Demand Response Transit Scheduling Research Based on Urban and Rural Transportation Station Optimization

Peiqing Li (), Longlong Jiang, Shunfeng Zhang and Xi Jiang
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Peiqing Li: School of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
Longlong Jiang: School of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
Shunfeng Zhang: School of Transportation, Southeast University, Nanjing 310058, China
Xi Jiang: School of Transportation, Southeast University, Nanjing 310058, China

Sustainability, 2022, vol. 14, issue 20, 1-17

Abstract: To reduce the operating cost and running time of demand responsive transit between urban and rural areas, a DBSCAN K-means (DK-means) clustering algorithm, which is based on the density-based spatial clustering of applications with noise (DBSCAN) and K-means clustering algorithm, was proposed to cluster pre-processing and station optimization for passenger reservation demand and to design a new variable-route demand responsive transit service system that can promote urban–rural integration. Firstly, after preprocessing the reservation demand through DBSCAN clustering algorithm, K-means clustering algorithm was used to divide fixed sites and alternative sites. Then, a bus scheduling model was established, and a genetic simulated annealing algorithm was proposed to solve the model. Finally, the feasibility of the model was validated in the northern area of Yongcheng City, Henan Province, China. The results show that the optimized bus scheduling reduced the operating cost and running time by 9.5% and 9.0%, respectively, compared with those of the regional flexible bus, and 4.5% and 5.1%, respectively, compared with those of the variable-route demand response transit after K-means clustering for passenger preprocessing.

Keywords: demand response transit; DBSCAN clustering; K-means clustering; genetic simulated annealing algorithm; station optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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