WO2017177965A1 - 一种基于时间顺序的轨迹匹配方法 - Google Patents

一种基于时间顺序的轨迹匹配方法 Download PDF

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WO2017177965A1
WO2017177965A1 PCT/CN2017/080575 CN2017080575W WO2017177965A1 WO 2017177965 A1 WO2017177965 A1 WO 2017177965A1 CN 2017080575 W CN2017080575 W CN 2017080575W WO 2017177965 A1 WO2017177965 A1 WO 2017177965A1
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trajectory
distance
determining whether
candidate
max
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李荣华
郭楷扬
刘刚
毛睿
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深圳大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2455—Query execution

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  • the invention belongs to the technical field of data processing, and more particularly to a chronological matching method based on time sequence.
  • Trajectory data plays an increasingly important role in daily life, business applications and scientific research. With the rapid development of wireless communication and mobile computing technology, the scale of the trajectory data we have acquired has become unprecedented. Such a large amount of trajectory data brings great research value.
  • trajectory data in addition to the moving lines of moving objects such as people or vehicles collected by GPS devices, also includes changes in the values collected by the sensors over time. Therefore, it can be said that the trajectory is almost ubiquitous, and with the popularity of smart devices equipped with various sensors in recent years, such as smart bracelets, smart phones, and trajectory data that can be acquired by various smart mobile devices. It is explosively growing. These rich trajectory data resources are a valuable asset that can be better served by mining these trajectory data.
  • millions of geo-location updates will be added to social networks such as Twitter, Weibo, WeChat, etc. Every day, the emerging Internet of Things will generate countless time-series data through sensors every day; these numbers are still growth of.
  • a basic and important operation is the similarity query, that is, given a database and a trajectory data, the candidate trajectory data most similar to the given trajectory data is queried in the database.
  • the object of the present invention is to provide a time-based trajectory matching method, which aims to solve the technical problem of low efficiency and high time complexity in the prior art method for finding similar paths.
  • the invention provides a chronological-based trajectory matching method, comprising the following steps:
  • S11 obtaining a maximum value of a distance between each point in the target trajectory and each point in the candidate trajectory, and assigning it to d max ; assigning 0 to d min ;
  • step S13 determining whether d meets the distance detection function, and if so, proceeds to step S14; if not, proceeds to step S15;
  • the invention also provides a chronological-based trajectory matching method, comprising the following steps:
  • S201 Initialize each variable so that the initial value of the auxiliary variable k is 1, the initial value of d min is positive infinity, and an array is used to record the position of each segment; ans is used to record the final distance, and the initial value of ans is positive infinity;
  • step S203 determining whether j is less than or equal to m, if yes, proceeding to step S204; if not, ending and returning ans;
  • step S205 determining whether L is less than R, and if so, then proceeds to step S206, and if not, proceeds to step S210;
  • step S207 determining whether d meets the distance detection function, and if so, proceeds to step S208; if not, proceeds to step S209;
  • step S211 determining whether d is greater than ans, if yes, then proceeds to step S212, and if not, proceeds to step S213;
  • the determining whether the d meets the distance detection function is specifically:
  • step (3.2) determining whether i is less than R, if yes, proceeding to step (3.3); if not, then d does not meet the distance detection function;
  • step (3.3) determining whether d(s i , t j ) is less than or equal to d, and if so, proceeding to step (3.4); if not, proceeding to step (3.5);
  • step (3.5) determining whether k is greater than m, and if so, then d meets the distance detection function; if not, returns to step (3.2);
  • k is used to represent the kth point to be compared in the target trajectory
  • k0 is the auxiliary variable, which is used to assign an initial value to k
  • i is an auxiliary variable
  • d(s i , t j ) represents the ith point of the candidate trajectory
  • m is a smaller value of the target trajectory length and the candidate trajectory length.
  • FIG. 1 is a flowchart of an implementation of a distance detecting method according to an embodiment of the present invention
  • FIG. 2 is a flowchart of an implementation of a first method for obtaining a distance between a candidate trajectory T and a target trajectory q according to an embodiment of the present invention
  • FIG. 3 is a flowchart of an implementation of a second method for obtaining a distance between a candidate track T and a target track q according to an embodiment of the present invention.
  • the present invention proposes a formula for measuring the distance between two trajectories and an efficient, low-time complex high-efficiency method for relatively quickly finding the most similar top K trajectories in a large amount of trajectory data.
  • the serial number of the last point, i indicates the serial number.
  • the problem we have to solve is to query the top k trajectories closest to the target sequence q in the trajectory database.
  • the most basic method for calculating dm is basic dynamic programming, but the query efficiency is very low.
  • the time complexity is O(mn 3 ).
  • the present invention proposes two methods for calculating the above dm, which can improve the efficiency of the method and reduce the time complexity of the method.
  • the embodiment of the present invention proposes a distance detecting function, which is a helper function.
  • the specific implementation process is as shown in FIG. 1 , and is used to detect whether the distance of each point on the target trajectory to the corresponding nearest point in the candidate trajectory is Less than the current d value (d is a custom error value), if true, return true, otherwise return false.
  • the time complexity is O(n).
  • k, k 0 and i are auxiliary variables; L represents the sequence number starting from the left in the candidate track, R is the sequence number ending in the right track of the candidate track; L and R are used to indicate the candidate track from the Lth element to the first Whether the elements of the R elements to the target trajectory conform to the distance detection function.
  • the determination of the values of L, R, and d is an initial value giving three values when the distance detection function is called by another program.
  • FIG. 2 is a flowchart showing an implementation of a first method for obtaining a distance between a target trajectory q and a candidate trajectory T according to an embodiment of the present invention; the method is to obtain a target trajectory q, and an approximate distance between candidate trajectories T
  • the error is ⁇ set artificially, indicating that the distance between the candidate trajectory and the target trajectory must be less than ⁇
  • the time complexity is O(mnlogn).
  • FIG. 3 is a flowchart showing an implementation of a second method for obtaining a distance between a target trajectory T and a candidate trajectory q according to an embodiment of the present invention; the method is to obtain a target trajectory T, and the distance between the candidate trajectories q is accurate. method. It is assumed that the target trajectory T has a total of m points, and the candidate trajectory q has n points.
  • the invention mainly solves a maximum and minimum problem, and the main idea of the method is to divide the candidate track q into n segments, and the T of the target track respectively correspond to n points corresponding to n segments. First, take the shortest distance from the corresponding point in the n-segment, and then select the largest distance from the shortest distance among all the segments.
  • the method time complexity is O(mnlogn).
  • the time-based active trajectory approximation search proposed by the invention has great significance, and defines a distance between two trajectories, and proposes two different methods for calculating distance, and based on the most basic dynamic programming method. Improve method efficiency and reduce time complexity.
  • trajectory data is almost ubiquitous, applications based on trajectory data are also endless.
  • the movement of objects has certain patterns. For example, people usually go to work along the same path on weekdays, while on holidays they may go shopping or watching movies. Therefore, through people's trajectories, they can dig out some of their behavioral habits, so that they can provide personalized help for their lives. Recommend potential friends to them through similar path matching, because friends will have similar interests, and his usual trajectory is similar.
  • taxi trajectories are excavated because taxi drivers are generally familiar with local road conditions. The route they choose usually avoids the roads that are prone to congestion. Searching through similar routes and finding routes similar to taxi drivers may be relative. A smoother and more convenient route.
  • the position prediction hopes to give a possible position of the object at some time in the future. If you can accurately predict the user's location, you can better provide them with various services, such as trip planning.

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Abstract

一种基于时间顺序的轨迹匹配方法，包括下述步骤：(1)获得目标轨迹中的各个点和候选轨迹中各个点之间的距离的最大值，并将其赋值给d max；将0赋值给d min；(2)判断(d max-d min)是否大于预设的误差阈值ε，若是，则获得d＝(d max-d min)/2；若否，则获得目标轨迹T与候选轨迹q之间距离为d max；(3)判断d是否符合距离检测函数，若是，则转入步骤(4)；若否，则转入步骤(5)；(4)令d＝d max并返回至步骤(2)；(5)令d＝d min并返回至步骤(2)。该方法能够提高效率，降低时间复杂度。

Description

一种基于时间顺序的轨迹匹配方法 技术领域
本发明属于数据处理技术领域，更具体地，涉及一种基于时间顺序的轨迹匹配方法。
背景技术
轨迹数据在日常生活、商业应用与科学研究等方面起着日益重要的作用。随着无线通信与移动计算技术的快速发展，我们所能获取到的轨迹数据规模也变得前所未有的庞大。如此大量的轨迹数据既带来了巨大的研究价值。
随着无线通信以及定位技术的快速发展，人们已经可以轻松地获取移动物体在不同时刻的位置信息。常见的轨迹数据，除了通过GPS设备采集到的人或车辆等运动物体的移动线路以外，还包括传感器采集到的数值随时间的变化情况。因此可以说，轨迹几乎是无处不在的，而且随着近年来配备了各种传感器的智能设备的普及，比如智能手环，智能手机以及各种各样的智能移动设备所能获取的轨迹数据更是爆炸性地增长。这些丰富的轨迹数据资源是一笔宝贵的财富，可以通过挖掘这些轨迹数据来更好地为人们服务。此外，Twitter、微博、微信等社交网络上每天还会新增数百万条含地理位置的状态更新，方兴未艾的物联网每天也会通过传感器生成不计其数的时序数据；这些数字仍然在不断的增长。
在有关轨迹数据的各种不同的应用中，一个基本且很重要的操作是相似性查询，即给定一个数据库和一条轨迹数据，在数据库中查询和给定轨迹数据最相似的候选轨迹数据。
目前对基于时间顺序的相似轨迹的研究相对较少，或者查找相似路径的方法效率低，时间复杂度高。
发明内容
针对现有技术的缺陷，本发明的目的在于提供一种基于时间顺序的轨迹匹配方法，旨在解决现有技术中查找相似路径的方法存在效率低和时间复杂度高的技术问题。
本发明提供了一种基于时间顺序的轨迹匹配方法，包括下述步骤：
S11：获得目标轨迹中的各个点和候选轨迹中各个点之间的距离的最大值，并将其赋值给dmax；将0赋值给dmin；
S12：判断(dmax-dmin)是否大于预设的误差阈值ε，若是，则获得d＝(dmax-dmin)/2；若否，则获得目标轨迹T与候选轨迹q之间距离为dmax；并在获得的目标轨迹与所有候选轨迹的距离中选取最短的一条候选轨迹作为与目标轨迹最匹配的轨迹；
S13：判断d是否符合距离检测函数，若是，则转入步骤S14；若否，则转入步骤S15；
S14：令d＝dmax并返回至步骤S12；
S15：令d＝dmin并返回至步骤S12；其中，dmax为目标轨迹与候选轨迹距离的最大值，dmin为目标轨迹与候选轨迹距离的最小值，ε为设定的距离误差值，d为辅助变量，用于暂存(dmax-dmin)/2的值。
本发明还提供了一种基于时间顺序的轨迹匹配方法，包括下述步骤：
S201：初始化各变量，令辅助变量k初始值为1，dmin的初始值为正无穷，并采用一个数组来记录各个分段的位置；ans来记录最终距离，ans的初始值为正无穷；
S202：令i＝1；
S203：判断j是否小于等于m，若是，则转入步骤S204；若否，则结束并返回ans；
S204：L＝lj+1，R＝n；将lj+1复制给L，即将L的值变为lj+1。将n的值赋 值给R(“＝“为赋值符号，将等号右边的值赋给等号左边的值)
S205：判断L是否小于R，若是，则转入步骤S206，若否，则转入步骤S210；
S206：lmid＝(L+R)/2，d＝d(T[lj，lmid]，tj)；
其中，d＝d(T[lj，lmid]，tj)的含义由
Figure PCTCN2017080575-appb-000001
可得，其含义为在候选轨迹T中第i个点至第j个点中选取一个与目标轨迹中tk的最短的距离，并赋给d；
S207：判断d是否符合距离检测函数，若是，则转入步骤S208；若否，则转入步骤S209；
S208：L＝lmid+1；并返回至步骤S205；
S209：R＝lmid；并返回至步骤S205；
S210：lj+1＝R；
S211：判断d是否大于ans，若是，则转入步骤S212，若否，则进入步骤S213；
S212：令ans＝d；并进入步骤S213；
S213：j＝j+1，并返回至步骤S203。
更进一步地，所述判断d是否符合距离检测函数具体为：
(3.1)初始化k＝k0，i＝L；
(3.2)判断i是否小于R，若是，则转入步骤(3.3)；若否，则d不符合距离检测函数；
(3.3)判断d(si，tj)是否小于等于d，若是，则转入步骤(3.4)；若否，则转入步骤(3.5)；
(3.4)令k＝k+1，并进入步骤(3.5)；
(3.5)判断k是否大于m，若是，则d符合距离检测函数；若否，则返回步骤(3.2)；
其中，k用于表示目标轨迹中被比较的第k个点，k0为辅助变量，用来给k赋初始值，i为辅助变量，d(si，tj)表示候选轨迹第i个点与目标轨迹第j个点之间的距离，m为目标轨迹长度与候选轨迹长度的较小的值。
通过本发明所构思的以上技术方案，与现有技术相比，由于提出了一种衡量两条轨迹间的距离的公式，能够取得高效，低时间复杂度的有益效果。
附图说明
图1是本发明实施例提供的距离检测方法的实现流程图；
图2是本发明实施例提供的第一种用于获得候选轨迹T与目标轨迹q之间距离的方法的实现流程图；
图3是本发明实施例提供的第二种用于获得候选轨迹T与目标轨迹q之间距离的方法的实现流程图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
本发明提出了一种衡量两条轨迹间的距离的公式以及高效，低时间复杂的高效方法，从而在大量轨迹数据中相对快速的查找最相似的前K个轨迹。
在本发明实施例中，定义基于时间顺序的目标序列q＝{t0，t1，t2，...，tm}和轨迹序列T＝{s0，s1，...，sn}，定义二者之间的距离为
Figure PCTCN2017080575-appb-000002
其中，l1＝1，lm+1-1＝n，d(si，tj)表示si与tj之间的欧几里得距离，T＝{s0，s1，...，sn}表示为一个轨迹，si(i＝1，…，n)是第i个二维序列，表示位置，tj表示目标序列上第j点；j表示序号，m表示目标序列最后一点的序号，i表示序号。
给定一个目标序列q，我们要解决的问题是在轨迹数据库中查询到前k个与 目标序列q最相近的轨迹，最基本的计算dm的方法是基本的动态规划，然而查询效率很低，时间复杂度为O(mn3)。为此，本发明提出了两种计算上述dm的方法，能够提高方法效率，降低方法的时间复杂度。
本发明实施例提出了一种距离检测函数，该函数为辅助函数，具体实现流程如图1所示，用来检测在目标轨迹上每一个点对于候选轨迹中相应最近的点的距离，是否都小于当前的d值(d值为自定义的误差值)，若是则返回true，否则返回false。时间复杂度为O(n)。图1中，k、k0、i均为辅助变量；L表示候选轨迹中左边开始的序号，R表示候选轨迹中右边结束的序号；L和R用于表示候选轨迹从第L个元素至第R个元素到目标轨迹的元素是否符合距离检测函数。且L、R、d的值的确定是由其它程序调用距离检测函数时给出三值的初始值。
图2示出了本发明实施例提供的第一种用于获得目标轨迹q与候选轨迹T之间距离的方法的实现流程图；该方法为求目标轨迹q，候选轨迹T之间距离的近似方法，误差为ε为人为设定的，表示候选轨迹与目标轨迹的距离必须小于ε，时间复杂度为O(mnlogn)。
具体包括下述步骤：
(1)获得目标轨迹和候选轨迹各个两点之间的距离的最大值，并将其赋值给dmax，将0赋值给dmin。
(2)然后进入循环，当误差值dmax-dmin＞ε时，一直通过距离检测函数来给dmax和dmin赋予新的值，从而进行二分查找(二分查找的基本思想是将n个元素分成大致相等的两部分，取a[n/2]即中间的元素与x做比较，如果x＝a[n/2]，则找到x，算法中止；如果x＜a[n/2]，则只要在数组a的左半部分继续搜索x，如果x＞a[n/2]，则只要在数组a的右半部搜索x。)，直至误差值小于ε时，循环结束。图2中dmin是否符合距离检测函数，具体可以将d代入距离检测函数，如果返回true即为符合，如果返回false即为不符合。
(3)最终将dmax值返回，即目标轨迹T与候选轨迹q之间距离。
图3示出了本发明实施例提供的第二种用于获得目标轨迹T与候选轨迹q之间距离的方法的实现流程图；该方法为求目标轨迹T，候选轨迹q之间距离的精确方法。假设目标轨迹T共有m个点，候选轨迹q共有n个点。本发明主要是解决一个最大最小问题，方法的主要思想为将候选轨迹q分为n段，目标轨迹的T分别对应n个点分别对应n段。首先分别在n段内取距离对应点最短的距离，然后再从所有段中最短距离中选取最大的距离。方法时间复杂度为O(mnlogn)。
具体包括下述步骤：
(1)给各变量赋初值，即令辅助变量k赋值为1，dmin赋值为正无穷，用l数组来记录各个分段的位置，ans来记录最终距离并赋值正无穷。
(2)为寻找m个分段位置，进入循环，并循环m次，首先确定要寻找分的位置，即L到R区间内。
(3)再进入循环，通过二分法查找分段内目标轨迹上的点至候选轨迹上的点的最短距离d，并找到分段位置L，R。
(4)记录找到的分段位置至l数组中。
(5)从所有分段中最短距离中取最大的距离并赋值给ans。
本发明提出的基于时间的活动轨迹近似搜索有很大的意义，且定义了一种计算两种轨迹之间的距离，并提出了两种不同计算距离的方法，且基于最基本的动态规划方法提高了方法效率，降低了时间复杂度。
由于轨迹数据几乎无所不在，基于轨迹数据的应用也是层出不穷。物体的运动都有一定的模式，比如人们在工作日通常沿着相同的路径上下班，而在假期则可能去购物或者看电影等。因此，通过人们的运动轨迹，可以挖掘出他们的一些行为习惯，从而可以为他们的生活提供个性化的帮助。通过相似路径匹配给他们推荐潜在的朋友，因为朋友会有相似的兴趣爱好，可能他平时的轨迹也近似。
还比如挖掘出租车轨迹，因为出租车司机一般都很熟悉本地的路况，他们选择的路径通常会避开容易发生拥堵的路段，通过相似路径搜索，查找与出租车司机相近的路线，可能是相对比较顺畅且便利的路线。
或者是已知运动物体的当前位置与历史轨迹，位置预测希望给出该物体在未来某个时候可能的位置。如果可以准确地预测用户的位置，则可以更好地为其提供各类服务，如行程规划等。
本领域的技术人员容易理解，以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。

Claims (3)

  1. 一种基于时间顺序的轨迹匹配方法，其特征在于，包括下述步骤：
    S11：获得目标轨迹中的各个点和候选轨迹中各个点之间的距离的最大值，并将其赋值给dmax；将0赋值给dmin；
    S12：判断(dmax-dmin)是否大于预设的误差阈值ε，若是，则获得d＝(dmax-dmin)/2；若否，则获得目标轨迹T与候选轨迹q之间距离为dmax；并在获得的目标轨迹与所有候选轨迹的距离中选取最短的一条候选轨迹作为与目标轨迹最匹配的轨迹；
    S13：判断d是否符合距离检测函数，若是，则转入步骤S14；若否，则转入步骤S15；
    S14：令d＝dmax并返回至步骤S12；
    S15：令d＝dmin并返回至步骤S12；其中，dmax为目标轨迹与候选轨迹距离的最大值，dmin为目标轨迹与候选轨迹距离的最小值，ε为设定的距离误差值，d为辅助变量，用于暂存(dmax-dmin)/2的值。
  2. 一种基于时间顺序的轨迹匹配方法，其特征在于，包括下述步骤：
    S201：初始化各变量，令辅助变量k初始值为1，dmin的初始值为正无穷，并采用一个数组来记录各个分段的位置；ans来记录最终距离，ans的初始值为正无穷；
    S202：令i＝1；
    S203：判断j是否小于等于m，若是，则转入步骤S204；若否，则结束并返回ans；
    S204：L＝lj+1，R＝n；
    S205：判断L是否小于R，若是，则转入步骤S206，若否，则转入步骤S210；
    S206：lmid＝(L+R)/2，d＝d(T[lj，lmid]，tj)；
    S207：判断d是否符合距离检测函数，若是，则转入步骤S208；若否，则 转入步骤S209；
    S208：L＝lmid+1；并返回至步骤S205；
    S209：R＝lmid；并返回至步骤S205；
    S210：lj+1＝R；
    S211：判断d是否大于ans，若是，则转入步骤S212，若否，则进入步骤S213；
    S212：令ans＝d；并进入步骤S213；
    S213：j＝j+1，并返回至步骤S203。
  3. 如权利要求1或2所述的轨迹匹配方法，其特征在于，所述判断d是否符合距离检测函数具体为：
    (3.1)初始化k＝k0，i＝L；
    (3.2)判断i是否小于R，若是，则转入步骤(3.3)；若否，则d不符合距离检测函数；
    (3.3)判断d(si，tj)是否小于等于d，若是，则转入步骤(3.4)；若否，则转入步骤(3.5)；
    (3.4)令k＝k+1，并进入步骤(3.5)；
    (3.5)判断k是否大于m，若是，则d符合距离检测函数；若否，则返回步骤(3.2)；
    其中，k用于表示目标轨迹中被比较的第k个点，k0为辅助变量，用来给k赋初始值，i为辅助变量，d(si，tj)表示候选轨迹第i个点与目标轨迹第j个点之间的距离，m为目标轨迹长度与候选轨迹长度的较小的值。
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