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

doi:10.13474/j.cnki.11-2246.2019.0294

Abstract

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

CLC Number:

P208

WEN Shuai, HUANG Zhengdong. Bus load factor estimation based on transit big data: a case study of Shenzhen[J]. 测绘通报, 2019, 0(9): 99-103.

References

[1] SHEN X, FENG S, LI Z, et al. Analysis of bus passenger comfort perception based on passenger load factor and in-vehicle time[J]. SpringerPlus, 2016, 5(1):62.
[2] TONG L C, ZHOU L, LIU J, et al. Customized bus service design for jointly optimizing passenger-to-vehicle assignment and vehicle routing[J]. Transportation Research Part C:Emerging Technologies, 2017, 85(12):451-475.
[3] 张桂芬, 沈伟, 刘杰. 智能公交信息采集系统的设计与实现[J]. 测绘通报, 2017(4):128-131,136.
[4] 刘欢, 李文权. 城市公交调度中满载率问题的研究[J]. 交通运输工程与信息学报, 2008, 6(4):104-109.
[5] 杨薇, 谭英嘉, 葛宏伟. 公交线路规划与服务评价中满载率指标的应用研究[J]. 公路, 2014(3):112-116.
[6] WARDMAN M, WHELAN G. Twenty years of rail crowding valuation studies:evidence and lessons from British experience[J]. Transport reviews, 2011, 31(3):379-398.
[7] TIRACHINI A, HENSHER D A, ROSE J M, et al. Crowding in public transport systems:effects on users, operation and implications for the estimation of demand[J]. Transportation research part A:Policy, 2013, 53(7):36-52.
[8] 蒋盛川, 孙轶凡, 杜豫川. 拥挤度对公共交通方式选择意愿的影响[J]. 同济大学学报(自然科学版), 2012, 40(12):1831-1835.
[9] BAGCHI M, WHITE P R. The potential of public transport smart card data[J]. Transport Policy, 2005, 12(5):464-474.
[10] 邬群勇, 苏克云, 邹智杰. 基于MapReduce的海量公交乘客OD并行推算方法[J]. 地球信息科学学报, 2018, 20(5):647-655.
[11] 杨万波, 王昊, 叶晓飞, 等. 基于GPS和IC卡数据的公交出行OD推算方法[J]. 重庆交通大学学报, 2015, 34(3):117-121.
[12] 胡郁葱, 梁杰荣, 梁枫明. 基于IC卡数据挖掘获取公交OD矩阵的方法[J]. 交通信息与安全, 2012, 30(4):66-70.
[13] 胡继华, 高立晓, 梁嘉贤. 基于交通大数据的公交线路OD矩阵推断方法[J]. 科学技术与工程, 2017, 17(11):309-314.
[14] ZHAO J. The planning and analysis implications of automated data collection systems:rail transit OD matrix inference and path choice modeling examples[J]. Environmental Science & Technology, 2004, 39(23):9309-9316.
[15] 李海波, 陈学武, 陈峥嵘. 基于公交IC卡和AVL数据的客流OD推导方法[J]. 交通信息与安全, 2015(6):33-39.
[16] BIRANT D, KUT A. ST-DBSCAN:an algorithm for clustering spatial-temporal data[J]. Data & Knowledge Engineering, 2007, 60(1):208-221.
[17] ESTER M, KRIEGEL H-P, SANDER J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise[C]//SIMOUDIS E, HAN J W, FAYYAD U, et al. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. Portland:AAAI Press, 1996:226-231.