A clustering-metaheuristic-simulation approach to determine air taxi operating site location
Urban Air Mobility (UAM) can improve the transportation service offered in urban areas by potentially solving traffic congestion, thereby allowing customers to travel more efficiently across any city. This research primarily focuses on determining optimal locations for establishing electric vertical...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Transportation Research Interdisciplinary Perspectives |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590198225000090 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823864274047467520 |
|---|---|
| author | Varshini Priyaa Senthilnathan Mohanapriya Singaravelu Suchithra Rajendran Sharan Srinivas |
| author_facet | Varshini Priyaa Senthilnathan Mohanapriya Singaravelu Suchithra Rajendran Sharan Srinivas |
| author_sort | Varshini Priyaa Senthilnathan |
| collection | DOAJ |
| description | Urban Air Mobility (UAM) can improve the transportation service offered in urban areas by potentially solving traffic congestion, thereby allowing customers to travel more efficiently across any city. This research primarily focuses on determining optimal locations for establishing electric vertical take-off and landing (eVTOL) air taxi infrastructure sites in urban cities using a three-phased approach. In Phase-1, Clustering Large Applications (CLARA) is developed to determine the potential set of air taxi infrastructure facilities. Next, an integrated metaheuristic-simulation approach is developed, in which the Genetic Algorithm (GA) model determines the sites to be opened, and based on this information, a simulation model (phase-3) is used to determine the routing-specific performance measures. We specifically consider New York City (NYC) as a case study and test the proposed approach using millions of estimated air taxi demands from prior studies. The results indicate that the number of operating stations required for efficient air taxi services is five (Central Park, JFK International Airport, lower Manhattan, Columbia University and Bronx), with an average customer time in system being about 32 min and a waiting time of 13 min per customer. |
| format | Article |
| id | doaj-art-c33ebf0c6aa541bf8a65ca6597f0026c |
| institution | Kabale University |
| issn | 2590-1982 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Transportation Research Interdisciplinary Perspectives |
| spelling | doaj-art-c33ebf0c6aa541bf8a65ca6597f0026c2025-02-09T05:01:19ZengElsevierTransportation Research Interdisciplinary Perspectives2590-19822025-01-0129101330A clustering-metaheuristic-simulation approach to determine air taxi operating site locationVarshini Priyaa Senthilnathan0Mohanapriya Singaravelu1Suchithra Rajendran2Sharan Srinivas3National Institute of Technology, Tiruchirappalli, Tamil Nadu 620015, IndiaDepartment of Computer Science, University of California, Irvine, USADepartment of Industrial and Systems Engineering, University of Missouri Columbia, MO 65211, USA; Department of Marketing, University of Missouri Columbia, MO 65211, USA; Corresponding author at: Department of Industrial and Systems Engineering, University of Missouri Columbia, MO 65211, USA.Department of Industrial and Systems Engineering, University of Missouri Columbia, MO 65211, USA; Department of Marketing, University of Missouri Columbia, MO 65211, USAUrban Air Mobility (UAM) can improve the transportation service offered in urban areas by potentially solving traffic congestion, thereby allowing customers to travel more efficiently across any city. This research primarily focuses on determining optimal locations for establishing electric vertical take-off and landing (eVTOL) air taxi infrastructure sites in urban cities using a three-phased approach. In Phase-1, Clustering Large Applications (CLARA) is developed to determine the potential set of air taxi infrastructure facilities. Next, an integrated metaheuristic-simulation approach is developed, in which the Genetic Algorithm (GA) model determines the sites to be opened, and based on this information, a simulation model (phase-3) is used to determine the routing-specific performance measures. We specifically consider New York City (NYC) as a case study and test the proposed approach using millions of estimated air taxi demands from prior studies. The results indicate that the number of operating stations required for efficient air taxi services is five (Central Park, JFK International Airport, lower Manhattan, Columbia University and Bronx), with an average customer time in system being about 32 min and a waiting time of 13 min per customer.http://www.sciencedirect.com/science/article/pii/S2590198225000090Urban Air Mobility (UAM)Clustering Large Applications (CLARA)Genetic Algorithm (GA)Simulation |
| spellingShingle | Varshini Priyaa Senthilnathan Mohanapriya Singaravelu Suchithra Rajendran Sharan Srinivas A clustering-metaheuristic-simulation approach to determine air taxi operating site location Transportation Research Interdisciplinary Perspectives Urban Air Mobility (UAM) Clustering Large Applications (CLARA) Genetic Algorithm (GA) Simulation |
| title | A clustering-metaheuristic-simulation approach to determine air taxi operating site location |
| title_full | A clustering-metaheuristic-simulation approach to determine air taxi operating site location |
| title_fullStr | A clustering-metaheuristic-simulation approach to determine air taxi operating site location |
| title_full_unstemmed | A clustering-metaheuristic-simulation approach to determine air taxi operating site location |
| title_short | A clustering-metaheuristic-simulation approach to determine air taxi operating site location |
| title_sort | clustering metaheuristic simulation approach to determine air taxi operating site location |
| topic | Urban Air Mobility (UAM) Clustering Large Applications (CLARA) Genetic Algorithm (GA) Simulation |
| url | http://www.sciencedirect.com/science/article/pii/S2590198225000090 |
| work_keys_str_mv | AT varshinipriyaasenthilnathan aclusteringmetaheuristicsimulationapproachtodetermineairtaxioperatingsitelocation AT mohanapriyasingaravelu aclusteringmetaheuristicsimulationapproachtodetermineairtaxioperatingsitelocation AT suchithrarajendran aclusteringmetaheuristicsimulationapproachtodetermineairtaxioperatingsitelocation AT sharansrinivas aclusteringmetaheuristicsimulationapproachtodetermineairtaxioperatingsitelocation AT varshinipriyaasenthilnathan clusteringmetaheuristicsimulationapproachtodetermineairtaxioperatingsitelocation AT mohanapriyasingaravelu clusteringmetaheuristicsimulationapproachtodetermineairtaxioperatingsitelocation AT suchithrarajendran clusteringmetaheuristicsimulationapproachtodetermineairtaxioperatingsitelocation AT sharansrinivas clusteringmetaheuristicsimulationapproachtodetermineairtaxioperatingsitelocation |