智能公交系统数据挖掘研究与应用
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摘要
发掘公交系统的车辆运行规律、客流规律以及对公交系统的可达性、可靠性等方面进行评价是智能公交系统的重要研究内容。随着智能公交系统的发展,智能公交系统收费、调度、客流监控等子系统中积累了海量数据,如何利用先进的数据挖掘技术分析智能公交系统中的这些海量信息,以期发现其中隐含的公共交通模式及规则,获得高层的、潜在的规律,如车辆运行规律、客流规律等,并评价公交系统的总体特征,如公交系统的可达性、可靠性等,日益受到相关研究者的关注。
本文首先根据智能公交数据特点及分析需要,结合数据挖掘系统的一般结构,提出了一个智能公交系统数据挖掘的体系结构,以便于实现智能公交系统数据挖掘算法。然后,对从智能公交卡付费信息和调度信息中挖掘公交车辆行程时间信息、进而利用行程时间和站点上下车人数信息估计公交动态OD矩阵以及利用行程时间和动态OD矩阵信息进行公交系统可达性评估等几个问题进行了研究,设计了适合的数据挖掘模型和算法。
这些问题的研究对于智能公交系统的公交规划、公交管理、公交调度与公交资源的分配、公交系统的评价等方面有着重要的意义。
本文主要研究内容和成果包括以下几个方面：
1)提出了一种从智能公交卡付费数据和调度数据挖掘公交车辆站点间行程时间的方法。
智能卡付费系统的广泛使用,为公交系统行程时间估计提供了新的途径。本文提出了一种利用公交调度信息和智能卡付费信息来估计行程时间的方法。该方法首先对同一辆车的连续两次刷卡进行朴素贝叶斯分类,区分是否是在同一个站刷卡；以此为依据,用极大似然估计、动态规划和二次规划方法估计出各个路段的行程时间；并运用交替MLE法,从不准确的初始参数出发,交替估计行程时间和行程时间的参数；最终推断出车辆运行的行程时间。最后,应用该方法从实际数据挖掘行程时间信息,并与实测的数据对比,对该方法的正确性和有效性进行验证。
2)提出了基于行程时间信息和各站点上下车客流信息估计动态公交OD矩阵的方法。
在智能公交系统背景下,公交系统各站的上下车乘客数和车辆行程时间可知。为了利用这些信息估计拥挤条件下公交系统的动态OD矩阵,本文提出了一个动态用户均衡条件下通过估计路径流量来估计动态公交OD的模型,并给出了相应的求解算法。为此,首先证明了无容量限制条件下,满足上下车乘客数约束的最小费用路径流为动态用户均衡流；其次证明了满足上下车乘客数约束的路径流蕴涵了容量约束,从而去除容量限制条件；最后通过在目标函数中引入一个路径费用的惩罚项,把满足上下车乘客数约束的最小费用路径流拓展到满足上下车乘客数约束的一般路径流；从而把动态公交OD估计问题转化为一个非负约束的凸二次规划问题。然后,应用Sherali算法,降低了问题的维度；并在小规模的公交网络上对算法进行了验证和分析。
3)提出了利用公交系统的行程时间信息以及动态OD矩阵信息,用等时线来描述公交系统可达性的方法。
可达性是衡量区位优势的重要指标。本文提出了一种利用动态OD矩阵信息和行程时间信息,以Voronoi图来划分各个公交站点服务区域,以公交服务区域中每一点的通勤时间数学期望值为指标,以通勤时间等时线来可视化地表示可达性的方法。最后,以昆明市公交网络为例,首先把该公交网络覆盖的区域划分为10米乘10米的格点,计算各格点的通勤时间,由此形成通勤等时线,表示该网络的可达性。
It is the focus areas of advanced public transportation systems (APTS) research to discover the patterns of travel times and passenger flows and assess the public transit network performance. Along with the rapid development of APTS, mass data have been collected and stored in smart card fare payment subsystem, scheduling subsystem and automatic passenger counting subsystem. And so, data mining, as one powerful data analysis technique, has been applied by APTS researchers to discover significant patterns and rules and retrieve potential high-level knowledge underlying the data.
In view of the requirements and characteristics of APTS, an extensible architecture for APTS data mining is first proposed. This architecture tries to incorporate the basic data mining algorithms and facilitate further developments to satisfy the needs of practical applications.
Travel time mining algortihm and dynamic OD matrix estimation algorithm are then presented. Finally, accessibility assessing method based on travel time and dynamic OD matrice is discussed.
Research to address these issues is significant to public transit planning, management and assessment. In general, the main contents and achievements of this dissertation consist of the following aspects:
1) Mining travel times from smart card fare payment data.
The recent adoption of smart card technology as a fare payment system by many transit operators provides a new way to infer travel times between adjacent stops in public transit network. One method is proposed to infer travel times from smart card fare payment data and bus scheduling data. An experiment is designed to test this algorithm with real-world data and the outcomes prove that the error of this method is small and the convergence is fast.
The method first classifies two sequential swipes to decide whether they occurred at the same stop with Naive Bayes Classifier (NBC). Travel times are then estimated from the NBC results using Maximum Likelihood Estimation (MLE), Dynamic Programming (DP) and Quadratic Programming (QP) methods.To solve the problem with imprecise initial parameters, alternative MLE method is proposed, which updates parameters and estimates values alternatively until convergence.
2) Dynamic OD matrix estimation of public transit network.
In the context of APTS, boarding and alighting passenger counts at each stop and travel times between adjacent stops are available. Dynamic OD matrix estimation in congested transit network from these data is examined. A dynamic OD matrix estimation model based on path flows estimation is developed. To solve the problem by path flows estimation, it is shown that, under dynamic user equilibrium constraints, if the capacity constraints are ignored, transit path flows satisfying boarding and alighting count constraints are minimum cost path flows. And then it is shown that capacity constraints are implied by boarding and alighting count constraints. Finally, the dynamic user equilibrium constraints are added to the objective function as one penalty term. And so, the dynamic transit OD matrix estimation problem is transformed into a nonnegative convex quadratic programming problem. Sherali algorithm is used to reduce the dimension of path flows. The experiment in a small size transit network shows that the error of prior OD matrix and the error of boarding and alighting count have an influence on the error of estimating results, and the proposed model and algorithm are feasible.
3) Accessibility assessment of public transit network.
Accessibility is a fundamental indicator of location advantages. This dissertation presents one method that seeks to measure time accessibility of public transit network with travel time and dyamic OD matrix information. It first bisects the service area of each bus stop by Voronoi diagram, and then calculates the expectation commute time of each grid in the service area. The public transit network of Kunming is assessed using this methodology. The service area is first divided into 10m*10m grids, and then the commute time of each grid is calculated, which forms time contours.
本文首先根据智能公交数据特点及分析需要,结合数据挖掘系统的一般结构,提出了一个智能公交系统数据挖掘的体系结构,以便于实现智能公交系统数据挖掘算法。然后,对从智能公交卡付费信息和调度信息中挖掘公交车辆行程时间信息、进而利用行程时间和站点上下车人数信息估计公交动态OD矩阵以及利用行程时间和动态OD矩阵信息进行公交系统可达性评估等几个问题进行了研究,设计了适合的数据挖掘模型和算法。
这些问题的研究对于智能公交系统的公交规划、公交管理、公交调度与公交资源的分配、公交系统的评价等方面有着重要的意义。
本文主要研究内容和成果包括以下几个方面：
1)提出了一种从智能公交卡付费数据和调度数据挖掘公交车辆站点间行程时间的方法。
智能卡付费系统的广泛使用,为公交系统行程时间估计提供了新的途径。本文提出了一种利用公交调度信息和智能卡付费信息来估计行程时间的方法。该方法首先对同一辆车的连续两次刷卡进行朴素贝叶斯分类,区分是否是在同一个站刷卡；以此为依据,用极大似然估计、动态规划和二次规划方法估计出各个路段的行程时间；并运用交替MLE法,从不准确的初始参数出发,交替估计行程时间和行程时间的参数；最终推断出车辆运行的行程时间。最后,应用该方法从实际数据挖掘行程时间信息,并与实测的数据对比,对该方法的正确性和有效性进行验证。
2)提出了基于行程时间信息和各站点上下车客流信息估计动态公交OD矩阵的方法。
在智能公交系统背景下,公交系统各站的上下车乘客数和车辆行程时间可知。为了利用这些信息估计拥挤条件下公交系统的动态OD矩阵,本文提出了一个动态用户均衡条件下通过估计路径流量来估计动态公交OD的模型,并给出了相应的求解算法。为此,首先证明了无容量限制条件下,满足上下车乘客数约束的最小费用路径流为动态用户均衡流；其次证明了满足上下车乘客数约束的路径流蕴涵了容量约束,从而去除容量限制条件；最后通过在目标函数中引入一个路径费用的惩罚项,把满足上下车乘客数约束的最小费用路径流拓展到满足上下车乘客数约束的一般路径流；从而把动态公交OD估计问题转化为一个非负约束的凸二次规划问题。然后,应用Sherali算法,降低了问题的维度；并在小规模的公交网络上对算法进行了验证和分析。
3)提出了利用公交系统的行程时间信息以及动态OD矩阵信息,用等时线来描述公交系统可达性的方法。
可达性是衡量区位优势的重要指标。本文提出了一种利用动态OD矩阵信息和行程时间信息,以Voronoi图来划分各个公交站点服务区域,以公交服务区域中每一点的通勤时间数学期望值为指标,以通勤时间等时线来可视化地表示可达性的方法。最后,以昆明市公交网络为例,首先把该公交网络覆盖的区域划分为10米乘10米的格点,计算各格点的通勤时间,由此形成通勤等时线,表示该网络的可达性。
It is the focus areas of advanced public transportation systems (APTS) research to discover the patterns of travel times and passenger flows and assess the public transit network performance. Along with the rapid development of APTS, mass data have been collected and stored in smart card fare payment subsystem, scheduling subsystem and automatic passenger counting subsystem. And so, data mining, as one powerful data analysis technique, has been applied by APTS researchers to discover significant patterns and rules and retrieve potential high-level knowledge underlying the data.
In view of the requirements and characteristics of APTS, an extensible architecture for APTS data mining is first proposed. This architecture tries to incorporate the basic data mining algorithms and facilitate further developments to satisfy the needs of practical applications.
Travel time mining algortihm and dynamic OD matrix estimation algorithm are then presented. Finally, accessibility assessing method based on travel time and dynamic OD matrice is discussed.
Research to address these issues is significant to public transit planning, management and assessment. In general, the main contents and achievements of this dissertation consist of the following aspects:
1) Mining travel times from smart card fare payment data.
The recent adoption of smart card technology as a fare payment system by many transit operators provides a new way to infer travel times between adjacent stops in public transit network. One method is proposed to infer travel times from smart card fare payment data and bus scheduling data. An experiment is designed to test this algorithm with real-world data and the outcomes prove that the error of this method is small and the convergence is fast.
The method first classifies two sequential swipes to decide whether they occurred at the same stop with Naive Bayes Classifier (NBC). Travel times are then estimated from the NBC results using Maximum Likelihood Estimation (MLE), Dynamic Programming (DP) and Quadratic Programming (QP) methods.To solve the problem with imprecise initial parameters, alternative MLE method is proposed, which updates parameters and estimates values alternatively until convergence.
2) Dynamic OD matrix estimation of public transit network.
In the context of APTS, boarding and alighting passenger counts at each stop and travel times between adjacent stops are available. Dynamic OD matrix estimation in congested transit network from these data is examined. A dynamic OD matrix estimation model based on path flows estimation is developed. To solve the problem by path flows estimation, it is shown that, under dynamic user equilibrium constraints, if the capacity constraints are ignored, transit path flows satisfying boarding and alighting count constraints are minimum cost path flows. And then it is shown that capacity constraints are implied by boarding and alighting count constraints. Finally, the dynamic user equilibrium constraints are added to the objective function as one penalty term. And so, the dynamic transit OD matrix estimation problem is transformed into a nonnegative convex quadratic programming problem. Sherali algorithm is used to reduce the dimension of path flows. The experiment in a small size transit network shows that the error of prior OD matrix and the error of boarding and alighting count have an influence on the error of estimating results, and the proposed model and algorithm are feasible.
3) Accessibility assessment of public transit network.
Accessibility is a fundamental indicator of location advantages. This dissertation presents one method that seeks to measure time accessibility of public transit network with travel time and dyamic OD matrix information. It first bisects the service area of each bus stop by Voronoi diagram, and then calculates the expectation commute time of each grid in the service area. The public transit network of Kunming is assessed using this methodology. The service area is first divided into 10m*10m grids, and then the commute time of each grid is calculated, which forms time contours.
引文
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