An Adaptive Policymaking Approach for Implementing Intelligent Speed Adaptation B. Agusdinata1*, V.A.W.J. Marchau2, and W.E. Walker3 Delft University of Technology, The Netherlands Jaffalaan 5, 2628 BX, Delft, The Netherlands. * Tel.: +3115 2784532; E-mail:
[email protected]
ABSTRACT Intelligent Speed Adaptation (ISA) could improve road traffic safety significantly. Large scale implementation of ISA, however, is hampered by uncertainties, such as the level of user acceptance, reliability of the technology, unexpected costs, etc. In this paper we suggest an adaptive policymaking approach that would enable ISA implementation to proceed despite these uncertainties. We propose five steps: (i) specifying the policy problem, (ii) assembling a basic policy, (iii) specifying the rest of the policy, (iv) learning from real world experience with the policy, and (v) changing the implemented policy. The adaptive approach is supported by exploratory analysis. Using a simple road safety model, the analysis involves making computational experiments across multiple plausible states of the system and multiple scenarios. We demonstrate how to use the insights from the exploratory analysis to design a basic policy and how to gain knowledge over time about the behavior of the system in order to resolve some of the uncertainties. By monitoring the change in mean speed to detect changes in the level of acceptance, the policy can be adapted to unfolding events. For example, for two target groups of drivers, young and middleaged ones, a policy that targets more young drivers with mandatory ISA is shown to be robust. KEYWORDS Intelligent Speed Adaptation, adaptive policy, exploratory analysis
INTRODUCTION A major objective for transport policies is the improvement of road traffic safety. For instance, in the Netherlands, national targets have been set at a 25% reduction in road fatalities and hospitalized injuries for 2010 [1]. On a European level, the road safety objectives are even higher. The Commission of the European Communities has targeted a 50% reduction in road fatalities within the European Union by 2010 [2]. Besides the more traditional preventive measures, such as driver-educational campaigns and legislation, current policy options include measures that directly intervene in vehicle driving tasks. In particular, the importance of Intelligent Speed Adaptation (ISA) systems is growing rapidly. These systems take into account local speed restrictions and warn the driver in case of speeding or may even automatically adjust the maximum driving speed to the posted maximum speed. Ex-
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pectations regarding the contribution of ISA to traffic safety are high. There are estimates of up to a 40% reduction of injury accidents [3] and up to a 59% reduction of fatal accidents [4] from the use of fully automatic speed control devices. Although the expectations concerning the positive impacts of ISA are high, there still is a considerable gap between what seems technologically possible and what has so far been implemented in practice [5]. The promising estimates of ISA benefits have been achieved using prototypes in experiments and field studies under controlled conditions, but these impressive experimental results do not mean that this is what can be expected in practice. The estimated contribution of ISA implementation to general transport safety and policy goals is based on the assumption that all vehicles are equipped with a perfectly functioning system, and that all drivers use the system in the way it was designed. These assumptions may lead to overoptimistic projections, since the participating vehicles have been equipped with ISA facilities that worked only within the test environments, the systems have occasionally malfunctioned within tests, and some drivers have occasionally ignored the instructions when using ISA systems within these studies. Some groups of (potential) users were more reluctant to accept the functionality of ISA than others [6]. Furthermore, there are many variations among ISA systems in terms of the level of driver intervention in case of speeding, the extent to which the driver can control the system, whether both fixed and variable speed limits are included, the origin of speed limit information, etc. Hence, ISA alternatives vary considerably in terms of their technological configurations. Clearly, different ISA options will produce different impacts on traffic safety, throughput, and environmental stress (e.g. fuel consumption and emissions) and will require different conditions for implementation (e.g. legislation, investments in infrastructure, etc.). These variations, in turn, have different implications for the stakeholders who will be involved in ISA implementation as well as their willingness to support its implementation. As a result of the uncertainties concerning the level of acceptance among drivers, reliability of the technology, unexpected costs, etc., decision makers are reluctant to implement ISA on a large scale basis, even though the estimated benefits are large.
AN INTEGRATED VIEW ON POLICYMAKING Policymaking on ADAS implementation requires an integrated view on the various ADAS applications, their possible consequences for transportation system performance, and societal conditions for implementation. The basis for such view has been provided by Walker [7]. According to this view, policymaking, in essence, concerns making choices regarding a system in order to change the system outcomes in a desired way (see Figure 1). The elements from this framework are assembled in a structure labeled ‘XPIORV’, where: • X= External forces: factors that are beyond the influence of policymakers. • P = Policies: instruments that are used by policymakers to influence the behavior of the system to help achieve their objectives. • I = Internal factors: factors inside the system that are influenced by external factors and policies. • O = Outcomes of interest. These are measurable system outcomes that are related to the objec-
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tives of policymakers and stakeholders. • R= Relationships: the functional, behavioral, or causal linkages among the external forces, policies, and internal factors that produce the outcomes of interest. Hence, O = R (X,I,P)
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• V = Value system of policymakers and stakeholders, which reflects their goals, objectives, and preferences. The value system contains the criteria for indicating the desirability of the various policy outcomes based on the resulting outcomes of interest. Policymaking process
Policymakers
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Figure 1: An integrated view of policymaking
ADAPTIVE POLICIES AND EXPLORATORY ANALYSIS An adaptive approach is an innovative way to proceed with ISA implementation despite the uncertainties [8]. This approach allows implementation to begin prior to the resolution of all major uncertainties, with the policy being adapted over time based on new knowledge. It is also quite different from traditional policymaking approaches that strive for an optimum policy based on best estimate predictions of system behavior and future external developments. However, in situations of deep uncertainty, in which knowledge about the system, developments both inside and outside the system, and responses to policy choices are either unknown or far from agreed upon, such methods have the risk of significant prediction errors, which might lead the chosen policy to fail. An adaptive approach will reduce such errors (see examples in [9], [10]). The specification of an adaptive policy is enabled by insights about uncertainties gained from a modeling approach called exploratory analysis, which can be viewed as sensitivity analysis in the broadest sense [11]. The approach assesses policy effectiveness across multiple dimensions of uncertainty. Insights are gained by performing large numbers of computational experiments across plausible scenarios of exogenous developments and states of the system. This paper demonstrates how the combination of exploratory analysis and adaptive policy analysis can be used to address the uncertainties surrounding ISA implementation. Our focus is primarily on the current dominating uncertainty -- the level of user acceptance.
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THE DESIGN AND IMPLEMENTATION OF AN ADAPTIVE POLICY Designing and implementing an adaptive policy for ISA involves five steps (adapted from [8]): 1. Specifying the policy problem: specification of the system model, objectives, constraints, and available policy options, which eventually lead to the definition of conditions for success 2. Assembling a basic policy: specification of a promising policy and identification of conditions for the policy to succeed 3. Specifying the rest of the policy: identification of the vulnerabilities of the policy, events that will make it fail, and corrective actions to be taken should the events occur. This step also involves identification of signposts that should be monitored; exceeding these thresholds will trigger remedial actions. 4. Learning from real world experience: establishment of a monitoring system, data collection, and resolution of some uncertainties. 5. Changing the policy: adjustment of the policy based on the information collected over time by the monitoring system. In the rest of the paper, each step will be discussed in detail. In the final section, we discuss the implications of our approach for policymaking in the ISA field and make some recommendations for further research.
STEP 1: SPECIFYING THE POLICY PROBLEM Description of the policy analysis framework. The framework for the analysis of the ISA implementation is summarized in Table 1. The elements listed are not meant to be exhaustive and are presented only for illustrative purposes. The framework is based on six elements: policies (P), external forces (X), internal factors (I), outcomes of interest (O), relationships (R), and value system of stakeholders (V). Using these structural elements, the ranges of uncertainties covered in the model are also highlighted.
Table 1: Framework for the analysis of ISA implementation problem Internal factors (I) Policies (P) • Driving behavior: • Two types of ISA: − Two Speed profiles: − Advisory ISA: static speed limit, providing warno Positive skewed (speeding being to the driver, voluntary intervention havior) − Mandatory ISA: static speed limit, controlling the o Symmetric (normally distributed) vehicle with haptic throttle, mandatory intervention − ISA level of compliance (LC): • Target groups are two age groups of drivers: [10,100%]. − Young: 18-25 years old • Road traffic density with variables: − Middle-aged: 30-40 years old − No. of drivers • Penetration level: 0, 10, and 50 % − Average vehicle-km per driver
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Table 1 (continued): Framework for the analysis of ISA implementation problem Outcomes of interest (O) External forces (X) • Road accidents • Demographics: Distribution of drivers by age − Proxy: reduction in accident risk • Economy: − Economic growth (We use the effects of demographics and economic devel- The outcomes of interest are measures considered to be correlated with policy objecopments on road traffic density.) tives (i.e. improving road safety) Value system (V) Relationships (R) • Two functional relationships between speed and acci- • Drivers dent risk: • Non-drivers (pedestrians, cyclists) − Klooden (1997) [12] • Politicians (local, national, supranational) − Fildes (1991) [13] • Speed profile shift mechanism: causal relationships on • Automobile industry the effect of ISA to driving behavior. This mechanism is related to level of compliance (LC) (e.g. LC=10% means only 10% of the drivers comply with the warning given by ISA)
A more detailed specification of the policy options (P) considered in our study is given in Table 2. Data for two variables, i.e. the number of drivers and the annual average vehicle-km (for the case of the Netherlands), are given in Table 3. The 16 scenarios formed by the combinations of the extreme values of these variables are given in Table 4. The speed–accident risk relationships are given graphically in Figure 2. Level of compliance (LC) is used as a proxy for uncertainty on user acceptance. Target: 18-25 years old 30-40 years old ISA: Mandatory Advisory Penetration level (%): Policy 1 10 50 Policy 2 50 10 Policy 3 50 0 Policy 4 0 50 Policy 5 50 50 Policy 6 10 10
Table 2: ISA policy options
External Variables No. of drivers with car possession Annual average vehicle-km per driver
(18-25 years old) (30-40 years old) Highest Lowest Highest Lowest 59,000 538,500 1,298,800 1,712,900 1,770
5,400
9,000
Total vehicle-km in The Netherlands in 1996 was approximately 107 billion, out of which 26 % of it (27.8 billion vehicle km) took place in the urban roads.
Table 3: Dutch statistics data for external variables Source: derived from CBS data 1991-2002
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30-40 years old No.Drivers Vehicle-km Highest Highest Highest Lowest …. ….. Lowest Highest Lowest Lowest
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Speed-accident risk relation for urban road
18-25 years old No.Drivers Vehicle-km Lowest Lowest Lowest Lowest …. ….. Highest Highest Highest Highest
10,100
Klooden (1997) Fildes (1991)
16 12 8 4 0
-16
-8
0
8
16
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Table 4: Specification of scenarios Figure 2: Speed-accident risk relationships
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Definition of success In order to know whether the objective of the policy is being reached, criteria of success need to be defined. We use the relative regret function (equation 1 below) to measure the degree to which a policy is successful. Relative _ regret(p, k, s) =
Max[Performance (p', k, s)] − Performance(p, k, s) p'
Max[Performance (p' , k, s)]
(1)
p'
Note that p′ indicates the policy that delivers the maximum risk reduction in a particular state of the system k and scenario s. Table 5: Definition of categories of regret Policy succeeds Policy fails
Category of regret No regret Mild A lot Overwhelming
Range of relative regret, r 0 ≤ r ≤ 0.04 0.05 ≤ r ≤ 0.94 0.95 ≤ r ≤ 9.94 r ≥ 9.95
Shade designation
A policy is considered to succeed when it has no regret or only mild regret. A policy fails when it has high or overwhelming regret (see Table 5). The choice of the boundaries of success and failure (i.e. r=0.04, 0.94, 9.94), which represent the value system of one particular stakeholder, is for illustrative purposes only. They are determined by asking stakeholders “how much worse (i.e. relative performance) from the optimum policy ( p' ) would alternative policies be considered to fall into categories of no, mild, a lot, or overwhelming regret?”.
STEP 2: ASSEMBLING A BASIC POLICY In the presence of, among others, the significant uncertainty about user acceptance, the implementation of ISA on a large scale will likely risk failure. Therefore, a basic policy is needed as a test case to learn about the system’s behavior and to resolve some of the major uncertainties. Criteria for basic policy The criteria that we use to identify a basic policy are that it should be: • a small scale pilot project to test both the ISA mandatory and advisory systems; • flexible; it should not be designed so rigidly that will make it difficult to adapt in the future. • able to be used for learning the behavior of the road safety system; • relatively robust across most dimensions of uncertainty. Exploratory analysis to support the assembly of a basic policy The performance of an ISA policy across computational experiments can be represented graphically as shown in Figure 3, which also indicates what we call the ‘robustness region’. The graph shows the circumstances in which a policy will succeed or fail. For example, for Policy 5 (50/50 6
combination) when the level of compliance (LC) is above 70%, the policy is robust and insensitive to changes in external scenarios. Below that, a pattern emerges. The lower the LC is, the greater the regret values are. The area where the policy fails increases as the [no. of drivers, vehicle-km] of young drivers increases. So in a graying population, for example, the policy will actually perform better. Policy 5
Functional Relationship: Klooden (1997)
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At this circumstance: 60% LC, scenario 15, both driver groups have speeding behavior, Klooden’ relationship suggests that Policy 5 has a mild regret compared to other five ISA policies No Regret Mild A lot Overwhelming 10%
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Figure 3: Example of a robustness region Another important finding from the exploratory analysis is that the performance of a policy is highly sensitive to the assumed speed-accident relationship. The way we deal with this sensitivity is to superimpose the results from each of the relationships. In this way, we are able to identify the zones in which both functional relationships suggest that the policy succeeds or fails (a way of dealing with this uncertainty). Continuing the example of Fig. 3, Fig. 4 provides robustness regions for Policy 5, but now for the two speed-accident risk relationships. Three zones in the robustness region can be identified: 1. Zone I: where both functional relationships suggest that the policy succeeds 2. Zone II: where both functional relationships suggest that the policy fails 3. Zone III: where no conclusion can be drawn because of conflicting results given by both functional relationships. Policy 5 Scenario1 Scenario2 Scenario3 Scenario4 Scenario5 Scenario6 Scenario7 Scenario8 Scenario9 Scenario10 Scenario11 Scenario12 Scenario13 Scenario14 Scenario15 Scenario16
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Functional Relationship: Fildes (1991)
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Figure 4: Example of dealing with uncertainty on speed-accident relationships in exploratory analysis The way to interpret Figure 4 is: ‘a policy still succeeds as long as it is within zone I and is not near to crossing the dashed line to zone III or zone II’.
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Specifying the basic policy Among the six policy options, Policy 6 (10/10 combination) is chosen as a basic policy. This choice fulfills the first three criteria. The 10/10 combination is a small-scale policy that can be easily adjusted, while at the same time it can be used to collect data for learning about the behavior of the system. For the last criterion -- that a basic policy must be robust across most dimensions of uncertainties -- we use insights from the exploratory analysis. Below we illustrate how to do so. We carry out computational experiments to reveal the robustness regions of Policy 6 across four combinations of speed profile and the two speed-accident risk relationships. This is because we are uncertain about the true speed profile and the true speed-accident risk relationship. The results are given in Figure 5. By tracking the dashed line, Policy 6 appears to be robust for levels of compliance (LC) as low as 30% (one case), 40 % (one case), and 50% (2 cases). Above an LC of 50 %, the robustness of the policy is not affected by changes in the external scenarios (i.e. number of drivers and average vehicle-km per driver). The conclusion is that the basic policy is robust for an LC above 50%. Policy 6
Functional Relationship: Klooden (1997)
Functional Relationship: Fildes (1991)
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a. Speed profile [18-25 yrs old, 30-40 yrs old] = [Positive skewed, Symmetric] Policy 6
Functional Relationship: Klooden (1997)
Functional Relationship: Fildes (1991)
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Policy 6
Functional Relationship: Klooden (1997)
Functional Relationship: Fildes (1991)
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c. Speed profile [18-25 yrs old, 30-40 yrs old] = [Symmetric, Symmetric] Policy 6
Functional Relationship: Klooden (1997)
Functional Relationship: Fildes (1991)
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d. Speed profile [18-25 yrs old, 30-40 yrs old] = [Symmetric, Positive skewed]
Figure 5: Insights from exploratory analysis to assemble a basic policy
STEP 3: SPECIFYING THE REST OF THE POLICY Specifying the rest of the policy involves identifying vulnerabilities, analyzing corrective actions when vulnerabilities occur, and defining signposts and threshold values for the signposts to trigger corrective action. The analysis of possible corrective actions and the determination of threshold values are supported by exploratory analysis. In this paper, we choose to focus on possible low driver compliance as the only vulnerability (source of failure) of the ISA policy. As has been shown, the effectiveness of the policy is very sensitive to a change in LC. The important question now is, what do we need to do if the level of acceptance dwindles; and what are the values of LC for each case, i.e. those acceptance rates beyond which the original safety goals will not be reached anymore. We use the insights from exploratory analysis to find answers to these questions. Exploratory analysis to analyze the performance of corrective actions The candidate corrective actions are the five policies besides Policy 6. The computation experiments conducted on the five other alternative policies reveal the robustness regions shown in Figs. 6a-6e. The results shown apply only to the speed-accident risk relationship of Klooden [12].
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Policy 2
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b. Robustness region of Policy 2 Policy 4
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e. Robustness region of Policy 5
Figure 6: Insights from exploratory analysis to specify adaptive policy Some of the insights from Fig. 6 are summarized below. Note that the various policies are the result of the different combinations of penetration level for the young and middle-aged drivers (see Table 2). • Policy 2 (50/10) has the largest favorable robustness region. This policy is expected to perform well when the level of acceptance is above 50%. • Policy 3 (50/0) has the second largest favorable robustness region. But it consistently performs well when the level of acceptance is low (below 40%). • Policy 1 (10/50) and Policy 4 (0/50) have relatively small robustness regions. They fail even when the level of acceptance is as high as 90%. • Policy 5 (50/50) can deliver good performance only when the level of acceptance is at the maximum. However a slight reduction in LC will lead to failure.
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Exploratory analysis to establish signposts and thresholds for LC We propose to use a change in mean speed as a signpost that signals a change in LC. We perform computational analysis for each of the six policy options to determine all the threshold values of LC. The result for Policy 2 is given in Table 6. Figure 6.b shows that Policy 2 (50/10) will lose its robustness if LC goes below 50%. Therefore, we can assert that if there is now an increase in mean speed, and if that increase reaches 0.2 km/hr, a new policy will have to be implemented. The implication of this threshold value is that in case there is a decrease in mean speed (i.e. increased level of acceptance), a higher level of penetration can be pursued. This means increasing the number of mandatory ISA installed in the 30-40 years old group (Figure 6.e, Policy 5). This can be done when the decrease in mean speed reaches 0.21 km/hr. However, because our model suggests that Policy 2 is still robust at a high LC, Policy 5 is redundant. Conclusion: we can only identify the lower threshold for LC (i.e. LC=50%). Table 6: Range of change in mean speed used as signpost Policy 2 (50/10) Change of mean speed for Total Traffic 0.61
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STEP 4: LEARNING FROM REAL WORLD EXPERIENCE The implemented basic policy provides a real world laboratory for learning. This will enable us to reduce some of the uncertainties. By measuring the actual change in mean speed of the total system, the range of uncertainty about the level of compliance and about the initial speed profile for each driver group can be narrowed down, if not resolved. We illustrate this by taking a hypothetical case. Suppose we know the figures for number of drivers and average vehicle-km for both driver groups. The measurement of change in mean speed reveals that the figure is between 0.41 and 0.44 km/hr. Our model reveals three possible conditions (see Figure 6.a). One is a condition in which both driver groups have symmetric risk profiles and the acceptance level is around 80%. Second is a condition in which 18-25 year-olds have a speeding speed profile (positive skewed), while the 30-40 year–olds have a symmetric speed profile, and the level of acceptance is approximately 70%. Overall the LC uncertainty ranges from 60% to 80%. At this point, the initial 40 (i.e. 10 x 4) uncertain states of the system have been already reduced to three. Assuming that measurement of the speed profile in vehicles installed with ISA is possible, a picture of the initial speed profile of each driver group can be established (See Figure 7.b). The result in this case is that the young drivers have a positive skewed speed profile, while the middleaged group has a symmetric one. We can then assert that the LC is around 70%. Let us also assume that the data collected during the implementation of the basic policy allows us to estimate the actual speed-accident risk relationship. Aarts [14] summarizes the methods to estimate speedaccident risk relationship. For illustrative purpose, let us assume that this is the relationship proposed by Klooden (1997).
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To sum up, at this point the following uncertainties have been resolved: level of compliance (around 70%), initial speed profile for both driver groups (positive skewed and symmetric, respectively), and speed-accident risk relationship of Klooden. Based on this unfolding information, the basic policy needs to be re-evaluated, either to capitalize on the opportunities that emerge or to correct the policy if the circumstances turn out to be unfavorable. For this purpose, we need to look back at the robustness region for the other five policies (Policy 1 to Policy 5) given in Fig. 6. The exploration now takes place in a reduced range of uncertainties because, as discussed above, some of the uncertainties have already been resolved.
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Figure 7: Illustration of process of learning from real world experience
STEP 5: ADAPTING THE POLICY With the monitoring system in place, a change of mean speed that exceeds the threshold value will trigger corrective action. This step is the adaptive part of the policy. In this step, events unfold, signpost information related to the trigger is collected, and policy actions are started, altered, stopped, or extended. The adaptive policymaking process is suspended until a trigger event is reached. As long as the original objectives and constraints remain in place, the responses to a trigger event have a defensive or corrective character – that is, they are adjustments to the basic policy that preserve its benefits or meet outside challenges. Under some circumstances, neither defensive nor corrective actions would be sufficient. Then, the entire policy might have to be reassessed and substantially changed or even abandoned. If so, however, the next policy deliberations would benefit from the previous real world experiences. The knowledge gathered in the initial adaptive policymaking process on outcomes, objectives, measures, preferences of stakeholders, etc., would be available and would accelerate the new policymaking process. Adjusting the basic policy The basic policy may need to be adjusted to take into account the information being gathered over time. As the result of data collection and analysis enabled by the implementation of the basic policy, three dimensions of uncertainty have already been resolved: we determined that the LC is 12
about 70%, the speed profiles, and the appropriate speed-accident risk relationship. According to Fig. 5.a, actions now need to be taken to adjust the basic policy (10/10), because it still has a mild regret and other policies might have no regret. The basic policy needs to be adjusted to Policy 2 (50/10), since it has no regret around an LC of 70% and has the largest favorable robustness region (see Fig. 6.b). Having adapted the basic policy to benefit from more information about the road safety system, a further monitoring of changes in mean speed needs to be carried out in order to determine when the new policy would need to be adapted. Policy adaptation as the result of a change in mean speed In the case of a decreasing level of acceptance, the threshold of 50% LC (which is equal to an increase of 0.2 km/hr in mean speed) will function as a trigger for policy adjustment (see Table 6). From the exploratory analysis (Fig. 5.c), Policy 3 is a better policy if this threshold is reached. This means making inactive the advisory ISA installed in the cars of 30-40 year old drivers. There might also be a situation in which the new policy needs to be completely reassessed. This would happen, for example, if the preferences of the stakeholders changed. The policy problem would then need to be reevaluated (i.e. go back to Step 1 of the process).
DISCUSSION AND POLICY IMPLICATIONS We have shown how exploratory analysis can provide insights to support an adaptive policy process. The entire process for adaptive ISA implementation can now be summarized in the flow chart in Fig. 8. 1. Specifying the policy problem – The first step involves the specification of the system model, objectives, constraints, and available policy options. The criteria of success are also defined in this step. 2. Assembling a basic policy -- The next step is to select a basic policy that meets the specified criteria, for example, that it should be small scale, adaptable, and robust across a wide range of uncertain circumstances. The selection of a basic policy is supported by exploratory analysis as depicted in Fig. 5. 3. Specifying the rest of the policy – Vulnerabilities of the basic policy need to be identified. In our case it will fail if the level of user acceptance (LC) is low. The decrease in LC is detected by the increase in mean speed in traffic. Exactly what corrective actions are available and the threshold value of the increase in mean speed are determined by exploratory analysis (see Fig. 6 and Table 6 respectively). 4. Learning from real world experience – Implementation of the basic policy enables decision makers to learn about the behavior of the system by collecting data on the speed profile, accident risk, and change in mean speed of the total traffic (see Fig. 7). As a result, some of the uncertainties are resolved (initial user acceptance of ISA and the functional relationship between speed and accident risk). Having resolved some of the uncertainties, decision makers will have enough information to adjust the basic policy to benefit from higher user acceptance or limit the effect of lower acceptance than expected. A monitoring system with signposts 13
should then be put into place to continuously monitor changes in mean speed. Another important use of exploratory analysis is to help identify the threshold value of the change in mean speed that would signal the need for a policy change. 5. Adapting the policy: If no change in mean speed is detected, the current policy will be maintained. However, when change occurs, depending on the size and direction of the change, the current policy may need to be adapted, either to correct deficiencies in the policy or to capitalize on the improved level of acceptance. The exploratory analysis results provide decision makers with clear guidance for policy adaptation. 1. Specifying the policy problem Specify objectives, constraints, and policy options
Establish criteria of success
5. Adapting the policy Correct the current policy
Yes
worse Need for Policy reassessment ?
Stay the course
No
2. Assembling a basic policy Specify criteria for basic policy
Select a basic policy
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better Change in mean speed?
Monitor change of mean speed to probe the change in level of acceptance
4. Learning from real world experience
3. Specifying rest of policy Identify vulnerabilities and establish signposts
Capitalize on the opportunities
Measure change of mean speed Implement the basic policy
Note: shaded box indicates the support of Exploratory Analysis method
Approximate the relation between speed and accident risk
Measure new speed profile of affected vehicle
Approximate level of acceptance (LC)
Figure 8: Flow chart of adaptive policymaking for ISA implementation The results from the exploratory analysis would support ISA policy implementation despite major uncertainties. Combined with the adaptive approach, uncertainties would be resolved over time. The difference between the adaptive approach and other approaches is that events or information that will lead to policy failures (vulnerabilities) are considered in advance. Appropriate responses can therefore be designed in advance and implemented when the monitoring system indicates the need for such actions. Therefore, although this approach might not deliver the optimum result (which would be possible if the future were known with certainty), it surely will reduce the costs associated with prediction errors in conditions of deep uncertainty (e.g., from external factors such as demographics and economy that will affect driving behavior and traffic density). When the population of young drivers increases, the implemented policy will become ineffective and will need to be adjusted again. But again, the change in policy will have been prepared for in advance, and again, the signposts can be used to indicate when the policy needs to be changed.
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What this means in practice is that analysts can explore all uncertainty dimensions in advance by doing computational experiments. The resulting database of solutions can then be stored and queried in assembling a basic policy. With the basic policy in place, data can be collected and analyzed to resolve some of the uncertainties (e.g., after one year’s data collection). Analysts will then have to carry out computational experiments again, but now with fewer uncertainties and with refined data, in order to establish a monitoring system and determine alternative policies. In our case, the extent of the reduction in uncertainty during the learning phase about system behavior depends on accurate measurement of mean speed and speed profiles. A study by Baruya [15] shows the possibility of getting 2-decimal accuracy for mean speed. Measurements will be facilitated by the fact that, under the basic policy, some vehicles will have ISA installed. Installing additional measuring devices will enable the required data to be collected. Further research is needed to refine and expand the approach described above: • Examining a more exhaustive set of policy options. • Using the entire population of drivers on all types of roads. • Experimenting with different variations in the mandatory and advisory systems for special conditions (fog, rain, accidents, etc). Also, using more refined scenarios (e.g., by time of day, day of the week). • Analyzing the impacts of a policy when each driver group has a varying level of compliance. • Refining the speed profile. In this paper, there are four variations of speed profile, which are rough representations. Additional research into refining the profiles will improve the plausibility of the range.
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