Chapter 5 discusses decision making using system modeling. The author briefly mentions an open source software tool, EMA Workbench, that can perform EMA and ESDMA modeling. Find EMA Workbench online and go to their main website (not the GitHub download site). Then do the following: 1) Under documentation, go to the Tutorials page. 2) Read through the Simple Model (in your chosen environment), and the Mexican Flu example. 3) Decide how you could use this software to create a model to help in developing a policy for a Smart City. To complete this assignment, you must do the following: A) Create a new thread. As indicated above, explain how you could use the EMA Workbench software to develop a model to help create a policy for a Smart City. Explain what policy you are trying to create (i.e. traffic light placement, surveillance camera coverage, taxi licenses issued, etc.), and what key features you would use in your model. Then, explain how EMA Workbench would help you. NOTE: keep your models and features simple. You don’t really need more than 2 or 3 features to make your point here. Note:attaching chapter 5 doc, pls ref

Using the EMA Workbench software, we can develop a model to help create a policy for a Smart City. One potential policy we could consider is the placement of traffic lights. The goal of the policy would be to optimize traffic flow and improve safety by strategically locating traffic lights in the city.

In the EMA Workbench, we can create a model that takes into account various factors that influence traffic flow, such as population density, road infrastructure, and current traffic patterns. These features would serve as inputs to the model, allowing us to simulate different scenarios and evaluate the impact of different traffic light placements.

To begin, we would define the relevant features in our model. For example, we could include the average number of vehicles on each road segment, the average speed of vehicles, and the average distance between intersections. By considering these features, we can estimate the current traffic conditions in the city.

Next, we would use the EMA Workbench to generate different scenarios by varying the placement of traffic lights. For each scenario, the model would simulate the flow of traffic and measure key performance indicators such as average travel time, congestion levels, and accident rates. By comparing the results of different scenarios, we can identify the traffic light placement that yields the best overall performance.

The EMA Workbench would greatly facilitate this process by allowing us to easily define and modify the model parameters. We can specify the range of possible traffic light locations and let the software run simulations to evaluate the impact of each option. In addition, the software provides visualization tools that help us analyze and interpret the results. We can generate visualizations of traffic patterns, congestion maps, and other relevant information to aid in decision making.

Moreover, the EMA Workbench supports sensitivity analysis and exploration of uncertainties. This means that we can assess how sensitive our model is to changes in the input parameters and account for uncertainties in real-world conditions. For example, we could introduce variations in traffic volume and observe how different traffic light placements perform in different scenarios. This feature allows us to assess the robustness of our policy recommendations and account for potential variations in the future.

In conclusion, the EMA Workbench software can be effectively utilized to develop a model for creating a policy on traffic light placement in a Smart City. By considering key features such as population density, road infrastructure, and current traffic patterns, the software allows us to simulate different scenarios and evaluate the impact of different traffic light placements. Its capabilities in parameter definition, scenario generation, and visualization greatly facilitate the decision-making process. Sensitivity analysis and the ability to account for uncertainties further enhance the robustness of our policy recommendations.