Routing multiple vehicles in Singapore
Date of Issue2017
School of Computer Science and Engineering
Traffic congestion is a major issue in most of the major metropolitan cities around the world with massive problems ranging from economic to environmental. All around the world various congestion control policies like road space rationing and congestion pricing, have been implemented to control congestion. With Singapore’s next generation ERP launching in 2020, it is speculated that every road will be charged. This project simulates the traffic situation in Singapore network with 30,000 randomly generated vehicles. Various pricing policies, like an ERP system, were applied and the overall network performance was analyzed. The results for shortest path routing, least travel time routing, least travel cost routing, and multi-agent routing are compared. For the multi-agent routing, the agents are split into distance sensitive, price sensitive, and time sensitive agents in a certain ratio. The simulations were performed using Simulation of Urban Mobility, and the tasks were automated using custom python scripts. Overall, it was observed that the network performance improved when each road was priced as it forced the majority of the agents to be price sensitive. Besides, the multi-agent routing performed better than the single-agent routing scenarios.
DRNTU::Engineering::Computer science and engineering
Final Year Project (FYP)
Nanyang Technological University