Research on Planning and Model Design of Micro-cycle Bus Line Based on Metro Station Connection

Qiushui Fang (Guangdong lingnan Pass co., LTD.,Guangzhou, 510000, China)
Zhiming Li (Guangdong lingnan Pass co., LTD.,Guangzhou, 510000, China)
Zhen Wang (Guangdong lingnan Pass co., LTD.,Guangzhou, 510000, China)
Jincheng Wu (Guangdong lingnan Pass co., LTD.,Guangzhou, 510000, China)
Hongling Yu (Guangdong lingnan Pass co., LTD.,Guangzhou, 510000, China)
Mengtian Leng (Guangdong lingnan Pass co., LTD.,Guangzhou, 510000, China)

Article ID: 996



Public transport coverage fails to keep pace with urbanization and urban expansion, which makes the “last kilometer" problem of residents’ travel increasingly prominent”. However, the practice has proved that microcirculation public transportation plays an important role in expanding the coverage of public transportation and promoting the integration of public transportation. Therefore, this paper takes a city bus community as an example. Firstly, it analyses the bus travel demand of commuters connecting to the subway station during the early workday rush hours on basis of IC Big Data, obtains candidate stations of microcirculation bus lines through K-means clustering. Secondly, it establishes the model, the target of which is to minimize the cost residents' travel and bus operation, under the limited condition of walking distance, passenger number, station spacing and departure frequency. Finally, the genetic algorithm is used to find the optimal solution of the model, so it’s no doubt that the most feasible circular bus route is obtained. The results have positive significance for promoting the construction and operation of public transport integration and promoting the convenience and efficiency of public transport travel.


Microcirculation Bus; Route planning; IC big data; GA

Full Text:



[1] Kuah G K, Perl J.A methodology for feeder-bus network design[M]. 1987.

[2] Avishai Ceder. Public Transit Planning and Operation-Theory, modeling and Practice[M]. Beijing: Tsinghua University Press.

[3] Ngamchai S, Lovell DJ. Optimal Time Tansfer in Bus Transit Route Network Design Using a Genetic Algorithm[J]. Journal of Transportion Engineering Asce, 2003, 129(5): 510-521.

[4] Zhang S L, Yuan Z Z,Cao Z CH. Study on locating setting of community shuttle feeder URT based on the user and operator costs. Journal of Beijing Jiaotong University (Nature Science), 2016, 40(6): 57-63.

[5] Xie C, Wang AN Q. A Case Study on the Optimization of Local Shuttle Bus System in Beijing. Journal of Beijing Union University( Humanities and Social Sciences) , 2016, 14(3): 118-124.

[6] Lu Q J, Chen Z P, Zhang L J. Multi-sources and Multi-destinations Route Search in Community Bus Service Based on ant Colony Algorithm.Journal of Hangzhou Dianzi University (Nature Sciences), 2016, 36(3): 84-88.

[7] Zhou X D, Kuang K, Liang C Y. Study on bus microcirculation network design under the block system. Journal of Transport Science and Engineering, 2017(4): 57-63.

[8] Chen J, Lv Y K,Cui M L. Estimation alighting stops of smart card public transportation passengers based on travel patterns[J] Xi’an Univ. of Arch.& Tech. (Natual Science Edition), 2018(01): 23-29.

[9] Hu J H, Gao L X, Liang J X. An inference method of public transit OD matrix based on Traffic big data. Science Technology and Engineering, 2017, 17(11): 309-314.


  • There are currently no refbacks.
Copyright © 2019 Author(s)

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.