The basic comparison I want to do is how fast they find shortest path when
there are many s-t queries and do rerouting (finding a new route) when
there are traffic jam in some edges.

On Tue, Feb 25, 2020 at 1:43 AM Jakob Erdmann <[email protected]> wrote:

> Given the distance/travelTime data for each edge, the shortest/fastest
> path in a typical network  is often unique and thus all algorithms reach
> the same result (manhatten grid networks are an obvious exception to this).
> Therefore it does not matter which algorithm you use as long as they use
> the same inputs.
> Thus, I was trying to understand what difference you would expect from
> using non-sumo algorithms.
>
> Am Di., 25. Feb. 2020 um 05:02 Uhr schrieb Raheleh Zarei <
> [email protected]>:
>
>> I am not sure if I understand the first paragraph of your email. The
>> algorithms are shortest/fastest path finders(I call them non-sumo
>> algorithms). Actually the thing I want to do is as follow:
>> -Import map of a city as road network
>> -Make some source and target points based on the map
>> -Run random number of source-target queries between these source and
>> target points and for every query (or in a time interval for example every
>> 20 second) get the current situation of the network (like the travel time
>> of edges because it might have changed due to heavy traffic in some edges)
>> and find the shortest path/fastest using non-sumo algorithms.
>> -calculate the path found (time/distance) by non-sumo algorithms for each
>> source-target query and also average of time-distance for the whole s-t
>> queries during simulation.
>>
>> Thanks for your help,
>> Raheleh
>>
>>
>>
>> On Mon, Feb 24, 2020 at 2:11 AM Jakob Erdmann <[email protected]>
>> wrote:
>>
>>> It depends on what these other algorithms are. If they are also
>>> shortest/fastest path finders that it would be sufficient to alter the
>>> weights/travel times assigned to the network edges (via TraCI or xml
>>> inputs) and keep using the sumo algorithms.
>>> You could also let these other algorithms compute their routes
>>> externally and then alter the routes in the simulation (via TraCI or xml
>>> inputs).
>>> regards,
>>> Jakob
>>>
>>> Am Mo., 24. Feb. 2020 um 02:58 Uhr schrieb Raheleh Zarei <
>>> [email protected]>:
>>>
>>>> Thanks for your explanation.
>>>> Yes, you are right. So, what you suggest to do if I want to compare
>>>> other routing algorithms with SUMO routing algorithms from traffic
>>>> efficiency point of view?
>>>> Do I need to develop SUMO source code and add other routing algorithms,
>>>> or there is another way I can call these algorithms inside SUMO.
>>>>
>>>> Thanks for your time and help.
>>>> Raheleh
>>>>
>>>> On Tue, Feb 18, 2020 at 3:57 PM Jakob Erdmann <[email protected]>
>>>> wrote:
>>>>
>>>>> Libsumo is useful if you need at lot of interaction between your code
>>>>> and sumo because it reduce communication overhead compared to TraCI.
>>>>> If you are comparing the algorithms in terms of overall traffic
>>>>> efficiency then most of the time is spent on simulation rather than 
>>>>> routing
>>>>> so you won't need Libsumo.
>>>>> If you are repeatedly calling the sumo routers to compare the found
>>>>> routes with your own routes then it makes more sense.
>>>>> For details, see https://sumo.dlr.de/docs/Libsumo.html
>>>>> regards,
>>>>> Jakob
>>>>>
>>>>> Am Mo., 17. Feb. 2020 um 10:19 Uhr schrieb Raheleh Zarei <
>>>>> [email protected]>:
>>>>>
>>>>>> Hello,
>>>>>>
>>>>>> I would like to test two routing algorithms using SUMO as a framework
>>>>>> for comparing these algorithms. Inside the program of these algorithms
>>>>>> (written in C++ in Linux), I need to call SUMO, start it, get the 
>>>>>> required
>>>>>> variables and reports and then close it and see the results. What do you
>>>>>> suggest for this? Do you think using Libsumo is a good idea? and if
>>>>>> yes what's the procedure of using it.
>>>>>>
>>>>>> Best,
>>>>>> Raheleh
>>>>>> --
>>>>>> *RAZ*
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>>>>
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