In order to make the simulation more consist to the real road, we should reset some traffic parameters in SUMO,such as max vehical speed, Max acceleration, minGAP e.t.
Which parameters need to be set in SUMO? And how?
This is a very generic question and it really depends on what you want to model and which input data you have. For starters you could try to calibrate the vehicle maximum speed acceleration and deceleration from observation points. using an iterative optimization. This is described in detail in https://sumo.dlr.de/docs/Tutorials/Calibration/San_Pablo_Dam.html
The resulting parameters can then be put into the vType for your vehicle.
Related
I want to use a python Min-Cost Flow solver to be able to construct new networks. This means that I have an initial complete graph, with the vertices being either suppliers or having a demand. Using the algorithm should tell me, based on their costs, what edges will be used to settle all demands. Different to the existing problems, the cost of an edge when being used are not only described by a unit cost but also have an investment of this edge which is independent of the flow. I have been looking into the source code of networkx and or-tools but cannot figure out how to adapt these to implement the investment cost of the edges. Does someone have a better idea or can help me adapting the code?
Best Regards
Justus
You cannot solve this with a standard graph algorithm (eg: MinCostFlow).
Instead you need to formulate it as a Mixed Integer Program.
You can start with this example:
https://developers.google.com/optimization/assignment/assignment_mip
But you need to tweak it a little bit:
You need two classes of decision variables: invest_var (binary) and flow_var (continuous).
The objective will look like this:
min: sum(flow_cost[i,j]*flow_var[i,j]) + sum(invest_cost[i,j]*invest_var[i,j])
And you need to add an additional constraint for each link:
flow_var[i,j] <= BIG_INT * invest_var[i,j]
The purpose of these to constrain flow_var to 0 if invest_var is 0.
Demand and Supply constraints will be similar as in the example.
BIG_INT is a constant. You can set it as:
BIG_INT=max(flow_upper_bound[i,j])
Where flow_upper_bound is an upper bound on your flow_var variables.
Notice, that the problem now becomes a Mixed Integer Linear Program instead of just being a Linear Program.
Lack of High Schools in remote areas is a problem for students in developing country. Students in some locations are better than that in other. So, I have to find those locations. Now, the main problem is defining "BETTER". I have made some rules that will define the profile of a location.
Right now, I am concerned with the good students.
So, what I have done is-
1. Used some inferential statistics to and made some rules to come up with the conclusion that Location A,B,C,etc are the most potential locations where you can put the high schools because according to my rules these locations contain quality students.
I did all of the things above to label the data because I required to define "BETTER" and label the data so that I can now use machine learning algorithm to learn the factors which makes a location a potential location so that if I give a data point from test data to the model, it will instantly tell if the location is better or not.
Overview of the method:
For each location, I have these 4 information:
total_students_staying_for_high_school_education(A),
total_students_leaving_for_high_school_education_in_another_place(B),
mean_grade_point_of_students_of_type_B,
ratio (calculated as B/A),
For the location whose ratio > 1
I applied the chi-squared significance test to come up with a statistic which would tell me if students are leaving that place in significant amount than staying. I used ANOVA and then Tukey test to compare means_grade points and then find combinations of pairs of locations whose means vary and whose is greater than the others.
I then wrote a python program with a custom comparator that first compares if mean_grade of those points vary and returns the one with greater mean. If the means don't vary, the comparator return the location with the one whose chi-squared value is greater.
This is how, the whole process comes up with few suggestions of location and I call those location "BETTER".
What I am concerned about is-
1. How do I verify if my rules are valid? Or do I even need to verify it?
2. Most importantly, is mingling statistics with machine learning as described above an appropriate approach?Is there any major leakage in the method?Can anyone suggest a more general method?
I know that my question is general, but I'm new to AI area.
I have an experiment with some parameters (almost 6 parameters). Each one of them is independent one, and I want to find the optimal solution for maximum or minimum the output function. However, if I want to do it in traditional programming technique it will take much time since i will use six nested loops.
I just want to know which AI technique to use for this problem? Genetic Algorithm? Neural Network? Machine learning?
Update
Actually, the problem could have more than one evaluation function.
It will have one function that we should minimize it (Cost)
and another function the we want to maximize it (Capacity)
Maybe another functions can be added.
Example:
Construction a glass window can be done in a million ways. However, we want the strongest window with lowest cost. There are many parameters that affect the pressure capacity of the window such as the strength of the glass, Height and Width, slope of the window.
Obviously, if we go to extreme cases (Largest strength glass, with smallest width and height, and zero slope) the window will be extremely strong. However, the cost for that will be very high.
I want to study the interaction between the parameters in specific range.
Without knowing much about the specific problem it sounds like Genetic Algorithms would be ideal. They've been used a lot for parameter optimisation and have often given good results. Personally, I've used them to narrow parameter ranges for edge detection techniques with about 15 variables and they did a decent job.
Having multiple evaluation functions needn't be a problem if you code this into the Genetic Algorithm's fitness function. I'd look up multi objective optimisation with genetic algorithms.
I'd start here: Multi-Objective optimization using genetic algorithms: A tutorial
First of all if you have multiple competing targets the problem is confused.
You have to find a single value that you want to maximize... for example:
value = strength - k*cost
or
value = strength / (k1 + k2*cost)
In both for a fixed strength the lower cost wins and for a fixed cost the higher strength wins but you have a formula to be able to decide if a given solution is better or worse than another. If you don't do this how can you decide if a solution is better than another that is cheaper but weaker?
In some cases a correctly defined value requires a more complex function... for example for strength the value could increase up to a certain point (i.e. having a result stronger than a prescribed amount is just pointless) or a cost could have a cap (because higher than a certain amount a solution is not interesting because it would place the final price out of the market).
Once you find the criteria if the parameters are independent a very simple approach that in my experience is still decent is:
pick a random solution by choosing n random values, one for each parameter within the allowed boundaries
compute target value for this starting point
pick a random number 1 <= k <= n and for each of k parameters randomly chosen from the n compute a random signed increment and change the parameter by that amount.
compute the new target value from the translated solution
if the new value is better keep the new position, otherwise revert to the original one.
repeat from 3 until you run out of time.
Depending on the target function there are random distributions that work better than others, also may be that for different parameters the optimal choice is different.
Some time ago I wrote a C++ code for solving optimization problems using Genetic Algorithms. Here it is: http://create-technology.blogspot.ro/2015/03/a-genetic-algorithm-for-solving.html
It should be very easy to follow.
We've been trying to figure out why we only achieve writing speed of ~53MBps on UHS104 cards that claim 90MBps.
Due to hardware constraints, clock frequency supplied to the card is only 148.5 MHz (instead of 208MHz).
Does that mean that we should achieve speed of (148.5 * 4)/8 = 74.25MBps?
Or is our caclulation wrong since it assumes that if card guarantees speed of 90MBps on frequency of 208MHz, then it should guarantee speed of 74.25MBps on frequency of 148.5?
The simplified physical layer spec states that for maximum performance you need to write full AU blocks - usually 2 or 4 MByte, otherwise the card will have to copy data around internally when writing across block boundaries. Unfortunately, most of the Speed Class Specification is missing in the 4.13 chapter.
The first AUs may have a different wear level strategy, as they are normally used for the FATs. This could make them slower to write to.
Y
I have 6 parameters for which I know maxi and mini values. I have a complex function that includes the 6 parameters and return a 7th value (say Y). I say complex because Y is not directly related to the 6 parameters; there are many embeded functions in between.
I would like to find the combination of the 6 parameters which returns the highest Y value. I first tried to calculate Y for every combination by constructing an hypercube but I have not enough memory in my computer. So I am looking for kinds of markov chains which progress in the delimited parameter space, and are able to overpass local peaks.
when I give one combination of the 6 parameters, I would like to know the highest local Y value. I tried to write a code with an iterative chain like a markov's one, but I am not sure how to process when the chain reach an edge of the parameter space. Obviously, some algorythms should already exist for this.
Question: Does anybody know what are the best functions in R to do these two things? I read that optim() could be appropriate to find the global peak but I am not sure that it can deal with complex functions (I prefer asking before engaging in a long (for me) process of code writing). And fot he local peaks? optim() should not be able to do this
In advance, thank you for any lead
Julien from France
Take a look at the Optimization and Mathematical Programming Task View on CRAN. I've personally found the differential evolution algorithm to be very fast and robust. It's implemented in the DEoptim package. The rgenoud package is another good candidate.
I like to use the Metropolis-Hastings algorithm. Since you are limiting each parameter to a range, the simple thing to do is let your proposal distribution simply be uniform over the range. That way, you won't run off the edges. It won't be fast, but if you let it run long enough, it will do a good job of sampling your space. The samples will congregate at each peak, and will spread out around them in a way that reflects the local curvature.