Forewarned is Forearmed Highways Agency approach to quantifying network safety risk

Transcription

1 Forewarned is Forearmed Highways Agency approach to quantifying network safety risk The Highways Agency Safety Risk Model 17 th May 2012 Elizabeth Mathie, Safety Risk Modelling Manager

2 The Highways Agency Safety Risk Model The Highways Agency operates the network of motorways and trunk roads in England In 2010, this included approximately: 4,340 miles of roads; 86 billion vehicle miles 1,250 Traffic Officers; 2 million hours worked 15,000 Supply Chain workers; 26 million hours worked

3 The Highways Agency Safety Risk Model In 2010, there were: 11,414 road user accidents on HA roads, resulting in 249 fatal, 1,637 serious and 16,136 slight injuries (from ~86 billion vehicle miles) 181 worker accidents (Traffic Officers and Supply Chain), resulting in 6 fatal, 21 serious and 154 slight injuries (from ~28.5 million hours worked) 2010 was an exceptional year, in the 3 years prior to it there was a total of 5 fatal accidents, 2011 saw no fatal accidents involving our workers By comparison, in 2009/10 on mainline railways, there were*: 5 fatalities and 238 major injuries to passengers (~32 billion miles travelled) 3 fatalities and 118 major injuries to members of the workforce (>200 million hours worked) *Source: RSSB, Annual Safety Performance Report 2009/10

4 The Highways Agency Safety Risk Model Highways Agency (HA) has developed a safety risk model and from that a report to present the safety risk profile for all populations using or working on our network; Information for Managing Safety on the Highways Agency Network Why? to help us understand the safety risks and the main factors driving these risks and so to inform priorities for investment and allocation of resources to help examine the potential safety implications of change on the network to help understand and explore how specific safety performance targets could be achieved

5 The Highways Agency Safety Risk Model The Scope of the model

6 The Highways Agency Safety Risk Model The base data in the model The Safety Risk Model has been constructed using only data that the Agency was already collecting; Stats 19 Personal Injury Accident data Accident data for Traffic Officer Service & supply chain workers HAPMS, network infrastructure data HATRIS, mileage travelled and average speed Command & Control, from the Traffic Officer Service SRW, details of road works. It reports on safety risk levels for all populations who either use or work on the Strategic Road Network (SRN) but does not look beyond it to site offices, motorway service areas etc It provides a broad range of outputs, but primarily Fatalities & Weighted Injuries (FWI) and FWI Risk for road users and road workers.

7 The Highways Agency Safety Risk Model - Structure Vehicle miles Worker exposure Classification by road type, user/worker type, location & time of day Road User Accidents Worker Accidents Contributing Factors Accident Sequences

8 The Highways Agency Safety Risk Model - Structure Root causes Accident Type Accident Progression End point consequences End point risk f /yr % f /yr FWI per accident FWI /yr 90% f /yr FWI per accident FWI /yr 10% f /yr f /yr 7.50 Vehicle-Vehicle collision % f /yr FWI per accident FWI /yr 90% f /yr FWI per accident FWI /yr 10% f /yr % f /yr FWI per accident FWI /yr 90% f /yr FWI per accident FWI /yr 10% FWI /yr Total risk 1.079

9 What are the model functions & outputs? Bowtie model offers the opportunity to examine the distribution of accident sequences that follow from particular contributing factors, for specified types of location or road user & by restricting the model to a single accident sequence the distribution of contributing factors that featured in that sequence Bespoke reporting more topic focused analysis for specific road users groups or contributing factors (or combinations of) to be examined What if? using off-line analyses to calculate the difference in contributing factors that potential changes could produce, the bowtie will model the what if effect of introducing those changes on accident sequences and FWI/FWI risk Standard outputs form the report Information for Managing Safety on the Highways Agency Network, describing the current safety profile on the network. The initial bulletin for 2009 created a base-line to review subsequent year s performance against

10 The Highways Agency Safety Risk Model - Outputs

11 The Highways Agency Safety Risk Model - Outputs

12 The Highways Agency Safety Risk Model - Outputs

13 The Highways Agency Safety Risk Model - Outputs

14 How is it different from the Agency s traditional reporting? FWI s give a combined measure of fatal, serious and slight casualties No. fatalities x (No. serious casualties) x (No. slight casualties) FWI risk is normalised by exposure vehicle miles travelled, hours worked or hours exposed to the network Provides a more complete picture for road users and workers; important because interventions for one group may have an impact on the other group and any 'trade-offs' must be understood Has more detailed information about the types of accidents (accident sequences) and root causes (contributing factors) Indicates what proportion of current levels of risk fall within the Agency s control or influence, so indicates what the Agency can realistically do to change current levels of safety risk, either itself, or by influencing or lobbying others

15 Highways Agency control or influence We can also control or influence certain accident sequences, predominantly through: the provision (or not) of barriers the design and positioning of roadside objects the removal of objects from the carriageway worker training, procedures and equipment provision decisions that influence the types of tasks that workers are required to undertake or the circumstances under which they are required to undertake them The report provides a matrix which brings the data together in a ranked format Factor represents <1% of total FWIs Factor represents between 1 and 5% of total FWIs Factor represents between 5 and 50% of total FWIs Factor represents >50%% of total FWIs

16 The Highways Agency Safety Risk Model - what-if scenarios Dynamic selection for modelling Left hand side of bow tie = accident causation Right hand side of bow tie = accident sequence and consequences Bow tie model allows you to configure both sides dynamically, depending on size of population being studied Small population of accidents -> limited breakdown of causes/consequences possible Large population of accidents -> more detailed breakdown of causes/consequences possible Results for each bow tie analysis stored in a results database The safety risks to all groups of interest can be calculated and compared.

17 The Highways Agency Safety Risk Model - what-if scenarios

18 The Highways Agency Safety Risk Model - what-if scenarios

19 The Highways Agency Safety Risk Model - what-if scenarios

20 Conclusions Use of risk exposure data normalisation of safety data gives an extra perspective and important information which can provide additional insight to safety decision making when considered alongside simple counts of accidents or casualties. Network level safety risks Cyclists are currently subjected to collective risk levels approximately 200 times the national average for all road users; risk levels for PTW riders/passengers are approximately 20 times the national average. Current collective risk levels for LGV and HGV occupants are approximately onethird of the national average. Influence of road type Motorways are currently the safest types of road, some variations are seen but they are not the riskiest type of road for any road user population. Dual carriageway A roads are safest for: LGVs, buses and coaches and riskiest for HGVs and cyclists. Single carriageway A roads safest for HGVs (jointly with motorways) and cyclists and they are riskiest for LGVs, cars, buses and coaches and PTWs. On current knowledge the majority of worker FWIs occur on motorways

21 Conclusions Contributing factors & accident sequences Road user behaviour is the biggest category of contributing factor for road user and worker risk. Inattention-distraction-carelessness Judgement Speed-aggression Worker judgement is the most significant worker contributing factor recorded. The causes and types of road user accidents on the HA network are specific to the particular road user and road type combination; HA controls & influence Directly control a small proportion of the FWIs; results from the model suggest the main areas where we can are: road adhesion provision of safety barriers For workers, we can impact safety risk through procedures & requirements infrastructure design (including design for maintenance) road works design (including any constraints on when & how road works take place) worker training (including Traffic Officer procedures)