1. EMA workbench
Smart cities require smart policies manage them. Implementing smart policies has become easier due to entry of advanced research methodologies that provide actionable data that helps in decision making and policy design. I would like to create a model for surveillance cameras that cover the city. Key features for this model include that it should cover all major transport routes, traffic stops, major social spots such as restaurants and entertainment spots. Another feature would be the coverage should be about the ninetieth percentile taking into account that certain areas would be impossible to mount surveillance cameras due to reasons like lack of connection to electric grids (Kon et al., 2012).
In continuation, I would use exploratory modelling and analysis as a research methodology to help me design the implementation of the surveillance camera coverage policy. Some of the uncertainties of this policy include the effectiveness of locations chosen and angles of the cameras. Expected outcomes would be quality and usefulness of surveillance once the system is in place. EMA will compute this experiment and will produces all possible future outcomes (Kwakkel, 2015). Once the results are out analysis will be done and decisions will be made depending on which outcome is best and robust policies will be set.
2. Exploratory Modelling and Analysis
Perhaps the most doubtless way to implement a policy is through informed decision making. Having reliable and accurate information enables you to consider all the factors in play and helps you make long-lasting solutions that work effectively. With the steady improvement in technology and computational prowess, there are many ways to acquire actionable information. One such way is the exploratory modelling and analysis workbench. This method is designed to compute experiments that analyze systems known to be very uncertain or very complex such that normal methods are not effective (Pruyt, 2013).
Smart cities are being developed even as we speak. It is important to know that the policies that we operate by now will not work for these smart cities because they are more advanced. EMA can be used to develop and implement polices such as traffic light placement which will enable efficient traffic control for our smart cities. The key features for this model include strategic placement of the traffic lights and well interconnected traffic light for remote operation (Alvin et al., 2012). The outcome should be specified as smooth traffic flow in majority of road routes and uncertain factors could include the interconnection of these traffic lights via the internet. EMA will analyze our simulated system and hence produce results that will guide the decisions when designing the policy.
3. The Exploratory Modeling and Analysis workbench aims at providing support for doing EMA on models developed in different environments modeling as well as settings. The EMA workbench includes support for designing experiments, performing the operations comprising of support for parallel processing on both a single machine as well as on clusters. The EMA workbench is highly applicable in various areas; this paper will briefly explore the application EMA workbench in the creation of a smart city.
A smart city should be able to promote the utilization of technology, information as well as data which helps in enhancing as well as improvement of its services as well as infrastructure. A smart city should be free of traffic congestion, low crime rates, and should have a substantial number of vehicles. This paper will briefly explore some of the areas where EMA workbench can be applied in the implementation of smart city policy. The first area where I will apply EMA workbench in the creation of a smart city is in the placement of traffic lights. Implementation of traffic lights makes use of simple systems which have deep uncertainties. This helps in the application of traffic lights, particularly in emergency services. Placement of traffic light systems might require the utilization of devices such as the android technology which will be able to communicate traffic lights in the smart city; this will give room for the drivers to be able to optimize the response time, particularly in emergencies (Kwakkel, 2015).
The second area where EMA workbench is applicable in the creation of a policy for a smart city is in the placement of surveillance cameras. One of the best ways of reducing crime rates in a smart city is having surveillance cameras which are properly placed. Proper installation of surveillance cameras can be achieved through the utilization of model-based integrated decision support systems; this will be achieved through the implementation of information and communication technology which helps in improving the efficiency by monitoring activities in the smart city through the strategic placement of sensors which can be able to collect data. Lastly, EMA workbench is also applicable in the issuance of vehicle license in the city. This will ensure that only a substantial number of vehicles can be accommodated in the town, thus reducing commotion (Keller et al.2016).
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