I have a mobility model created by SUMO with area around 2 KM * 2 Km for real map.
I want to compute the results for only part of this model. I read that I can use roiroad or roirect.
Roirect take (x1,y1-x2,y2) as Traci coordination, however, I want to use roiroad to take exactly the cars in specific road.
My question is: if the roiroad function take a string of road name , from where in sumo that I can get this value.
should I construct the map again with Netconvert and using --output-street-names
Edges in SUMO always have an ID. It is stored in the id="..." attribute of the <edge> tag. If you convert a network from some other data format (say, OpenStreetMap) to SUMO's XML representation, you have the option to try and use an ID that closely resembles the road name the edge represents (this is the option you mentioned). The default is to allocate a numeric ID.
Other than by opening the road network XML file in a text editor, you can also find the edge ID by opening the network in the SUMO GUI and right clicking on the edge (or by enabling the rendering of edge IDs in the GUI).
Note that, depending on the application you simulate, you will need to make sure that you have no "gaps" in the Regions Of Interest (ROIs) you specify. When a vehicle is no longer in the ROI its corresponding node is removed from the network simulation. Even if the same vehicle later enters another (or the same) ROI, a brand new node will be created. This is particularly important when specifying edges as ROI (via the roiRoads parameter). Keep in mind that SUMO uses edges not just to represent streets, but also to represent lanes crossing intersections. If you do not specify these internal edges, your ROIs will have small gaps at every intersection.
Note also that up until OMNeT++ 5.0, syntax highlighting of the .ini file in the IDE will (mistakenly) display a string containing a # character as if it were a comment. This is just a problem with the syntax highlighting. The simulation will behave as expected. For example, setting the roiRoads parameter to "-5445204#1 :252726232_7 -5445204#2" in the Veins 4.4 example as follows...
...will result in a Veins simulation where only cars on one of the following three edges are simulated:
on the edge leading to the below intersection; or
on the edge crossing the below intersection; or
on the edge leaving the below intersection.
Related
I have a problem where I have a road that has multiple entry points and exits. I am trying to model it so that traffic can flow into an entry and go out the exit. The entry points also act as exits. All the entrypoints are labelled 1 to 10 (i.e. we have 10 entry and exits).
A car is allowed to enter and exit at any point however the entry is always lower number than the exit. For example a car enters at 3 and goes to 8, it cannot go from 3 to 3 or from 8 to 3.
After every second the car moves one unit on the road. So from above example the car goes from 3 to 4 after one second. I want to continuously accept cars at different entrypoints and update their positions after each second. However I cannot accept a car at an entry if there is already one present at that location.
All cars are travelling at the same speed of 1 unit per second and all are same size and occupy just the space at the point they are in. Once a car reaches its destination, its removed from the road.
For all new cars that come into the entrypoint and are waiting, we need to assign a waiting time. How would that work? For example it needs to account for when it is able to find a slot where it can be put on the road.
Is there an algorithm that this problem fits into?
What data structure would I model this in - for example for each entrypoints, I was thinking something like a queue or like an ordered map and for the road, maybe a linkedlist?
Outside of a top down master algorithm that decides what each car does and when, there is another approach that uses agents that interact with their environment and amongst themselves, with a limited set of simple rules. This often give rise to complex behaviors: You could maybe code simple rules into car objects, to define these interactions?
Maybe something like this:
emerging behavior algorithm:
a car moves forward if there are no cars just in front of it.
a car merges into a lane if there are no car right on its side (and
maybe behind that slot too)
a car progresses towards its destination, and removes itself when destination is reached.
proposed data structure
The data structure could be an indexed collection of "slots" along which a car moves towards a destination.
Two data structures could intersect at a tuple of index values for each.
Roads with 2 or more lanes could be modeled with coupled data structures...
optimial numbers
Determining the max road use, and min time to destination would require running the simulation several times, with varying parameters of the number of cars, and maybe variations of the rules.
A more elaborate approach would us continuous space on the road, instead of discrete slots.
I can suggest a Directed Acyclic Graph (DAG) which will store each entry point as a node.
The problem of moving from one point to another can be thought of as a graph-flow problem, which has a number of algorithms for determining movement in a graph.
I started (since about one week) using veins (4.4) under omnet++ (5.0).
My current task is to let vehicles adjust their transmission range according to a specific context. I did read a lot of asked questions like these ones (and in other topics/forums):
Dynamical transmission range in the ieee802.11p module
Vehicles Receive Beacon Messages outside RSU Range
How coverage distance and interference distance are affected by each other
Maximum transmission range vs maximum interference distance
Reduce the coverage area between vehicles
how to set the transmission range of a node under Veins 2.0?
My Question:
How to -really- change the transmission range of just some nodes?
From the links above, I knew that the term "transmission range", technically, is related to the received power, noise,sensitivity threshold, etc. which defines the probability of reception.
Since I am new to veins (and omnet++ as well), I did few tests and I concluded the following:
"TraCIMobility" module can adjust the nodes' parameters (for each vehicle, there is an instance) such as the ID, speed, etc.
I could, also, instantiate the "Mac1609_4" (for each vehicle) and changed some of its parameters like the "txPower" during simulation run-time but it had no effect on the real communication range.
I could not instantiate (because it was global) the "connection manager" module which was the only responsible of (and does override) the effective communication range. this module can be configured in the ".ini" file but I want different transmission powers and most importantly "can be changed during run-time".
The formula to calculate the transmission range is in the attached links, I got it, but it must be a way to define or change these parameters in one of the layers (even if it is in the phy layer, i.e., something like the attached signal strength...)
Again, maybe there is some wrong ideas in what I have said, I just want to know what/how to change this transmission range.
Best regards,
You were right to increase the mac1609_4.txPower parameter to have a node send with more power (hence, the signal being decodable further away). Note, however, that (for Veins 4.4) you will also need to increase connectionManager.pMax then, as this value is used to determine the maximum distance (away from a transmitting simulation module) that a receiving simulation module will be informed about an ongoing transmission. Any receiving simulation module further away will not be influenced by the transmission (in the sense of it being a candidate for decoding, but also in the sense of it contributing to interference).
Also note that transmissions on an (otherwise) perfectly idle channel will reach much further than transmissions on a typically-loaded channel. If you want to obtain a good measurement of how far a transmission reaches, have some nodes create interference (by transmitting broadcasts of their own), then look at how the Frame Delivery Rate (FDR) drops as distance between sender and receiver increases.
Finally, note that both 1) the noise floor and 2) the minimum power level necessary for the simulation module of a receiver to attempt decoding a frame need to be calibrated to the WLAN card you want to simulate. The values chosen in the Veins 4.4 tutorial example are very useful for demonstrating the concepts of Veins, whereas the values of more recent versions of Veins come closer to what you would expect from a "typical" WLAN card used in some of the more recent field tests. See the paper Bastian Bloessl and Aisling O'Driscoll, "A Case for Good Defaults: Pitfalls in VANET Physical Layer Simulations," Proceedings of IFIP Wireless Days Conference 2019, Manchester, UK, April 2019 for a more detailed discussion of these parameters.
I am just giving my opinion in case someone was already in my situation:
In veins (the old version that I am using is 4.4), the "connection manager" is the responsible for evaluating a "potential" exchange of packets, thus, its transmission power is almost always set to the upper-bound.
I was been confused after I changed the vehicles "Mac1609_4" transmission power and "graphically", the connection manager was still showing me that the packets are received by some far nodes which in fact was not the case, it was just evaluating whether it is properly received or not (via the formula discussed in the links above).
Thus: changing the "TxPower" of each vehicle had really an effect beside graphically (the messages were not mounted to the upper layers).
In sum, to make a transmission range aware scheme, this is what must be done:
In the sender node (vehicle), and similarly to the pointer "traci" which deals with the mobility features, a pointer to the "mac1609" must be created and pointed to it as follows:
In "tracidemo11p.h" add ->
#include "veins/modules/mac/ieee80211p/Mac1609_4.h"//added
#include "veins/base/utils/FindModule.h"//added
and as a protected variable in the class of "tracidemo11p" in the same ".h" file ->
Mac1609_4* mac;//added
In "tracidemo11p.cc" add ->
mac = FindModule<Mac1609_4*>::findSubModule(getParentModule());
now you can manipulate "mac" as in "traci", the appropriate methods are in the "modules/mac/ieee80211p/Mac1609_4.cc & .h"
for our work, the method will be:
mac->setTxPower(10);//for example
This will have an impact on the simulation in real-time for each node instance.
It may had described it with basic concepts because I am new to omnet-veins, these was done in less than one week (and will be provided for new users as well).
I hope it will be helpful (and correct)
I have a set of water meters for water consumers drawn up as geojson and visualized with ol3. For each consumer house i have their usage of water for the given year, and also the water pipe system is given as linestrings, with metadata for the diameter of each pipe section.
What is the minimum required information I need to be able to visualize/calculate the amount of water that passed each pipe in total of the year when the pipes have inner loops/circles.
is there a library that makes it easy to do the calculations in javascript.
Naive approach, start from each house and move to the first pipe junction and add the used mater measurement for the house as water out of the junction and continue until the water plant is reached. This works if there was no loops within the pipe system.
This sounds more like a physics or civil engineering problem than a programming one.
But as best I can tell, you would need time series data for sources and sinks.
Consider this simple network:
Say, A is a source and B and D are sinks/outlets.
If the flow out of B is given, the flow in |CB| would be dependent on the flow out of D.
So e.g. if B and D were always open at the same time, the total volume that has passed |CB| might be close to 0. Conversely, if B and D were never open at the same time the number might be equal to the volume that flowed through |AB|.
If you can obtain time series data, so you have concurrent values of flow through D and B, I would think there would exist a standard way of determining the flow through |CB|.
Wikipedia's Pipe Network Analysis article mentions one such method: The Hardy Cross method, which:
"assumes that the flow going in and out of the system is known and that the pipe length, diameter, roughness and other key characteristics are also known or can be assumed".
If time series data are not an option, I would pretend it was always average (which might not be so bad given a large network, like in your image) and then do the same thing.
You can use the Ford-Fulkerson algorithm to find the maximum flow in a network. To use this algorithm, you need to represent your network as a graph with nodes that represent your houses and edges to represent your pipes.
You can first simplify the network by consolidating demands on "dead-ends". Next you'll need pressure data at the 3 feeds into this network, which I see as the top feed from the 90 (mm?), centre feed at the 63 and bottom feed near to the 50. These 3 clusters are linked by a 63mm running down, which have the consolidated demand and the pressure readings at the feed would be sufficient to give the flowrate across the inner clusters.
I'm working on character recognition (and later fingerprint recognition) using neural networks. I'm getting confused with the sequence of events. I'm training the net with 26 letters. Later I will increase this to include 26 clean letters and 26 noisy letters. If I want to recognize one letter say "A", what is the right way to do this? Here is what I'm doing now.
1) Train network with a 26x100 matrix; each row contains a letter from segmentation of the bmp (10x10).
2) However, for the test targets I use my input matrix for "A". I had 25 rows of zeros after the first row so that my input matrix is the same size as my target matrix.
3) I run perform(net, testTargets,outputs) where outputs are the outputs from the net trained with the 26x100 matrix. testTargets is the matrix for "A".
This doesn't seem right though. Is training supposed by separate from recognizing any character? What I want to happen is as follows.
1) Training the network for an image file that I select (after processing the image into logical arrays).
2) Use this trained network to recognize letter in a different image file.
So train the network to recognize A through Z. Then pick an image, run the network to see what letters are recognized from the picked image.
Okay, so it seems that the question here seems to be more along the lines of "How do I neural networks" I can outline the basic procedure here to try to solidify the idea in your mind, but as far as actually implementing it goes you're on your own. Personally I believe that proprietary languages (MATLAB) are an abomination, but I always appreciate intellectual zeal.
The basic concept of a neural net is that you have a series of nodes in layers with weights that connect them (depending on what you want to do you can either just connect each node to the layer above and beneath, or connect every node, or anywhere in betweeen.). Each node has a "work function" or a probabilistic function that represents the chance that the given node, or neuron will evaluate to "on" or 1.
The general workflow starts from whatever top layer neurons/nodes you've got, initializing them to the values of your data (in your case, you would probably start each of these off as the pixel values in your image, normalized to be binary would be simplest). Each of those nodes would then be multiplied by a weight and fed down towards your second layer, which would be considered a "hidden layer" depending on the sum (either geometric or arithmetic sum, depending on your implementation) which would be used with the work function to determine the state of your hidden layer.
That last point was a little theoretical and hard to follow, so here's an example. Imagine your first row has three nodes ([1,0,1]), and the weights connecting the three of those nodes to the first node in your second layer are something like ([0.5, 2.0, 0.6]). If you're doing an arithmetic sum that means that the weighting on the first node in your "hidden layer" would be
1*0.5 + 0*2.0 + 1*0.6 = 1.1
If you're using a logistic function as your work function (a very common choice, though tanh is also common) this would make the chance of that node evaluating to 1 approximately 75%.
You would probably want your final layer to have 26 nodes, one for each letter, but you could add in more hidden layers to improve your model. You would assume that the letter your model predicted would be the final node with the largest weighting heading in.
After you have that up and running you want to train it though, because you probably just randomly seeded your weights, which makes sense. There are a lot of different methods for this, but I'll generally outline back-propagation which is a very common method of training neural nets. The idea is essentially, since you know which character the image should have been recognized, you compare the result to the one that your model actually predicted. If your model accurately predicted the character you're fine, you can leave the model as is, since it worked. If you predicted an incorrect character you want to go back through your neural net and increment the weights that lead from the pixel nodes you fed in to the ending node that is the character that should have been predicted. You should also decrement the weights that led to the character it incorrectly returned.
Hope that helps, let me know if you have any more questions.
My software receives information from several sensors. The number of sensors is not fixed - they can be added and removed, each sensor one has its own unique identifier. Sensors send data irregularly - they can keep silent for weeks or push data every second. Each sensor generates a value from a fixed set of values - so sensors are discrete. My program logs each message from each sensor into an SQL database table (sensorId, time, value).
The task is to filter the information. I need to select only one record from this log, which I'm considering to be the actual information. For example, if I get the latest record from a single sensor, which says that value is A, but before it 10 different sensors told me that the value is B, then I shall still consider B to be the actual information. At the same time the problem is not just the usual noise filtering, because if there was one sensor which told me for a month every second that value was C, and then five sensors recently tell that in fact the value is D, I shall immediately consider D to be the actual data despite long history - I want to say that the number of independent sources also must have weight.
So, I think I get a kind of a function of two variables - time (ageing) and the number of unique sensors in the moment of time. So, I think, I must somehow calculate the weight of each record and then just select the one with the biggest weight. And I suppose that to calculate the record weight I should use not only the information from current record, but information from all the previous ones.
I need some help with the algorithm. Maybe there is actually some well-known solution which I'm not aware of?