Reduction in accidents, and in particular injury and fatal accidents, is a primary focus of many ITS deployments. Integral to these is an understanding of how the existing traffic situation (driver behaviour, vehicle dynamics and road environment) relate to safety. Also key is understanding that an ITS scheme which is not in itself targeted specifically at road safety – may nevertheless result in changes in the level of safety as an unintended side effect.
Deployment of ITS can alter the balance of accident types. It is not uncommon with traffic schemes for one type of incident to be substantially reduced and another type to increase (perhaps of lower severity). In general, ITS deployments that reduce congestion and smooth traffic flows will reduce accidents. High variability in speeds — of the vehicle (rapid deceleration or acceleration) and between vehicles — is more likely to cause disturbances and incidents than steady vehicle speeds. In general higher overall speeds will increase both accident risk and severity.
Accident analysis is a major tool in obtaining an understanding of the existing situation and how it could be improved by ITS. It helps to provide an understanding of the most effective solutions and is essential for monitoring and evaluating the safety of the road network. It should be undertaken, following deployment of a system, to:
Monitoring is used to verify that, after deployment, the system has produced the desired effects and there are no unexpected negative side-effects. An example might be the case of a VMS, where incidents could occur as a result of drivers slowing down - in order to read the VMS or to give themselves more time for decision-making after passing the VMS.
Evaluation compares the before and after situation - ideally also comparing it with a control road or location in which there has been no intervention. This provides reassurance that an observed improvement was not simply the result of an overall trend such as a general improvement in safety performance. A rigorous evaluation will require a statistically significant change in the number of accidents to demonstrate that the change observed is not the result of chance. (See Evaluation)
ITS systems themselves can provide data to enhance accident analysis. The systems can notify the emergency services and traffic management centre directly that an accident has taken place – for example via eCall, for which the in-vehicle technology is mandated in new European cars from 2018. (See Driver Support)
More generally, the data that is available from in-vehicle data recorders and roadside systems can be used to enhance accident analysis. Accident data could include information on traffic flow, weather conditions or the status of real-time traffic management systems. For some accidents, relevant information may be captured on roadside video.
ITS has also improved data collection at the accident scene through providing sophisticated mobile hardware which is capable of:
ITS systems can provide a large amount of data that is relevant to accident analysis – such as data on weather and traffic conditions. Digital road maps may contain information on road horizontal curvature and slope in addition to other roadway information such as vehicle restrictions or number of lanes. In-vehicle data recording provides an additional source of information. Arrangements need to be put in place to archive this data for accident investigation and analysis.
Hardware advances in recently years have also improved accident investigation and recording (see the example, CRASH, in the display box below). Similar systems include the Road Accident Data Management System (RADMS) developed by the World Bank and the Road Accident Data Recorder (RADaR) developed by the International Road Federation.
The CRASH electronic system used by police forces in England and Wales for data capture at the scene of collision combines digital technology with information management. It enables secure collection, validation, transmission and storage of road traffic collision reports. It supports police business needs and the Department for Transport's statistical requirements.
CRASH is hosted on the Police National Computer and imports and exports data to and from other agreed agencies and their systems – such as the vehicle record at the Driver and Vehicle Licensing Agency. By providing automated access to complementary sources of information, it maximises the efficiency of police time when reporting an accident. A police officer only needs to record the vehicle’s registration number – rather than other details, such as the make, model and colour of a vehicle, and the owner. Collision locations are more accurately positioned using built-in GPS receivers and interactive maps.
Roadside systems can supply information on weather, road surface conditions and traffic flow. Video of the accident scene may be available from CCTV cameras and the Traffic Control Centre. The data can be transmitted to a national or regional Traffic Control Centre (TCC), which can then initiate appropriate action – such as dynamic speed limit management. For example, the TCC may set a temporary lower speed limit in response to adverse weather conditions or road accidents – and communicate this to the road users through a range of media, such as VMS or subscription based news/traffic channels.
Real time monitoring of traffic conditions via sensors and imaging technologies also support TCC operator awareness of unexpected events – such as road accidents and stranded vehicles – so they can take appropriate action. Video of the accident scene may be available from CCTV cameras and the Traffic Control Centre. (See: CCTV, Weather Monitoring, Vehicles and Roadways, and Traffic Control Centres)
The Active Traffic Management (ATM) system in the UK consists of sensors buried in the lane to monitor traffic flow and speeds. If any abnormal patterns are detected, the TCC operator can confirm the incident by looking at CCTV images and setting the VMS systems – to show temporary speed limits or specific messages, as below. The ATM system was trialled on the M42 in 2003, fully implemented in 2006, and has gradually evolved into the current Smart Motorway system. (See Case Study)
Active Traffic Management Systems. Key: CCTV = Closed-Curcuit Television; AMS = Advanced Message Sign; ERA = Emergency Rest Area; HSR = Hard Shoulder Running; AMI = Advanced Motorway Indicator.
Tachographs and fleet management systems can provide data on driver hours of service and vehicle speeds. The use of video cameras as an integral part of fleet management systems is becoming more common. The camera view may be of the forward roadway only or it may also extend to a cabin (driver) view allowing investigation of driver attention in the pre-accident period. Fleets typically use such data for feedback to drivers, driver training and investigation following an incident. The saving of data for a time window is typically automatically triggered by an accelerometer that detects rapid acceleration or deceleration.
So-called “blackbox” Event Data Recorders (EDRs) are mandated for other modes of transport such as civil aviation but are not yet required for road vehicles. The recorders provide enhanced quality and accuracy of accident data. Typically, they store recent data in short-term memory – and the memory store is replaced at frequent intervals. Once an event, such as airbag deployment, is detected, the data in the memory store is permanently saved. This will comprise information on the status of vehicle sensors and control systems which can be accessed from the vehicle’s Controller Area Network (CAN). Data can include information on speed, accelerator pedal position, brake activation, driver use of seatbelt, as well as use of on-board vehicle systems such as cruise control or speed limiter prior to and during an accident.
EDRs are already present in a large proportion of vehicles, including over 90% of light vehicles in the USA. There is a US standard for EDRs fitted in light vehicles (Code of Federal Regulations Title 49 part 563). It is intended to ensure that data from an accident is usable for accident investigation purposes and can assist in analysing the performance of advanced safety systems such as restraint systems. The standard specifies common requirements for EDRs in terms of vehicle information such as speed, accelerator position, brake application, engine speed and speed change through a collision. It also requires vehicle manufacturers to provide data retrieval tools. There has been extensive discussion, particularly in North America, about mandating the fitting of EDRs in all new light vehicles - but to date, no legislation has been enacted. (See Probe Data)
Reliable accident reporting systems have value in enhancing understanding of conflict and behavioural issues and in identifying common causes of accidents and developing effective countermeasures.
Procedures need to be put in place to store and archive relevant data from roadside systems. There are privacy issues associated with data stored by fleet management systems and in-vehicle Event Data Recorders. In some countries the consent of drivers may be needed to access the EDR information unless there are legal provisions that provide s of access in certain situations. (See Legal and Regulatory Issues)
Analysis of prior accident history can be used to guide decisions on the deployment of safety-related ITS systems – systems primarily targeted at reducing accident numbers and accident severity. The analysis can help identify what systems to install and where to install them. There is little benefit to road network operations from deploying systems and technologies where they are not needed - and where they will have little impact. For example in many countries the location of speed cameras is determined by considering the relationship between the number of speed violations and excessive numbers of accidents. A similar approach, focused on the relationship between accidents and violations, can be used to determine the best location for red light cameras.
High-quality accident databases and appropriate tools for data extraction and analysis are required to identify problem sites. Proper procedures need to be applied to site identification to avoid selecting sites that are not fundamentally unsafe but may be subject to random fluctuations in accident numbers from one year to another. This problem is known as “bias by selection” where the resulting observed “improvements” in performance are in large part the result of random variation (“regression-to-the-mean”).
Before deciding that an ITS solution is needed, a proper assessment needs to be made of the alternatives. Standard tools are cost benefit analysis (CBA) and cost effectiveness analysis (CEA). CBA evaluates whether the predicted monetised benefits outweigh the costs. CEA measures alternative interventions against success criteria - such as lives saved or improvement in quality-adjusted life years (QALY). The QALY measurement represents the gap between an ideal scenario where everyone lives into old age free of disease and disability and the real-life situation in the population. (See Project Appraisal)
Some cooperative systems, such as intersection collision avoidance systems, are particularly targeted at blackspots. (See New and Emerging Applications)
There is considerable literature on problem site identification in road safety. The standard technique is to apply the Empirical Bayes (EB) method, which considers both the regression-to-the-mean effect and the expected safety performance of a particular site, based on the safety performance of similar locations.
Practitioners in developing countries need to consider the reliability and consistent quality of the available accident data. Where toll roads exist, the data may be fairly complete, in contrast to other parts of the road network. In some countries, data may be recorded where the police have relatively easy access to the accident locations but coverage may be poor elsewhere.
A useful introduction to the Empirical Bayes (EB) method is provided by Hauer, Harwood, Council and Griffith (2002), Estimating Safety by the Empirical Bayes Method: A Tutorial, Transportation Research Record 1784. A preprint version of this paper can be found at http://ezrahauer.files.wordpress.com/2012/08/trbpaper.pdf.