6. Case Study: Effectiveness of Traffic Safety Measures
6.1. Traffic Safety Measurements: Evaluating Effectiveness
As the conclusion from the above chapters, based on fundamental theory of human factors and driver behaviors, along with empirical data from field survey (manual counting and interviews), we can then describe the driver behaviour chain of violating road traffic regulations.
Talking briefly, it is concluded that the violation behaviour are caused from general attitudes towards regulations (long-term) and specific-scenario acceptance of rules (short-term). Those influencing parameters which have effects to the probability of violating traffic regulations can be categorized into two groups of:
- internal elements which are mainly related to driver personality (e.g. age, education, driving experience, etc.);
- external elements which are related to traffic environment (egg. Infrastructures, traffic flow, traffic rules and regulations, traffic operation and management elements, etc.)
In order to construct the model of risk analysis in the approach of driver behaviour, all above-mentioned elements will be treated as independent parameters of the whole system. Those process from normal situation to critical situations (which means driver behaviour chain, in the definition from the aspect of accident progress) consists of driver natural logics such as attitudes, perceptions, experience, skill, etc.
We then have the risk analysis model of driver behaviour of violating road traffic regulations (the figure can be seen in Annex A. The model parameters (situations and developments) are clarified in the Annex A. Summarization can be seen as follows:
A. Independent parameters.
1. Parameters have influence on general attitudes towards rules (long-term) - Education level
- Age of driver
- Experience of enforcement: are those experiences of getting punishment at the intersection when violating traffic rules (his/her own experience/observation on spot/ from other relatives, friends, etc…). In fact, this parameter is also influenced by enforcement level after a long period of time.
2. Parameters have influence on specific-scenario acceptance of rules (short-term)
- Enforcement level. In reality, an effective enforcement level in cooperation with some other measurements such as education, promotion, etc., will effect the driver experience of enforcement (as mentioned in chapter 5), which in its turn will have effects on (long-term) general attitudes towards road traffic regulations. However, in this research, we temporarily take into consideration the enforcement level´s influence on driver specific-scenario acceptance of rules. Enforcement level is defined as the enforcement punishment fee and effectiveness of monitoring works (automatic monitoring tools, or the presence of policemen).
- Perception skill: describe the driver knowledge on the specific situation of the intersection, including its entire layout, its traffic management and operation (whether there is a traffic signal or not, whether there is any priority rules for left-turning movement or any other rules, etc.) and the driver concentration level of recognizing the status of traffic signal, etc.
- Those parameters have impact on ―fear for congestion‖:
Experience of getting stuck: are those experiences of getting stuck even in the local area (city, this crowded area, etc.) or at the specific intersection (his/her own experience/ from other relatives, friends, etc…).
Intersection level of service (LOS): including intersection capacity and traffic volume
Trip motivation: in this specific scenario, how urgent is the trip.
B. Dependent parameters
- ―Fear for congestion‖ is result from ―intersection LOS‖, ―experience of getting stuck‖ and ―trip motivation‖. From this position, the parameter of ―fear for congestion‖ goes along with
―enforcement level‖ and ―perception skill‖ to create a ―specific-scenario acceptance of rules‖
inside the driver´s mind.
- The dependent parameters of ―general attitudes towards rules‖ and ―specific-scenario acceptance of rules‖ come together to lead to the driver behaviour of violating traffic regulation (so-called violation behaviors in the model).
In order to calculate the risk level (how many percentage/ probability for the driver to violate the traffic regulations), it is required to quantify all above-mentioned parameters and their relationships. The methodology of risk analysis DRAM is constructed based on distribution probability of variables.
Required data will be:
- data on probability distribution of parameters ´value
- data on relationship (also in probability distribution) among parameters in the model
In the approach of risk analysis, parameters´ values and relationships among them can be firstly determined as assumptions (the assumption may be verified and adjusted afterwards). When the knowledge is improved, the model can be developed and expanded later on. The important thing is that collected data to be further analysed must be consistent (comparable).
Relationships among parameters are described in the following assumption table.
Table 16. Describing relationships among parameters
number development number of
relations input situations output situation
I gaining attitude towards legislation 3 aod eoe eod etl
II calculating intersection level of service 2 ic tcv ilos III gaining attitude towards congestion 3 ilos mot eogt ffc
IV balancing between two risks 2 el ffc misc
V deciding to violate traffic regulations 2 atl misc vb
Assumption related to developments:
I. High experience of enforcement leads to higher percentage of high attitude towards legislation
People having higher education have higher attitudes towards legislation
Old people have higher attitude towards legislation than the young people
II. Calculating LOS by comparing intersection capacity (Cc) and traffic volume (Vc)
III. The high motivation of driver leads to higher fear for congestion
More often of getting stuck in the intersection cause to higher fear for congestion Intersection level of service decrease means higher fear for congestion
IV. High enforcement leading to lower motivation in scenario to violate the traffic regulations
High fear for congestion leading to higher motivation in scenario to violate the traffic regulations V. High attitude towards legislation leads to lower percentage of violating traffic regulations
High motivation in scenario leads to higher percentage of violating traffic regulations
Applying the methodology of risk analysis, parameters are displayed in the form of probability distribution. Relationships among them are therefore such fomulars which can apply Bayes formular for conditional probability distribution.
Just take one relationship as an example: In the first relationship (I) from independent parameters of
―education level‖ (el), ―age of driver‖ (aod), ―experience of enforcement‖ (eoe), and the dependent parameter of ―general attitudes towards traffic rules‖ (ga).
We have (from the above mentioned table the probability values of independent parameters as follows:
P(eof = high) means the probability for the driver to have the high experience of enforcement.
P(eof = high) + P (eof = medium) + P (eof = low) = 1
The probability for the driver to have the high ―general attitude towards traffic rules‖ is the probability of every cases:
P(ga = high| eoe = high, el = high, eod = old) + P(ga = high| eoe = high, el = high, eod = young) + P(ga = high| eoe = high, el = medium, eod = old) + ….
+ P(ga = low| eoe = low, el = low, eod = young)
In the specific case study, assumptions will be adjusted step by step, based on empirical data (knowledge), as well as data from field survey (observation and interview).
6.1.2. Vietnam Experiences
So far, guidelines to conduct traffic safety measurements, such as AASHTO (NHCRP report 500, 2004), often classify measurements into groups based on their oriented objectives (due to elements of the whole traffic system). Such system of 3E (Engineering, enforcement, and education) to evaluate traffic measurements are applied now in Vietnam (Source: JICA, ALMEC, TRAHUD projects).
In fact, when applying such measurements of traffic management and operation, they are not conducted separately. More often, such traffic safety measurements are conducted in the broad scope (in the whole route or some routes, in one or some local areas, etc.). Moreover, influences of traffic safety measurements on driver awareness and behaviors are very essential to take into consideration, even before applying. Evaluating effectiveness of such measurements is important but not very simple.
There is a fact that many traffic safety measurements, which have been successfully applied in developed countries with considerable results in removing traffic accidents, require a rather long time to have the initial results when being applied in Vietnam. The first result mentioned here is the high probability of road users to obey such regulations though they are very useful in traffic safety. Many measurements need the procedure to be adjusted and come into effects (see examples in the next part). The problem is that, almost measurements of traffic management and operation in general and traffic safety in particular are very costly, with the large social effects.
In order to improve the current situation, the research suggests using the risk analysis model to evaluate effectiveness of traffic safety measurement before, during and after applying into the reality.
The first criteria to evaluate are influencing level of the measurement to driver behaviours of violating traffic regulations. Recommend using expert methodology to evaluate the influence before applying.
During the process of operating the measurement in the reality, other evaluation criteria will be determined and calculated (by collecting and processing data appropriately with the standard forms).
Traffic safety measurements are evaluated based on the objective-oriented fundamental. The expert opinion may be collected in the form of ranking (grades) as the following table.
Applying the model in order to evaluate the traffic safety measurement before conducting in the reality.
Table 17. Influencing elements of traffic safety measurement Name of
measurement
Measurement´s main elements
Percentage (%)
Measurement´s objectives Influencing level (%)
Results (%) Enforcement
Enforcement level
Experience of enforcement
Egineering
Infrastructure LOS
Experience of getting stuck Fear for congestion
Education
Education level Perception skill
Total influence
This evaluation table can be more precious when knowledge on influencing parameters and their influence levels increase and are accumulated through periods of time. It is also possible to use the evaluation criteria of the time required for the measurement to be effect (long-term or short-term), the criteria of finance (applying costs), etc.
Briefly speaking, the model of driver behaviour of violating traffic regulations is useful to evaluate the effectiveness of the new measurement, particularly its impacts on the road users´ behaviors.