Demand Response Transit Scheduling Research Based on Urban and Rural Transportation Station Optimization
Summary. Researchers developed a clustering algorithm combining DBSCAN and K-means to optimize demand-responsive transit routes between urban and rural areas. Testing in Henan Province, China, the system reduced operating costs by 9.5% and running time by 9.0% compared to regional flexible buses. The approach preprocesses passenger demand and optimizes station locations to improve service efficiency while promoting urban-rural integration.
Cite this article
Li, P., Jiang, L., Zhang, S., & Xi, J.. (2022). Demand Response Transit Scheduling Research Based on Urban and Rural Transportation Station Optimization. Sustainability. https://doi.org/10.3390/su142013328
Li, Peiqing, et al. “Demand Response Transit Scheduling Research Based on Urban and Rural Transportation Station Optimization.” Sustainability, 2022. https://doi.org/10.3390/su142013328.
Li, Peiqing, Longlong Jiang, Shunfeng Zhang, and Jiang Xi. 2022. “Demand Response Transit Scheduling Research Based on Urban and Rural Transportation Station Optimization.” Sustainability. https://doi.org/10.3390/su142013328.
@article{li-2022-demand-response-transit-scheduling-research,
title = {Demand Response Transit Scheduling Research Based on Urban and Rural Transportation Station Optimization},
author = {Peiqing Li and Longlong Jiang and Shunfeng Zhang and Jiang Xi},
journal = {Sustainability},
year = {2022},
doi = {10.3390/su142013328},
url = {https://doi.org/10.3390/su142013328}
}
TY - JOUR TI - Demand Response Transit Scheduling Research Based on Urban and Rural Transportation Station Optimization AU - Peiqing Li AU - Longlong Jiang AU - Shunfeng Zhang AU - Jiang Xi JO - Sustainability PY - 2022 DO - 10.3390/su142013328 UR - https://doi.org/10.3390/su142013328 ER -
Details
- DOI
- 10.3390/su142013328
- Countries
- China
- Regions
- Asia
- Categories
- transportation, regional-innovation-systems
- Added
- 2026-04-28