I'm using SUMO for (indeed) simulating traffic in a large-scale environment.
I would like to simulate different traffic densities and I noticed that the "--scale" option allows scaling the amount of traffic. However, this value and how it affects the simulation is still obscure. What is the bound of this value? How precisely traffic is scaled? Is there a precise explanation of how this works?
thanks :)
The --scale option really scales the whole demand by the factor. So if the factor is 2 whenever a vehicle is going to be inserted by the input definitions two vehicles will be inserted instead. For values < 1 it gets interpreted a s a probability so --scale 0.5 means a 50% chance that the vehicle will be skipped. In theory there is no limit to the value you give here but if the street is full no further vehicles will be inserted immediately and they pile up in the insertion buffer instead. This means they get inserted later when there is sufficient space.
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I'm simulating drivers with Eclipse SUMO using TraCi. I have a road segment of 1Km (highway) that contains five lanes: two right lanes that are leading to a right turn (exit), and three left lanes that are leading straight. It seems that drivers that want to go right will stand on the right lanes regardless of the queue size, resulting in two lanes that are very slow, and beside them, three lanes that can have a speed of 120 Km/h.
In reality, some drivers are not waiting in the 1 Km queue, but deciding to jostle to the queue in the middle, while other might regret standing in the queue and decide to go straight instead. This is resulting in a slow down on the three left lanes i.e., it is rare to have two adjacent lanes
(lanes 2 and 3 from the right on this case) with a 100 km/h speed difference (safety reasons)
My question is if a parameter that limits the speed ratio between two adjacent lanes exists, or if there is another way to simulate this kind of behavior, as I could not find any.
There are two different behaviors you propose as alternative to standing in the queue. The first one is changing the lane later which you might achieve by reducing the eagerness to do a strategic lane change with the lcStrategic parameter.
The second idea is to change the route and/or the destination. To change the route automatically you can enable the rerouting device for the vehicles. This will work only if there really is a faster route (taking the jam into account) in your network which leads to the same destination. Another possibility is to employ rerouters to set new routes or destinations. You can define a probability here but it is not possible to let this depend on the size / delay of the jam .
I'm developing an Android application where we use the OBDII to read car's engine parameters. Currently we are obtaining speed (kmh), engine's RPM and mass airflow live while driving the car.
We now have to find a way how using these parameters we are able to obtain from OBDII determine which gear is set at the moment.
I thought about just specifying that for instance given RPM level and given speed the car is driving on a particular gear, but I thing it will not do the trick.
Maybe some of you have some experience in such a field - I will be very helpful for any help!
Without any prior physics knowledge (i.e. knowing if there is any formula to calculate the gear from a set of input parameters), you could make use of the area of data mining.
You just calculate a lot of data (including gear!) and then check, if it is possible to find a formula including the channels you think might be relevant, that gives you the correct gear often enough (how often is your choice, might be 90% or 99%).
Apart from this, I'd say that it is quite hard to find a formula valid for each car with your input parameters (engine rpm, air mass flow, speed in km/h). The problem is, that the actual speed in km/h is also dependant on the wheel size and such stuff. And I do not know if all transmissions use the same transmission ratios (probably not, because we have transmissions with 5 gears and such with 6 gears). Thus, measuring your rpm at the engine might result in totally different rpm's on the wheel which might have totally different diameters per car.
So for the future users dealing with the same problem:
It is sufficient to divide the current speed by the current RPM actually. By doing so, driving on a particular gear you will get a constant value (+- some small mistake value).
It means that if you have 5 gears in your car, you can ask the user to calibrate the device on each gear meaning that while user is driving on a parcitular gear (does not matter on which RPM/speed level) diving the current speed by current RPM will give you a constant value. If you count this constant using different "levels" of RPMs and speed on a particular gear, it will give you some small mistake value (lets say mistake will be 0,001). So for example, on 2nd gear you will get constant = 0,02 +- 0,001. Defining such a constant value on each gear you can almost certainly classify on which gear you are driving at the moment by simply dividing the current speed by current RPM and checking, for which gear's constant value (+- mistake value) that value fits.
I tested it - it works perfectly fine ;).
We're building a GIS interface to display GPS track data, e.g. imagine the raw data set from a guy wandering around a neighborhood on a bike for an hour. A set of data like this with perhaps a new point recorded every 5 seconds, will be large and displaying it in a browser or a handheld device will be challenging. Also, displaying every single point is usually not necessary since a user can't visually resolve that much data anyway.
So for performance reasons we are looking for algorithms that are good at 'reducing' data like this so that the number of points being displayed is reduced significantly but in such a way that it doesn't risk data mis-interpretation. For example, if our fictional bike rider stops for a drink, we certainly don't want to draw 100 lat/lon points in a cluster around the 7-Eleven.
We are aware of clustering, which is good for when looking at a bunch of disconnected points, however what we need is something that applies to tracks as described above. Thanks.
A more scientific and perhaps more math heavy solution is to use the Ramer-Douglas-Peucker algorithm to generalize your path. I used it when I studied for my Master of Surveying so it's a proven thing. :-)
Giving your path and the minimum angle you can tolerate in your path, it simplifies the path by reducing the number of points.
Typically the best way of doing that is:
Determine the minimum number of screen pixels you want between GPS points displayed.
Determine the distance represented by each pixel in the current zoom level.
Multiply answer 1 by answer 2 to get the minimum distance between coordinates you want to display.
starting from the first coordinate in the journey path, read each next coordinate until you've reached the required minimum distance from the current point. Repeat.
In a soccer game, I am computing a steering force using steering behaviors. This part is ok.
However, I am looking for the best way to implement simple 2d human locomotion.
For instance, the players should not "steer" (or simply add acceleration computed from steering force) to its current velocity when the cos(angle) between the steering force and the current velocity or heading vectors is lower than 0.5 because it looks as if the player is a vehicule. A human, when there is an important change of direction, slows down and when it has slowed enough, it starts accelerating in the new direction.
Does anyone have any advice, ideas on how to achieve this behavior? Thanks in advance.
Make it change direction very quickly but without perfect friction. EG super mario
Edit: but feet should not slide - use procedural animation for feet
This is already researched and developed in an initiative called "Robocup". They have a simulation 2D league that should be really similar to what you are trying to accomplish.
Here's a link that should point you to the right direction:
http://wiki.robocup.org/wiki/Main_Page
Maybe you could compute the curvature. If the curvature value is to big, the speed slows down.
http://en.wikipedia.org/wiki/Curvature
At low speed a human can turn on a dime. At high speed only very slight turns require no slowing. The speed and radius of the turn are thus strongly correlated.
How much a human slows down when aiming toward a target is actually a judgment call, not an automatic computation. One human might come to almost a complete stop, turn sharply, and run directly toward the target. Another human might slow only a little and make a wide curving arc—even if this increases the total length to the target. The only caveat is that if the desired target is inside the radius of the curve at the current speed, the only reasonable path is to slow since it would take a wide loop far from the target in order to reach it (rather than circling it endlessly).
Here's how I would go about doing it. I apologize for the Imperial units if you prefer metric.
The fastest human ever recorded traveled just under 28 mph. Each of your human units should be given a personal top speed between 1 and 28 mph.
Create a 29-element table of the maximum acceleration and deceleration rates of a human traveling at each whole mph in a straight line. It doesn't have to be exact--just approximate accel and decel values for each value. Create fast, medium, slow versions of the 29-element table and assign each human to one of these tables. The table chosen may be mapped to the unit's top speed, so a unit with a max of 10mph would be a slow accelerator.
Create a 29-element table of the sharpest radius a human can turn at that mph (0-28).
Now, when animating each human unit, if you have target information and must choose an acceleration from that, the task is harder. If instead you just have a force vector, it is easier. Let's start with the force vector.
If the force vector's net acceleration and resultant angle would exceed the limit of the unit's ability, restrict the unit's new vector to the maximum angle allowed, and also decelerate the unit at its maximum rate for its current linear speed.
During the next clock tick, being slower, it will be able to turn more sharply.
If the force vector can be entirely accommodated, but the unit is traveling slower than its maximum speed for that curvature, apply the maximum acceleration the unit has at that speed.
I know the details are going to be quite difficult, but I think this is a good start.
For the pathing version where you have a target and need to choose a force to apply, the problem is a bit different, and even harder. I'm out of ideas for now--but suffice it to say that, given the example condition of the human already running away from the target at top stpeed, there will be a best-time path that is between on the one hand, slowing enough while turning to complete a perfect arc to the target, and on the other hand stopping completely, rotating completely and running straight to the target.
I'm using GPS units and mobile computers to track individual pedestrians' travels. I'd like to in real time "clean" the incoming GPS signal to improve its accuracy. Also, after the fact, not necessarily in real time, I would like to "lock" individuals' GPS fixes to positions along a road network. Have any techniques, resources, algorithms, or existing software to suggest on either front?
A few things I am already considering in terms of signal cleaning:
- drop fixes for which num. of satellites = 0
- drop fixes for which speed is unnaturally high (say, 600 mph)
And in terms of "locking" to the street network (which I hear is called "map matching"):
- lock to the nearest network edge based on root mean squared error
- when fixes are far away from road network, highlight those points and allow user to use a GUI (OpenLayers in a Web browser, say) to drag, snap, and drop on to the road network
Thanks for your ideas!
I assume you want to "clean" your data to remove erroneous spikes caused by dodgy readings. This is a basic dsp process. There are several approaches you could take to this, it depends how clever you want it to be.
At a basic level yes, you can just look for really large figures, but what is a really large figure? Yeah 600mph is fast, but not if you're in concorde. Whilst you are looking for a value which is "out of the ordinary", you are effectively hard-coding "ordinary". A better approach is to examine past data to determine what "ordinary" is, and then look for deviations. You might want to consider calculating the variance of the data over a small local window and then see if the z-score of your current data is greater than some threshold, and if so, exclude it.
One note: you should use 3 as the minimum satellites, not 0. A GPS needs at least three sources to calculate a horizontal location. Every GPS I have used includes a status flag in the data stream; less than 3 satellites is reported as "bad" data in some way.
You should also consider "stationary" data. How will you handle the pedestrian standing still for some period of time? Perhaps waiting at a crosswalk or interacting with a street vendor?
Depending on what you plan to do with the data, you may need to supress those extra data points or average them into a single point or location.
You mention this is for pedestrian tracking, but you also mention a road network. Pedestrians can travel a lot of places where a car cannot, and, indeed, which probably are not going to be on any map you find of a "road network". Most road maps don't have things like walking paths in parks, hiking trails, and so forth. Don't assume that "off the road network" means the GPS isn't getting an accurate fix.
In addition to Andrew's comments, you may also want to consider interference factors such as multipath, and how they are affected in your incoming GPS data stream, e.g. HDOPs in the GSA line of NMEA0183. In my own GPS controller software, I allow user specified rejection criteria against a range of QA related parameters.
I also tend to work on a moving window principle in this regard, where you can consider rejecting data that represents a spike based on surrounding data in the same window.
Read the posfix to see if the signal is valid (somewhere in the $GPGGA sentence if you parse raw NMEA strings). If it's 0, ignore the message.
Besides that you could look at the combination of HDOP and the number of satellites if you really need to be sure that the signal is very accurate, but in normal situations that shouldn't be necessary.
Of course it doesn't hurt to do some sanity checks on GPS signals:
latitude between -90..90;
longitude between -180..180 (or E..W, N..S, 0..90 and 0..180 if you're reading raw NMEA strings);
speed between 0 and 255 (for normal cars);
distance to previous measurement matches (based on lat/lon) matches roughly with the indicated speed;
timedifference with system time not larger than x (unless the system clock cannot be trusted or relies on GPS synchronisation :-) );
To do map matching, you basically iterate through your road segments, and check which segment is the most likely for your current position, direction, speed and possibly previous gps measurements and matches.
If you're not doing a realtime application, or if a delay in feedback is acceptable, you can even look into the 'future' to see which segment is the most likely.
Doing all that properly is an art by itself, and this space here is too short to go into it deeply.
It's often difficult to decide with 100% confidence on which road segment somebody resides. For example, if there are 2 parallel roads that are equally close to the current position it's a matter of creative heuristics.