Proposal (Part 2) 


ISA has it advantages, but on the other side there are uncertainties attached to the idea of implementing ISA.  It being a new technology not much is known about implementing it on a large scale and tests results on ISA shows reduction of accidents, emissions, improve traffic flow, etc. on the road but all these results are based on small scale implementation and simulation studies. These results give us an indication of performance of ISA but as there is no large scale implementation of ISA, which makes it difficult to understand the relation between ISA implementation and transport system performance & operation in real time. Also all these results are based on assumption of optimal technological performance of ISA with 60% to 100% penetration level of ISA. Also all the simulation and other results, which shows positive impact on safety are with 60% to 100% penetration level of the technology and there exists uncertainty related to attaining the required penetration level. There is also uncertainty related to the standards for implementing ISA. Uncertainty also exists related to the user acceptance and human behaviour [Human Machine Interface] towards the implemented system in the car.  There also exist economic [market scenario], organisational/political, legal and technological uncertainties. Also there are external factors that affect the system which are uncertain like transportation demand, fuel price, economic change, etc. Hence there are large uncertainties attached to implementation of ISA and it becomes important to deal with these uncertainties for smooth implementation of ISA as a policy option.  The consequence of making wrong policy decision by the government may have fatal effects on many others. Hence it becomes important to develop policies that would cope up with the fast changing information world.

The Problem with Traditional Policy Making

The traditional policy making is based on the assumption that the future can be predicted well enough to identify policies that will produce favourable outcomes in one or more specific plausible future world. The future worlds are called scenarios [10].  The process focuses on identifying uncertainties and the selection of policy/alternative is based on its performance across different scenarios, constructed considering different uncertainties [external factors] and their dimensions. If the policy produces desired outcomes across various plausible scenarios, the policy is considered robust and is then implemented.
The traditional policy making process is shown in figure.

The problems with traditional approach are:
•    The policy developed would be best for specific scenarios that are fairly certain not to occur, since any given scenario has a probability zero of actually occurring [10]. 
•    The policy is based on assumptions about the future and in a case where the assumptions fail or the uncertainty is overlooked in developing scenario, which might lead to the failure of the policy.
•    It is also very difficult to implement single policy that would perform successfully in fast changing world across all the plausible scenarios. Fixed policies can fail for particular scenarios because they fail to exploit opportunities that arise, ignore crucial vulnerabilities, or depend for their performance on critical assumptions that fail to hold [10]. 
•    More important, the resulting policy has implications for the future that actually occurs, that were probably not examined in the course of the analysis and that are generally not revisited as the future unfolds [10].
•    The scenario analysis is based on the trend explorations and the occurrence of trend breaking might lead to policy failure as there are fewer chances to incorporate the change in the existing policy that is implemented. This is because of limited monitoring, mainly on ex-post basis.
•    Participation of different stakeholders in decision making process is viewed as dysfunctional as they would slow down the decision making process [8]. Because of this there is possibility of opposition against the policy that is implemented, which might lead to reconsideration of the policy.

Current policy making is often characterized by a ‘sit and wait’ attitude in response to the uncertainties or by developing scenarios, allowing developments to be largely determined by market forces [8].  These policies don’t provide flexibility to the changing circumstances and don’t incorporate a plan for learning over time as part of decision making process.

Looking at the uncertainties related to implementing ISA and the shortcomings of traditional policy making for handling uncertainties by a static policy, it can be concluded that  traditional policy making approach can lead to policy failure. This is because as ISA is a new technology not much is know about it when implemented on large scale and the information will change continuously during implementation related to different aspects of ISA. Hence one static policy would not work when ISA is implemented as the policy has to be adapted to the changing information.

From the policy makers’ point of view, it is very essential to deal with uncertainties that exists in implementation of ISA, as to what policy actions or strategies to follow and adapted to deal with uncertainties. Hence the Adaptive Policy Making Approach is adopted in which policy is updated over the period of time with respect to the changes in the system and as new information is available. The basic difference between the traditional and adaptive policymaking approach is the way to deal with the uncertainties. Adaptive policy analysis is based on identifying in advance the uncertain conditions or events that could make the policy fail and specifying the actions to be taken in anticipation or in response to them. The process has a systematic approach for monitoring the environment, gathering information about the events that have happened and events that are yet to happen, implementing pieces of the policy over time and adjusting it to the new circumstances as the information about different developments and/or uncertainties are know in the future. The adaptive process looks both from the present to the future and from the future to the present, in order to develop ways of comparing where we are going to where we would like to go.
Hence the policies themselves would be designed to be incremental, adaptive and conditional [10].
(The adaptive approach has been developed by Warren Walker, Jonathan Cave and Adnan Rahman, RAND)

Hence knowing the uncertainties related to the implementation of ISA, shortcomings of traditional policy making to handle uncertainties and knowing that adaptive policymaking is a better approach to deal with uncertainties, the research is directed in developing an Adaptive Policy for implementing ISA.


“How can Adaptive Policy be developed to deal with vulnerabilities (uncertainties) in implementing ISA (as a policy option) in the existing Dutch Transportation System?”


An Adaptive Approach would be used in research for implementing ISA technology a policy option. The steps are explained below:

1. Literature Study:-    The literature study is done related to following topics – Intelligent Speed Adaptation, Intelligent Transportation System and Advanced Driver Assistance System, Dutch Transportation system and Adaptive Policy Making
2. Basic Analysis:-   System Model for Transportation System [causal model]. Actor and Network Analysis and Objective Analysis. This would help at various points in developing policy
3. Identify the objectives, constraints, policy option and definition of success:– In this step the stage setting is done    In this step of Adaptive policy making the objective is identified. As mentioned in the scope the objective would be related to traffic safety. The policy option is implementing ISA.  Actor Analysis would help us to know the interest and role of different actors participating in implementing ISA, which are set as the constraint of the policy and finally the definition of success is then identified.
4. The basic policy is assembled – necessary conditions of success is identified and policy actions are listed:-    In this step the basic policy is specified for implementation and the necessary conditions of success are identified considering the basic policy to be implemented. Causal model would help in identifying them the necessary conditions.
5. In this step vulnerabilities are identified and their mitigating and hedging actions are set:-   The vulnerabilities are identified considering the necessary conditions of success set in step 4. The mitigating and hedging actions identified for certain and uncertain vulnerabilities respectively. The actions are identified using the causal model and means end hierarchy.
6. In this step Sign posts and triggers are identified:-     The signposts are developed directly from then necessary conditions and for the vulnerabilities identified. The causal model and objective analysis will be used to identify the signposts. The triggers are the critical value of the signposts. Setting the trigger level would hep us in identifying the corrective and defensive action in the next step.
7. In this step corrective and defensive actions are identified along with reassessment conditions:-     The identification of these actions would help during the implementation phase as it would help to take relative actions as per the critical value of the trigger. The conditions of reassessment of the existing policy are also established in this step.


•    The outcomes of interest would be traffic safety. The analysis done during the study would be related to the specified outcome of interest.
•    The study would be restricted to the implementation of ISA technology as a policy option in Dutch transportation system.
•    The basic policy would not be developed for all the types of ISA but just for one type of ISA.
•    The study would be related to Dutch Transportation System and Ministry of Transport, Public Works and Water Management as the problem owner.
•    Only structural uncertainties are dealt while policy making and not the parametric or model uncertainties.
•    The screening of the policy actions is not considered in scope.
•    The values of the trigger level are not identified.


The adaptive approach would help to deal with the uncertainties for implementing ISA. It would give a better result while dealing with uncertainty as compared to approach to deal with uncertainty in usual policy analysis. Over a period of time, when the knowledge base is increased or improved about the behaviour of the real world, it would give better results to the outcomes of policy.






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