Southampton Road Traffic Accident Reports Data Briefing

Transcription

1 Southampton Road Traffic Accident Reports Data Briefing Dan King Public Health Intelligence Briefing: Road traffic accidents 1

2 Introduction: All road traffic accidents (RTAs) involving human death or personal injury occurring on the highway and notified to the police within 30 days of occurrence, and in which one or more vehicles are involved, are reported to local authorities via the police STATS19 dataset. This briefing summarises the main findings from this dataset in order to help public health professionals and commissioners better understand the health implications of RTAs in the city. All data presented in this briefing relates to accidents occurring within the Southampton City boundary rather than all accidents involving Southampton residents. Trends in the number of accidents, injuries and vehicles involved: RTA data has been collected in the city for many years. Figure 1 below illustrates the trend in the number of accidents occurring within the city, as well as the number of vehicles involved and number of people injured between 2000 and Over this period: The annual number of accidents fell from 870 in 2000 to 632 in 2012; a 27.4% reduction. The annual number of casualties fell from 1089 in 2000 to 777 in 2012; a 28.7% reduction. The annual number of vehicles involved fell from 1613 in 2000 to 1160 in 2012; a 28.1% reduction. However, although these numbers have fallen steadily over the period studied, the number of casualties suffering serious injury or death has not (see appendix A for STATS19 definition of serious injury). Figure 2 shows the proportion of all RTA casualties killed or seriously injured (KSI) between and (3-year pooled data). The proportion of casualties KSI remained fairly constant up until , but has slowly increased in the last three 3-year pooled periods. For example, in , 16.2% of casualties were KSI, compared to 10.5% in , a statistically significant increase. Of these, the majority were seriously injured, with very few deaths occurring on Southampton roads in recent years. In fact the number of deaths from RTAs has gradually fallen over time from 20 in to 6 in (see table 1). Figure 1 Public Health Intelligence Briefing: Road traffic accidents 2

3 Public Health Intelligence Briefing: Road traffic accidents Road traffic accidents Figure 2 Table 1 Trend in the number of recorded deaths resulting from an RTA Period No. Deaths Figures 3 & 4 show the number of RTAs by day and time of occurrence respectively during the 2010 to 2012 pooled period. Perhaps unsurprisingly, both show that accidents are more likely to occur when there are more vehicles on the road: Figure 3 Accidents occur more frequently during week days (Monday to Friday) compared with weekends. Fridays had the highest number of recorded accidents (17.4%), whilst Sundays had the lowest (9.1%). There are two clear peaks in RTAs in Southampton coinciding with rush hour periods; between 7:00am and 9:00am, and between 3:00pm and 6:00pm which account for 16.3% and 35.9% of daily accidents respectively. 3

4 Figure 4 Analysis by vehicle type and casualty: During the 2010 to 2012 (pooled) period there were 1,953 accidents recorded on Southampton roads, involving a total of 3,559 vehicles and resulting in 2,378 casualties. Table 2 summarises the number and proportion of vehicles involved in accidents by vehicle type. Table 2 Number and proportion of vehicles involved in accidents: Southampton (pooled) Accident type Car Motorcycle Pedal cycle Goods vehicle Coach, bus or minibus Taxi/Priva te hire car Unknown All 2407 (67.6%) 347 (9.7%) 382 (10.7%) 223 (6.3%) 100 (2.8%) 83 (2.3%) 17 (0.5%) KSI 336 (52.8%) 115 (18.1%) 99 (15.6%) 48 (7.5%) 20 (3.1%) 15 (2.4%) 3 (0.5%) Figure 5 below shows the proportion of accidents that involve each type of vehicle, along with how they change with the severity of the accident. Please note that these proportions will differ from the table above as they consider the proportion of all accidents rather then the proportion of all vehicles. Over 83% of all accidents involved a car, 19.4% involved a cyclist and 17.6% involved a motorcycle. Figure 5 Public Health Intelligence Briefing: Road traffic accidents 4

5 Compared to all accidents, those that resulted in death or serious injury (KSI) involved a larger proportion of cyclists (26.1%; a difference of 6.7%) and motorcyclists (29.5%; a difference of 11.9%) suggesting they are more vulnerable to serious injury. This is further demonstrated in figure 6 which considers the proportion of vehicles involved in accidents resulting in KSI by vehicle type. Of all vehicles involved in an accident, almost a third of all motorcycles and a quarter of all pedal cycles are involved in accidents resulting in death or serious injury, compared to only 14% of all cars. Figure 6 Table 3 and figure 7 show the number and proportion of casualties in Southampton by casualty type and severity of injury for the period 2010 to 2012 (pooled). Overall, drivers and riders account for 63.5% of all casualties, whilst passengers account for 19.6% and pedestrians 16.9%. However, when considering only those casualties who are killed or seriously injured in accidents, pedestrians account for over a quarter of all casualties (26.8%). Table 3 Number and proportion of casualties involved in accidents by casualty type and severity of injury: Southampton (pooled) Figure 7 Casualty type Driver or rider Pedestrian Vehicle passenger All casualties 1510 (63.5%) 402 (16.9%) 466 (19.6%) KSI casualties 270 (61.2%) 118 (26.8%) 53 (12%) Public Health Intelligence Briefing: Road traffic accidents 5

6 Accident locations, road types and conditions: The majority of accidents in Southampton occur on single carriageway roads. During the 2010 to 2012 period these accounted for 80.2% of all accidents, with 13.1% occurring on dual carriageways (1.8% on motorways), 5.2% on roundabouts and less than 1% on both slip roads and one way streets (see table 4 below). Table 4 Number of accidents by road type: 2010 to 2012 (pooled) Road type Dual Carriageway One Way Street Roundabout Single Carriageway Slip Road No. Accidents % of total 13.1% 0.9% 5.2% 80.2% 0.6% Overall during the (pooled) period, over 87% (1709) of accidents occurred on 30mph limited roads, with 5.9% (116) on 40mph limited, 4.4% on 50mph (85) limited and only 1.5% (29) on roads with a 70mph limit. Figure 8 below illustrates how these proportions change by road type. Figure 8 Figure 9 shows the proportion of accidents resulting in death or serious injury (KSI) by road speed limit. Roads with a 30mph speed limit appear to have the highest proportion of KSI (over 20%), with this proportion falling gradually as the road speed limit increases (10.3% for 70mph roads). However, the figures for higher limit roads should be treated with caution as they are based on fairly small numbers and therefore are subject to a greater degree of natural variation. Figure 9 Public Health Intelligence Briefing: Road traffic accidents 6

7 Over 85% (1675) of accidents occurred during fine weather, with only 13% (256) happening during rain. Similarly, 75% (1470) of accidents happened on dry roads, with 23.5% (459) happening on wet or damp roads and just over 1% (23) on frosty or icy roads. Figure 10 below shows the location of all recorded accidents in Southampton by severity for the 2010 to 2012 (pooled) period. Although accidents are distributed throughout Southampton, the central area of the city appears to show a higher density of accidents. This is more clearly illustrated in figure 11, which shows the number of accidents over the same period broken down by electoral ward. Bargate ward has by far the highest number of accidents with 328 in total recorded over the 3 year period; this is almost twice as many as any other ward and 16.9% of the total accidents for the city (19% of KSI). Bevois ( %) and Freemantle ( %) wards had the second and third highest number of accidents, whilst Coxford (46 2.4%) and Bitterne (61 3.1%) wards had the lowest. Figure 10 Figure 11 Public Health Intelligence Briefing: Road traffic accidents 7

8 Figures 12 and 13 show the geographic distribution of accidents involving vulnerable road users (i.e. pedestrians, motor cyclists and pedal cyclists) over the same period. Once more the central part of the city, and especially Bargate ward, had the highest number of accidents involving pedestrians (86; 22.3% of total for the city), cyclists (79; 21.1%) and motorcyclists (41; 12.4%). Over a third (36.5%) of all accidents resulting in pedestrian injury occurred in Bargate and Bevois wards alone. Outside of the city centre, Sholing ward had a high number of accidents involving motorcyclists (33; 10%) and Portswood had a high number of accidents involving pedal cyclists (36; 9.6%). Figure 12 Figure 13 Public Health Intelligence Briefing: Road traffic accidents 8

9 Accident contributory factors: For every reported RTA, the investigating officer records up to six contributory factors ; These 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. Although there are clear guidelines for recording contributory factors to minimise the scope for differences in reporting, it is worth noting that they are largely subjective and depend on the skill and experience of the investigating officer to reconstruct events 1. Therefore, any analysis of contributory factors should take this into account. In an attempt to minimise any uncertaintly and in order to help identify the most important primary causes of accidents, only those factors recorded as very likely (rather than possible ) to have contributed to an accident are considered in the following analysis. Figure 14 Figure 14 above shows the proportion of accidents in Southampton by type of contributory factor and accident severity for the pooled period 2010 to It is worth noting that the proportions in the chart will not necessaily add up to 100% as more than one factor, or factor category can be recorded for any single accident. It is clear that the majority of accidents (62.8%) involved some form of driver error, such as failing to look properly, signal, judge anothers path or speed, sudden braking or loss of control. Pedestrian action (19.1%) was the second most common type of factor, whilst driver injudicious action (illegal or dangerous) and driver behaviour/experience (careless, aggressive, inexperience, nervous etc) were reported in 15.7% and 11.1% of accidents respectively. Very few accidents were reported to be as a result of external factors such as road conditions (5.7%) or vehicle defects (0.8%). There is a broadly similar distribution of contributory factor types when considering accidents resulting in death or serious injury (KSI). However, pedestrian action is more common; reported in 32.9% of KSI accidents. This is likely to be a result of pedestrians being more vulnerable and therefore more likely to be involved in accidents resulting in serious injury. Tables 5 and 6 provide more detail by showing the top 10 contributory factors for all accidents and accidents resulting in death and serious injury respectively. The tables are colour coded to show which category each factor belongs to (the colour key is shown in figure 15). Drivers failing to look properly (28.1%) and failing to judge anothers direction or speed (15.6%) feature in the top two, whilst pedestrians failing to look properly (7.7%) was third for all accidents and those resulting in KSI. As discussed above, factors involving pedestrians are more prevalent in the top 10 causes of serious accidents. Interestingly, 5% of serious accidents were partly caused by a pedestrian imparied by alcohol. 1 Department for Transport (2004) STATS 20 Instructions for the completion of road accident reports. Public Health Intelligence Briefing: Road traffic accidents 9

10 Figure 15 Driver Injudicious Action Driver Behaviour or Experience Driver Error Driver Vision Affected Pedestrian Contributed Road Environment Table 5 Top 10 factors very likely to have contributed to an accident: All accidents 2010 to 2012 (pooled) Pos All Accidents: Top 10 contributory factors No. Accidents % of Accidents 1 Driver failed to look properly % 2 Driver failed to judge others path or speed % 3 Pedestrian failed to look properly % 4 Driver careless, reckless, or in a hurry % 5 Driver poor turn or manoeuvre % 6 Driver loss of control % 7 Slippery road due to weather % 8 Sudden braking % 9 Driver disobeyed Give Way or Stop % 10 Vision affected by stationary or parked vehicle % Table 6 Top 10 factors very likely to have contributed to an accident: Accidents resulting in death or serious injury 2010 to 2012 (pooled) Pos KSI Accidents: Top 10 contributory factors No. Accidents % of Accidents 1 Driver failed to look properly % 2 Driver failed to judge others path or speed % 3 Pedestrian failed to look properly % 4 Driver careless, reckless, or in a hurry % 5 Driver loss of control % 6 Pedestrian impaired by Alchohol % 7 Driver poor turn or manoeuvre % 8 Vision affected by stationary or parked vehicle % 9 Crossing road masked by static/parked vehicle % 10 Pedestrian careless, reckless or in a hurry % Table 7 Top 10 factors very likely to have contributed to an accident: All accidents resulting in pedestrian injury 2010 to 2012 (pooled) Pos All Accidents: Top 10 contributory factors No. Accidents % of Accidents 1 Pedestrian failed to look properly % 2 Driver failed to look properly % 3 Crossing rd masked by static or parked vehicle % 4 Pedestrian failed to judge vehicles path or speed % 5 Pedestrian impaired by Alchohol % 6 Pedestrian careless, reckless or in a hurry % 7 Pedestrian dangerous action in carriageway % 8 Driver failed to judge others path or speed % 9 Wrong use of pedestrian crossing facility % 10 Driver passing too close to cycle, horse, rider, ped % Public Health Intelligence Briefing: Road traffic accidents 10

11 Figure 16 shows the distribution of contributory factor types over the same period for only those accidents resulting in pedestrian injury in Southampton. The vast majority of these accidents (93.6% total and 100% KSI) record at least one factor where the pedesrian was at fault. Only 22.3% of these accidents report driver error (falling to 16.7% for KSI), whilst driver injudicious action and driver behaviour/experience were reported in only 5.9% and 3.8% of accidents respectively. Table 7 provides more detail of the top 10 contributory factors. Pedestrians failing to look properly is the most common contributory factor, reported in 37.9% of accidents resulting pedestrian injury, whilst driver failing to look properly is only reported in 13%. One in ten (10.2%) accidents are partly caused by pedestrians impaired by alcohol, which rises to 15.8% (2 nd most common factor) for those accidents resulting in death or serious injury. Figure 16 Profile of drivers / riders involved in accidents: The STATS19 dataset records a number of personal details for each driver/rider involved in an accident (including age, gender and postcode of residence), allowing a basic profile to be built up of those involved in accidents on Southampton roads. It should be noted that the STATS19 data does not directly attach blame to any accident, and so the following analysis merely profiles those involved in accidents rather than those at fault. Over half of drivers/riders (59.7%) involved in accidents occurring in Southampton are resident within Southampton local authority, with 8% living in Eastleigh, 4.8% in the New Forest and 2.7% in Test Valley. A further 9.3% come from other Local Authority areas, whilst 15.6% were untraced or had an unknown postcode. At least a quarter of all drivers/riders (24.7%) come from outside the city. Figure 17 shows the proportion of drivers/riders living in Southampton and elsewhere by vehicle type. A greater proportion of those travelling by motorbike (70.6%) or pedal cycle (84.6%) are resident in Southampton, whilst perhaps unsurprisingly a larger proportion of goods vehicle (36.3%) and coach, bus or minibus drivers (29%) come from areas outside of the city, indicative of the greater distances travelled by these types of vehicle. Public Health Intelligence Briefing: Road traffic accidents 11

12 Figure 17 Figures 18 shows the gender profile of vehicle drivers and riders involved in accidents on Southampton roads. For all recorded accidents during the 2010 to 2012 (pooled) period 68.1% of drivers/riders were male compared to 31.9% who were female. When considering only those accidents that resulted in death or serious injury the proportion of drivers/riders who were male increased to 72.4%. This suggests that males are over 2.5 times more likely to be involved in serious accidents compared to females, assuming an equal number of male and female drivers on the road. Figure 18 Figure 19 shows the gender split by selected vehicle type over the same period. The split for car drivers is more equal, with 59.3% male and 40.7% female. For other vehicle types, the gender split is more heavily weighted towards males, with males accounting for 78.2% of pedal cyclists, 88.1% of motorcyclists, 84.4% of coach, bus or minibus drivers, 95% of goods vehicles drivers and 96.2% of taxis drivers involved in an accident. This may be partly a result of more males driving/riding these types of vehicle compared to females. Public Health Intelligence Briefing: Road traffic accidents 12

13 Figure 19 Figures 20 and 21 show the number of accidents occurring in Southampton by age group for the period 2010 to Those in the younger age groups are involved in the largest number of accidents in the city, with 62% aged under 39. The age band has the highest number of accidents (448; 14.6%) amongst this group. Accidents resulting in death or serious injury have a slightly younger age profile with an larger number involving drivers/riders aged 19 or under (80; 14.5%). Figure 20 An analysis by vehicle type highlights that the majority (63.8%) of those in the 0-19 age group involved in accidents were riding motorcycles or pedal cycles. This age group accounted for 26.6% and 39.1% of all accidents involving a pedal cyclist and motorcylist respectively. The age profile for car drivers was more evenly split between age groups, whilst those for coach, bus, minibus, goods vehicles and taxi drivers is weighted more in favour of the older age groups. The full breakdown can be seen in figure 22. Public Health Intelligence Briefing: Road traffic accidents 13

14 Figure 21 Figure 22 Figure 23 below shows the number of car, van and taxi drivers involved in accidents per 1,000 cars/vans available to households by electoral ward in the city. Bitterne Park (9.7) and Portswood (9.5) wards have a significantly lower rate compared to the city average (14.0), whilst Bevois ward stands out as having a rate significantly higher than the average (26.6). As this is a rate per 1,000 registered vehicles, any differences observed should not be due to the fact there are more drivers or vehicles in any one area of the city. More accidents occur in the center of the city (see figures 11 and 13), which may in part help to explain why residents of Bevois have a high accident involvment rate as they are more likely to be driving in the areas in which they live. However, Bargate ward has the highest number of recorded accidents in the city, yet Bargate residents do not have a similarly high accident involvement rate (14.9). When interpreting this data it must be remembered that the rate is based on the number of vehicles available to households and may not reflect the number of drivers (multiple drivers per vehicle) or levels of local traffic. Both of these factors may help to explain why Bevois has such a high rate as it has a relatively low number of vehicles available to households (4741 compared to ward average of 6347). Public Health Intelligence Briefing: Road traffic accidents 14

15 Figure 24 shows the same data by local deprivation quintile. There does appear to be a relationship between deprivation and accident involvement, with those car/van drivers living in the 20% most deprived areas of the city having the highest accident involvement rate (20.8 per 1,000; significantly higher than all other quintiles), whilst those living in the 20% least deprived have the lowest rate (10.1 per 1,000). Again this might in part be explained by relatively low levels of car ownership yet relatively high levels of traffic in some of the most deprived areas of the city. Figure 23 Figure 24 Public Health Intelligence Briefing: Road traffic accidents 15

16 Appendix A STATS19 Severity of casualty definition 2 : The STATS19 dataset codes each RTA casualty as Fatal, Serious or Slight. Fatal injuries include only those cases where death occurs in less than 30 days as a result of the accident. It does not include death from natural causes or suicide. Serious injuries include: Fractures, internal injuries, severe cuts, crushing, burns (exclusing friction burns), concussion, severe shock (requiring hospital treatment), detention in hospital as an in-patient, injuries to casualties who die 30 or more days after the accident from injuries sustained in that accident. Slight injuries include: Sprains, neck whiplash injury, bruises, slight cuts and slight shock requiring roadside attention. Persons who are merely shaken and who have no other injury are not included unless they receive or appear to need medical treatment. 2 Department for Transport (2004) STATS 20 Instructions for the completion of road accident reports. Public Health Intelligence Briefing: Road traffic accidents 16