Simulation of traffic scenarios using SUMO

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SUMO-based simulations of Bengaluru’s Electronic City run for about one hour across low/moderate/high traffic and implement deadlock detection, emission detection, and speed detector placement. Stress is a linear combo of five weighted parameters (halted vehicles, CO2, occupancy, queue length, waiting time). Making five traffic lights adaptive reduces mean travel time and waiting time most in high traffic; one junction’s pollutants drop from ~24,000 mg to much lower and fall in rank. Code is open-sourced and available via PyPI.

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" chapters (20)

1. 0:00Traffic in India
2. 0:47Traffic in E-City
3. 1:22Adaptive Traffic Lights
4. 1:37Introduction
5. 1:56Traffic Scenarios
6. 2:07Traffic Properties
7. 2:18Stress function
8. 3:30Metrics in Action
9. 5:01Use Cases
10. 6:16Simple Interventions
11. 6:34Python Library
12. 6:59Traffic Interventions - Algorithm
13. 7:55Mean Travel Time
14. 8:23Mean Waiting Time
15. 8:48Stress vs Time
16. 9:32Emission Analysis
17. 11:03Speed Camera Placement
18. 11:57Real World Data: Yulu bikes
19. 12:39Placing Yulu bike stands
20. 13:24Future Plans