When I run sumo simulation via command line, there's a line that reads vehicle TOT 12500 ACT 323 what does that mean?
I guess it has something to deal with current vehicles that is on the road.
I am simulating a large network and using randomTrip.py to generate trips and then using DUAROUTER to generate routes. I would like to take a look into the vehicles on the road to monitor whether are the routes generated feasible
TOT refers to the total number of vehicles which have been simulated in the current simulation run (including the vehicles which already arrived), ACT denotes the number vehicles currently in the network.
For having a look at the vehicles simply start sumo with gui (using the sumo-gui executable)
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I have been started to learn Opataplanner for sometime, I try to figure out a model design for my use case to progress the solution calculation, here is my case in real world in manufactory's production line:
There is a working order involved list of sequential processes
Each kind of machine can handle fixed types of processes.(assume machine quantity are enough)
The involved team has a number of available employees, each employee has the skills for set of processes with their different own working cost time
production line has fixed number of stations available
each station put one machine/employee or leave empty
question: how to design the model to calculate the maximum output of completion product in one day.
confusion: in the case, the single station will have one employee and one machined populated, and specified dynamic processed to be working on. but the input of factors are referred by each other and dynamic: employee => processes skill , process skill => machines
can please help to guide how to design the models?
Maybe some of the examples are close to your requirements. See their docs here. Specifically the task assignment, cheap time scheduling or project job scheduling examples.
Otherwise, follow the domain modeling guidelines.
I'm modifying the highway_overtake.py controller to be able to track the position and speed of the neighboring SUMO vehicles (the ones that are highlighted in transparent colored boxes). How can I access the SUMO vehicle information that is in close proximity of the Lincoln car? I want to get the position, acceleration, speed etc. of the SUMO cars highlighted near the Lincoln car. Here is what I have tried so far:
I have changed the 'supervisor' field to TRUE in the highway overtake world for the lincoln MKZ vehicle node.
I imported the Supervisor from controller in my higway_overtake.py controller file.
I tried calling the traci functions getIDList() and getPosition() but it gives an error that says controller doesn't have any such attributes.
I believe this information can be retrieved from the SumoSupervisor.py file which is the controller file for Sumo Interface. But I do not know how to access this file in my highway_overtake.py script either.
Any information that can help me solve this will be very helpful!
Thanks in advance.
If it's for debugging, you could use this supervisor function to get the positon of all cars in the simulation, and then figure out which one is closed to your target car (which ID you know).
https://cyberbotics.com/doc/guide/supervisor-programming#tracking-the-position-of-robots
Otherwise, if you want to use only functions that are available to actual machines, try the emitter/receiver devices. You might be able to use signal strength as a proxi for proximity, or, if there's a synced tick, use signal reception time as a proximity detector.
Or use GPS modules (with an high enough resolution) on each car and compare the coordinates.
When using SUMO to create a grid network, it seems we have to define route for different types of vehicles. But for a large grid network such as 10*10, it would be impossible to manually input the routes for different flow, especially when considering turning at intersections.
My goal is to have a large network, let flow run throught it with certain turning probabilities at intersections. Then I wish I could use traCI to control the signal lights.
There are a few ways as to how you could manage multiple routes:
Define trip and/or flow with to edge and from edge attributes. The DUAROUTER application will find the shortest route possible or the best route possible (if edge-weights are provided)
The above (point 1) can also be achieved if fromTaz/toTaz (Traffic Assignment Zones) are assigned
NOTE - For both point 1 and 2, the via attribute can force the vehicles to travel through a given edge or a given set of edges.
Another way to generate multiple routes is to generate the 10*10 network and to note down (in the program) all the connections (this is done so that SUMO does not throw any no connection errors). A simple program can be written in conjunction with TraCI, that turns the vehicle from a given edge to a different edge on any junction. Given that this will be time consuming, but if your focus is not on the overall simulation time, this approach will be the most apt for you.
Another way is to add rerouter devices on all edges leading to a junction. You can define new destinations and routes here. This will be the easiest solution for a large network.
I am looking for a machine learning strategy that will allow me to load in test data, along with a test outcome, and optimize for a particular scenario to adjust future testing parameters.
(See example in the edit)
Original example: For example, consider that I have a 3 dimensional space (environmental chamber) that I place a physical test device into. I will then select a number of locations and physical attributes with which to test the device. First I will select to test the device at every location configuration, across multiple temperatures, humidities, and pressures. At each test increment, or combination of variables, I log the value of each feature, e.g. x,y,z positional data, as well as the temperature, humidity, pressure, etc.. after setting these parameters I will initiate a software test on the physical device that is affected by the environmental factors in ways too complex for me to predict. This software test can output three outputs that vary with an unknown (until tested) probability based on the logged physical parameters. Of the three outputs, one is a failure, one is a success, and one is that the test finishes without any meaningful output (we can ignore this case in processing).
Once the test finishes testing every physical parameter I provide it, I would like to then run an algorithm on this test data to select the controllable parameters e.g. x,y,z positions, or temperature to maximize my chance of a successful test while also minimizing my chance at a failure (in some cases we find parameters that have a high chance at failure and a high chance at success, failures are more time expensive, thus we need to avoid them). The algorithm should use these to suggest an alternative set of test parameter ranges to initiate the next test iteration that it believes will get us closer to our goal.
Currently, I can maximize the success case by using an iterative decision tree and ignoring the results that end in failure.
Any ideas are appreciated
Edit:
Another example (this is contrived, lets not get into the details of PRNGS)-- Lets say that I have an embedded device that has a hardware pseudo random number generator (PRNG) that is affected by environmental factors, such as heat and magnetometer data. I have a program on the device that uses this PRNG to give me a random value. Suppose that this PRNG just barely achieves sufficient randomization in the average case, in the best case gives me a good random value, and in the worst case fails to provide a random value. By changing the physical parameters of the environment around the device I can find values which with some probability cause this PRNG to fail to give me a random number, give me an 'ok' random number, and which cause it to succeed in generating a cryptographically secure random number. Lets suppose in cases in which it fails to generate a good enough random number the program enters a long running loop trying to find one before ultimately failing, which we would like to avoid as it is computationally expensive. To test this, we first start off by testing every combination of variables in which we can control (temperature, position, etc..) perhaps by jumping several degrees at a time, to give a rough picture on what the device's response looks like over a large range. I would like then to run an algorithm on this test data, narrow my testing windows and iterate over the newly selected feature parameters to arrive at an optimized solution. In this case, since failures are expensive, the optimized solution would be something that minimizes failures, while simultaneously maximizing successes.
At work we perform demanding numerical computations.
We have a network of several Linux boxes with different processing capabilities. At any given time, there can be anywhere from zero to dozens of people connected to a given box.
I created a script to measure the MFLOPS (Million of Floating Point Operations per Second) using the Linpack Benchmark; it also provides number of cores and memory.
I would like to use this information together with the load average (obtained using the uptime command) to suggest the best computer for performing a demanding computation. In other words, its 3:00pm; I have a meeting in two hours; I need to run a demanding process: what node will get me the answer fastest?
I envision a script which will output a suggestion along the lines of:
SUGGESTED HOSTS (IN ORDER OF PREFERENCE)
HOST1.MYNETWORK
HOST2.MYNETWORK
HOST3.MYNETWORK
Such suggestion should favor fast computers (high MFLOPS) if the load average is low and, as load average increases for a given node, it should favor available nodes instead (i.e., I'd rather run in a slower computer with no users than in an eight-core with forty dudes logged in).
How should I prioritize? What algorithm (rationale) would you use? Again, what I have is:
Load Average (1min, 5min, 15min)
MFLOPS measure
Number of users logged in
RAM (installed and available)
Number of cores (important to normalize the load average)
Any thoughts? Thanks!
You don't have enough data to make an well-informed decision. It sounds as though the scheduling is very volatile: "At any given time, there can be anywhere from zero to dozens of people connected to a given box." So the current load does not necessarily reflect the future load of the machines.
To properly asses what hosts someone should use to minimize computation time would require knowing when the current jobs will terminate. If a powerful machine is about to be done doing most of its jobs, it would be a good candidate even though it currently has a high load.
If you want to guess purely on the current situation, you can do a weighed calculation to find out which hosts have the most MFLOPS available.
MFLOPS available = host's MFLOPS + (number of logical processors - load average)
Sort the hosts by MFLOPS available and suggest them in a descending order.
This formula assumes that the MFLOPS of a host is linearly related to its load average. This might not be exactly true, but it's probably fairly close.
I would favor the most recent load average since it's closer to the current/future situation, whereas, jobs from 15 minutes ago might have completed by now.
Have you considered a distributed approach to computation? Not all computations can be broken up such that more than one cpu can work on them. But perhaps your problem space can benefit from some parallelization. Have a look at Hadoop.
You don't need to know FLOPS. beowulf modules paralell computing center has I go to has the script for sure
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