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Optimized Platoon Pathfinding for Ma...

Conner-Strunk, Jessica.

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Optimized Platoon Pathfinding for Maximized Fuel Savings.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimized Platoon Pathfinding for Maximized Fuel Savings./
作者:	Conner-Strunk, Jessica.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	25 p.
附註:	Source: Masters Abstracts International, Volume: 82-02.
Contained By:	Masters Abstracts International82-02.
標題:	Computer science. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27834445
ISBN:	9798662493490

Optimized Platoon Pathfinding for Maximized Fuel Savings.
Conner-Strunk, Jessica.

Optimized Platoon Pathfinding for Maximized Fuel Savings. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 25 p.

Source: Masters Abstracts International, Volume: 82-02.

Thesis (M.S.)--Southern Illinois University at Carbondale, 2020.

This item must not be sold to any third party vendors.

Fuel efficiency is an ever present problem in today's modern world. The United States in particular is in need of a solution to lowering greenhouse gas emissions caused by transit and freight across its spread out cities. In fact in the United States the average commute time of an individual is 26 minutes, meaning that round trip people are driving about an hour every day, to and from work. But that gas consumption is pittiling compared to that of the freight industry. Heavy Duty Vehicles (HDVs) commonly known as semi trucks, account for three quarters of US freight emissions and 7.5% of total US greenhouse gas emissions. But this can be cut down considerably with the implementation of platooning. Platooning is when multiple vehicles follow in close distance to reduce aerodynamic drag, causing significant fuel savings. In this paper, we will go over an algorithm to help vehicles join with already formed platoons on the road, increasing their fuel efficiency and therefore saving cost to the driver in addition to lessening the negative effect on the planet. This will be done using a modified A* algorithm. The base weight of zero will be the amount of gas the vehicle would consume taking the shortest path that google maps recommends, alone, with no platooning. Paths may end up with negative weights due to the fuel savings caused by joining existing platoons during the vehicles' travel. The algorithm will have access to a map of the roadways and the GPS data of nearby platoons. It will then perform a cost-benefit analysis to determine if the fuel savings from joining a platoon will outweigh the cost of going outside of its original path in order to join the other vehicles.

ISBN: 9798662493490Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Dijkstra

Optimized Platoon Pathfinding for Maximized Fuel Savings.
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520 $a Fuel efficiency is an ever present problem in today's modern world. The United States in particular is in need of a solution to lowering greenhouse gas emissions caused by transit and freight across its spread out cities. In fact in the United States the average commute time of an individual is 26 minutes, meaning that round trip people are driving about an hour every day, to and from work. But that gas consumption is pittiling compared to that of the freight industry. Heavy Duty Vehicles (HDVs) commonly known as semi trucks, account for three quarters of US freight emissions and 7.5% of total US greenhouse gas emissions. But this can be cut down considerably with the implementation of platooning. Platooning is when multiple vehicles follow in close distance to reduce aerodynamic drag, causing significant fuel savings. In this paper, we will go over an algorithm to help vehicles join with already formed platoons on the road, increasing their fuel efficiency and therefore saving cost to the driver in addition to lessening the negative effect on the planet. This will be done using a modified A* algorithm. The base weight of zero will be the amount of gas the vehicle would consume taking the shortest path that google maps recommends, alone, with no platooning. Paths may end up with negative weights due to the fuel savings caused by joining existing platoons during the vehicles' travel. The algorithm will have access to a map of the roadways and the GPS data of nearby platoons. It will then perform a cost-benefit analysis to determine if the fuel savings from joining a platoon will outweigh the cost of going outside of its original path in order to join the other vehicles.
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