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|Title:||Routing multiple vehicles in Singapore||Authors:||Lin, Jian Kai||Keywords:||DRNTU::Engineering::Computer science and engineering||Issue Date:||2015||Abstract:||With the continuous increase in the world population of motorized vehicles, which has exceeded 1 billion units in 2010. Severe tra c congestion is becoming a daily problem, faced by commuters in metropolitan cities. Its impacts on the commuters include but not limited to wastage of fuel, opportunity costs, and serious health issues. The purpose of this project is to ease tra c congestion and its impacts from the drivers' perspective. The proposed solution is a central routing algorithm that considers the overall tra c conditions, and assigns cooperative routes to all active vehicles. In order to determine the algorithm's performance, a simulation engine that involved numerous software components has been developed. The simulation engine allows user to execute di erent tra c scenarios, and capture the simulation data by changing con gurations of both tra c network and tra c model. A total of 6 simulations were executed in a grid environment, to identify the optimal performance of the cooperative algorithm with rerouting intervals of 10, 15, 20, 25, 30, 35 seconds. Each simulation was executed in a grid network that comprised 29 nodes and 88 edges, with a 12-hour tra c model that consisted of 3 peak periods (e.g. morning, lunch, evening rush hour). The same set of experiments was repeated for the Dijkstra's Algorithm to identify its optimal rerouting interval. A set of MATLAB scripts has been written to process the simulation data of best performance by each algorithm. The output diagrams have enabled comparison of the performances in relation to number of completed trips, mean travel time, fuel consumption, and CO emissions. The preliminary result of the experiments conducted in the grid network, had displayed positive e ects that cooperative algorithm possessed in reducing tra c congestion. Similarly, bene ts of cooperative algorithm were observed from the experiments conducted in the realistic environment. The con gured realistic network contained 53 nodes and 150 edges of di erent lengths. At the end of this project, a fully tested and functional simulation engine has been developed to support future work. The data processing capabilities of the components in the simulation engine have been optimized. This is achieved by employing appropriate data structures and creating Java classes, that has reduced computation time and simpli ed the source codes respectively. The next phase of this project is to execute large scale simulations on actual maps, which will involve massive level of computation in the hardware. One feasible solution is to host the routing algorithm on a cloud server, such as Amazon Web Service and Google Compute Engine.||URI:||http://hdl.handle.net/10356/62619||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Student Reports (FYP/IA/PA/PI)|
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