公交浮动车辆到站时间实时预测模型

孙棣华; 赖云波; 廖孝勇; 赵敏; 刘卫宁

doi:10.19818/j.cnki.1671-1637.2011.02.014

公交浮动车辆到站时间实时预测模型

doi: 10.19818/j.cnki.1671-1637.2011.02.014

孙棣华^{1, 2,},
赖云波^{1, 2},
廖孝勇^{1, 2},
赵敏^{1, 2},
刘卫宁^{2, 3}

1.
重庆大学自动化学院, 重庆 400044
2.
重庆大学信息物理社会可信服务计算教育部重点实验室, 重庆 400044
3.
重庆大学计算机学院, 重庆 400044

基金项目:

高等学校博士学科点专项科研基金项目 20090191110022

详细信息

作者简介:
孙棣华(1962-), 男, 重庆人, 重庆大学教授, 工学博士, 从事智能交通系统与计算机集成制造研究

中图分类号: U491.17
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- 文章访问数: 563
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- 被引次数: 0
出版历程
- 收稿日期: 2010-12-15
- 刊出日期: 2011-04-25

Real-time prediction model of arrival time for floating transit vehicle

SUN Di-hua^{1, 2
,},
LAI Yun-bo^{1, 2},
LIAO Xiao-yong^{1, 2},
ZHAO Min^{1, 2},
LIU Wei-ning^{2, 3}

1.
School of Automation, Chongqing University, Chongqing 400044, China
2.
Key Laboratory of Dependable ServiceComputing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044, China
3.
School of Computer Science, Chongqing University, Chongqing 400044, China

More Information

Author Bio:
SUN Di-hua(1962-), male, professor, PhD, + 86-23-65106953, d3sun@163.com

摘要

摘要: 根据公交浮动车辆实时GPS数据, 考虑不同时段的路段平均速度、公交车站、信号灯等多因素的影响, 建立了一种新的公交车辆到站时间预测模型。通过估计到达下游最临近站点的时间和判断道路上GPS数据的有效性等方法, 改善了预测模型的精度, 并应用重庆公交车辆数据对模型进行验证。计算结果表明: 该模型能够实时预测公交浮动车辆到达下游站点的时间, 预测精度优于现有方法, 在高峰时段预测误差小于9%, 在非高峰时段预测误差约为6%, 并对各种道路交通条件具有较好的适应性。
- 公共交通 /
- 公交浮动车辆 /
- 到站时间 /
- 路段平均速度 /
- 实时预测
Abstract: According to the real-time GPS data of floating transit vehicle, the effects of road section average speeds, bus stations and traffic lights were considered, and a novel prediction model of bus arrival time was proposed.The precision of prediction model was improved by estimating arrival time to the nearest downstream station and judging the effectiveness of GPS data on raod section.The prediction model was tested and verified by using the transit vehicle GPS data of Chongqing City.Calculation result indicates that the arrival time of floating transit vehicle to the downstream station can be real-time predicted by using the model, the prediction precision is better than the existing methods, the prediction error is less than 9% in peak periods and is about 6% in non-peak periods, and the prediction model has good adaptability to the various of traffic conditions.
- public transport /
- floating transit vehicle /
- arrival time /
- road section average speed /
- real-time prediction

HTML全文

图 1 公交车辆运行过程

Figure 1. Running process of transit vehicle

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图 2 公交车辆到达下游站点模型

Figure 2. Model of transit vehicle arrive at downstream stations

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图 3 863线路

Figure 3. No.863 route

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图 4 时间散点

Figure 4. Time scattered plots

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图 5 非高峰时段到站时间比较

Figure 5. Comparison of entering times in non-peak periods

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图 6 高峰时段到站时间比较

Figure 6. Comparison of entering times in peak periods

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图 7 非高峰时段到达时间误差比较

Figure 7. Error comparison of entering times in non-peak periods

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图 8 高峰时段到达时间误差比较

Figure 8. Error comparison of entering times in peak periods

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表 1 进站速度

Table 1. Speeds of entering station km·h^-1

表 2 出站速度

Table 2. Speeds of leaving station km·h^-1

表 3 站点特征

Table 3. Characteristics of stations

参考文献(12)

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