Abstract
| Original language | English |
|---|---|
| Title of host publication | 2019 23rd International Conference on Mechatronics Technology (ICMT) |
| Publisher | IEEE |
| Pages | 1-6 |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-7281-3998-2 |
| ISBN (Print) | 978-1-7281-3999-9 |
| DOIs | |
| Publication status | Published - 16 Dec 2019 |
| Externally published | Yes |
| Event | 23rd International Conference on Mechatronics Technology - Salerno, Italy Duration: 23 Oct 2019 → 26 Oct 2019 http://www.icmt2019.org/ |
Conference
| Conference | 23rd International Conference on Mechatronics Technology |
|---|---|
| Abbreviated title | ICMT |
| Country | Italy |
| City | Salerno |
| Period | 23/10/19 → 26/10/19 |
| Internet address |
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Bibliographical note
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Keywords
- autonomous vehicles
- intelligent transportation systems
- fleet management
Cite this
An Evolutionary Approach to the Optimisation of Autonomous Pod Distribution for Application in an Urban Transportation Service. / Woodman, Roger; Hill, W.; Birrell, S.; Higgins, M. D.
2019 23rd International Conference on Mechatronics Technology (ICMT). IEEE, 2019. p. 1-6.Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding
}
TY - GEN
T1 - An Evolutionary Approach to the Optimisation of Autonomous Pod Distribution for Application in an Urban Transportation Service
AU - Woodman, Roger
AU - Hill, W.
AU - Birrell, S.
AU - Higgins, M. D.
N1 - © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
PY - 2019/12/16
Y1 - 2019/12/16
N2 - For autonomous vehicles (AVs), which when deployed in urban areas are called “pods”, to be used as part of a commercially viable low-cost urban transport system, they will need to operate efficiently. Among ways to achieve efficiency, is to minimise time vehicles are not serving users. To reduce the amount of wasted time, this paper presents a novel approach for distribution of AVs within an urban environment. Our approach uses evolutionary computation, in the form of a genetic algorithm (GA), which is applied to a simulation of an intelligent transportation service, operating in the city of Coventry, UK. The goal of the GA is to optimise distribution of pods, to reduce the amount of user waiting time. To test the algorithm, real-world transport data was obtained for Coventry, which in turn was processed to generate user demand patterns. Results from the study showed a 30% increase in the number of successful journeys completed in a 24 hours, compared to a random distribution. The implications of these findings could yield significant benefits for fleet management companies. These include increases in profits per day, a decrease in capital cost, and better energy efficiency. The algorithm could also be adapted to any service offering pick up and drop of points, including package delivery and transportation of goods.
AB - For autonomous vehicles (AVs), which when deployed in urban areas are called “pods”, to be used as part of a commercially viable low-cost urban transport system, they will need to operate efficiently. Among ways to achieve efficiency, is to minimise time vehicles are not serving users. To reduce the amount of wasted time, this paper presents a novel approach for distribution of AVs within an urban environment. Our approach uses evolutionary computation, in the form of a genetic algorithm (GA), which is applied to a simulation of an intelligent transportation service, operating in the city of Coventry, UK. The goal of the GA is to optimise distribution of pods, to reduce the amount of user waiting time. To test the algorithm, real-world transport data was obtained for Coventry, which in turn was processed to generate user demand patterns. Results from the study showed a 30% increase in the number of successful journeys completed in a 24 hours, compared to a random distribution. The implications of these findings could yield significant benefits for fleet management companies. These include increases in profits per day, a decrease in capital cost, and better energy efficiency. The algorithm could also be adapted to any service offering pick up and drop of points, including package delivery and transportation of goods.
KW - autonomous vehicles
KW - intelligent transportation systems
KW - fleet management
U2 - 10.1109/ICMECT.2019.8932138
DO - 10.1109/ICMECT.2019.8932138
M3 - Conference proceeding
SN - 978-1-7281-3999-9
SP - 1
EP - 6
BT - 2019 23rd International Conference on Mechatronics Technology (ICMT)
PB - IEEE
ER -