Traffic: Negative Emergence of Cars
Frank Hui, Charley Cai
Burnaby North Secondary
Floor Location : S 011 F
In our current traffic system, congestion has become a major problem and governments often cannot afford to construct more roads due to their high cost and maintenance fees. Furthermore, roads can lead to habitat segregation and environmental damage as large areas of landscape are cleared for their construction. As the transportation needs of humanity grows and with the increasingly problematic traffic congestions in cities, we set out to find the optimal and most efficient road system through simulated swarm intelligence.
The hypothesis was that the grid system would be the most efficient road system as it allows many possible routes to allow cars to bypass traffic congestion.
The experiment used a simulator that we coded, which allows users to create customs maps. Each car has an objective and uses a typical breadth-first path-finding algorithm to navigate to its set destination. Due to the nature of computerized simulations, we had the ability to monitor and control each individual variable very precisely. This allowed us to maintain a very high level of consistency between each one of the trials.
The grid system, orbital system and tributary trees were tested to see which one was the most efficient. Each system was simulated 25 times, each one for a total of 30 000 ticks. After each trial, all the conditions would be reset so as not to affect future trials. Time was also kept to keep track of the performance of each trial.
The results prove that the grid system is the most efficient road system as it had an average of 4.28 cars remaining at 30 000 ticks. In comparison, the orbital system had an average of 7.32 cars remaining and the tributary trees had 34.12 cars remaining. Observing the simulation, we could determine that the cars in the grid system had a variety of routes that allowed each individual car to have multiple paths at its disposal. Overall, this allowed the grid system to perform the best that supports our original hypothesis.