Abstract
With the improvement of people's living standards and the desire to pursue quality of travel, the number of people who choose self-guided itineraries is increasing sharply. However, the problem is that planning routes during the trip are exhausting, and the efficiency of transportation. In response to this and considering the psychology of people making the most of their holidays along with current boom in chain travel, the paper proposes a frame and an algorithm that aims to optimize the travel route. This paper studies route planning for chain travel of city groups within a specific range based on cluster analysis. The algorithm refers to the solution of TSP, knowledge in Graph Theory, and is suitable for optimizing travel itineraries for multiple cities for several days, characterized by providing users with time and money-saving options. It reduces waste on the road, waiting times for travelers, and provides convenience for self-guided itineraries.
| Original language | English |
|---|---|
| Title of host publication | CICTP 2020 |
| Subtitle of host publication | Advanced Transportation Technologies and Development-Enhancing Connections - Proceedings of the 20th COTA International Conference of Transportation Professionals |
| Editors | Haizhong Wang, Heng Wei, Lei Zhang, Yisheng An |
| Publisher | American Society of Civil Engineers (ASCE) |
| Pages | 2107-2117 |
| Number of pages | 11 |
| ISBN (Electronic) | 9780784482933 |
| DOIs | |
| State | Published - 2020 |
| Externally published | Yes |
| Event | 20th COTA International Conference of Transportation Professionals: Advanced Transportation Technologies and Development-Enhancing Connections, CICTP 2020 - Xi'an, China Duration: 14 Aug 2020 → 16 Aug 2020 |
Publication series
| Name | CICTP 2020: Advanced Transportation Technologies and Development-Enhancing Connections - Proceedings of the 20th COTA International Conference of Transportation Professionals |
|---|
Conference
| Conference | 20th COTA International Conference of Transportation Professionals: Advanced Transportation Technologies and Development-Enhancing Connections, CICTP 2020 |
|---|---|
| Country/Territory | China |
| City | Xi'an |
| Period | 14/08/20 → 16/08/20 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 11 Sustainable Cities and Communities
Keywords
- Chain travel
- City groups
- Multi-objective programming
- TSP
- Transportation big data
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