测绘通报 ›› 2019, Vol. 0 ›› Issue (9): 99-103.doi: 10.13474/j.cnki.11-2246.2019.0294

• 技术交流 • 上一篇    下一篇

基于公交大数据的满载率推算方法——以深圳市为例

闻帅, 黄正东   

  1. 深圳大学智慧城市研究院, 广东 深圳 518060
  • 收稿日期:2019-03-14 出版日期:2019-09-25 发布日期:2019-09-28
  • 作者简介:闻帅(1991-),男,硕士,主要研究方向为GIS、城市公交。E-mail:oddo2012@163.com
  • 基金资助:
    深圳市科技创新委员会基础研究(学科布局)项目(JCYJ20170412105839839);国家自然科学基金(41271396)

Bus load factor estimation based on transit big data: a case study of Shenzhen

WEN Shuai, HUANG Zhengdong   

  1. Research Institute for Smart Cities, Shenzhen University, Shenzhen 518060, China
  • Received:2019-03-14 Online:2019-09-25 Published:2019-09-28

摘要: 城市可持续发展需要提升公共交通的供给能力。公汽满载率是公交规划、调度和服务评价等方面的重要参数。在公交信息化水平不断提升的背景下,由公交IC卡数据和公共汽车GPS数据等构成的公交大数据为获得相对精确的客流提供了可能。虽然已有相对稳定的OD推算方法,但对于公汽满载率的研究尚不够充分。本文提出基于历史公交大数据的大规模公交出行链搜素算法,在此基础上构建公共汽车满载率数据库,并以深圳市为例揭示了高满载率线路段的时空分布特征。本文研究对于揭示公汽服务整体水平和探测关键公交廊道具有较大价值。

关键词: 公交大数据, 公汽满载率, 公交出行链, IC卡数据, 深圳市

Abstract: Sustainable urban development needs to improve the supply capacity of public transport. The load factor is an important parameter in bus planning, dispatching and service evaluation. The level of public transit information provision has been improved. With the availability of transit IC card data and bus GPS data, it is possible to obtain relatively accurate information of passenger flow. Although relatively stable OD estimation methods have been suggested, there is still research gap on bus load factor. In this paper, a large-scale public transit trip chain search algorithm based on historical public transit big data is proposed, and a bus load factor database is constructed. Taking Shenzhen as an example, this paper reveals the spatial and temporal distribution characteristics of high load factor road sections. The research has great value for revealing the overall level of bus service and detecting key bus corridors.

Key words: transit big data, bus load factor, public transit trip chain, IC card, Shenzhen

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