Road Safety Manual
A manual for practitioners and decision makers
on implementing safe system infrastructure

You are here

5.4 Non-Crash Data and Recording Systems

Crash data is generally considered a key source of information when assessing and treating risk. However, in some countries, particularly LMICs, crash data may not be reliable or available at all. Additional surveys and data sources other than crash data may be the only reliable source of safety data available. As discussed in Identifying Data Requirements, this additional information (safety performance indicators) is also important in road safety management. These enable assessment of different policies, programmes and projects to identify their effect on road safety outcomes. This occurs through the collection and assessment of details on the interventions implemented and the intermediate outcomes.

A variety of other data sources are available, including information on road design and features, traffic data, survey data and exposure data.

Additional Sources of Information

Road inventory

Road inventory data are a major source of information that can assist in assessing safety. Because the impact of different road elements is well known, different elements or combinations of elements can provide vital insight into crash problems, including the key crash types contributing to fatal and serious injury outcomes (see The Safe System Approach). The following data are of particular use:

  • road class – e.g. freeway, arterial, local road;
  • road width and type – divided (plus width and type of median) or undivided;
  • adjacent land use – e.g. residential, commercial;
  • lane number and widths – in each direction;
  • intersection or crossing type – cross or ‘T’ intersection, roundabout, railway or other;
  • traffic control devices – signals, stop signs, give way signs;
  • road alignment – horizontal and vertical alignment;
  • street lighting;
  • road surface – type (asphalt, concrete, brick, unpaved) and condition (roughness, potholes, skid resistance);
  • shoulders – width, type (paved, unpaved);
  • speed limit;
  • roadside hazards – including distance to hazard and type;
  • pedestrian facilities – including presence of footpath, pedestrian crossing and type.

This presents a basic list of relevant road element types, but there are many other factors that may have an influence on safety outcomes. The International Road Assessment Programme (iRAP) collects around 70 attributes (see http://www.irap.org/about-irap-3/methodology for details of these attributes, and Proactive Identification for more information on iRAP, and Box for examples of the data collection undertaken in Mexico). In the United States, the Model Inventory of Roadway Elements (MIRE) provides a list of 202 elements that may be needed for making road safety decisions. Further information on MIRE can be found at http://www.mireinfo.org/about.html.

Box 5.7: Case study – road safety data collection in Mexico

The problem: Nationally, Mexico has about 18,000 deaths in road accidents per year, with an annual average growth of 1.9%. Of these deaths, 25% occur in the Federal Highway Network, which constitutes the backbone of the road and national transportation system.

The solution: In order to improve road safety within the country and thereby the quality of life of the population, as well as safety conditions along the major national and international trade corridors, Mexico launched a preventative project aimed at assessing 46,000 km of Federal Highways during a first stage in 2012 and 19,000 km of collector roads during a second stage in 2013. The start of this project coincided with the launch of the UN Decade of Action for Road Safety, to which Mexico adhered, so that this project was thought as a breakthrough to improve road safety and as the start of a periodic road assessment program.

The outcome: The project, in each of its two stages, made it possible to generate a safety rating for each type of road user and the investment plan at 20 years for safer roads. For the 46,000 km assessed during the first stage, the evaluation in both stages showed about 50% of the total length assessed with one or two stars for vehicle occupants and higher percentages for the other more vulnerable road users. The project has made it possible to address significant problems throughout the system by implementing large-scale treatments (i.e. installation of median and roadside barriers), as well as to incorporate asset management measures (i.e. signs and pavement markings) within the annual program of the maintenance and rehabilitation agency, and to apply the results in the updating of the road design standards. It is also making it possible to count on an updated inventory of the roads assessed. The adoption of this technology as a periodic program will make it possible to monitor the effectiveness of the measures and programs implemented.

Case study provided by Dr Alberto Mendoza, IMT, Mexico.

 

Road features need to be spatially located (ideally through a GIS-based system) to allow effective analysis and cross-linkage.

Road inventory data relevant to road safety may already exist (e.g. through road asset database systems) or it might need to be collected. A situational assessment should be performed to see whether this data exists (see Establishing and Maintaining Crash Data Systems). Road inventory data has traditionally been used for road safety audit or inspection (see Assessing Potential Risks and Identifying Issues), but in more recent times, methods have been developed to quantify the likely safety outcomes based on these elements.

The collection of this data can be based on ad hoc approaches (e.g. through periodic inspection, public complaints, etc.), but should ideally be through a comprehensive programme conducted on a regular basis. The most common approach involves the collection of data through video images, and subsequent rating or coding of this data by trained experts. This information is then fed into a database or asset register (see Box 5.8).

Box 5.8: Collection and rating of safety data through video images

Although the collection of road inventory data is highly useful, it needs to be done in a way that minimises costs (i.e. it must be quick), it must be accurate, and it must be safe. Several methods for data collection are available. In the most basic technique, data can be recorded on data collection sheets while travelling along the road of interest. This approach can only really be used on relatively short lengths, and it can be difficult to collect all relevant road variables when travelling at normal traffic speed.

For more extensive data gathering, computer-aided collection can be undertaken using a tablet or laptop, or information can be collected via video and coded safely back in the office. With a tablet, information is added to a database while travelling along the road of interest. Touch screen technology is typically used to select relevant road variables. Different symbols may be displayed on screen to facilitate quick data entry. As mentioned earlier, it is often difficult to enter all relevant variables when travelling at high speeds or in busy environments, and so video is often recorded to assist in later data entry and checking.

Another option involves the desktop assessment of video data. One or more video cameras can be used to collect data along the network of interest. A single camera can be used to gather information in a forward direction. Alternatively, more cameras can be used to allow better collection of road and roadside information. This video imagery is then used to code the variables of interest. The video images can be calibrated to allow measurement for more accurate collection of information (such as road width or distance to roadside hazards) and to ensure accurate spatial location of assets.

Video images are assessed, and can be paused to study more complex environments. Information from the images is added to a database for later analysis. This data entry may be through a dropdown menu system or manual population of a database. Data is typically collected for a discrete length of road (e.g. a 10 m section).

Figure 5.4: Populating a database with safety-related inventory data.

 

New technologies are being developed that will assist in more automated collection of road and roadside data. As an example, it is possible to collect information on features such as road width, horizontal and vertical alignment, and road surface condition using Light detection and ranging (LiDAR) and other vehicle sensors.

Traffic data

Traffic data is important to collect and analyse, particularly traffic volumes (or Average Annual Daily Traffic, AADT). This data can be used to generate crash rates, which provide a good indication of safety performance, including the safety performance of specific routes, road types or even infrastructure elements. Other types of traffic data include the traffic mix (e.g. percentage of different vehicle type; motorcyclists, bicyclists and pedestrians) and vehicle speeds (mean and 85th percentile speeds, compliance with speed limits). Traffic data can be collected using manual traffic counts or through automated means (e.g. pneumatic tubes or permanent data collection devices installed in the pavement).

Other exposure data

Aside from traffic data, other sources of exposure data include population data (total number of people; number by each age group) for an area or country. This data is typically available from national census data. Vehicle registration data is also often collected and used.

Attitude survey data

Attitude surveys collect information on the views of drivers, other road users and residents. This information is considered an important source of feedback for assessing the effectiveness of a new programme or treatment, and can provide insight into driver behaviour (for example, low compliance levels with the posted speed limit).

Enforcement data

Information on the number of police checks (e.g. for speed, alcohol, restraint use), number of violations (e.g. number of vehicles speeding; motorcycle riders without helmets), and number of drivers punished (e.g. fined, penalties provided or imprisoned) are all useful measures. These will help assess the impact of new policies or actions on safety outcomes.

Other sources

In addition to the data sources mentioned above, other useful information can be gathered from the following sources, where available:

  • hospital files – an additional source of information on crashes other than police files, which provides more detailed information on the injuries sustained, the length of stay in hospital, etc. (see Sources Data in Establishing and Maintaining Crash Data System for further detail);
  • maintenance and operations files – information on the type and timing of any maintenance work;
  • expenditure data – information relating to spending on safety-related initiatives;
  • project history files – information relating to any previous major corrective works, including safety infrastructure improvements;
  • insurance company data – crash history of the driver and the car;
  • weather reports;
  • vehicle data, including information from periodic vehicle inspection.

Other types of compliance data are discussed in the following section.

Additional Survey Methods

Often traffic data and driver behaviour data are not readily available. There is no set list of additional data that must be collected, and given the cost of any type of data collection, careful thought needs to be given to this task, regardless of whether this is conducted at national level or for specific locations. Additional data should be collected when there is a need for it, and collection of this data should be cost-effective.

The following section provides a brief description of some of the more common data surveys performed, as well as different methods that can be used. References to useful material is also provided.

Measuring speeds and traffic volumes

A spot speed survey involves the collection of a sample of speeds at a specific road location, or at a number of locations. This can then be used to determine the speed distribution of vehicles, which is useful for the following reasons:

  • establishing the speed of vehicles (e.g. mean and 85th percentile speed) on the selected road;
  • checking the proportion of vehicles that are travelling over the speed limit;
  • assessing the likely contribution of vehicle speeds to crashes;
  • checking for differences in speeds between different road users;
  • tracking these speeds for monitoring purposes;
  • evaluating the effectiveness of a new treatment installed to reduce speeds.

Vehicle speeds can be measured using manual methods (radar or laser guns, stop watch), or using automatic methods (loops or tubes). Automatic methods are better for studies that require a larger sample. Loops and tubes can also record more than just average speeds, such as traffic volumes, vehicle turning movements and traffic mix. These components are essential to understanding the safety issues that exist at a location. The GRSP Speed Manual (GRSP, 2008) and the UK government (DETR, 2001) provide in-depth guides to speed and volume measurements and how to manage speed-related safety issues. See Box 5.9 for a case study on speed data collection in India.

Box 5.9: Case study – Speed data collection

The problem: data was required on vehicle speeds for a study in four states in India.

On-site traffic speed, traffic volume and crash data on four sample sites were selected from the road corridors that formed part of the 2011 Four States Project in India. The research team conducted traffic operating speed and volume studies for determining the 85th percentile speed and the traffic density on the corridors by type of vehicle. Researchers also collected available police crash data and conducted on-site crash investigations for a period of up to two months to obtain a better understanding of the accidents. These crash site visits were made possible through close liaison with the local police departments and also through regular routine patrols of the road sections.

The study was conducted on four state highways (two in Karnataka, and two in Gujarat) totalling around 300 km.

The outcome: It was found that the 85th percentile traffic speeds were well above the posted speed limit (in some cases the posted speed limit was not obvious). Large differences in speed were observed between types of vehicles. Cars had much higher 85th percentile speeds than other road users such as motorised two-wheelers, buses and trucks. The spread of speed for the same vehicle type was also quite high. For example, motorised two-wheelers travelled at speeds ranging from 25 to 80 km/h.

Some of the key learnings from this data collection were that:

  1. Due to the lack of detailed data held by some road authorities and police departments in India, on-site speed and traffic volume data collection such as this is needed to truly understand how the road is being used.
  2. With rapid urbanisation, traffic speed and volume studies need to be conducted on an ongoing basis.
  3. Proper documentation needs to be maintained to ensure uniformity in data collection and, most importantly, data interpretation.
  4. The activity can be recorded on video cameras to ensure that the data sample can be rechecked; this also acts as a safety measure.
  5. Better equipment may help realise larger samples and faster data collection, but there must be a way to check the data collected to ensure reliability and accuracy.
  6. he safety of the data collection team and other road users is of paramount importance. Careful planning and risk assessments are recommended prior to working in or adjacent to the live carriageway.

In addition to the production of safety ratings, the speed data led to recommendations for the setting of evidence-based speed limits as well as follow-up enforcement of these limits.

Source: Ravishankar Rajaraman, Manager - Safety Group, JPRI.

 

Measuring seatbelt and crash helmet usage

GRSP has developed two separate manuals, one dedicated to seatbelts and child restraints (GRSP, 2009) and the other dedicated to helmets (GRSP, 2006). Each of the manuals provides information on how to assess the extent of non-seatbelt and non-helmet use in a project region, as well as how to design, implement and evaluate a programme to target these issues. With regard to measuring seatbelt and helmet usage, the guides list possible sources of this information, as well as how to collect the data through conducting community surveys and observational studies.

Measuring drink-driving levels

Much like measuring helmet and seatbelt usage, GRSP has developed a road safety manual for drink-driving (GRSP, 2007). This provides information on how to assess the situation and choose priority actions, as well as how to design, implement and evaluate a drinking and driving programme.

The guide suggests collecting data from relevant authorities, such as the police, road authorities and health sectors, to understand the size of the problem. Data on the level of compliance with existing laws can be collected through a combination of crash data (i.e. crashes involving drivers and riders with Blood Alcohol Content (BAC) levels exceeding the legal limit), the number of alcohol offences detected by police, the percentage of drivers stopped with a BAC over the legal limit, and by performing driver surveys (GRSP, 2007).

There are a variety of other intermediate outcomes that could be measured, depending on the interventions implemented.

Data Recording Systems

As for crash data, it is important that survey data be recorded in a way that can be analysed easily. It is also beneficial for the system to be developed so that data can be linked with other data sources. This is particularly important for surveys that cover a broad geographic area (such as traffic volume, asset or population data). Such systems may already exist for this data. A common method is to link data by location using a Geographic Information System (GIS). These systems can typically store information that is linked geographically for future analysis. Different types of data can be added to such systems as a ‘layer’, allowing more powerful assessment of risk (see Analysis of Data and Using Data to Improve Safety).

 

 

Reference sources

No reference sources found.