I would be thankful if you could answer the following queries:
1. How to measure the simulation default time? If the simulation runs for 600 time steps then is it equal to the actual 10 minutes? If so then why does the simulation of (4404) end in just a few minutes?
2. <flow id="f" type="type1" probability="0.3" from="E1" to="E1" begin="0" end="1000" /> Does it mean that after every second there is 0.3 probability that a vehicle will depart and this flow will end after 1000 seconds?
3. How can to create traffic congestion using flow.
3. I created one edge with two lanes and generated traffic using sumo repeated flow but why are the vehicles traveling only on the right lane?
4. For one flow I defined this setting "<vType id="type1" vClass="truck" maxSpeed="30" color="red" arrivalLane="E1"/>" but in the trip output file I noticed that the vehicles are not following the set arrivalLane and mingap. I want the vehicles to arrive at Edge 1 lane 1 not one edge 1 lane 0
5. If the sumo measures the speed in meters per second then how can we convert it into kilometers per hour while plotting a graph?
6. Plotting a graph, "plotXMLAttributes.py edge_data.xml -i id -x density -y speed -s --yfactor 3.6 -ylabel vehs/hour" gives this the below warning. What does it mean?
Warning: found idattr 'id' in element 'interval' (level 2) and element 'edge' (level 3). Using 'edge'.
7. <edgeData id="dump_15" freq="400" file="edge_data.xml" excludeEmpty="true"/> here freq=400 means that the edges is collected after each six minutes?
8. In the attached graph speed versus density (>plotXMLAttributes.py edge_data.xml -i id -x density -y speed -s --yfactor 3.6) what does density represent? Does it mean that at a certain interval if the density is 10 then the average speed of the vehicles is 44 km/h.
9. Please guide me on how can we calculate traffic efficiency? For example, if I say on a 2 km of road 1000 vehicles pass in a day due to congestion or delay but with a new approach, 1500 vehicles can pass in a day? How can we measure this kind of KPI.