 - Disobeyed pedestrian crossing facility CF 306 and CF307 - Speed-related factors CF310 - Cyclist entering road from pavement CF401 - Junction overshoot CF402 - Junction restart (moving off at junction) CF403 - Poor turn or manoeuvre CF407 Passing too close to cyclist, horse rider or pedestrian CF 410 Loss of control CF507 - Cyclist wearing dark clothing at night CF707 Vision affected by rain - sleet - snow or fog CF804 Pedestrian Wrong use of pedestrian crossing facility Use of CF999 other please specify 36 TRL iii PPR488

6 5 Recommended guidance for Contributory Factor analysis General guidelines Specific issues 42 6 Specification of system to analyse contributory factors Accident level Factor types by accident variable(s) Number of accidents with each factor by accident variable(s) Number of accidents with each contributory factor by accident variable(s) for subsets of accidents based on vehicle or casualty data Vehicle level Number of vehicles with each contributory factor by accident or vehicle variable(s) Casualty level Number of pedestrian casualties with each contributory factor by accident, vehicle or casualty variable(s) Number of casualties associated with vehicles with each contributory factor by accident, vehicle or casualty variable(s) More complex analyses 47 7 Development of the STATS19 database 48 8 Conclusions 49 Acknowledgements 50 References 50 Appendix A Number of contributory factors recorded by year 52 TRL iv PPR488

7 Executive summary STATS19 is the national database of records of road accidents involving personal injury. Since 1949, the police have recorded statistical details of road accidents throughout Great Britain in this database, which thus forms a principal resource for research into road safety. TRL maintains a version of the STATS19 database which supplies fundamental data for the company s numerous road safety projects. The STATS19 system is updated regularly to ensure that it continues to meet the evolving needs of users, including researchers. In 2005, as part of the latest revision of the system, another section was added to the STATS19 form where police reporting officers could enter Contributory Factors. A maximum of six Contributory Factors can be applied to an accident together with its level of confidence, i.e. an indication of whether each contributory factor is very likely or possible. The Contributory Factors in a road accident are the key actions and failures that led directly to the actual impact. They show why the accident occurred and provide important indications about how accidents may be prevented in future, so offer valuable extra information for road safety research. However, these data are conceptually more complex than the traditional STATS19 variables such as driver age or road number. The TRL Academy therefore commissioned TRL to undertake research in this important area. This project has consisted of three main tasks. Firstly, a literature review of contributory factor analysis in the UK and elsewhere has been carried out and the approaches have been compared and their strengths and weaknesses reported. Secondly, the guidance provided by the STATS20 manual for recording contributory factors has been assessed in conjunction with analysis of the 2005 to 2007 contributory factor data. Thirdly, the TRL version of the STATS19 database has been developed to allow Contributory Factor data to be readily extracted for these analyses. These three tasks are reported here. From the literature it was apparent that the main issues that needed to be decided as part of the analysis were: Whether to use only accidents where the police officer attended or to use all reported accidents. Whether to restrict the levels of confidence to very likely or possible. Whether the analysis being conducted relates to numbers of accidents, vehicles or casualties and how to present the findings. In summary very few examples of countries that collect and use contributory/causation factors as part of their ongoing national system were found: namely New Zealand which has a similar system to GB and the Netherlands. When the STATS19 system was expanded in 2005 to include contributory factors, no consistency checks were specified. Consequently, invalid codes can be entered, or valid codes can be entered inappropriately. A range of analyses were carried out to see whether this lack of consistency checks may have led to an appreciable level of misreported factors. A validity assessment of the assignment of contributory factors to vehicles, casualties or uninjured pedestrians showed that the incidence of detectable errors is relatively low (less than 1%) and the percentage of contributory factors that have been incorrectly assigned decreases from 2005 to 2007 as the system becomes more familiar. Overall, 91% of the pedestrian only factors were correctly assigned to a pedestrian in an accident. The proportion of accidents with no contributory factors reported reduced from 2005 (9%) to 2007 (6%) and is likely to be due to increasing experience of the new system as 2005 was the first year of reporting contributory factors. For those accidents where TRL v PPR488

8 contributory factors were reported, the average number of very likely and possible contributory factors was similar for each year Overall, the guidance for completing the Stats19 form seemed reasonably clear. Some analyses of the contributory factor data omit accidents where the police officer did not attend (DfT, 2009). In general, one or two contributory factors were found to be recorded per accident; more contributory factors were more frequently recorded when a police officer attended. Several contributory factors have links with one or more other contributory factors; others have links with other STATS19 variables, i.e. those recorded on the attendant circumstances, vehicle or casualty records. A review of these linked factors and variables has been carried out. The TRL version of the STATS19 database has been developed to allow Contributory Factor data to be readily analysed and it is hoped that this will encourage the use of these data as part of TRL research. However, it will be necessary to review experience with the new software after it has been in use for about one year to check how well it has met users requirements, and whether any further development may be necessary. References Department for Transport (2009). Reported Road Casualties Great Britain: Annual Report. London: The Stationery Office. TRL vi PPR488

9 Abstract In 2005, the STATS19 system was expanded to record Contributory Factors, i.e. the key actions and failures that led directly to individual accidents. These data are conceptually more complex than the traditional STATS19 variables such as driver age or road number, so more options are available for analysis. A TRL self-funded project has been carried out with the main aim of providing guidance about these data and effective methods for analysing them. This report documents the range of analyses that has been carried out. TRL 1 PPR488

10 1 Introduction STATS19 is the national database of records of road accidents involving personal injury. Since 1949, the police have recorded statistical details of road accidents throughout Great Britain in this database, which thus forms a principal resource for research into road safety. TRL maintains a version of the STATS19 database which supplies fundamental data for the company s numerous road safety projects. The STATS19 system is updated regularly to ensure that it continues to meet the evolving needs of users, including researchers. In 2005, as part of the latest revision of the system, another section was added to the STATS19 form where police reporting officers could enter Contributory Factors. In the words of the STATS20 manual which provides guidance to reporting officers: The Contributory Factors in a road accident are the key actions and failures that led directly to the actual impact. They show why the accident occurred and give clues about how it may have been prevented. The contributory factors provide important indications about how accidents may be prevented in future, so offer valuable extra information for road safety research. However, these data are conceptually more complex than the traditional STATS19 variables such as driver age or road number, so more options are available for analysis. This partly explains the relative slowness of researchers both at TRL and outside to exploit this new resource. This document is a principal output from a TRL self-funded project whose main aim is to promote the use of contributory factors by TRL researchers by providing guidance about these data and effective methods for analysing them, including development of the database software. The structure of the report is as follows: Sections 1.1 and 1.2 give more details of the current STATS19 contributory factor coding system, while Section 1.3 presents details of the facilities for analysing contributory factors within the SQL STATS19 database that has recently been developed at TRL; Section 2 presents a review of the technical literature relating to police systems for recording contributory factors; Section 3 reports the results of an assessment of the internal consistency of the contributory factors in the STATS19 contributory factors as currently recorded; Section 4 reviews individual factors to assess the reporting. Section 5 provides recommendations for analysing contributory factors. Section 6 proposes suggestions for the current and future capability of the STATS19 system at TRL. Section 7 describes the development of the STATS19 database at TRL. The STATS19 database includes only records of road accidents that have been reported to and by the police. It is widely recognised that many other accidents are not included because either they were not reported to the police or they were reported but the police failed to record them in the database. Concern to make this distinction clear has recently led the Department for Transport to rename their principal annual publication of STATS19-based statistics Reported Road Casualties Great Britain. This research examines the way in which contributory factors are reported via the STATS19 system, so by definition all of the accidents being considered have been reported. The term reported will be used as sparingly as possible in this report in order to focus on how these accidents have been reported and whether there may be implications for users of the data. TRL 2 PPR488

11 Note that a short guide (Broughton et al., 2010) has been written to accompany this report. It provides practical guidance for researchers, including some material from this report as well as and advice about the most appropriate analytical methods. 1.1 A brief history It is useful to begin with an account of the process that led to the STATS19 contributory factor coding system that exists at present. As a Government survey, the STATS19 system is reviewed at intervals of about five years to ensure that it continues to be fit for purpose, and changes resulting from the latest review are likely to take effect from January It is possible that some details of the current system will be revised at that point. One important reason for the success of the STATS19 system is that accident data are collected in a consistent way in all parts of Great Britain, so local data can be brought together in a national database. In addition to objective factors such as time of day and speed limit, the original system implemented in 1949 also collected 'contributory factors', i.e. the factors which the reporting officer considered had contributed to the causation of the accident. Subsequent doubts over the reliability of the factors being collected meant that collection of these data ceased to be a national requirement in Nevertheless, a TRL survey of the 43 police forces in England and Wales in 1994 found that over one half were still recording contributory factors, but the systems being used had diverged over time so that patterns of accident causation in different areas could not be compared. At that time, the information was mainly used by the Local Authorities, for example in developing remedial measures at accident hot spots and for road safety publicity. It was recognised, however, that its value would be greatly enhanced by recording the factors in a consistent way by all forces, and linking the data to the national STATS19 database. Accordingly, the then Department of Transport (now DfT) commissioned the Transport Research Laboratory to develop a prototype system, which was trialled by eight police forces for three months in the summer of Broughton et al., (1998) describes the development of the system and presents analyses of the data that were collected during the trial. Twelve police forces adopted the TRL system during 1999; two more adopted it in 2000 and a further force in The 2002 Quality Review of the STATS19 recording system concluded that the STATS19 system should be expanded to include contributory factors, and a revised and simplified version of the TRL system was introduced in January This used the coding system shown in Figure 1.1, and guidance for reporting officers was provided in the revised version of the STATS20 manual (Department for Transport, 2004). The main simplification consisted in the removal of the two tiers of factors in the TRL system, which separated the factors summarising what led to the accident from the factors summarising why. Prior to 2005, the STATS19 system had included three record types: the Attendant Circumstances record (1 completed per accident), the Vehicle record (1 completed per accident-involved vehicle) and the Casualty record (1 completed per injured person). The new contributory factors form constitutes the fourth record type, with one set of codes to be entered per accident in the boxes at the bottom of the form. The wording of the form is intended to be self-explanatory, as far as possible, with the STATS20 manual supplying clarification of specific issues. Up to six factors can be entered per accident. These are attributed to either vehicles (actually their drivers or riders), casualties (including injured pedestrians) or uninjured pedestrians, and the reporting officer s confidence in each factor is recorded as either very likely or possible. This level of complexity arises because of the wide range of potential causes of accidents and provides one motivation for this project: to provide guidance to researchers that will assist them to deal with this complexity. TRL 3 PPR488

12 Figure 1.1 Contributory factor recording system of 2005 (DfT, 2004) TRL 4 PPR488

13 1.2 Recording contributory factors The STATS20 manual (DfT, 2004) sets out the following procedure to be used by police officers to record contributory factors, using the form reproduced in Figure 1.1: 1. Select up to six factors from the grid, relevant to the accident. 2. Only include factors which have contributed to the accident (e.g. do not include "Poor road surface" unless it was relevant to the accident). 3. More than one factor may, if appropriate, be related to the same road user. 4. The same factor may be related to more than one road user. 5. Factors may be shown in any order, but an indication must be given of whether each factor is very likely (A) or possible (B). 6. The participant should be identified by the relevant vehicle or casualty reference number (e.g. 001, 002 etc), preceded by "V" if factor applies to a vehicle, driver/rider or the road environment (e.g. V002), or "C" if the factor relates to a pedestrian or passenger casualty (e.g. C001). 7. Enter "U000" if the factor relates to an uninjured pedestrian. Code 999 is Other Please specify below, but the text that should be recorded on the form is not entered in the national STATS19 database. Hence, it is impossible to judge whether these 999 codes represent officers failure to use the system properly (i.e. to identify the appropriate code from those listed on the form) or an incompleteness of the form. The coding system needs to be relatively compact if it is be used effectively by the large number of police officers who compile accident reports, so a degree of incompleteness is inevitable, but the question of whether the existing system strikes an effective balance between usability and completeness cannot be answered with the available data. The Department for Transport specifies a series of consistency checks for the three longestablished record types, but has not specified consistency checks so far for the new contributory factor system. Thus, the STATS19 database potentially contains inconsistencies, such as a pedestrian code from the 800 series attributed to a vehicle. Section 4 reports the results of several checks of the consistency of the coding of contributory factors. Ideally, the Department for Transport will introduce consistency checks for the contributory factors so that reporting officers would be prompted to correct data entry errors when compiling their reports. That would still leave the problem of the errors that already exist, but a simple example shows that editing of the database in an attempt to rectify errors that had been detected could introduce further errors. Suppose that code 806 (pedestrian impaired by alcohol) had been entered for a car involved in a car/pedestrian accident. It is impossible from the available information to decide which of the following is true: The pedestrian was impaired by alcohol; the correct code was chosen but should have been entered for the casualty rather than the vehicle, The driver of the vehicle was impaired by alcohol; the code 501 should have been entered for the vehicle rather than the casualty Arbitrarily selecting one rather than the other could well lead to a coding of the accident that was false but consistent. The analyst should be aware of the possibility of such inconsistencies; section 5 considers the possible responses. 1.3 STATS19 at TRL For many years, a copy of the STATS19 database has been maintained at TRL. Recently, software has been developed that allows a version of the database to be accessed via the TRL intranet by staff with the necessary permission. This allows researchers to TRL 5 PPR488

14 extract STATS19 data personally, rather than needing to request the database manager to extract the data for them. The software takes account of the guidance about analysis of contributory factors that is summarized Section 5, so that researchers should now be able to carry out these types of analysis from their desktops. TRL 6 PPR488

15 2 Literature Review 2.1 Introduction The purpose of the literature review was to collect information about existing methods used to analyse contributory factor data. This was done in two parts: Review the analysis methods used by Department for Transport, TRL and other UK research organisations; Establish whether other countries collect and use contributory factor type data as part of a national system. Published data was searched using the following methods: Searches using databases held by TRL (The International Transport Research Database (ITRD) and Science Direct); Web-based search tools (e.g. Google and Google Scholar); Browsing the reference lists of relevant articles to identify additional relevant pieces of work (whose reference lists were examined etc.); Consulting TRL colleagues and other contacts about relevant TRL projects and other research. The searches were conducted using a systematic approach and the list of search terms is listed below. term 1 and term 2 and term 3 or term 4 Road Accident Contributory Reporting system Traffic Collision Causation National system Crash Cause(s) Collection system Incident Etiology Police investigation Accidentology The grading of literature found in the searches was undertaken to select only those articles which were: directly relevant to the project; of a suitable quality, robustness, and academic rigour; and sufficiently recent to still be relevant. This grading was based on an analysis of the abstract text contained in the search results; if no abstract was present then the article title was used. A 'filtering' methodology was therefore set up as a consistent and quick means of selecting the search results based on three criteria: Relevance; Quality; and Timeliness. Relevance was interpreted to mean: Does the publication present any evidence, and is it likely to be relevant to UK practice? Quality was interpreted to mean: Is the publication peer reviewed or from a trusted source, does its methodology appear robust? Timeliness refers to when the article was published (articles published before 2000 were not considered). TRL 7 PPR488

16 2.2 UK analysis of STATS19 Contributory Factors The DfT has presented results in RRCGB and discussed the scope and limitations of the 2006, 2007 and 2008 contributory factor data (Robinson et al., 2007, Tranter 2008 and Waite 2009). They note that the contributory factor data are largely subjective and depend on the skill and experience of the reporting police officer and are not usually based on an extensive investigation. This should be kept in mind when interpreting the data. They also recognise that some factors may be difficult to identify at the scene after the accident has occurred and as a result are less likely to be reported. For example, a defective traffic signal may be more obvious for a police officer to identify at the scene than exceeding the speed limit or driver was nervous, uncertain or panicked. In addition these contributory factors are disclosable in court and police officers would require some supporting evidence to record them. The Scottish Government Transport Statistics Branch present contributory factor data in their annual publication entitled Reported Road Casualties Scotland, the latest edition being The numbers of very likely and possible CFs by CF code are reported. In the course of their work they found that there was an inconsistency between the CF code and the type of participant code in about 3-4% of cases in The then Scottish Executive introduced additional computer cross-checks of the data submitted by police forces which has reduced the inconsistency to 1% of cases in the 2008 data. TRL has also analysed the contributory factor data for a number of projects. Crinson et al., (2008) studied the factors in rural road accidents in Scotland; Broughton and Knowles (2009) used contributory factors as part of the road casualty target monitoring project; Knowles et al., (2009) studied the factors in cyclist accidents and Crinson et al., (2010) studied the factors in pedestrian accidents. From the literature it was apparent that the main issues that needed to be decided as part of the analysis were: 1. Whether to use only accidents where the police officer attended or to use all reported accidents; 2. Whether to restrict the levels of confidence; 3. Whether the analysis being conducted relates to numbers of accidents, vehicles or casualties and how to present the findings. Each will be discussed below. 1. Restricting to use only accidents when the police officer attended The decision of whether to include only the accidents where the contributory factors were recorded by the attending police officer may be made to exclude potential bias from self-reported contributory factors. However this may exclude certain categories of accident and produce an unrepresentative sample. The DfT analysis by Robinson et al., (2007), Tranter (2008) and Waite (2009) included only accidents in which a police officer attended the scene (81% of accidents in 2006 and 2007) as they concluded that it was not always possible for the reporting officer to accurately reflect the correct contributory factors when they did not attend. They also excluded accidents that had no contributory factors attributed to them. The proportion of accidents that satisfied both conditions was 77% for all three years, although this proportion varied by severity, decreasing from 94% of fatalities to 75% of slight accidents, and vehicle type (ranging from 64% of pedal cyclists to 85% of heavy goods vehicles). Crinson (2010) adopted a similar approach by including only those accidents where a police officer attended the scene and excluding accidents with no contributory factors recorded from the analysis. In contrast Broughton and Knowles (2009) included all accidents with recorded contributory factors whether the police officer attended or not. Knowles et al., (2009) found clear differences for cyclists in the reporting and attribution TRL 8 PPR488

17 of contributory factors depending whether or not the police officer attended the accident. Contributory factors were more likely to be recorded for accidents attended by the police and attribution was split fairly equally between the cyclist and the driver/rider, but when they did not attend then the factors were attributed more often to the driver/rider rather than the cyclist. Whether or not the police attended had far less effect in terms of the type of contributory factors reported. 2. Restricting the levels of confidence The decision whether to include only the contributory factors recorded as very likely (which can be regarded as more reliable) and exclude the factors recorded as possible or to use all levels of confidence is also not straight-forward as the recording of confidence varies amongst police forces. Table 3-4 in Section 3 shows the joint distribution of very likely and possible factors reported per accident. The majority of the analysis in the reviewed literature used both the very likely and possible contributory factors combined although reasons for this decision were not always given by the authors such as Robinson et al., (2007), Tranter (2008) and Waite (2009). Crinson et al., (2008) also used all levels of confidence in their analysis. They presented the joint distribution of very likely and possible factors reported per accident and concluded that to exclude the possible factors would present an unbalanced summary of the causation of the accidents under study. Broughton (2007) performed his analysis using all the factors and only the very likely factors and noted that the results were similar for both levels of confidence. Broughton and Knowles (2009) and Knowles et al., (2009) based the analysis on the set of accidents with at least one very likely factor and excluded the possible factors. The aim was to focus on the information in which the reporting officers had greater confidence, whilst accepting that the division between very likely and possible was poorly defined. 3. Presentation The most direct way to analyse the contributory factor data is to calculate the incident rate or proportion of accidents in which a particular factor was recorded (i.e. the number of times the factor code was recorded divided by the total number of accidents). Up to six factors can be recorded per accident so an accident may still have occurred even if that factor had not been present. Despite this these proportions should show the relative importance of the potential reason for the accident. Crinson et al., (2008) calculated the incident rate for each contributory factor and presented the top 10 factors for serious accidents (noting any differences between serious and fatal results). This type of presentation has also been adopted by other authors such as Broughton and Knowles (2009), Robinson et al., (2007), Tranter (2008) and Waite (2009). 2.3 International experience The second aim of the literature review was to establish whether other countries collect and use contributory factor type data as part of a national system. This information was gathered in two ways: scrutinising the data sources used in the international research papers and following this up using internet (Google) searches. In summary very few examples of countries that collect and use contributory/causation factors as part of their ongoing national system were found: namely New Zealand which has a similar system to GB and the Netherlands. In New Zealand the national traffic crash database holds records of police reported traffic crashes which include a set of 400 factor codes which probably contributed to the crash. They are grouped into approximately 50 categories found to be the most common factors in crashes. These factors are determined from a written explanation of what TRL 9 PPR488

18 happened in the driver statements and police descriptions. There are driver and vehicle related factors and also codes for other factors such as the road and environment. Each factor recorded in the crash is assigned to the vehicle or driver. Gordon (2007) used the contributory cause codes to study driver distractions. The Netherlands (SWOV) have a road crash database based on the report forms filled in by the police and sent to the Ministry of Transport. The database includes a cause of the crash variable which is determined by the information on the police report form (i.e. it is the opinion of the policeman who attended the crash). The approach is a legal one: did the driver behave in such a way that he/she committed an offence? Up to three causes per crash are recorded. Other examples of using contributory factors were found in Hong Kong and Washington. Sze and Wong (2007) conducted research into pedestrian injuries in traffic crashes using pedestrian casualty contributory factors. The Hong Kong Transport Department publish annual Road Traffic Accident Statistics which show traffic accidents by contributory factor related to the driver, the casualty (driver, passenger, pedestrian), the vehicle and the environment. Australia does not collect contributory factors as part of their national system. Brace et al., (2007) noted that many useful sets of information are gathered by the police at the scene of an accident to describe the vehicles, the individuals involved, the road infrastructure, and the environmental conditions. However, it was also obvious that there is currently no clear way of recording more than a very basic level of human factors information or any specific contributing factors for road accidents attended by the police. TRL 10 PPR488

19 3 Review of the contributory factors This section reports the results of an assessment of the internal consistency of the recorded contributory factors as well as considering the levels of confidence recorded and whether a police officer attended the accident. 3.1 Consistency of factors The STATS19 database system maintained by the DfT includes a range of consistency checks so that, for example, the value entered for variable 1.5 Number of vehicles for a particular accident is required to match the number of vehicle records. When the STATS19 system was expanded in 2005 to include contributory factors, however, no new checks were specified. Consequently, invalid codes can be entered, or valid codes can be entered inappropriately. This section reports a range of relevant analyses to see whether the lack of consistency checks may have led to an appreciable level of misreported factors. Note that, while the following tables identify a certain level of error in the reporting of contributory factors, there is no way of assessing the reliability of the factors chosen by individual reporting officers from the database. The full list of contributory factors together with the reported numbers is given in Appendix A. The contributory factors are assigned to a participant and should be identified by the relevant vehicle or casualty reference number. They are preceded by "V" if the factor applies to a vehicle, driver/rider or the road environment, or "C" if the factor relates to a pedestrian or passenger casualty, or "U" if the factor relates to an uninjured pedestrian. For each a reference is given which should match with either a vehicle or a casualty in the accident. A validity assessment of the assignment of contributory factors to vehicles, casualties or uninjured pedestrians is shown in Table 3-1. A detectable error (see Table 3-1) is one where the contributory factor has been assigned to either a vehicle or casualty that does not exist in the accident. It is encouraging to see that the incidence of detectable errors is relatively low (less than 1%) and the percentage of contributory factors that have been incorrectly assigned decreases from 2005 to 2007 as the system has become more familiar. Table 3-1: Contributory factors that have been incorrectly assigned Factors attributed to vehicles that do not exist Factors attributed to vehicles that do exist 377, , ,946 % not valid 0.09% 0.03% 0.02% Factors attributed to casualties that do not exist Factors attributed to casualties that do exist 41,710 43,112 40,193 % not valid 0.70% 0.19% 0.06% Factors attributed to Uninjured pedestrian 1, All contributory factors 420, , ,776 The pedestrian only factors (801 to 810) should always be assigned to a pedestrian casualty or an uninjured pedestrian. Table 3-2 shows how the pedestrian only TRL 11 PPR488

20 contributory factors have been assigned for the years 2005 to Where the contributory factor was assigned to a casualty, using the casualty reference in STATS19 data, the table also shows the casualty class. Again, the incidence of error is relatively low. Overall, 91% of the pedestrian only factors were correctly assigned to a pedestrian in the accident. The most common error was assigning a pedestrian only code to a vehicle. The pedestrian only codes that were most frequently incorrectly assigned relate to injudicious action and impairment or disability types of codes. This suggests that when investigating these contributory factors it is advisable to ensure that they were assigned to a pedestrian, whether uninjured or a casualty. Table 3-2: How the pedestrian only contributory factors have been assigned. Casualties by class Contributory factor Driver or rider Passenger Pedestrian No match Vehicle U* Total Percent valid 801 Crossing road masked by stationary or parked vehicle , ,845 94% 802 Failed to look properly , , ,510 91% 803 Failed to judge vehicle's path or speed , ,482 86% 804 Wrong use of pedestrian crossing facility , ,034 95% 805 Dangerous action in carriageway (e.g. playing) , ,935 92% 806 Impaired by alcohol , ,156 91% 807 Impaired by drugs (illicit or medicinal) % 808 Careless - reckless or in a hurry , ,228 92% 809 Pedestrian wearing dark clothing at night , ,275 94% 810 Disability or illness - mental or physical , ,144 86% All pedestrian factors 2, , ,728 1, ,389 91% * U indicates the contributory factor was attributed to an uninjured pedestrian The percentage valid is the percentage of factors recorded which follows the guidance, that is, the percentage of factors attributed to a casualty who was a pedestrian or an uninjured pedestrian There were a small number of invalid contributory factor codes in the STATS19 data, the most common are shown in Table 3-3 and the full list is provided in Appendix A. The invalid codes are where the contributory factor code is not one of those listed in Figure 1.1. As with the other examples the percentage of contributory factor codes that are TRL 12 PPR488

21 invalid is very small for all three years. The number of invalid factors reduced from 70 in 2005 to 47 in It is not possible to determine the number of the intended code from the data and although, a range check for the codes may be desirable it would probably mean going back to the original data source to correct. Table 3-3 Invalid Contributory factors Invalid contributory factor code Other Total % of all factors not on list 0.017% 0.003% 0.012% In conclusion the lack of consistency checks have not led to an appreciable level of misreported factors and the reader should not worry unduly about inconsistencies when analysing this type of data. 3.2 Recording of confidence Reporting police officers record their confidence (see Section 1.2) in the contributory factors by coding individual factors as either very likely or possible. Table 3-4 shows the joint distribution of the very likely and possible contributory factors reported per accident. Table 3-4: Number of very likely and possible contributory factors per accident Number of very likely contributory factors Number of possible contributory factors Total 0 7.1% 5.7% 5.4% 2.4% 1.0% 0.3% 0.3% 22.2% % 7.9% 3.1% 0.9% 0.3% 0.2% 33.3% % 4.6% 1.6% 0.5% 0.3% 25.4% 3 9.0% 1.7% 0.6% 0.4% 11.6% 4 3.9% 0.6% 0.4% 5.0% 5 1.3% 0.3% 1.7% 6 0.8% 0.8% Total 61.5% 20.9% 11.1% 4.1% 1.6% 0.5% 0.3% 100.0% TRL 13 PPR488

22 Overall, for the three years of data for all police forces: 7.1% of accidents had no reported contributory factors; It was most common for just one very likely factor to be recorded (20.9%) followed by just two very likely factors (18.4%); 2.7% of accidents were recorded with the maximum number of factors (6) Over one half (61.5%-7.1%= 54.4%) of accidents were assigned at least one very likely contributory factor and no possible contributory factors One fifth of accidents (23.4%) were assigned a combination of very likely and possible contributory factors In 2005, there were more accidents with no contributory factors reported (9%) than for 2006 and 2007 (6%). This is likely to be due to inexperience with the new system since 2005 was the first year of reporting contributory factors. For those accidents where contributory factors were reported, the average number of very likely and possible contributory factors was similar for each year. 3.3 Did a police officer attend the accident? The STATS19 variable 1.26 asks Did a police officer attend the scene and obtain the details for this report? Where the answer is yes, then the contributory factors presumably reflect the police officer s opinion at the time of reporting. However, if the answer is no the contributory factors were presumably based on the account of whoever reported the accident to the police, so there is a risk of bias. Table 3-5: Summary of contributory factors by police officer attended Police officer attended scene? Yes No Total Number of accidents 459, , ,011 Number of accidents with contributory factors 433,721 93, ,410 % of accidents with contributory factors reported 94% 86% 93% Average number of contributory factors Average number of very likely contributory factors Average number of possible contributory factors % of contributory factors that were very likely for each accident 71% 65% 70% % of accidents where police officer attended scene 81% 19% 100% The percentage of accidents attended by a police officer was constant for the three years (2005 to 2007), 81%. There were 93,666 accidents where the police officer did not attend the scene but contributory factors were reported. Unsurprisingly, both the proportion (86%) of these accidents where contributory factors were recorded and the number of factors (2.16) recorded per accident was lower than for the accidents that were attended by a police officer. Some analyses of the contributory factor data omit accidents where the police officer did not attend (DfT, 2009). A likely reason for this decision is a view that these contributory factors should be more reliable. On the other hand, certain groups of accident are more TRL 14 PPR488

23 likely to be attended by the police so this selection may bias results, and including the unattended accidents would increase the number available for analysis by 22%. Figure 3-1 shows the number of contributory factors recorded per accident by whether the police attended the accident. Figure 3-1: Number of contributory factors recorded by whether the police attended In both cases one or two contributory factors were most commonly recorded. When a police officer did attend, more contributory factors tended to be recorded. 3.4 By accident severity Analysis by severity showed that a higher proportion of fatal accidents were attended by the police (99%) compared to 92% for serious and 79% for slight. However, the proportion of accidents with contributory factors reported was similar for all severities. Figure 3-2 shows the average number of very likely and possible contributory factors for each severity of accident. TRL 15 PPR488

24 Figure 3-2: Number of contributory factors recorded by severity and confidence The average number of factors assigned to fatal accidents was 2.5 with marginally fewer for serious and slight accidents respectively. The higher number of contributory factors in fatal accidents was mainly due to an increased number of possible contributory factors. This may be because these accidents are investigated more thoroughly and this should be taken into account when considering whether to exclude possible contributory factors from analysis of fatal accidents. Figure 3-3 shows the distribution of the number of contributory factors by accident severity. Figure 3-3: Distribution of number of factors recorded by severity 35% Fatal Serious Slight 30% 25% % of accidents 20% 15% 10% 5% 0% Number of factors TRL 16 PPR488

25 3.5 Police force There may be differences in the way each police force reports the contributory factors. The percentage of accidents attended by the police varied from 70% (Merseyside) to 98% (Tayside). The percentage of accidents where contributory factors were recorded showed more variation; from 54% (Dorset) to 100% (5 police forces). Table 3-6 shows the police forces with more than 10% of accidents without any factors. Table 3-6: Police Forces with less than 90% of accidents reported with contributory factors Police Force Total number of accidents % of accidents with contributory factors recorded Dorset 7,308 54% Devon & Cornwall 17,522 61% Avon & Somerset 15,508 68% West Yorkshire 21,306 80% Cambridgeshire 9,384 83% Greater Manchester 25,289 83% Leicestershire 9,171 85% Northumbria 13,231 86% Lancashire 16,054 89% Table 3-7: Summary of contributory factors recorded and confidence by Police Force. Mean Minimum Maximum % of accidents with contributory factors reported 93% 46% Dorset 100% West Midlands, Wiltshire, South Wales, Central, Number of contributory factors Average number of very likely contributory factors Average number of possible contributory factors % of accidents where police officer attended scene Dumfries & Galloway Dumfries & Galloway 2.92 W Mercia 2.61 Metropolitan Metropolitan 1.22 W Mercia 81% 70% Merseyside 98% Tayside Table 3-7 shows that the West Midlands, Wiltshire, South Wales and Central Police Forces have contributory factors recorded for every accident and that the Dorset Police Force (46%) has the smallest proportion of accidents with contributory factors recorded. The Dumfries and Galloway Police Force has both the lowest number of contributory factors and the lowest number of very likely contributory factors recorded per accident. In contrast, the Metropolitan Police Force has both the highest number of very likely contributory factors and the lowest number of possible contributory factors recorded per accident. A possible reason for this that for a number of years the Metropolitan Police TRL 17 PPR488

26 Force have been recording contributory factors using their own system and this may have led to increased confidence. The police attended the scene of the accident more frequently in Tayside and less often in Merseyside. The reasons for these variations are unclear; they may be due to differences in policing policy, resources or practice. TRL 18 PPR488

27 4 Review of individual contributory factors This section reports a review of contributory factors where the STATS20 guidance was unclear or misleading and also considers consistency checks for some contributory factors that have links to other contributory factors or variables in the STATS19 attendant circumstances, vehicle or casualty records. 4.1 Review of Stats20 guidance The guidance for completing the Stats19 form was reviewed to assess whether the overall instructions and the explanatory text for each contributory factor were clear. The contributory factors are numbered in STATS20 and these are used within this section when referencing the contributory factors. Overall, the guidance seemed reasonably clear. However, the STATS20 guidance for contributory factor 803 pedestrian failed to judge vehicle s path or speed is rather questionable. The STATS20 guidance for contributory factor 803 explains that before entering the carriageway the pedestrian saw the approaching vehicle but misjudged its speed or intended manoeuvre. There is also a note that this factor may be used in conjunction with contributory factor code 404. The 400 series of contributory factors relate to an error of judgement by a driver/rider, or an action resulting from another party s actions. In many cases, further codes can be used to explain why these actions were taken. Contributory factor code 404 is specifically where a driver/rider misled another road user (including a pedestrian) by either giving a bad signal or by not signalling at all. This appears to lead to confusion as it is reasonable to assume that the pedestrian only failed to judge the vehicle s path or speed because the driver/rider failed to signal or gave a misleading signal. 4.2 Checks between selected contributory factors and other STATS19 variables For some of the contributory factors, there are links between other contributory factors or other STATS19 variables, i.e. those recorded on the attendant circumstances, vehicle or casualty records. These were reviewed CF 101 Poor or defective road surface The STATS20 guidance for this factor states: Includes any obvious road surface defect such as potholes and cracks. Also includes roads where a worn surface or poor skid resistance is thought to have contributed to the accident. There is also a field on the attendant circumstances part of STATS19 to record any special conditions at site, which includes a value for road surface defective. STATS20 guidance for special conditions at site states: This section should be completed for every personal injury accident whether or not such conditions were considered to be contributory to the accident. Use code 0 only if there were no special conditions at the accident site. A defective road surface (code 5) includes any obvious road surface defect, such as pot holes, cracks (but not lack of skid resistance) and surface melting, but not the presence of ice, snow or flood. TRL 19 PPR488

28 This slight difference in definition means that these two fields are not directly comparable, since if lack of skid resistance was a contributory factor, then this would not be recorded under special conditions. Table 4-1 shows the number of accidents with contributory factor 101 poor or defective road surface by the special conditions recorded at the site and year. Table 4-1: Number of accidents with CF 101 (poor or defective road surface) by special conditions at site and year Special conditions at site Total None ,829 Auto traffic signal out Auto traffic signal partially defective Permanent road signing or marking defective or obscured Roadworks Road surface defective Oil or diesel Mud Total ,783 Table 4-1 shows that the road surface was defective for 22% of accidents where this factor was contributory. Two-thirds of accidents where poor or defective road surface was contributory had no special conditions recorded; suggesting that these are due to the lack of skid resistance, but there may be inconsistencies in reporting CF Deposit on road STATS20 guidance for this factor states: Include any deposit arising from human (or animal) activity which has made sections of the road surface slippery or which has caused traction control problems for a vehicle. STATS20 guidance for special conditions includes values for oil, diesel or mud. Therefore, if the deposit on road that has contributed to the accident is oil, diesel or mud, the special conditions should reflect this. However, if the deposit is something else there is no code for this. Table 4-2 shows the special conditions at site recorded for those accidents with deposit on road as a contributory factor. TRL 20 PPR488

29 Table 4-2: Accidents with CF 102 (deposit on road) by special conditions at site and year Special conditions at site Total None 1,049 1,104 1,060 3,213 Auto traffic signal out Auto traffic signal partially defective Permanent road signing or marking defective or obscured Roadworks Road surface defective Oil or diesel ,674 Mud ,201 Total 2,151 2,220 2,177 6,548 About one-half of accidents where deposit on road was a contributory factor were recorded with no special conditions at site. This suggests that either the deposit on the road was something other than oil, diesel or mud, or that there are inconsistencies in the coding CF 104 Inadequate or masked signs or road markings STATS20 guidance states: The warning or directional road signs or road markings, at the accident location, could not be clearly seen or read. Includes road signs which are obscured by trees/vegetation or where the sign is unreadable due to glare or poor condition. Also includes road markings which have been worn away or covered by snow. This code should also be used where the size, siting or lack of road signs or markings contributed to the accident. There is a code in the field special conditions at site to record permanent road signing or marking defective or obscured. The special conditions at site field does not cover a lack of signing. Table 4-3 shows the number of accidents with contributory factor 104 (inadequate or masked signs or road markings) by special conditions at site and year. TRL 21 PPR488

30 Table 4-3: Accidents with CF 104 (inadequate or masked signs or road markings) by special conditions at site and year Special conditions at site Total None ,434 Auto traffic signal out Auto traffic signal partially defective Permanent road signing or marking defective or obscured Roadworks Road surface defective Oil or diesel Mud Total ,121 About one-fifth of the accidents with contributory factor 104 assigned were recorded as having special conditions at site permanent road signing or marking defective or obscured. The remainder were mostly recorded without any special conditions at site. This suggests that the size, siting or lack of road signs or markings contributed to the accident rather than the signing being defective or obscured CF 105 Defective traffic signals STATS20 guidance for this contributory factor states: A totally, or partially, defective automatic traffic signal contributed to the accident. Includes traffic signals which have previously been damaged by vehicle impact and have become misaligned. Part-time signals outside the times of normal operation (and, therefore, not illuminated) should not be regarded as defective. There is a code in the field special conditions at site to record automatic traffic signals out or partially defective. STATS20 guidance for this states: An automatic traffic signal (including a pelican/puffin/toucan crossing) is 'out' (code 1) if it is totally inoperative at the time of the accident (i.e. no light(s) whatsoever in operation). Do not use this code to record part time signals which are not in operation (unless there is clear evidence that the part time signals are defective). An automatic traffic signal (including a pelican/puffin/toucan crossing) is 'partially defective' when any defect not covered by Code 1 exists. The STATS20 guidance therefore suggests that where CF105 is used to record that defective signals were contributory to the accident, the special conditions field should be used to record the presence of defective traffic signals. Table 4-4 shows the special conditions at site recorded for those accidents with defective traffic signals as a contributory factor. TRL 22 PPR488

31 Table 4-4: Accidents with CF 105 (defective traffic signals) by special conditions at site and year Special conditions at site Total None Auto traffic signal out Auto traffic signal partially defective Permanent road signing or marking defective or obscured Roadworks Road surface defective Oil or diesel Mud 1 1 Total A special condition at site was not recorded for 38% of the accidents. This is appears to be contrary to the STATS20 guidance CF 107 Temporary road layout STATS20 Guidance for this contributory factor states: Includes contraflow sections on dual carriageways, single alternate line working at roadworks on two way roads and all other locations where a temporary road layout has contributed towards an accident. The special conditions at site includes a code for roadworks. If roadworks are present and any of the other conditions are also present, code 4 is preferred if the roadworks were hit by at least one vehicle. Includes accidents occurring within the coned area approaching (or immediately following) the roadworks. This guidance therefore means that when temporary road layout is a contributory factor, the special conditions at site should be recorded as roadworks. Table 4-5 shows the relationship between these two variables. TRL 23 PPR488

32 Table 4-5: Accidents with CF107 (temporary road layout) by special conditions at site and year Special conditions at site Total None Auto traffic signal out Auto traffic signal partially defective Permanent road signing or marking defective or obscured Roadworks Road surface defective Oil or diesel Mud Total ,247 About three-quarters (73%) of accidents were temporary road layout was recorded as a contributory factor had roadworks recorded as a special condition CF 109 Animal or object in carriageway STATS20 Guidance for this contributory factor states: 'Animal' includes any animal (not just those defined in the Road Traffic Act) which contributed to the accident, whether or not the animal was hit. Excludes ridden horses and animals pulling vehicles (e.g. horse and trap) because these are treated as vehicles in their own right. Pedestrians are also not included. 'Object' is defined as any object which the driver would not expect to find in the carriageway (e.g. dislodged vehicle load, fallen tree) and which caused or contributed to the accident. Excludes permanent features such as bollards, refuges, kerb etc. The attendant circumstances field carriageway hazards includes the following codes: 0. None 1. Dislodged vehicle load in carriageway 2. Other object in carriageway 3. Involvement with previous accident 6. Pedestrian in carriageway - not injured 7. Any animal in carriageway (except ridden horse) The carriageway hazard variable has various types of hazards, and should include any animal or object that was identified as contributory. Table 4-6 shows the carriageway hazard for those accident with contributory factor 107 (animal or object in carriageway) by year. TRL 24 PPR488

33 Table 4-6: Accidents with CF107 (animal or object in carriageway) by carriageway hazard and year Carriageway hazard Total None ,945 Dislodged vehicle load in carriageway Other object in carriageway Involvement with previous accident Pedestrian in carriageway - not injured Any animal in carriageway (except ridden horse) ,112 Blank 1 1 Total 1,727 1,754 1,660 5,141 Whilst the most common carriageway hazard in accidents where CF109 was assigned was any animal in carriageway (41%), 38% were recorded with no carriageway hazards CF301 - Disobeyed automatic traffic signal STATS20 Guidance for this contributory factor states: Driver/Rider did not stop at automatic traffic signal set at red. Code applies to permanent and temporary traffic signals (e.g. at roadworks). This code should not be used for non-junction pedestrian crossings (e.g. pelican or toucan - see code 304). This contributory factor should either be used where automatic traffic signals (ATS) are present, or where special conditions at site is roadworks. Table 4-7 shows the accidents with CF301 compared with the junction control, roadworks and pedestrian crossing facilities. Table 4-7: Accidents with CF301 (disobeyed automatic traffic signal) by junction control, roadworks and pedestrian crossing facilities and year Junction control Special conditions at site Pedestrian crossing physical facilities within 50m Total Automatic traffic signal 2,212 2,357 2,147 6,716 Other Roadworks Other Other Pedestrian phase at ATS Other Other Other Total 2,556 2,683 2,456 7,695 The proportion of accidents with CF301 recorded that did not occur at ATS, roadworks or pedestrian phase at ATS was 12%. These may have been at other sites with traffic signals, for example past a narrowing or bridge, or maybe incorrect coding of the junction control, special conditions or the contributory factor. TRL 25 PPR488

34 4.2.8 CF302 - Disobeyed - Give Way - or - Stop - sign or markings STATS20 Guidance for this contributory factor states: Driver/Rider did not stop at "Stop" sign or give way at "Give Way" sign or road markings. Includes manually operated Stop/Go signs at roadworks. Code will often be used in conjunction with codes 401 and 402. This contributory factor should only be used where a give way or stop sign is present, or at roadworks where signs may operate. Table 4-8 shows the junction control and special conditions at site for this contributory factor. Table 4-8: Accidents with CF302 (disobeyed - Give Way - or - Stop - sign or markings) by junction control, special conditions and year Junction control Special conditions Total Stop sign Give way or uncontrolled 5,284 5,025 3,986 14,295 Other Roadworks Other Total 5,797 5,498 4,375 15,670 The majority (95%) of accidents with CF302 were at a junction controlled with a stop sign, give way or uncontrolled or at roadworks. As with factor 301, there may be giveway or stop signs or markings at other locations, such as narrowing, where this factor may be used CF303 - Disobeyed double white lines STATS20 Guidance for this contributory factor states: Driver/rider deliberately crossed double white line road markings prior to accident. Do not use this code to record a vehicle crossing the double white lines as result of losing control. Double white lines are only used on single carriageways, and therefore all accidents where factor 303 (disobeyed double white lines) is present should have occurred on a single carriageway road. Table 4-9: Accidents with CF303 (disobeyed double white lines) by road type and year Road type Total Roundabout One-way street Dual carriageway Single carriageway Slip road Unknown Total ,081 The majority (92%) of these accidents occurred on a single carriageway road. TRL 26 PPR488

35 CF304 - Disobeyed pedestrian crossing facility STATS20 Guidance for this contributory factor states: Driver/rider caused accident by failing to stop (whether intentionally or not) at a pedestrian crossing. For this code, a pedestrian crossing is defined as a Zebra crossing or Pelican, Puffin, Toucan or similar light controlled crossing. Central refuges, without any traffic control, are not treated as pedestrian crossings for the purposes of this code. Use code 301 for driver/riders causing pedestrian accidents at traffic signal junctions. The presence of a crossing facility within 50m is also recorded in the STATS19 data, and is shown in the Table 4-10 for those accidents where CF304 was recorded. Table 4-10: Pedestrian crossing facilities at accidents with CF304 (disobeyed pedestrian crossing facility) by pedestrian crossing and year Physical Pedestrian crossing Human Total Zebra Pelican, puffin, toucan Pedestrian phase at ATS Footbridge or subway Central refuge No physical facility School crossing patrol No human control Total ,935 There were 92 accidents (5% of total) which had CF304 as a contributory factor but were not recorded as being at a pedestrian crossing facility CF 306 and CF307 - Speed-related factors There are two contributory factors relating to speed, exceeding speed limit and travelling too fast for conditions. The guidance for the use for these given in STATS20 is reproduced below: 306 Exceeding speed limit: Driver/rider caused, or contributed to the accident, by exceeding the posted speed limit. This code should also be used in cases where the actions of another road user were the immediate cause of the accident but a speeding vehicle also contributed to causing the collision. Includes exceeding variable speed limits (e.g. on motorways) and speed limits based on vehicle type (including towing). Use this code (not code 307) if driver/rider was exceeding the speed limit and travelling too fast for the conditions. 307 Travelling too fast for conditions: Driver/rider was travelling within the speed limit, but their speed was not appropriate for the road conditions and/or vehicle type (including towing), and contributed to the accident. TRL 27 PPR488

36 The STATS19 data was analysed to assess how well the guidance of using exceeding speed limit rather than travelling too fast for conditions when both apply is followed. Table 4-11 shows the reporting of these two contributory factors by year. Table 4-11: Number of vehicles with speed factors by year Speed factor Total CF306 only 10,717 10,530 11,127 32,374 CF307 only 29,558 26,917 22,230 78,705 CF306 and CF307 2,979 3,132 3,389 9,500 % over-reporting of CF307 9% 10% 13% 11% Note: only here means without the other one from 306/307, but may have other factors In the three-year period, there were 9,500 vehicles where both of these speed contributory factors were recorded. If the instructions had been followed, these accidents should have been recorded with CF306 (exceeding speed limit) and not CF307 (travelling too fast for conditions). This suggests that CF307 is over-reported by 11%, with the highest rate in 2007 (13%). Table 4-12 shows the number of vehicles reported with the two speed-related factors by accident severity. Table 4-12: Number of vehicles with speed factors by accident severity Speed factor Fatal Serious Slight Total CF306 only 1,442 6,388 24,544 32,374 CF307 only 1,939 11,734 65,032 78,705 CF306 and CF ,014 7,005 9,500 % over-reporting of CF307 20% 15% 10% 11% This shows that incorrectly using both contributory factors for the same vehicle is more common in fatal accidents (20%) compared with serious (15%) and slight (10%). Analysis by police force also showed large variations, with some police forces having almost no vehicles with both contributory factors, and the maximum over-reporting of factor CF307 was in Staffordshire (20%). Where both of the speed factors were used (9,500 cases), more than half (56%) were coded as very likely for both factors, with 22% as possible for both factors CF310 - Cyclist entering road from pavement STATS20 Guidance for this factor states: Pedal cyclist contributed to the accident by riding from the pavement into the road. Also includes cyclist crossing road on pedestrian or toucan crossing. This code should only apply to pedal cyclists. Table 4-13 shows the number of vehicles and casualties with contributory factor 310 by vehicle type and year. TRL 28 PPR488

37 Table 4-13: The type of vehicles with CF310 (cyclist entering road from pavement) by year Contributory Factor type Vehicle type Total Vehicle Pedal cycle 1,150 1,137 1,096 3,383 Other vehicle Vehicle Total 1,234 1,208 1,174 3,616 Casualty Pedestrian Pedal cycle Other vehicle No match Casualty Total Total 1,317 1,273 1,225 3,815 The majority (94%) of the vehicles coded with CF310 were pedal cyclists; the most common other vehicle was car (5%). This contributory factor may have been applied to the vehicle which was affected by the pedal cycle entering from pavement rather than the pedal cyclist. There were 199 occurrences of CF310 that were applied to casualties rather than vehicles, which is not how this factor is meant to be recorded. However, in these cases, the majority (152) of the casualties were pedal cyclists. There were also a number of pedestrians with this contributory factor applied; it is unknown whether an incorrect reference has been recorded or whether this code has been used to record pedestrians entering the road from pavement CF401 - Junction overshoot STATS20 Guidance for this contributory factor states: Code should be used to record a driver/rider who did not stop at a junction and overshot the stop line or give way markings. This code will frequently be used in conjunction with other codes which will give further detail on why the overshoot happened. Code should not be used to record an overshoot at working automatic traffic signals (use code 301) but may be used if the automatic traffic signals are defective. In order for junction overshoot to be a contributory factor, it is expected that the site of the accident would be at a junction. TRL 29 PPR488

38 Table 4-14: Accidents with CF401 (junction overshoot) by junction detail and year Junction detail Total Not at or within 20m of a junction At or within 20m of a junction 3,249 3,050 3,121 9,420 Total 3,473 3,263 3,349 10,085 This shows that 93% of accidents where the contributory factor junction overshoot was recorded as a contributory factor occurred at a junction. For the remaining 7% there may be errors in the recording of either the junction detail or of CF CF402 - Junction restart (moving off at junction) STATS20 Guidance for this contributory factor states: Identifies a driver/rider who has successfully stopped (or slowed to give way) at a junction, but has then moved off, and in doing so, has contributed to an accident. As with code 401, other codes can be used to explain why this action was taken (e.g. impairment or distraction). As with CF401, this contributory factor should only apply at junctions. Table 4-15 shows the use of this contributory factor for junction and non-junction accidents. Table 4-15: Accidents with CF402 (junction restart, moving off at junction) by junction detail and year Junction detail Total Not at or within 20m of a junction At or within 20m of a junction 2,718 2,423 2,265 7,406 Total 2,815 2,501 2,354 7,670 Overall, 3.4% of accidents where junction restart was a contributory factor were away from a junction. This could be due to an error in the recording of the junction detail or the contributory factor and was fairly constant over the three years CF403 - Poor turn or manoeuvre STATS20 Guidance for this contributory factor states: Applies to any manoeuvre performed by the driver/rider which caused, or contributed to, the accident. Examples include reversing, turning left, right or U- turn, changing lanes or overtaking. Also include poor vehicle positioning (e.g. in middle of road, in wrong lane at junction or encroaching into bus/cycle lane). Where codes 402 [junction restart] and 403 could both apply use code 402. The table below shows how vehicles have been coded with these two contributory factors. TRL 30 PPR488

39 Table 4-16: Vehicles with CF402 (junction restart, moving off at junction) and CF403 (poor turn or manoeuvre) by year Contributory Factors present Total Factor 403 only 53,052 49,334 46, ,067 Factor 402 only 6,240 5,582 5,056 16,878 Factors 402 and ,150 Total 60,144 55,548 52, ,095 Note: only here means without the other one from 402/403, but may have other factors There were 2,150 vehicles which were coded with both contributory factors 402 and 403. This does not follow the guidelines and suggests an over-reporting of contributory factor 403 by 1.4% CF407 Passing too close to cyclist, horse rider or pedestrian STATS20 guidance for this contributory factor states: Use this code whether or not there was contact between the cyclist/horse rider/pedestrian and the passing vehicle. Identify the passing vehicle in the which participant box (not the cyclist, horse rider or pedestrian). Includes contact with overhanging wing mirrors. Table 4-17 shows the types of vehicle which were recorded with this factor. Table 4-17: Vehicles with CF407 (passing too close to cyclist, horse rider or pedestrian) by year Vehicle type Total Pedal cycle PTW Car 2,230 2,151 2,403 6,784 Bus/coach Other Other non motor Horse Goods vehicle Unknown Total 3,294 3,154 3,420 9,868 Overall, there were 231 pedal cycles, equestrians or other non-motorised users recorded with this factor, 2% of all uses of this factor, the majority of these were pedal cycles. The vast majority of uses of this contributory factor were for the other vehicle types. Table 4-18 shows the number of accidents recorded with CF407 by whether pedestrians, pedal cycle, or horse riders were involved. Pedal cycles and ridden horses may have TRL 31 PPR488

40 been recorded as part of the incident even if the rider was not injured; however, uninjured pedestrians are not recorded. Table 4-18: Accidents with CF407 (passing too close to cyclist, horse rider or pedestrian) by year Accident type Total Horse accident Cycle accident 1,395 1,462 1,621 4,478 Pedestrian accident 1,458 1,361 1,434 4,253 Pedestrian and horse Pedestrian and cycle No pedestrian, cycle or horse ,045 Total 3,301 3,147 3,415 9,863 The majority of the accidents with CF407 involved either a pedestrian or a pedal cycle. However, there were 1,045 accidents (11%) where the presence of a pedestrian, pedal cyclist, or horse rider was not recorded. It may be that these were uninjured and not recorded CF 410 Loss of control STATS20 Guidance for this contributory factor states: This code should be used where a driver/rider lost control of their vehicle, thereby causing or contributing to an accident, whether or not they were considered to be at fault. Wherever possible, at least one more code should be allocated to the same driver/rider to give an indication of why they lost control. Includes ridden horses. The STATS19 contributory factor data were analysed to assess the frequency of using loss of control without other contributory factors for the same driver/rider. Table 4-19 shows the number of other contributory factors recorded for vehicles which had loss of control as a factor. TRL 32 PPR488

41 Table 4-19: Number of other factors recorded for vehicles with CF410 (loss of control) by year Number of other factors recorded for vehicle with CF Total 0 3,024 3,073 2,870 8, ,646 6,645 6,231 19, ,579 5,723 5,505 16, ,242 3,220 3,163 9, ,563 1,609 1,624 4, ,236 1,240 1,261 3,737 Total 21,290 21,510 20,654 63,454 % with no other factors 14.2% 14.3% 13.9% 14.1% Overall, 14% of vehicles recorded with CF410 (loss of control) were not recorded with any other factors, contrary to the guidance given in STATS20, although there may have been other factors attributed to other participants of the accident. Analysis by severity of the accident (Table 4-20) showed that this was more common for fatal accidents (19%). Table 4-20: Number of other factors recorded for vehicles with CF410 (loss of control) by accident severity Number of other factors recorded for vehicle with CF410 Fatal Serious Slight Total ,903 6,547 8, ,894 14,856 19, ,247 12,895 16, ,939 7,281 9, ,615 4, ,848 3,737 Total 2,684 12,728 48,042 63,454 % with no other factors 19.3% 15.0% 13.6% 14.1% There was variation between police forces in the percentage of vehicles with CF410 without other factors; with West Mercia having the lowest (4.7%) and Dumfries and Galloway the highest (53.6%). TRL 33 PPR488

42 CF507 - Cyclist wearing dark clothing at night STATS20 Guidance for this factor states: To be used in accidents where, if the cyclist s clothing had been brighter, then they would have been seen in time and the accident would not have occurred. Can be used in conjunction with code 506 if necessary. It would be expected that this factor was only attributed to pedal cyclists, and used for accidents which occurred in darkness. Table 4-21 shows the use of CF507 by type of vehicle and lighting conditions. Table 4-21: Use of CF507 (cyclist wearing dark clothing at night) compared with vehicle type and lighting conditions by year Vehicle or casualty Vehicle type Lighting condition Total Vehicle Pedal cycle Daylight Darkness Other vehicle Daylight Darkness Vehicle Total ,161 Casualty Pedal cycle Daylight Darkness Other vehicle Darkness Casualty Total Total ,238 The majority (74%) of occurrences of CF507 were applied to a vehicle which was a pedal cycle and the light conditions were darkness. A further 5% were in accidents in the dark and assigned to a casualty which was a cyclist. The remaining 20% were contradictory codes, either not applied to a cyclist, or not during darkness CF707 Vision affected by rain - sleet - snow or fog STATS20 Guidance for this contributory factor states: VISION AFFECTED BY: DRIVER/RIDER ONLY. Codes are used to record all factors which affected a driver s or rider's vision. These factors are used when a failure or poor manoeuvre occurs because a driver/rider's view is obscured; preventing them from seeing a hazard, such as an approaching vehicle or a pedestrian in the road, and it appears that they could have avoided the hazard if they had been able to see it clearly. 707 Rain, sleet, snow or fog Only use this code where the driver/rider's vision was affected by these weather conditions and this contributed to the accident. Use code 103 where the weather contributed to poor road conditions. TRL 34 PPR488

43 It would be expected that those accidents with contributory factor 707 occurred in weather conditions that were not fine. Table 4-22 shows the weather conditions, and also the road surface conditions for accidents with contributory factor 707. Table 4-22: Accidents with CF707 (vision affected by rain - sleet - snow or fog) by weather, road surface condition and year Weather Road surface condition Total Rain, Snow, fog or mist 2,418 2,600 2,368 7,386 dry Fine wet/damp/frost/snow/ice/flood dry Other wet/damp/frost/snow/ice/flood dry Unknown wet/damp/frost/snow/ice/flood Total 2,543 2,721 2,485 7,749 The vast majority (95%) of accidents with this factor occurred in weather conditions of rain, snow, fog or mist. Of the remainder, the most common was fine weather, but with a road surface that was damp, wet, snow, ice or flood, although these may have been better coded with factor 708 (spray from other vehicles) CF804 Pedestrian Wrong use of pedestrian crossing facility STATS20 Guidance for this contributory factor states: Pedestrian entered the carriageway at any crossing with pedestrian lights while the lights indicated that the pedestrian should wait. Includes pedestrians entering the carriageway at a zebra crossing but failing to give the driver sufficient time to stop. Code should not be used at traffic signal junctions without pedestrian phase. Only pedestrian casualties (or uninjured pedestrians) should be coded with this contributory factor, and the attendant circumstances should record the presence of a pedestrian crossing facility within 50m. Table 4-23 shows the accidents with CF804 by pedestrian crossing facility. TRL 35 PPR488

44 Table 4-23: Accidents with CF804 (pedestrian, wrong use of pedestrian crossing facility) by pedestrian crossing facilities Physical Pedestrian crossing facilities Human Total Zebra Non junction light controlled pedestrian crossing ,481 Pedestrian phase at ATS ,404 None/footbridge/refuge Crossing patrol Other authorised person none Total 1,143 1,142 1,150 3,435 The majority of accidents coded with CF804 were also reported as being within 50m of a pedestrian crossing facility (or person). However, there were 278 accidents (8%) where there was no facility reported on the attendant circumstances Use of CF999 other please specify STATS20 Guidance for this contributory factor states: To be used only when no contributory factor is available to describe a particular circumstance which contributed to the accident. Includes all cases where a passenger caused or contributed to their own injury (e.g. jumping from vehicle, "stealing a ride" and falling from vehicle, or being drunk and falling in bus). Code will also include all vehicle defects not listed in codes (e.g. Wheel became detached from vehicle, engine fire or engine seized). Overall, between 2005 and ,602 accidents were coded with this contributory factor (3% of accidents), with little variation between the years. There was variation between police forces, ranging from 0.2% for Dumfries and Galloway to 6.3% for Dyfed-Powys. In 38% of accidents with CF999 this was the only factor recorded. The text descriptions accompanying the CF999 ( other ) were obtained from Greater Manchester Police (GMP) for a sample of 743 accidents between 1 April 2005 and 31 December The following tables in this section (Table 4-24 to Table 4-27) use these GMP data to provide further information on the occasions when CF999 is recorded. Table 4-24 shows the number of other contributory factors by the number of contributory factors recorded in the accident. TRL 36 PPR488

45 Table 4-24: Number of other contributory factors in the GMP sample by number of contributory factors in accident and year Number of contributory factors in accident Total Total This shows a similar pattern to nationally (GB), with 36% of other contributory factors being the only contributory factor recorded for the accident. The text descriptions were reviewed individually and either categorised with an alternative existing contributory factor using the STATS20 guidance (DfT, 2004) and the contributory factor codes listed in Figure 1.1 or into groups of genuine others with similar descriptions. There were also some coded as not valid, where the text did not give a contributory reason for the accident. Examples of factors which were categorised as not valid included: Pending enquiries Passenger no seatbelt Busy road traffic Contravenes bus lane Table 4-25 shows the number of other contributory factors categorised by whether they appeared to be genuine use of other or whether an existing code should have been used. Table 4-25: Validity of other contributory factors Type of other factor Number Existing contributory factor code could have been used 334 Not valid 83 Unsupervised child 20 Police Pursuit 36 Other genuine others 259 Unknown 32 Total 764 Just under half (43%) of the other contributory factors could have been described with one of the existing codes. About one-third (34%) were grouped together as being genuine others. The number of other contributory factors that were assessed to be not valid was 83. TRL 37 PPR488

46 In some cases, from the description given, there was more than one of the existing contributory factors that could have been used, but it was not possible to say which. The existing contributory factors that could have been used instead of other on ten or more occasions for this sample of other contributory factors from the GMP are shown in Table 4-26, together with the occurrence of that contributory factor in Greater Manchester in the STATS19 data for 2005 to Table 4-26: Number of other contributory factors by the number of contributory factors in accidents CF code Description Illness or disability, mental or physical Tyres illegal, defective or under-inflated Existing number in Greater Manchester* Example of description 179 Sneezed Poor turn or manoeuvre 2, Aggressive driving 1, Pedestrian failed to look properly 2, Distraction in vehicle Overloaded or poorly loaded vehicle *Note: STATS19 data ( ) 46 O/s Front Tyre Blow Out Moving From Left Exit Into Middle Lane Deliberate Act Drove Into Veh2 Ped Runs Into Rd Causing Bus To Brake Heavily Looked Inside Own Car To Change Radio Load Strapping Gives Way Causing It To Shift Number of other contributory factors In total, between 2005 and 2007 there were over 25,000 accidents recorded in Greater Manchester. If the accidents with other contributory factors had been recorded as suggested above, the occurrence of those contributory factors listed in Table 4-26 would increase by a maximum of 0.2%. The types of other contributory factor with ten or more occurrences which were not easily placed into the existing contributory factors and are genuine uses of other contributory factor are shown in Table TRL 38 PPR488

47 Table 4-27: Number and description of other contributory factors which could not be easily placed Group Number Description Passenger error 40 police pursuit 36 foot slip 30 vehicle defect 22 Passenger Fell Whilst Alighting Bus Slipped From Seat Police Veh Pursuing V1 Makes Off From Police Foot Slipped Off Brake Driver Got Boot Stuck Under Accelerator Pedal V1 Suffers Mechanical Breakdown Lane 2 Engine Failure On Motorcycle fts 21 Failed To Stop At Scene unsupervised child 20 Pedestrian fall 17 2 Yr Old Child Wandering Into The Rd Young Child In Roadway Ped Slipped And Fell Into Carriageway Pedestrian Fall N C/way While Crossing Road handbrake 12 Driver V1 Failed To Set Hand Brake Ped pushed into road 12 Ped Was Pushed Into Oncoming Veh Unknown Caused C1 To Jump Into Road Attempt high wind 10 High winds Parked vehicle 10 inattention 10 Parked Vehs N/s Road Prior To Bend Veh2 Poorly Parked Rear Sticking Out Into Road Lapse In Concentration Lack Of Attention These other contributory factors which could not be easily placed were a small proportion of all accidents in the Greater Manchester area. TRL 39 PPR488

48 5 Recommended guidance for Contributory Factor analysis Perhaps the most fundamental item of advice is to be aware of the nature and limitations of the contributory factors (CF) recorded in the STATS19 database. The caveat on the STATS19 form should be borne in mind These factors reflect the reporting officer s opinion at the time of reporting and may not be the result of extensive investigation. The data are subjective and provide useful indications rather than definitive evidence of how road accidents are caused. This awareness should influence the terminology used. It is factually correct to say that factor X was attributed to individual Y by the police, not that Y committed the action or failure covered by factor X. Questions of accident causation are emotive because of the pain and suffering caused by road accidents, so it is important that these issues are presented sensitively when reporting results. The previous sections have drawn attention to two important decisions that need to be taken when designing an analysis of contributory factors: Whether to include only accidents where the STATS19 report was compiled from details collected by police officers who attended the scene the main reason for making this choice would be to exclude potential bias from self-reported contributory factors, but accidents that the police attend may form an unrepresentative sample. Whether to include only contributory factors recorded as very likely the main reason would be to exclude the potentially less reliable possible factors but standards for recording confidence vary markedly (see Table 3-7) among police forces. At the moment there is no way of determining which decision is correct, but it would probably be worthwhile to examine the results of parallel analyses to understand their sensitivity to the decision (which should be easily carried out). A consensus may emerge once there has been sufficient experience of using this relatively new data source. 5.1 General guidelines The contributory factors reported following a road accident are the key actions and failures that led directly to the actual impact. The broadest type of question that can be answered by analysing the factors for a group of accidents is who was responsible for these accidents? The analysis simply needs to consider which of the people involved in these accidents had factors attributed to them, not the details of the factors. One approach is to compare the proportion of each group of participants that had factors attributed to them: the group with the highest proportion was judged by the police to bear the greatest share of responsibility for these accidents. Many accidents involve a conflict between the people involved, and the distribution of factors between them can be analysed. When studying car-pedestrian accidents, for example, it is useful to compare the proportions of accidents where: One or more factors were attributed to the car driver and no factor was attributed to the pedestrian One or more factors were attributed to the pedestrian and no factor was attributed to the car driver One or more factors were attributed to both the car driver and the pedestrian At the next level of detail, the individual factors reported for a group of accidents can be analysed to examine questions such as why did these accidents happen?, with the supplementary question of what can be done to stop further accidents from happening?. The general approach is to calculate the proportion of accidents in which TRL 40 PPR488

49 each contributory factor was reported, and then rank the factors in diminishing order. This will show the principal and secondary factors that led to these accidents, in the collective opinion of the police reporting officers. Various issues need to be considered when designing the analysis, in particular whether it should be restricted to accidents attended by the police and to very likely factors. If there is no restriction then calculate: A = number of accidents with contributory factors reported, F(n) = number of accidents with contributory factor n reported, for all values of n F(n)/A is the required proportion for factor n. If there is a restriction then it is applied when counting accidents and factors, e.g. A = number of accidents with very likely contributory factors reported, F(n) = number of accidents with contributory factor n reported as very likely F(n)/A is the required proportion for factor n. It will occasionally be the casualties in these accidents that are of interest, e.g. what proportion of casualties are injured in speed-related accidents. In this case, A and F(n) are counts of casualties rather than accidents. The question can be refined by specifying conditions. When the specification is in terms of a variable from the accident record, such as road type or time of day, then it is simply necessary to select an appropriate group of accidents for the analysis. Questions such as why do elderly car drivers have accidents? are slightly more complicated as they involve a specification in terms of variables from the vehicle or casualty record. Each STATS19 contributory factor is linked to a participant, either a vehicle (V), casualty (C, usually a pedestrian, or exceptionally a passenger) or U if the factor relates to an uninjured pedestrian (although their details are not recorded so there is little scope for analysis). When the question involves drivers, riders or vehicles: Identify those factors that relate to the relevant group of drivers or vehicles, based on the V-code associated with the contributory factor and the STATS19 record of the vehicle identified by this code Calculate A and F(n) accordingly. In the case of the elderly car driver question, each factor is considered in turn, and the vehicle identified by the V-code is checked to see whether it is a car and the driver s age is in the range defined as elderly. The denominator A is the number of elderly drivers of cars in accidents with contributory factors reported (including those in accidents with factors reported only for non-elderly car drivers), the numerator F(n) is the number of these drivers with contributory factor n reported. The approach is very similar when the question relates to pedestrians or passengers: Identify those factors that relate to the relevant group of pedestrians or passengers, based on the C-code associated with the contributory factor and the STATS19 record of the casualty identified by this code Calculate A and F(n) accordingly. For example, the question why are year olds run over? would be answered by considering each factor in turn and checking whether the casualty identified by the C- code is a pedestrian aged between 16 and 19. The question could be refined by specifying the type of vehicle that struck the pedestrian. The denominator A is the number of pedestrians aged in accidents where factors were recorded, including those accidents where factors were only applied to a vehicle in the accident. TRL 41 PPR488

50 5.2 Specific issues This section presents advice about further issues that need to be considered, based on the experience of actual analyses. 1. Analyse aggregate data whenever possible In view of the difficulties that accident investigators (including those completing STATS19 forms) have in establishing why accidents occur, it would be unwise to place great confidence in analyses of relatively small groups of accidents. Analyses of aggregate data should yield results that are reliable (but take note of 2). When designing remedial measures at a specific site or range of sites, however, it is inevitable that the groups of accidents to be analysed will be relatively small. 2. Be aware of evidential problems with certain factors This affects the way in which the incidence of certain contributory factors should be reported. These factors relate to demonstrable risk factors in road accidents, but they are likely to be underreported because of the problems that accident investigators generally face when collecting the necessary evidence. Driver using mobile phone and fatigue are obvious examples, but equivalent difficulties also arise with the speed-related factors. 3. Be aware of the possibility that incidence can vary between groups of accidents Perhaps the most notable example of this arises from the relationship between speed and accident severity. The faster a vehicle travels, the more likely its occupants and those that it may impact are to be fatally injured. Consequently, an analysis of all accidents rather than fatal accidents would fail to show the important role of speed in this most important group of accidents. 4. Be prepared to calculate confidence limits When comparing the incidence of a factor in various groups of accidents, the numbers of factors will often be relatively small, especially when fatal accidents are studied. Consequently, seemingly large differences can arise by chance. It is relatively straightforward to test the significance of differences, and when reporting results only draw attention to those that are significant at, for example, the 95% level. Alternatively, report 95% confidence intervals, although this can make larger tables more difficult to read. 5. Accident avoidance One potential use of the contributory factor data is to assess the potential for avoiding accidents. For example, what is the potential effect of equipping vehicles with a device that prevents close following? In the opinion of the reporting officers, any accident with the single contributory factor close following would have been prevented. On the other hand, only a proportion of those accidents with close following reported in combination with other factors would have been prevented: the continued presence of these other factors may mean that the accident would still have occurred even if the close following had been prevented. Thus, it is possible to estimate the likely range of the proportion of accidents that might be avoided, rather than a single value. TRL 42 PPR488

51 6 Specification of system to analyse contributory factors This section suggests types of cross tabulation that an analysis system should ideally be able to compute. 6.1 Accident level Factor types by accident variable(s) Cross tabulations of the number of accidents by contributory factor type (e.g. 100s road environment contributed). If possible by any attendant circumstances (accident) variables, for example: What proportion of fatal accidents had vehicle defects compared with serious accidents? How does the proportion of accidents with impairment or distraction vary by year? How many accidents had road environment contributed on each road class? The total number of accidents with contributory factors should also be available, so that percentage of accidents can be calculated. All figures should be restricted to those where a police officer attended the scene. Options to include all contributory factors, or only those recorded as very likely may be useful. The ability to include more than one accident variable may also be useful. The table below shows an example table, based on data in Reported Road Casualties Great Britain 2009 (DfT, 2009). Table 6-1: Example table: Contributory factors: Reported accidents by severity: GB 2008 (DfT, 2009) Contributory factor reported in accident Fatal accidents Serious accidents Slight accidents All accidents No. % No. % No. % No. % Road environment contributed , , , Vehicle defects , ,574 2 Injudicious action , , , Driver/rider error or reaction 1, , , , Impairment or distraction , , , Behaviour or inexperience , , , Vision affected by: , , , Pedestrian only (casualty or uninjured) , , , Special codes , , ,678 4 Total number of accidents 2, , , , Number of accidents with each factor by accident variable(s) This type of table is similar to that described above, but with figures for each individual contributory factor. For example: TRL 43 PPR488

52 How many accidents at roundabouts had CF101 (poor or defective road surface)? What were the top 10 contributory factors in accidents in darkness and daylight? What proportion of accidents with CF306 (exceeding speed limit) were fatal? As above, the total number of accidents with contributory factors should also be available, so that percentage of accidents with each contributory factor can be calculated. All figures should be restricted to those where a police officer attended the scene. Options to include all contributory factors or only those recorded as very likely may be useful. The table below shows an example table, based on data in Reported Road casualties Great Britain (RRCGB) 2009 (Dft, 2009). Table 6-2: Example table: Contributory factors: GB (DfT, 2009) Contributory factor reported in accident No. % No. % No. % No. % Failed to look properly 46, , , , Failed to judge other person's path or speed 26, , , , Careless, reckless or in a hurry 23, , , , Loss of control 21, , , , Poor turn or manoeuvre 22, , , , Slippery road (due to weather) 14, , , , Pedestrian failed to look properly 13, , , , Travelling too fast for conditions 17, , , ,281 9 Sudden braking 10, , , ,292 7 Following too close 10, , , ,195 6 Total number of accidents 147, , , , Number of accidents with each contributory factor by accident variable(s) for subsets of accidents based on vehicle or casualty data This type of table is similar to those described above, but for a subset of accidents based on querying the vehicle or casualty data, for example: What are the top 10 contributory factors in heavy good vehicle accidents? What percentage of pedestrian accidents had each contributory factor? The table below shows an example (Crinson et al., 2010), giving the most commonly reported contributory factors in pedestrian accidents, by both accident severity and rural/urban marker (note that rural/urban is a derived STATS19 field) TRL 44 PPR488

53 Table 6-3: Example table: Percentage of pedestrian accidents where principal factors were reported (Crinson et al., 2010) Code Contributory factor Fatal Serious Slight Severity urban rural urban rural urban rural urban rural 802 Pedestrian failed to look properly 808 Pedestrian careless, reckless or in a hurry 805 Dangerous action in carriageway 806 Pedestrian impaired by alcohol 803 Pedestrian failed to judge vehicle's path or speed 809 Pedestrian wearing dark clothing at night 801 Crossing road masked by stationary or parked vehicle 405 Driver/Rider failed to look properly 602 Driver/Rider careless, reckless or in a hurry 501 Driver/Rider impaired by alcohol 701 Stationary or parked vehicle(s) 804 Wrong use of pedestrian crossing facility All pedestrian accidents with contributory factors 51% 19% 55% 46% 52% 43% 7% 9% 11% 13% 27% 20% 20% 17% 3% 13% 8% 13% 8% 18% 7% 10% 8% 14% 14% 38% 17% 18% 13% 14% 6% 32% 19% 9% 14% 17% 11% 8% 10% 11% 10% 31% 5% 14% 2% 10% 14% 34% 6% 6% 21% 11% 17% 15% 2% 11% 31% 19% 11% 9% 13% 12% 18% 32% 14% 6% 5% 8% 6% 5% 17% 15% 7% 3% 2% 6% 1% 1% 26% 3% 5% 1% 5% 5% 4% 3% 5% 1% 3% 1% 4% , % 19% Severity=Percentage of fatal and serious accidents that were fatal (minimum of 10 Fatal and Serious accidents) 6.2 Vehicle level Number of vehicles with each contributory factor by accident or vehicle variable(s) This type of analysis gives the number of vehicles with each contributory factor. This requires the contributory factor data to be linked with the vehicle data using the participant reference with participant type V. This analysis can answer queries such as: How many goods vehicles were coded with CF206 (overloaded or poorly loaded vehicle? TRL 45 PPR488

54 What were the top 10 contributory factors for drivers aged 70 and over? What were the contributory factors for vehicles which skidded? It may also be useful to have the ability to analyse accident variables in conjunction with this, for example, road class and year. An example table, based on a table in RRCGB (DfT, 2009), showing contributory factors by vehicle type is shown below. Table 6-4: Example table: Contributory factors: Vehicles in reported accidents by vehicle type: GB 2008 (DfT, 2009) Contributory factor assigned to vehicle Motorcycles Cars LGV HGV No. % No. % No. % No. % Poor or defective road surface Deposit on road , Slippery road 1, , Inadequate or masked signs or road markings Defective traffic signals Traffic calming Temporary road layout Road layout , Animal or object in carriageway , Number of vehicles 18, , , , Casualty level Number of pedestrian casualties with each contributory factor by accident, vehicle or casualty variable(s) This type of analysis is only suitable for pedestrian casualties, and requires the casualty details to be linked with the contributory factor data via the participant reference for those contributory factors attributed to casualties (C). These normally only apply to the pedestrian contributory factors, but there may be other valid codes such as CF999 (other). Example analyses include: How many pedestrian casualties were reported with CF801 (pedestrian crossing road masked by stationary or parked vehicle)? How does the occurrence of each pedestrian contributory factor vary by pedestrian age? How many pedestrians with CF806 (pedestrian impaired by alcohol) were killed? It may be useful to include accident variables in the tables as shown in the vehicle tables in section 6.2. The example table shown below shows the number of pedestrian casualties with each of the pedestrian factors by age group (based on data from RCGB 2007 (DfT, 2008)). TRL 46 PPR488

55 Table 6-5: Example table: Contributory factors assigned to pedestrian casualties, by age group: GB 2007 (DfT, 2008) All Contributory factor recorded No. % No. % No. % No. % No. % Pedestrian failed to look properly 5, , , , , Pedestrian careless, reckless or in a hurry 2, , , , Pedestrian crossing road masked by stationary or parked vehicle 1, , Pedestrian failed to judge vehicle s path or speed 1, , , Pedestrian impaired by alcohol , , Dangerous action in carriageway (eg. playing) ,439 6 Pedestrian wrong use of pedestrian crossing facility ,108 5 Pedestrian wearing dark clothing at night Pedestrian disability or illness, mental or physical Pedestrian impaired by drugs (illicit or medicinal) Number of casualties 7, , , , , Number of casualties associated with vehicles with each contributory factor by accident, vehicle or casualty variable(s) This type of table gives the number of casualties associated with (occupants and pedestrians) vehicles recorded with a contributory factor. This analysis is based on linking from the casualty details and the vehicle details with the contributory factor data linked on the vehicle reference with participant type V. Examples include: How many casualties were occupants of vehicles with CF501 (driver/rider impaired by alcohol)? 6.4 More complex analyses The types of analysis described in sections 6.1, 6.2 and 6.3 should be fairly straightforward to create and interpret. More complex analyses need to be carried out by an analyst with expertise in contributory factor analysis. Examples include: What other contributory factors were recorded for vehicles with loss of control as a contributory factor? What contributory factors were recorded for the vehicles which hit pedestrians? What was the total number of casualties in accidents where CFxxx was a factor? How many accidents had contributory factor 306 or 307? TRL 47 PPR488

56 7 Development of the STATS19 database The TRL software for accessing the STATS19 database has been enhanced as part of this project to provide a range of options for analysing contributory factors. This should help to encourage the use of these data as part of TRL research, but it will be necessary to review experience with the new software after it has been in use for about one year to check how well it has met users requirements, and whether any further development may be necessary. TRL 48 PPR488

57 8 Conclusions The STATS19 accident reporting system was extended in 2005 to record Contributory Factors. The new data provide indications about the causes of accidents, and hence help to develop new approaches for preventing accidents in future. The new data are hence of great potential value to road safety research, but their complexity may have tended to limit the extent to which they have been used. The main aim of this project has been to encourage the use of the new data by improving access and summarising use experience of analysing them. A review of the technical literature has shown that only two other countries record similar information as part of their national accident reporting system: New Zealand and the Netherlands. Hence, the relevant experience comes largely from this country, specifically TRL and Government statisticians at the Department for Transport and the Scottish Government. The national STATS19 database includes a range of consistency checks, but no new checks were specified when contributory factors were added to the system in This means, for example, that non-existent contributory factor codes are present in the database. Ideally, the Department for Transport will introduce consistency checks to prevent this from happening in future. Analyses have shown, however, that this is not a major problem with the existing data and should not deter their use. Various detailed analyses of the contributory factor data suggest that the police are generally applying the new system in a reasonably consistent and reliable way, although examples of inconsistencies and misunderstandings have inevitably been identified. These problems are also likely to affect the traditional STATS19 variables, and their effects could only be diminished if the Police Service nationally was prepared to accord greater priority to their task of recording information about accidents. The TRL software for accessing the STATS19 database has been enhanced as part of this project to provide a range of options for analysing contributory factors. This should help to encourage the use of these data as part of TRL research, but it will be necessary to review experience with the new software after it has been in use for about one year to check how well it has met users requirements, and whether any further development may be necessary. TRL 49 PPR488

58 Acknowledgements The work described in this report was carried out in the Statistics and Engineering Group of the Transport Research Laboratory. The authors are grateful to B Sexton who carried out the technical review and auditing of this report. References Brace C, Lenne M and Archer J, (2007). Establishing methods to understand human error at intersections, Australasian Road Safety Research Policing Education Conference, 2007, Melbourne, Victoria, Australia. Collingwood, Victoria: The Meeting Planners. Broughton J, Markey K A and Rowe D (1998). A new system for recording contributory factors in accidents. TRL Report 323. Crowthorne, UK: Transport Research Laboratory (TRL). Broughton J. (2007). Contributory factors in road accidents, TRL Staff Paper (PA/TRS/5756/07). Wokingham, Berkshire: Transport Research Laboratory (TRL). Broughton J and Knowles J, (2009). Monitoring progress towards the 2010 casualty reduction target data, TRL Report 671. Crowthorne, UK: Transport Research Laboratory (TRL). Crinson L, Cuerden R and Vandrevala T, (2010). Review of pedestrian fatal road traffic accidents, TRL Report (In Press). Crowthorne, UK: Transport Research Laboratory (TRL). Crinson L, Scoons J and Broughton J, (2008). Rural road safety: drivers and driving - annex: factors contributing to rural road accidents in Scotland. Edinburgh, UK: Scottish Government. Department for Transport (2004). STATS20 instructions for the completion of road accident reports. London: Department for Transport. Department for Transport (2009). Reported Road Casualties Great Britain: Annual Report. London: The Stationery office. Department for Transport (2008). Road Casualties Great Britain: Annual Report. London: The Stationery office. Gordon C, (2007). Driver distraction related crashes in New Zealand, International Conference on the Distractions in Driving, 2005, Sydney, New South Wales, Australia. Mawson, Act: Australasian College of Road Safety. Knowles J, Adams S, Cuerden R, Savill T, Reid S and Tight M (2009). Collisions involving cyclists on Britain s roads: establishing the causes, TRL Report PPR 445. Crowthorne, UK: Transport Research Laboratory (TRL). NZ Transport Agency. Coded crash reports from the Crash Analysis System (CAS): an interpretation guide v3. [Accessed Robinson D and Singh R (2007). Contributory factors to road accidents. Road Casualties Great Britain London: Department for Transport. Sze N and Wong S, (2007). Diagnostic analysis of the logistic model for pedestrian injury severity in traffic crashes, Accident Analysis & Prevention (v39 (n6) p ). Kidlington, Oxford: Elsevier Science Ltd. The Scottish Government (2009). Reported Road casualties Scotland: Edinburgh, UK: Scottish Government. Tranter M (2008). Contributory factors to road accidents. Road casualties Great Britain 2007 London: Department for Transport. TRL 50 PPR488

59 Waite C (2009). Contributory factors to reported road accidents. Reported road casualties Great Britain 2008 London: Department for Transport. TRL 51 PPR488

60 Appendix A Number of contributory factors recorded by year Table A 1: The number of valid contributory factors recorded by year Valid Contributory Factor Poor or defective road surface 1,128 1,047 1, Deposit on road (e.g. oil - mud - chippings) 2,426 2,520 2, Slippery road (due to weather) 16,542 15,530 15, Inadequate or masked signs or road markings Defective traffic signals Traffic calming (e.g. speed cushions - road humps - chicanes) Temporary road layout (e.g. contraflow) Road layout (e.g. bend - hill - narrow carriageway) 5,328 4,635 4, Animal or object in carriageway 2,042 2,054 1, Tyres illegal - defective or under-inflated 1,177 1,056 1, Defective lights or indicators Defective brakes 1,106 1,107 1, Defective steering or suspension Defective or missing mirrors Overloaded or poorly loaded vehicle or trailer Disobeyed automatic traffic signal 3,275 3,345 3, Disobeyed - Give Way - or - Stop - sign or markings 7,076 6,641 5, Disobeyed double white lines Disobeyed pedestrian crossing facility 1, Illegal turn or direction of travel 1,542 1,379 1, Exceeding speed limit 8,389 8,260 8, Travelling too fast for conditions 20,436 19,155 16, Following too close 16,022 14,536 12, Vehicle travelling along pavement Cyclist entering road from pavement 1,763 1,657 1, Junction overshoot 4,310 3,890 3, Junction restart (moving off at junction) 3,553 3,109 2, Poor turn or manoeuvre 27,855 26,047 24, Failed to signal or misleading signal 3,338 3,306 3, Failed to look properly 62,878 68,038 67, Failed to judge other person's path or speed 35,295 36,202 35, Passing too close to cyclist - horse rider or pedestrian 3,308 3,159 3, Sudden braking 13,443 13,441 13, Swerved 6,220 6,102 5, Loss of control 22,675 22,750 21,916 TRL 52 PPR488

61 Valid Contributory Factor Impaired by alcohol 8,397 8,155 7, Impaired by drugs (illicit or medicinal) Fatigue 2,046 2,058 2, Uncorrected - defective eyesight Illness or disability - mental or physical 1,886 1,924 1, Not displaying lights at night or in poor visibility Cyclist wearing dark clothing at night Driver using mobile phone Distraction in vehicle 3,361 3,258 3, Distraction outside vehicle 2,555 2,429 2, Aggressive driving 7,907 7,629 7, Careless - reckless or in a hurry 30,407 33,424 30, Nervous - uncertain or panic 2,723 2,594 2, Driving too slow for conditions or slow vehicle (eg. tractor) Learner or inexperienced driver/rider 8,323 8,350 8, Inexperience of driving on the left Unfamiliar with model of vehicle 1,396 1,332 1, Stationary or parked vehicle(s) 5,987 5,790 5, Vegetation Road layout (eg. bend - winding road - hill crest) 3,277 2,797 2, Buildings - road signs - street furniture Dazzling headlights Dazzling sun 3,512 3,561 3, Rain - sleet - snow or fog 3,076 3,257 2, Spray from other vehicles Visor or windscreen dirty or scratched Vehicle blind spot 2,028 2,081 2, Crossing road masked by stationary or parked vehicle 4,810 4,599 4, Failed to look properly 16,619 16,794 16, Failed to judge vehicle's path or speed 5,103 4,826 4, Wrong use of pedestrian crossing facility 1,351 1,342 1, Dangerous action in carriageway (e.g. playing) 2,062 1,948 1, Impaired by alcohol 2,909 3,129 3, Impaired by drugs (illicit or medicinal) Careless - reckless or in a hurry 7,259 7,430 6, Pedestrian wearing dark clothing at night 1,081 1,104 1, Disability or illness - mental or physical Stolen vehicle 1,250 1,264 1, Vehicle in course of crime TRL 53 PPR488

62 Valid Contributory Factor Emergency vehicle on a call Vehicle door opened or closed negligently Other - please specify below 5,656 5,424 5,045 All contributory factors 420, , ,776 Table A 2: The number of invalid contributory factors recorded by year (i.e. invalid Contributory Factor numbers recorded) Invalid Contributory Factor number TRL 54 PPR488

63 All invalid contributory factors TRL 55 PPR488

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66 Guidance for the analysis of STATS19 Contributory Factors In 2005, the STATS19 system was expanded to record Contributory Factors, i.e. the key actions and failures that led directly to individual accidents. These data are conceptually more complex than the traditional STATS19 variables such as driver age or road number, so more options are available for analysis. A TRL self-funded project has been carried out with the main aim of providing guidance about these data and effective methods for analysing them. This report documents the range of analyses that has been carried out. Other titles from this subject area TRL323 A new system for recording contributory factors in road accidents, J Broughton, KA Markey, D Rowe TRL671 Monitoring progress towards the 2010 casualty reduction target data, J Broughton, J Knowles PPR241 Factors influencing pedestrian safety: a literature review. A Martin PPR242 PPR445 Reporting of road traffic accidents in London: Matching Police STATS19 with hospital accident and emergency data. Supplementary report for St. Thomas Hospital Central London. H Ward, S Robertson, K Townley and A Pedler Collisions involving pedal cyclists on Britain s roads: establishing the causes, J Knowles, S Adams, R Cuerden, T Savill, S Reid, M Tight Price code: 3X ISSN TRL Crowthorne House, Nine Mile Ride Wokingham, Berkshire RG40 3GA United Kingdom T: +44 (0) F: +44 (0) E: enquiries@trl.co.uk W: Published by IHS Willoughby Road, Bracknell Berkshire RG12 8FB United Kingdom T: +44 (0) F: +44 (0) E: trl@ihs.com W: PPR488