19 Oct model for Traffic management
EMA model for Traffic management
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Exploratory Modelling and Analysis (EMA) Workbench helps in developing the computational software on complex systems so that various possible outcomes can be derived at early stage. I would like to discuss few features in smart city policy making and develop a algorithm using EMA model in Python
Smart City policy Making: Must implement latest wireless technology and utilize various internet of thing (IoT) in its development. List of services to include in policy making are Transportation, Emergency Health services, Traffic management, Schools, Shopping centers, Crime Detection, Power and Water supply, Waste management.
Python Model on Traffic management: Traffic lights will change the signal light by collecting data from GPS. GPS will help in giving real-time data feed to traffic signals so that signals can go Green as per the traffic on each side of the Road. One pre-requisite is vehicles having GPS system. While manufacturing vehicles, GPS should be included in basic model of vehicles so as to reach smart city Goals
from __future__ import (absolute_import, print_function, division,
unicode_literals)
from ema_workbench import (Model, RealParameter, ScalarOutcome, ema_logging,
perform_experiments, GPS)
#traffic signal lights use x1, x2, x3 to print traffic light
def traffic_signal(x1=None, x2=None, x3=None):
return {‘y’: x1 * x2 + x3}
if __name__ == ‘__main__’:
ema_logging.LOG_FORMAT = ‘[%(name)s/%(levelname)s/%(processName)s] %(message)s‘
ema_logging.log_to_stderr(ema_logging.INFO)
model = Model(‘simpleModel’, function=traffic_signal) # instantiate the model
# Location, speedOfVehicle, NoOfVehicles depends on Live data collected from GPS
model.uncertainties = [Location(“x1”, -0.1, 10),
SpeedOfVehicles(“x2”, 0, 100)
NoOfVehicles(“x3”, 0, 500)]
# y would be Yellow, Green or Red
model.outcomes = [ScalarOutcome(‘y’)]
results = perform_experiments(model, 100)
Expected Result: Traffic signal turns to Green by calculating the number of vehicles (NoOfVehicles), SpeedOfVehicles, Location depending on the GPS data.
Reply 2:
EMA Workbench
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EMA supports the design of workbench experiments, experiments that support parallel processing and results analysis. One key design principle is that people can do EMA on regular computers, without resorting to HPC.EMA Workbench software can be used to develop a model to help create a policy for a Smart City for traffic light placement in the city.
EMA is useful when there is relevant information that can be exploited by architectural models, but this information is not sufficient to specify a single model then we can use set of single models. In many situations, multiple models can be constructed that correspond to the available information .
Important steps in EMA are
1) conceptualize the decision problem and associated uncertainties;
2) develop a set of fast and easily manageable models of the interest system;
3) State the uncertainties that will be explored. Subsequently, depending on the purpose of applying the EMA, several further steps are possible. Depending on the specific application or EMA usage, different next steps are possible. In the case of open exploration with the aim of identifying the variability of the dynamics represented by the models and associated uncertainties,
4) produces a series of computational experiments,
5) Execute these experiments and through various visualization techniques.
6) Focus on possible types of dynamics.
In other scientific fields interested in model-based decision support, there are consequences that can be summarized using Samples for Systematic Exploratory Use. Exploratory Modeling and Analysis (EMA) aims to make systematic findings based on models and directional searches. In this paper we have tried to explain and explain what SD and EMA look like combined to address the major social challenges characterized by their dynamic complexity and profound uncertainty. A systematic search is needed to address such problems.Various hypotheses regarding the structure of the model, the parameterization of the model, and the types of behavior dynamics that occur, as well as the input uncertainties about the proposed searches.
By considering all the above points and analysis we can design any type system by using EMA.
References:
Agusdinata, D. (2008). Exploratory Modeling and Analysis. A Promising Method to Deal with Deep Uncertainty. Phd dissertation, Delft University of Technology, Delft. 3, 4
Albrechts, L. (2004). Strategic (spatial) planning reexamined. Environment and Planning B:Planning and Design 31 (5), 743–758. 14
Reply 3 :
Using EMA Workbench to develop a model to help create a traffic light placement policy
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Exploratory Modeling and Analysis (EMA) is a research methodology that uses computational experiments to analyze complex and uncertain systems (Bankes, 1993). That is, exploratory modeling aims at offering computational decision support for decision making under deep uncertainty and Robust decision making. The Exploratory Modeling and Analysis (EMA) Workbench is an evolving set of tools and methods.
The policy I am trying to create is traffic light placement in smart cities. The features I would use for modeling will be, No of accidents when the traffic signal is 160 ft away from the stop sign, No. of accidents when it is placed 120 ft away, no. of accidents when placed 40 ft away and finally when placed 10 ft away.
My model will have the above-mentioned features with their upper limit and lower limit as model uncertainties. It will have average accidents as the outcome. The perform_experiments function in the EMA Workbench will help us execute the model with the random values in the respective upper and lower range and provide a number of accidents for each placement distance of the signal. Based on this model we can formulate a policy to place the traffic signals.
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