Online monitoring of local taxi travel momentum and congestion effects using projections of taxi GPS-based vector fields

Ubiquitous taxi trajectory data has made it possible to apply it to different types of travel analysis. Of interest is the need to allow someone to monitor travel momentum and associated congestion in any location in space in real time. However, despite an abundant literature in taxi data visualization and its applicability to travel analysis, no easy method exists. To measure taxi travel momentum at a location, current methods require filtering taxi trajectories that stop at a location at a particular time range, which is computationally expensive. We propose an alternative, computationally cheaper way based on preprocessing vector fields from the trajectories. Algorithms are formalized for generating vector kernel density to estimate a travel-model-free vector field-based representation of travel momentum in an urban space. The algorithms are shared online as an open source GIS 3D extension called VectorKD. Using 17 million daily taxi GPS points within Beijing over a 4-day period, we demonstrate how to generate in real time a series of projections from a continuously updated vector field of taxi travel momentum to query a point of interest anywhere in a city, such as the CBD or the airport. This method allows a policy-maker to automatically identify temporal net influxes of travel demand to a location. The proposed methodology is shown to be over twenty times faster than a conventional selection query of trajectories. We also demonstrate, using taxi data entering the Beijing Capital International Airport and the CBD, how we can quantify in nearly real time the occurrence and magnitude of inbound or outbound queueing and congestion periods due to taxis cruising or waiting for passengers, all without having to fit any mathematical queueing model to the data.     

基于地理格网的复杂路线车辆通行时间估算方法

车辆通行时间隐含了特定时隙的交通状况，准确地计算该时间在交通监测和路径规划中具有重要意义。现有研究通常利用车辆历史轨迹估算一定距离内选定路径的通行时间，然而当路径距离较长时，限于很难找到完整穿越指定路径的历史轨迹而无法对其通行时间进行准确估计；此外，海量历史轨迹在估计路径通行时间时会产生巨大的数据管理和计算压力。因此，本文引入地理格网，首先构建统一的时空索引，将路网及其历史轨迹分别划分为一系列落在地理格网单元（Cell）中的路段模式及轨迹段；然后利用一系列频繁共享轨迹在Cell中的停留时间计算车辆在当前路段模式的通行时间；最后通过一组历史时段相似路径模式的通行时间估算较长路线的车辆通行时间。通过对北京市10 000辆出租车一周的轨迹数据进行试验，验证了本文方法在处理海量历史轨迹数据上的有效性，以及在估算较长路径上车辆通行时间的优越性。     

Integrated compression of vehicle spatio‐temporal trajectories under the road stroke network constraint

With fast growth of all kinds of trajectory datasets, how to effectively manage the trajectory data of moving objects has received a lot of attention. This study proposes a spatio‐temporal data integrated compression method of vehicle trajectories based on stroke paths coding compression under the road stroke network constraint. The road stroke network is first constructed according to the principle of continuous coherence in Gestalt psychology, and then two types of Huffman tree—a road strokes Huffman tree and a stroke paths Huffman tree—are built, based respectively on the importance function of road strokes and vehicle visiting frequency of stroke paths. After the vehicle trajectories are map matched to the spatial paths in the road network, the Huffman codes of the road strokes and stroke paths are used to compress the trajectory spatial paths. An opening window algorithm is used to simplify the trajectory temporal data depicted on a time–distance polyline by setting the maximum allowable speed difference as the threshold. Through analysis of the relative spatio‐temporal relationship between the preceding and latter feature tracking points, the spatio‐temporal data of the feature tracking points are all converted to binary codes together, accordingly achieving integrated compression of trajectory spatio‐temporal data. A series of comparative experiments between the proposed method and representative state‐of‐the‐art methods are carried out on a real massive taxi trajectory dataset from five aspects, and the experimental results indicate that our method has the highest compression ratio. Meanwhile, this method also has favorable performance in other aspects: compression and decompression time overhead, storage space overhead, and historical dataset training time overhead.     

出租车轨迹数据的频繁轨迹识别

为识别城市交通中的频繁路径,本文提出了一种出租车轨迹数据的频繁轨迹识别方法。该方法首先对轨迹数据进行轨迹压缩,以降低计算复杂度;然后基于最长公共子序列和动态时间规整算法进行轨迹相似性度量计算,利用计算得到的轨迹间相似度生成距离矩阵;最后将生成的距离矩阵结合HDBSCAN算法进行聚类得到频繁轨迹。选取厦门岛内两个区域进行试验分析,结果表明,该方法能够识别出轨迹数据集中的频繁轨迹,进而得到城市区域之间通行的频繁路径,对道路规划、路径优化与推荐、交通治理等应用提供帮助。     

支持城市多种交通方式的最佳路径分析

分析了影响出行者路径选择的因素,研究了不同交通方式即出行时间、出行距离、拥挤程度、通行费用和服务水平上的差别,把通行费用和服务水平作为重要区别因素,实现了城市公交车和的士两种交通方式下的最优路径。     

符合认知规律的时空轨迹融合与路网生成方法

以行驶在城市大街小巷的出租车GPS时空轨迹数据为研究对象,研究了符合"感知—认知—经验"认知规律3层次的轨迹融合与路网生成方法,提出了基于Delaunay三角网的时空轨迹融合模型,实现了从GPS时空轨迹中对符合认知规律需求的路网信息的获取,并以武汉市出租车GPS轨迹为试验,实现了对武汉市出租车时空轨迹的融合与武汉市路网数据的生成,证明了该方法的有效性。     

基于出租车GPS数据的城市居民出行时空分布研究

随着城市的快速发展,道路拥堵、打车难等问题越来越突出,严重影响了居民的出行效率和生活质量。出租车GPS数据,在一定程度上包含了部分居民出行行为的丰富信息。考虑到出租车载客事件发生于一维道路网络空间,本文提出对出租车上下客事件所在路段进行分析,得到不同时段居民出行的热门路段和区域,分析居民出行时空分布特征,有助于了解交通现状和居民出行需求,提高城市对居民出行活动的服务水平。     

Personalized travel route recommendation using collaborative filtering based on GPS trajectories

Travelling is a critical component of daily life. With new technology, personalized travel route recommendations are possible and have become a new research area. A personalized travel route recommendation refers to plan an optimal travel route between two geographical locations, based on the road networks and users’ travel preferences. In this paper, we define users’ travel behaviours from their historical Global Positioning System (GPS) trajectories and propose two personalized travel route recommendation methods – collaborative travel route recommendation (CTRR) and an extended version of CTRR (CTRR+). Both methods consider users’ personal travel preferences based on their historical GPS trajectories. In this paper, we first estimate users’ travel behaviour frequencies by using collaborative filtering technique. A route with the maximum probability of a user’s travel behaviour is then generated based on the naïve Bayes model. The CTRR+ method improves the performances of CTRR by taking into account cold start users and integrating distance with the user travel behaviour probability. This paper also conducts some case studies based on a real GPS trajectory data set from Beijing, China. The experimental results show that the proposed CTRR and CTRR+ methods achieve better results for travel route recommendations compared with the shortest distance path method.     

利用词向量模型分析城市道路交通空间相关性

刻画城市道路之间的交通相关性是提高交通插值及预测水平的基础。现有研究及应用通常假设一定空间或拓扑距离内的道路相互之间具有相关性,这种方式忽视了道路之间交通影响的时空异质性。例如,上游道路交通流通常不会均匀扩散到所有下游道路,而是集中在特定方向上。道路之间产生交通影响和交互作用的根本原因是大量机动车辆穿梭其中。为从数据驱动的角度度量道路之间的交通相关性,从而顾及其时空异质性,本文利用词向量模型Word2Vec从大量机动车出行路径中挖掘道路之间的交通交互影响关系。首先把"路段-路径"类比为"词-文档";其次利用Word2Vec模型从大量路径(文档)中为每条路段(词)训练出一个实数向量(词向量);然后以向量之间的余弦相似度度量对应路段之间的交通相关性;最后利用交通状态数据对结果进行验证。以北京市200万条出租车出行路径为数据进行试验,结果表明:(1)平均水平上,向量相似度越高的邻近路段,其交通状态变化趋势也越相似,证明了本文方法可以正确度量道路之间的交通相关性,并刻画出其空间异质性;(2)工作日早、晚高峰及节假日路段之间的交通相关性大于工作日平峰和周六日,其合理性体现了本文方法可以正确捕捉道路交通相关性的时间异质性。本文方法及分析可为交通规划、诱导等提供方法论和理论基础。     

Inferring trip purposes and uncovering travel patterns from taxi trajectory data

Global positioning system-enabled vehicles provide an efficient way to obtain large quantities of movement data for individuals. However, the raw data usually lack activity information, which is highly valuable for a range of applications and services. This study provides a novel and practical framework for inferring the trip purposes of taxi passengers such that the semantics of taxi trajectory data can be enriched. The probability of points of interest to be visited is modeled by Bayes’ rules, which take both spatial and temporal constraints into consideration. Combining this approach with Monte Carlo simulations, we conduct a study on Shanghai taxi trajectory data. Our results closely approximate the residents’ travel survey data in Shanghai. Furthermore, we reveal the spatiotemporal characteristics of nine daily activity types based on inference results, including their temporal regularities, spatial dynamics, and distributions of trip lengths and directions. In the era of big data, we encounter the dilemma of “trajectory data rich but activity information poor” when investigating human movements from various data sources. This study presents a promising step toward mining abundant activity information from individuals’ trajectories.     

基于GPS时空轨迹的路网拓扑自动变化检测

充分利用出租车GPS时空轨迹数据分布广和时效性强的特点,提出一种基于车载GPS轨迹数据的路网拓扑自动变化检测新方法。该方法首先利用向量相似性度量模型,度量GPS轨迹向量与路网局部拓扑向量之间的相似性,检测疑似道路拓扑变化点,然后通过比较疑似道路拓扑变化点与路网拓扑关系,完成新增、废弃、改建等道路变化,实现基于车载GPS轨迹的路网拓扑自动变化检测。实验结果表明,该方法不仅有效地检测出道路新增、道路废弃与道路改扩建等变化,而且能利用出租车实时和大范围分布特点来实现城市路网大范围实时变化检测。     

Detecting vehicle traffic patterns in urban environments using taxi trajectory intersection points

Abstract

Detecting and describing movement of vehicles in established transportation infrastructures is an important task. It helps to predict periodical traffic patterns for optimizing traffic regulations and extending the functions of established transportation infrastructures. The detection of traffic patterns consists not only of analyses of arrangement patterns of multiple vehicle trajectories, but also of the inspection of the embedded geographical context. In this paper, we introduce a method for intersecting vehicle trajectories and extracting their intersection points for selected rush hours in urban environments. Those vehicle trajectory intersection points (TIP) are frequently visited locations within urban road networks and are subsequently formed into density-connected clusters, which are then represented as polygons. For representing temporal variations of the created polygons, we enrich these with vehicle trajectories of other times of the day and additional road network information. In a case study, we test our approach on massive taxi Floating Car Data (FCD) from Shanghai and road network data from the OpenStreetMap (OSM) project. The first test results show strong correlations with periodical traffic events in Shanghai. Based on these results, we reason out the usefulness of polygons representing frequently visited locations for analyses in urban planning and traffic engineering.     

基于众源数据的室内外一体化行人路网构建

行人路网的完整性和准确性是保障步行导航服务的关键。当前的行人路网大多是基于室外道路设施构建的,缺乏室内可步行路径的数据支持,无法在导航应用中提供准确、真实的最优路径规划服务。鉴于此,本文提出了一种基于众源数据的室内外一体化行人路网构建方法,采用智能手机定位传感器与惯性传感器记录的众源轨迹,首先对缺失或者漂移的室内步行数据进行筛选,然后使用改进的行人航位推算(PDR)方法推算出准确的室内轨迹,进而采用莫尔斯理论生成涵盖室内外行人路径的完整行人路网。试验分析中对搜集到的260条步行轨迹数据进行行人路网构建,并使用高精度测量设备采集真实路网数据进行对比分析,结合OSM数据对试验结果进行综合评价。试验结果表明,本文方法能够准确、完整地生成室内外一体化行人路网。     

利用道路网眼实现路网的增量式更新

获取现势性的交通道路数据是数字城市和智慧城市建设的基础，基于传统测绘的道路网更新方法存在一定局限性，而基于众源数据及行车轨迹数据更新道路网近年来则倍受关注。首先提出了一种新的道路变化增量更新方法，该方法先对历史道路网建立面拓扑结构，生成由道路网组成的最小闭合面域（道路网眼）；然后以道路网眼为基本控制单元，综合利用轨迹点上下文距离信息和隐马尔可夫模型（hidden Markov model，HMM），提取失配轨迹点和失配轨迹段；最后采用缓冲区分析和最大密度法对失配轨迹提取骨架线，创建新增道路，增量更新历史道路网。实验结果表明，以道路网眼为控制单元，利用轨迹点上下文距离分析和HMM捕获失配轨迹点，可提高失配轨迹点的提取效率，改善道路网更新效果。该方法可用于大规模路网的增量式更新。     

应用人工免疫算法快速规划3DTF/TA~2航线

针对复杂环境下单(双)机TF/TA2飞行路径规划问题,提出了满足飞机可飞性和可操作性的人工免疫算法飞行航线规划方法;设计了综合3D威胁信息的惩罚函数(亲和力函数);构建了包含动态、静态威胁及不可飞区域的综合威胁模型。据此,设计了不同威胁度环境下的单、双飞行航线,并将人工免疫算法规划的航线与遗传算法进行比较。结果表明,在简单威胁环境下,遗传算法规划时间短、路径较长;在复杂环境下,遗传算法的单机规划失败率极高(大于95%),双机规划失败。人工免疫算法能够为单(双)机规划出一条最优及多条待选飞行航线。     

Assessing Raster GIS Approximation for Euclidean Shortest Path Routing

Identifying a route that avoids obstacles in continuous space is important for infrastructure alignment, robotic travel, and virtual object path planning, among others, because movement through space is not restricted to a predefined road or other network. Vector and raster GIS (geographic information system) solution approaches have been developed to find good/efficient routes. On the vector side, recent solution approaches exploit spatial knowledge and utilize GIS functionality, offering significant computational advantages in finding an optimal solution to this path routing problem. Raster‐based shortest path techniques are widely applied in route planning for wayfinding, corridor alignment, robotics and video gaming to derive an obstacle avoiding path, but represent an approximation approach for solving this problem. This research compares vector and raster approaches for identifying obstacle‐avoiding shortest paths/routes. Empirical assessment is carried out for a number of planning applications, highlighting representational issues, computational requirements and resulting path efficiency.     

网络约束下局部空间同位模式的扫描统计方法

空间同位模式挖掘旨在发现空间数据库中频繁发生在邻近位置的地理事件。由于空间异质性,地理事件在不同区域邻近出现的频繁程度亦存在差异,进而形成局部同位模式。现有局部同位模式挖掘方法多基于欧氏空间的平面假设,难以客观揭示网络空间（如城市道路）内地理事件间的局部同位规律,因此基于空间扫描统计思想,提出了一种网络约束下的局部同位模式挖掘方法。首先,发展了网络约束下的路径扩展方法,识别可能存在局部网络空间同位模式的候选路径;其次,基于网络约束下的二元泊松分布构建显著性检验的零模型,判别候选路径中局部网络空间同位模式的有效性。通过模拟实验与北京市出租车供需模式分析,发现该方法比现有方法得到的结果更精细、更客观,能够有效地挖掘网络约束下的局部同位模式。     

A multi‐modal transportation data‐driven approach to identify urban functional zones: An exploration based on Hangzhou City,China

Recent urban studies have used human mobility data such as taxi trajectories and smartcard data as a complementary way to identify the social functions of land use. However, little work has been conducted to reveal how multi‐modal transportation data impact on this identification process. In our study, we propose a data‐driven approach that addresses the relationships between travel behavior and urban structure: first, multi‐modal transportation data are aggregated to extract explicit statistical features; then, topic modeling methods are applied to transform these explicit statistical features into latent semantic features; and finally, a classification method is used to identify functional zones with similar latent topic distributions. Two 10‐day‐long “big” datasets from the 2,370 bicycle stations of the public bicycle‐sharing system, and up to 9,992 taxi cabs within the core urban area of Hangzhou City, China, as well as point‐of‐interest data are tested to reveal the extent to which different travel modes contribute to the detection and understanding of urban land functions. Our results show that: (1) using latent semantic features delineated from the topic modeling process as the classification input outperforms approaches using explicit statistical features; (2) combining multi‐modal data visibly improves the accuracy and consistency of the identified functional zones; and (3) the proposed data‐driven approach is also capable of identifying mixed land use in the urban space. This work presents a novel attempt to uncover the hidden linkages between urban transportation patterns with urban land use and its functions.     

Geovisualization of Human Hybrid Activity-Travel Patterns

While GIS‐based geovisualization techniques have been fruitfully employed in a wide range of fields, they have not been applied to explore human hybrid activity‐travel patterns (i.e. the patterns of people's activities and/or travel in both the physical and virtual worlds). This study develops two geovisualization approaches using 3D and 2D GIS techniques to address the difficulties faced by past studies. First, the notions of information cube and hybrid 3D space‐time paths are developed to accommodate cyberspatial activities in exploratory data analysis. Second, a new representation of 2D space‐time paths that incorporates parallel coordinate plots is developed for exploring the multiple attributes of people's Internet and physical activities. These two methods were implemented using ArcObjects and the Visual Basic for Applications environment in ArcGIS. They were used to visualize a complex Internet‐activity diary dataset collected in Columbus (Ohio, USA). The study shows that the 3D and 2D geovisualization methods developed allow us to uncover important patterns in hybrid human activity‐travel behavior.     

轨迹分割与图层融合的车辆轨迹线构建道路地图方法

传统道路地图构建方法将轨迹点（线）同等对待提取道路数据,忽略车辆轨迹的空间差异性,制约其结果精度与应用范围。为此,本文根据轨迹速度将轨迹线集分割滤选为3个轨迹线子集,将轨迹方向与Delaunay三角网模型集成探测路网拓扑结构;顾及轨迹线子集的特征差异选取不同参数值和约束条件并分层提取道路几何、交通语义数据,分别构建3个道路图层;运用缓冲区方法并根据道路几何、语义特征将多个道路图层融合为单个完整道路地图。运用出租车轨迹数据进行试验分析,结果表明：该方法顾及轨迹分布差异性,能将道路几何、交通语义信息融合提取,更适于处理复杂道路结构下的轨迹线。