基于GPS轨迹的出行信息提取研究
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摘要
一个城市的活力得益于顺畅的交通系统。出行预测模型为交通规划和交通政策的制定提供了科学依据。从20世纪50年代发展至今,出行预测模型发生了从基于“出行”(Trip-based)向基于“活动”(Activity-based)的范式转变。活动分析法的基本思路是通过对在驱动因素和限制条件下微观个人的出行决策分析来获得大尺度群体行为的宏观涌现,因此对出行细节数据的需求更为迫切,而传统的出行调查方法无论在精度上还是在数据采集频率上已经远远落后于需要。
传统的出行调查方法采用入户访谈(Face-to-Face)、计算机辅助电话调查(CATI)的方式,这些方法本质上都依赖于被访者对行程的回忆和主观认知,因此不可避免的存在受访回应率低、数据质量不高的问题,更无法提供活动模型所需的路径选择信息。
将全球定位系统(GPS)应用于出行调查,变传统的人工记录为仪器记录,可解决长期困扰传统调查方法的诸多难题,但同时又对从轨迹中自动提取出行信息提出了挑战。对该问题的解决,不仅可革新出行调查技术,也可为行为学、时间地理学等研究提供数据支撑。从更广泛的意义来讲,这种从仪器记录的时间位置数据向可认知的语义信息的转化,为计算设备理解人的行为,实现“智慧化”,提供了重要契机。
本文关注的问题是,在无需借助任何辅助数据的情形下,是否存在一些算法,单纯依靠GPS轨迹的时空特征,可以获得对常规出行信息的自动有效提取。换言之,如果能最大限度地利用GPS轨迹数据固有的时空结构,结合先验知识挖掘出高质量的出行信息,便可少用甚至不用辅助数据,降低运作成本,提高工作效率。这些信息包括三种交通规划模型的基本参数,即出行端点、出行方式、以及出行目的。为此,本文研究的主要内容包括：
(1)面向对象的轨迹数据分割方法设计
从理论上增进对轨迹数据的深入认识,提出了面向对象的轨迹数据分割方法。该方法依据轨迹中信息的多层次规律,参照面向对象的图像理解思路,实现自下而上的轨迹逐级合并。为本文后续的行程识别、出行方式判定等工作打下了基础,同时也丰富了轨迹数据的理论和实践研究。
(2)出行端点提取方法研究
针对当前从GPS轨迹中提取出行端点的研究中多是以单点为计算对象的不足,根据本文提出的面向对象的轨迹分割思路,采用多次合并的方法实现出行端点的提取,可有效地避免噪音干扰。根据训练样本,对识别端点的时间阈值进行优化,发现180s的阈值更适合识别出行端点。
(3)出行方式判别研究
根据对样本数据的速度、加速度、方向、信号质量等特征的观察,优选出合适的统计量,并对比了多层感知器神经网络、贝叶斯网、决策树三个方法的分类效果。结果表明,在C4.5决策树中使用速度的75分位数、速度离差和信号缺失比例三个统计量取得了最佳的总体分类精度,达92.34%。
(4)出行目的推断研究
人的出行决策受制于时间、空间、社会关系和个人认知,因此从这四个方面选取推断依据,结合GPS数据特征,使用停留时长、到访时段、职业、到访频率等属性,利用C4.5决策树进行出行目的推断,但结果并不理想。
(5)国内出行调查试验初探
从华东师范大学的教师和学生中征集志愿者,开展出行调查试验。试验结果表明,基于被动式GPS的出行调查方式精度高,用户负担小,在这两方面显著优于传统调查方式。
(6)出行信息提取软件开发
基于Matlab GUI开发了出行信息提取软件,可实现GPS轨迹数据的自动入库、行程自动识别、交互式修改行程、交通方式和出行目的样本库建立。
论文结论表明,本文提出的面对对象的轨迹分割方法有助于从轨迹中提取信息,在行程识别和换乘点识别中取得了不错的效果。在无辅助数据的情况下,从GPS轨迹中可较好的提取行程和交通方式信息,但出行目的推断精度不高。总体来讲,基于被动式GPS调查方法不仅在调查精度和用户负担上优于传统调查方法,而且可以提供路径选择信息,有着良好的应用前景。
本文的不足之处在于对轨迹数据的理论认识不够深入,而且研究所用的样本量较小,出行目的推断的精度有待提高,在下一阶段的研究中应着力解决这些问题。
Urban and regional planning is mainly based on travel demand models which aim at estimating changes in transportation activity over time. Recently, activity-based models have attracted an increasing interest. This new paradigm is based on the bottom-up idea, and as a result, raises an urgent need for household/personal travel survey (HTS/PTS) data.
Traditionally, HTS Data was collected using Face-to-Face home interviews or computer-assisted-telephone interviews (CATI), which need the respondents recall travel details such as trip origins, destinations, start time, end time, trip modes and trip purpose. Compared to trip-based model, activity-based model also needs trip routes. Respondents often forget or slip some trips, which leads to poor accuracy to travel models. At the same time, high burden is also responsible for a low overall response rate.
GPS can record location and time, which give us an accurate and detailed trip trajectories. This new technology presents a revolutionary method of travel survey. However, it calls for automated identification trips from trajectories and derive trip modes and trip purposes.
The main study contents of this research include:
(1) An object-oriented trajectory segmentation method was proposed, which used a bottom-up procedure to merge each level of trajectory details to form a semantic trajectory with trip information.
(2) Combined with machine learning methods, trip information was derived, which included stops, trip modes and trip purposes.
(3) An experimental travel survey was conducted in East China Normal University, which use 13 vehicle respondents and 11 personal respondents. Their travel data was collected to do the trip information deriving research described above.
(4) A software was also developed to assist the research, which can automated import raw GPS data to a simple database, identify stops and gave an interactive GUI for modify derived trips and construct sample for machine learning.
The results indicated that passive GPS travel survey method has low burden and high accuracy. The procedure we proposed have done well in trip identification and trip mode classification, however the method to infer trip purpose need to be improved.
传统的出行调查方法采用入户访谈(Face-to-Face)、计算机辅助电话调查(CATI)的方式,这些方法本质上都依赖于被访者对行程的回忆和主观认知,因此不可避免的存在受访回应率低、数据质量不高的问题,更无法提供活动模型所需的路径选择信息。
将全球定位系统(GPS)应用于出行调查,变传统的人工记录为仪器记录,可解决长期困扰传统调查方法的诸多难题,但同时又对从轨迹中自动提取出行信息提出了挑战。对该问题的解决,不仅可革新出行调查技术,也可为行为学、时间地理学等研究提供数据支撑。从更广泛的意义来讲,这种从仪器记录的时间位置数据向可认知的语义信息的转化,为计算设备理解人的行为,实现“智慧化”,提供了重要契机。
本文关注的问题是,在无需借助任何辅助数据的情形下,是否存在一些算法,单纯依靠GPS轨迹的时空特征,可以获得对常规出行信息的自动有效提取。换言之,如果能最大限度地利用GPS轨迹数据固有的时空结构,结合先验知识挖掘出高质量的出行信息,便可少用甚至不用辅助数据,降低运作成本,提高工作效率。这些信息包括三种交通规划模型的基本参数,即出行端点、出行方式、以及出行目的。为此,本文研究的主要内容包括：
(1)面向对象的轨迹数据分割方法设计
从理论上增进对轨迹数据的深入认识,提出了面向对象的轨迹数据分割方法。该方法依据轨迹中信息的多层次规律,参照面向对象的图像理解思路,实现自下而上的轨迹逐级合并。为本文后续的行程识别、出行方式判定等工作打下了基础,同时也丰富了轨迹数据的理论和实践研究。
(2)出行端点提取方法研究
针对当前从GPS轨迹中提取出行端点的研究中多是以单点为计算对象的不足,根据本文提出的面向对象的轨迹分割思路,采用多次合并的方法实现出行端点的提取,可有效地避免噪音干扰。根据训练样本,对识别端点的时间阈值进行优化,发现180s的阈值更适合识别出行端点。
(3)出行方式判别研究
根据对样本数据的速度、加速度、方向、信号质量等特征的观察,优选出合适的统计量,并对比了多层感知器神经网络、贝叶斯网、决策树三个方法的分类效果。结果表明,在C4.5决策树中使用速度的75分位数、速度离差和信号缺失比例三个统计量取得了最佳的总体分类精度,达92.34%。
(4)出行目的推断研究
人的出行决策受制于时间、空间、社会关系和个人认知,因此从这四个方面选取推断依据,结合GPS数据特征,使用停留时长、到访时段、职业、到访频率等属性,利用C4.5决策树进行出行目的推断,但结果并不理想。
(5)国内出行调查试验初探
从华东师范大学的教师和学生中征集志愿者,开展出行调查试验。试验结果表明,基于被动式GPS的出行调查方式精度高,用户负担小,在这两方面显著优于传统调查方式。
(6)出行信息提取软件开发
基于Matlab GUI开发了出行信息提取软件,可实现GPS轨迹数据的自动入库、行程自动识别、交互式修改行程、交通方式和出行目的样本库建立。
论文结论表明,本文提出的面对对象的轨迹分割方法有助于从轨迹中提取信息,在行程识别和换乘点识别中取得了不错的效果。在无辅助数据的情况下,从GPS轨迹中可较好的提取行程和交通方式信息,但出行目的推断精度不高。总体来讲,基于被动式GPS调查方法不仅在调查精度和用户负担上优于传统调查方法,而且可以提供路径选择信息,有着良好的应用前景。
本文的不足之处在于对轨迹数据的理论认识不够深入,而且研究所用的样本量较小,出行目的推断的精度有待提高,在下一阶段的研究中应着力解决这些问题。
Urban and regional planning is mainly based on travel demand models which aim at estimating changes in transportation activity over time. Recently, activity-based models have attracted an increasing interest. This new paradigm is based on the bottom-up idea, and as a result, raises an urgent need for household/personal travel survey (HTS/PTS) data.
Traditionally, HTS Data was collected using Face-to-Face home interviews or computer-assisted-telephone interviews (CATI), which need the respondents recall travel details such as trip origins, destinations, start time, end time, trip modes and trip purpose. Compared to trip-based model, activity-based model also needs trip routes. Respondents often forget or slip some trips, which leads to poor accuracy to travel models. At the same time, high burden is also responsible for a low overall response rate.
GPS can record location and time, which give us an accurate and detailed trip trajectories. This new technology presents a revolutionary method of travel survey. However, it calls for automated identification trips from trajectories and derive trip modes and trip purposes.
The main study contents of this research include:
(1) An object-oriented trajectory segmentation method was proposed, which used a bottom-up procedure to merge each level of trajectory details to form a semantic trajectory with trip information.
(2) Combined with machine learning methods, trip information was derived, which included stops, trip modes and trip purposes.
(3) An experimental travel survey was conducted in East China Normal University, which use 13 vehicle respondents and 11 personal respondents. Their travel data was collected to do the trip information deriving research described above.
(4) A software was also developed to assist the research, which can automated import raw GPS data to a simple database, identify stops and gave an interactive GUI for modify derived trips and construct sample for machine learning.
The results indicated that passive GPS travel survey method has low burden and high accuracy. The procedure we proposed have done well in trip identification and trip mode classification, however the method to infer trip purpose need to be improved.
引文
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