A master thesis from Aalborg University
Travel Time and Fuel Consumption Optimization in Vehicle Routing under Fuzzy Congestion: A study of dynamic traffic routing with congestion levels calculated by applying fuzzy logic
[Travel Time and Fuel Consumption Optimization in Vehicle Routing under Fuzzy Congestion]
Author(s)
Term
4. term
Education
Publication year
2023
Submitted on
2023-06-02
Abstract
In research, there is increasing interests in both efficient vehicle routing and quantified definitions of traffic congestion. As congestion can have a significant impact on most common routing parameters, especially travel time and fuel consumption, it could be beneficial to incorporate the traffic congestion into vehicle routing. As such, we define a fuzzy inference system to determine the level of congestion from the average speed and traffic density on a given road segment. The traffic congestion is then used to define penalties applied to the objective parameter in a Dijkstra's algorithm. The objective parameter to be minimized will be either fuel consumption or travel time, both described by a function of speed. The use of weighted moving average to forecast the input values of the routing is also investigated. The tests indicates that utilizing fuzzy determined traffic congestion can indeed improve the accuracy of routing algorithms, though when it comes to forecasting, there might be better options than the weighted moving average.
Keywords
Documents
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