交通运输系统工程与信息

交通运输系统工程与信息 ›› 2017, Vol. 17 ›› Issue (2): 126-135.

• 系统工程理论与方法 • 上一篇    下一篇

贝叶斯框架下快速公交的站间运行状态推断

胡继华*1a, 2，梁嘉贤1b, 2   

  1. 1. 中山大学a. 公共实验教学中心；b. 工学院智能交通研究中心，广州510006； 2. 广东省智能交通系统重点实验室，广州510006
  • 收稿日期:2016-07-22 修回日期:2016-10-19 出版日期:2017-04-25 发布日期:2017-04-25
  • 作者简介:胡继华(1971－)，男，河南人，讲师，博士.
  • 基金资助:

    国家自然科学基金项目/National Natural Science Foundation of China(41271181)；广东省环境监测大数据公共服务平台及其创新应用/Public Service Platform of Big Data of Environmental Monitoring and its Innovative Applications in Guangdong Province(2015B010110005).

Inferring the Travel States of Bus Rapid Transit between Neighboring Stations within a Bayesian Framework

HU Ji-hua 1a, 2, LIANG Jia-xian 1b, 2   

  1. 1a. Public Experiment Teaching Center; 1b. Research Centre of Intelligent Transportation System, School of Engineering, SunYet-sen University, Guangzhou 510006, China; 2. Guangdong Provincial Key Laboratory of Intelligent Transportation System, Guangzhou 510006, China
  • Received:2016-07-22 Revised:2016-10-19 Online:2017-04-25 Published:2017-04-25

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被引次数

1

摘要：

站间行程车速是度量快速公交运营服务和乘客体验的重要指标.由于红绿灯、行人过街等影响，快速公交车速分布往往呈现多峰现象，很难利用单一分布描述.本文建立了贝叶斯框架下基于多相站间车速的高斯混合模型，利用可逆回跳马尔科夫链蒙特卡洛方法推断快速公交站间运行状态，包括状态个数和各状态权重、速度均值和方差.以广州市快速公交为例，展示了该模型可很好地拟合多相车速数据，合理划分快速公交运行状态.发现广州市快速公交在早晚高峰容易衍生出新的运行状态，对于车速低于25 km/h 的走廊，其运行状态个数较多，且更容易随时段发生变化，符合实际调查结果.可见本文提出的模型能有效地分析快速公交的速度分布特征和演变规律，验证了该模型的有效性.

关键词: 智能交通, 快速公交运行状态, 贝叶斯估计, 高斯混合模型, 可逆回跳马尔科夫链蒙特卡洛方法, 快速公交速度

Abstract:

Travel speed is a fundamental measure of operation service and passenger experience for bus rapid transit (BRT). The distributions of travel speed of BRT show multimodal shapes, because of the influence of the traffic lights, the pedestrian, etc. Traditional mathematical models with simple distribution lack the ability to explain these factors that lead to different speed distribution curves. A Gaussian mixture model based on the multimodal travel speed data is constructed under a Bayesian framework in this paper. A reversible-jump Markov chain Monte Carlo approach (RJMCMC) is used to infer the detailed travel states information of BRT, including the number of travel states and the weight, speed and variation for each travel state. A case study was presented in Guangzhou, China. The results show the distributions of the multimodal travel speed are well fit with the model and the travel states are divided properly. New travel state is easily derived during morning or evening peak, and the corridor with the speed lower than 25 km/h had higher number of travel states and the states are easier to change in time of day. The study shows that the model could be used to analyze the distributed features and evolution rules of travel states and proves the availability of the proposed model

Key words: intelligent transportation, BRT travel state, Bayesian estimation, Gaussian mixture model, reversible-jump Markov chain Monte Carlo approach, BRT travel speed

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