The Geography of Transport Systems
FOURTH EDITION
Jean-Paul Rodrigue (2017), New York: Routledge, 440 pages.
ISBN 978-1138669574
Risk Assessment
Author: Dr. Jean Andrey
NOTE: Contents phased out but kept online for reference.
1. Transportation Safety
Risk is a combination of uncertainty and negative consequences. The development of the field of risk assessment was fostered by a series of unrelated but significant health studies and was based very much on the dual development of probability theory and scientific method. Risk assessment involves three steps : hazard identification, risk estimation and risk evaluation. A fourth aspect of risk studies follows from the risk assessment process and deals with ameliorating the risk; it has been labeled risk response, risk control or, most commonly, risk management.
Transportation safety is a term that is intended to convey something about the avoidance of risk from bodily harm and property damage associated with the operation of transport systems. However, there is a paradox in the conventional usage of the term in that most safety statements instead discuss the amount of risk present. Indeed, safety typically refers to the characteristic or long-term average risk of an entity. Low levels of risk are thus equated with safe conditions/systems.
Government agencies maintain databases that document the frequency and nature of unsafe incidents for the various transportation modes. There are different indicators for the different modes, e.g. air crashes, pipeline spills, capsized boats, train derailments and road collisions. Not all modes contribute equally to the overall transportation risk experienced by a society. In fact, in most nations of the world, including Canada, more than 90 percent of casualties (fatalities and injured persons combined) are victims of road collisions.
2. Road Safety
Motor vehicle accidents are not a simple stimulus-response process; rather, many factors interact to create collisions events. Sometimes the phrase 'cause' is used to identify the factor that assigns legal responsibility for the accident (e.g. driving while alcohol-impaired; exceeding the speed limit). However, in most accident research, the concept of cause has been replaced with the concept of contributing factor. The term contributing factor refers to those conditions/ whose variation will alter the risk of collision.
The first formal articulation of the factors contributing to motor vehicle collisions was developed by the public health community in the 1940's and is referred to as the Haddon Matrix. The Haddon Matrix is a useful conceptual framework for organizing both risk factors and accident-countermeasures strategies.
Because there are so many potential hazards affecting the safe operation of motor vehicles, risk analysis tend to focus mainly on risk estimation, i.e. quantifying and comparing collision-involvement rates for different combinations of drivers, vehicles and environments.
3. Collision Involvement Rates
Collision-involvement rates are calculated as as follows :
NUMBER OF COLLISIONS
EXPOSURE
Depending on the focus of the study, the collision data may pertain to all reported collisions or to specific subsets, for example according to severity (e.g. property damage only (PDO), injury or fatal), number of motor vehicles involved (e.g. one, two, three or more), or pre-collision maneuver (e.g. turning maneuver, ran off road).
It is also common to focus on particular subsets of drivers (e.g. by age or sex), vehicles (e.g. passenger vs. freight), roads (e.g. rural vs. urban; two-lane vs. multi-lane), and driving conditions (e.g. according to time of day, weather condition or political jurisdiction). In these cases, it is important that the collision and exposure data are compatible.
4. Exposure to Risk and the Calculation of Accident Rates
The concept of exposure has been an integral part of road safety analysis since WWII. Although defined in various way, the idea is fairly straightforward : one would expect accident frequency to increase as travel increases, both at the individual and societal level. As a result, exposure is used to normalize accident data to allow for comparison. For example, if Driver A has 10 accidents while driving one million kilometers and Driver B has 5 accidents while driving 200,000 kilometers, then one would conclude that Driver A's accident risk is lower because, per kilometer, Driver A is less likely to be involved in a collision (.00001 involvements/km for Driver A vs. .000025 involvements/km for Driver B). This difference in risk could be due to differences in driver characteristics (e.g. skill, experience, risk taking), vehicle characteristics (e.g. size, state of repair) or the quality of exposure (e.g. proportion of driving at night, during inclement weather, in high traffic, in urban areas, on undivided highways).
Because exposure can be interpreted very broadly, a wide variety of exposure measures have been used in the field of road safety; ultimately, the choice of exposure measure is dependent upon the elements under investigation. Byun et al (1979) conceptualize that there are four denominations of exposure measures : time-, event-, activity-, and population-based measures. Chipman et al (1991) suggest that risk exposure can be measured in terms of population, driver-distance, or driver-time. This latter classification seems the most appropriate because travel, and thus exposure, occurs within these three dimensions. The following table lists examples for each class of measure. Throughout the literature, distance travelled and time travelled are the most widely used measures of driver exposure to risk.
Exposure Dimension Exposure Measure
Space
  • Driver/vehicle kilometres
  • Passenger kilometres
Time
  • Driver/vehicle hours
  • Passenger hours
  • Average Daily Traffic (ADT)
Population
  • Number of licensed drivers
  • Number of registered vehicles
  • Number of trips
  • Number of passengers
  • Population
Another way of comparing accident rates is to calculate risk ratios.
5. Measuring Exposure
To be of use in risk analysis, exposure data need to be assembled from reliable sources. There are three widely accepted methods of collecting exposure data : direct observation, survey techniques, and induced measurement. A less popular method is to use fuel sales to estimate total distance travelled. The advantages and disadvantages of each are discussed below.
Direct methods include personal observation and mechanical counting. In the health sciences, direct measurements can be obtained through laboratory experiments. The driving environment, however, cannot be as easily modeled in a lab; however, personal observations about driving behavior are possible. Researchers can observe driver behavior as a passenger or as a roadside observer (Wolfe, 1982). These method allows the researcher to collect a wide variety of exposure measures in a diverse range of situations. Direct Observation is, obviously, very time consuming and not very efficient for studies of large groups of drivers or vehicles. A less costly method of direct observation uses mechanical counting devices to record the number of vehicle axles that pass by (Wolfe, 1982). This method is useful in local site evaluations, but again is not amenable to studies of large driver or vehicle groups, and provides no information about the quality of trips. Overall, direct methods can provide very reliable data, but these methods tell us little about driver or trip characteristics.
Surveys are the most widely used method to gather exposure information about drivers in the road safety community (e.g., see Wolfe, 1982; Cerrelli, 1989; Joly et al., 1991; Chipman et al., 1991; Stutts and Martell, 1992). Two examples of exposure databases compiled from survey data include the National Personal Transportation Survey (NPTS) in the United States and the 1988 and 1994 Exposure Surveys for Ontario funded by the Ontario Ministry of Transportation. Typically, a random sample is drawn from the entire driver population and trip data are collected for a period of one or more days. Exposure information is then extrapolated to make inferences about various driver sub-populations.
There are several methods of conducting surveys. Researchers may use personal interviews, self-administered questionnaires, or mail-back surveys. Personal interviews may consist of roadside surveys or telephone interviews. The presence of an interviewer is likely to reduce the number of incomplete records (Wolfe, 1982). The presence of a researcher may or may not be required for self-administered questionnaires. Mail-back questionnaires typically provide less complete data than the personal interviews. Problems may arise when drivers are asked to recall past trips. Also, the season in which data are collected may introduce bias. Respondents are usually asked to provide trip details for a period of one or more days. The lure of the mail-back questionnaires is, however, the opportunity to gather data for selected variables for a larger sample. Studies of driver populations at the regional, provincial, or national level are very expensive and for this reason, empirical data on travel behaviour are not collected annually.
In 1964, Thorpe (1964) introduced a technique that can be used to by-pass the need for traditionally gathered empirical estimates of driver exposure. The technique is known as the induced exposure method, and is based on several assumptions. The technique has been applied in modified form in several recent studies (e.g. Stamatiadis and Deacon, 1997). Theoretically, the induced-exposure method allows risk comparisons to be made for a wide range of situations.
Another clever way to bypass the need to collect exposure data is to use the matched-pair (also known as the matched-sample) approach. This approach is commonly used in studies of driving-related weather hazards.
6. GIS Applications in Road Safety Research
An early application of GIS was to determine optimal routes for emergency vehicles (Bridgehouse, 1993). Addresses obtained from incoming 911 calls are matched to a road network and the quickest route can be resolved. The cost of implementing such a system is enormous, primarily because of the time needed to create an accurate province- or state-wide address database. Like the emergency vehicle application, the transport of hazardous materials is another area of safety research where GIS technology has been successfully employed (Lepofsky and Abkowitz, 1993; Brainard et al., 1996). These studies evaluate the variations in risk along any given road and then determine the safest route of transport. The topological structure of a vector-based GIS is well suited to route assignment based on origin-destination data. Overlay and buffer operations are particularly helpful when examining different hazard scenarios.
The application of GIS to issues in accident analysis has been minimal, although the number of publications has increased recently. Most studies using GIS have been limited primarily to black spot analyses. Black spots occur on a map when several accidents occur at the same location. The use of GIS permits analysts to visually inspect the spatial distribution of accidents and interactively query individual sites. It is also possible to use GIS to evaluate the safety benefits of various local countermeasure activities by examining the frequency and spatial distribution of accidents (e.g., Affum and Taylor, 1997). As a consequence, most of the studies that focus on the accident ignore the social factors that give rise to the risk of an accident.
Hence, there is an opportunity to use GIS to move beyond the accident and focus on the travel that gives rise to traffic accidents. Many researchers in the road safety community have stated that poor exposure data compromise their ability to estimate accident rates, especially when the data are disaggregated by driver and driving situation. Two recent studies illustrate the use of GIS to estimate risk exposure.
Although the above studies found the use of GIS beneficial, there are some limitations of GIS technology that have restricted the amount of research on travel. The data structure in a GIS over-simplifies the transportation environment. This has serious implications for modeling accuracy in large scale urban transportation modeling. Furthermore, there are two fundamental problems when using the network assignment procedures of a GIS to predict travel or travel demand. First, most travel models require traffic flow or congestion information. These values vary with every trip and most GISs are not able to account for any iterative variations in traffic flow in a user friendly manner. Second, the topological data model used by vector GISs cannot adequately account for the "virtual" relationships between unconnected map features essential to calculating and predicting urban travel demand. Shaw (1993) surmises that a GIS is not presently suitable for any of the four steps in the urban travel demand model. He concludes that most GISs are insufficient for present traffic demand modeling requirements because the strengths of a GIS lie in its spatial analytic capabilities rather than its spatial modeling capabilities. Researchers have therefore questioned the validity of GIS model outputs (Hartgen et al., 1993). Many of the studies cited above had to transfer data back and forth between the GIS and other statistical software complicating the use of GIS for transport and safety-related studies. Many software developers have recognized these limitations and have begun to produce GIS packages oriented toward the transportation modeller (e.g., TransCAD, GIS-Trans).