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。     

Travel time estimation at intersections based on low-frequency spatial-temporal GPS trajectory big data

ABSTRACT

Intersections are the critical parts where different traffic flows converge and change directions, forming “bottlenecks” and “clog points” in urban traffic. Intersection travel time is an important parameter for public route planning, traffic management, and engineering optimization. Based on low-frequency spatial-temporal Global Positioning System (GPS) trace data, this article presents a novel method for estimating intersection travel time. The proposed method first analyzes the different travel patterns of vehicles through an intersection, then determines the range of an intersection dynamically and reasonably, and obtains traffic flow speed and delay at the intersection under different travel patterns using a fuzzy fitting approach. Finally, the average intersection travel time is estimated from traffic flow speed and delay and intersection range in different travel patterns. Wuhan road network data and GPS trace data from taxicabs were tested in the experiments and the results show that the proposed method can improve the accuracy of travel time estimation at city intersections.     

人类行为时空特性的统计力学

利用手机话单数据分析城市个体居民移动活动的时间熵和空间熵特征,一方面探讨了从原始话单记录中进行出行识别的必要性,另一方面提出了一种考虑空间邻近性的轨迹近似熵特征分析方法。其中,出行识别可以克服手机定位数据采样频率较低的缺陷;近似熵分析方法具有强空间鲁棒性,可以减少因手机定位数据空间精度较低带来的影响。实证结果表明,城市居民出行活动既具有强烈的目的地选择倾向,同时也具有强烈的移动路径选择偏好。     

城市手机用户移动轨迹时空熵特征分析

利用手机话单数据分析城市个体居民移动活动的时间熵和空间熵特征,一方面探讨了从原始话单记录中进行出行识别的必要性,另一方面提出了一种考虑空间邻近性的轨迹近似熵特征分析方法。其中,出行识别可以克服手机定位数据采样频率较低的缺陷;近似熵分析方法具有强空间鲁棒性,可以减少因手机定位数据空间精度较低带来的影响。实证结果表明,城市居民出行活动既具有强烈的目的地选择倾向,同时也具有强烈的移动路径选择偏好。     

顾及交叉路口和车辆模态的轨迹重构与分析

由于数据传输和存储成本的限制,大多数轨迹数据采样率低且不确定,而城市精细模型往往需要高频轨迹数据,例如,微观交通碳排模型需要时间间隔为1 s的轨迹数据。因此,对低频轨迹数据进行高频重构有非常重要的意义。提出了一种顾及交叉路口和车辆模态的轨迹重构方法,采用高频轨迹数据训练车辆运动模态的理论概率模型,结合交叉路口来确定低频轨迹点之间的模态序列,并通过遗传算法求解理论概率模型来完成各模态时间和距离的分配,进而完成轨迹点的高频重构。结果表明,所提方法重构轨迹的均方根误差（root mean square error,RMSE）值相较于传统的数学插值方法降低了62.9%,相较于未考虑交叉路口的模态方法,降低了12.2%。因此,该方法在低频轨迹数据重构中具有很好的应用价值。     

Extracting spatial patterns in bicycle routes from crowdsourced data

Much is done nowadays to provide cyclists with safe and sustainable road infrastructure. Its development requires the investigation of road usage and interactions between traffic commuters. This article is focused on exploiting crowdsourced user‐generated data, namely GPS trajectories collected by cyclists and road network infrastructure generated by citizens, to extract and analyze spatial patterns and road‐type use of cyclists in urban environments. Since user‐generated data shows data‐deficiencies, we introduce tailored spatial data‐handling processes for which several algorithms are developed and implemented. These include data filtering and segmentation, map‐matching and spatial arrangement of GPS trajectories with the road network. A spatial analysis and a characterization of road‐type use are then carried out to investigate and identify specific spatial patterns of cycle routes. The proposed analysis was applied to the cities of Amsterdam (The Netherlands) and Osnabrück (Germany), proving its feasibility and reliability in mining road‐type use and extracting pattern information and preferences. This information can help users who wish to explore friendlier and more interesting cycle patterns, based on collective usage, as well as city planners and transportation experts wishing to pinpoint areas most in need of further development and planning.     

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,准确地汁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佔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-系列落在地理格网单;(Cell)中的路段模式及轨迹段;然肩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;最肩通过一组防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10000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,以及在估算较氏路径上车辆通行时间的优越性。     

车辆轨迹数据提取道路交叉口特征的决策树模型

众源车辆轨迹数据隐含最新的道路分布信息,研究利用轨迹数据提取道路特征有益于基础路网数据的快速建库与更新。道路网由交叉口和连接交叉口的道路线构成,其中交叉口特征识别是整个道路网生成的关键。由于缺乏精细的交叉口识别模型,轨迹数据生成的道路网容易出现路口遗漏、结构失真等现象。针对这一问题,本文提出一种利用轨迹数据提取道路交叉口的方法。首先,分析车辆在交叉口与非交叉口区域移动轨迹几何形态及隐含动力学特征的变化情形;然后,利用决策树方法构建轨迹片段分类模型,并结合移动开窗式的轨迹线剖分模型建立交叉口区域变道轨迹片段提取方法;最后,依据Hausdorff距离对交叉口区域轨迹片段进行聚类,并提取中心线获得完整的道路交叉口结构。采用真实的车辆轨迹线作为测试数据,验证了本文提出方法的有效性。     

一种利用低频轨迹数据提取道路交叉口的方法

为实现从低频轨迹数据中提取城市道路交叉口,本文设计了一种基于数据预处理与聚类算法的道路交叉口精准识别方法。首先结合轨迹数据的特征,采用启发式滤波算法对原始数据进行清洗,剔除冗余点与异常点;然后依据车辆的运行规律,提出了一种分步式道路交叉口的提取算法,由此计算出疑似道路交叉口的特征点;最后利用层次密度聚类算法(HDBSCAN)对筛选过后的轨迹点进行聚类并提取质心,得到道路的交叉口,最终以成都市某日的出租车行驶轨迹为数据源,进行试验分析。结果表明,使用该算法提取交叉口,精确率达95.33%、召回率达82.11%、F值达88.46%,能有效且准确识别城市道路交叉口信息,在城市管理与交通规划中具有一定的应用价值。     

Trajectory analysis at intersections for traffic rule identification

In this paper,we focus on trajectories at intersections regulated by various regulation types such as traffic lights,priority/yield signs,and right-of-way rules.We test some methods to detect and recognize movement patterns from GPS trajectories,in terms of their geometrical and spatio-temporal components.In particular,we first find out the main paths that vehicles follow at such locations.We then investigate the way that vehicles follow these geometric paths(how do they move along them).For these scopes,machine learning methods are used and the performance of some known methods for trajectory similarity measurement(DTW,Hausdorff,and Fréchet distance)and clustering(Affinity propagation and Agglomerative clustering)are compared based on clustering accuracy.Afterward,the movement behavior observed at six different intersections is analyzed by identifying certain movement patterns in the speed-and time-profiles of trajectories.We show that depending on the regulation type,different movement patterns are observed at intersections.This finding can be useful for intersection categorization according to traffic regulations.The practicality of automatically identifying traffic rules from GPS tracks is the enrichment of modern maps with additional navigation-related information(traffic signs,traffic lights,etc.).     

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。     

出租车GPS轨迹集聚和精细化路网提取

针对利用GPS数据提取双向路网和交叉路口转向信息精度低的不足,本文提出一种顾及位置与行驶方向的轨迹集聚和精细化路网提取方法,实现了精细化路网提取。为提高交叉路口路网的提取精度,首先剔除原始轨迹中的离散和异常轨迹点,并按一定的步长对轨迹段进行加密;然后引入行驶方向角来表达车辆在轨迹点处的行驶方向,顾及位置和行驶方向获取每个轨迹点的相似轨迹点集合;接着依次计算每个轨迹点的偏移距离,通过对轨迹点的迭代偏移完成轨迹集聚;最后剔除未成功集聚的轨迹点,将完成集聚的轨迹点连成轨迹线并作缓冲区,运用栅格数据数字化方法提取得到能够反映道路精细转向关系的道路网。以福州市出租车GPS数据进行轨迹集聚和路网提取试验,结果表明:本文方法能有效地将GPS轨迹按车辆行驶方向分别进行集聚,提取的道路网为双向道路并且能反映交叉路口处道路的精细转向关系。     

一种有效的复杂多边形裁剪算法

提出了一种基于扫描线思想和梯形分割技术的多边形裁剪算法,其主要步骤包括:计算主多边形(集)与窗口多边形(集)的交点,提取所有交点和多边形边界结点的纵坐标(y)并进行排序;以排序后的y作水平扫描线,分别对主多边形和窗口多边形进行梯形分割,获得两组梯形集合;对这两组梯形集合逐行执行梯形单元“交”运算,最后对结果交集进行边界追踪建立裁剪多边形。实验结果表明,该算法具有较好的计算效率,是一种比较实用的多边形裁剪算法。     

Discovering spatial and temporal patterns from taxi-based Floating Car Data: a case study from Nanjing

Floating Car Data (FCD) refers to the trajectories of vehicles equipped with Global Positioning System-enabled devices that automatically record location-related data within a short time interval. As taxies in Chinese cities continually drive along the streets seeking passengers, FCD can easily traverse the entire street network in a city on a daily basis. Taking advantage of this situation, this study extracted passenger pickup and drop-off locations from FCD sourced from 6445 taxis over a 2-week period in Nanjing, China to discover human behavioral patterns and the dynamics behind them. In this study, road nodes are converted to the points, based on which Thiessen polygons are generated to divide the study area into small areas with the goal of exploring the spatial distribution of pickup and drop-off locations. Moran’s I index is used to calculate the spatial autocorrelation of the spatial distribution of pickup and drop-off locations, and hot spot analysis is used to identify statistically significant spatial clusters of hot and cold spots. The spatial and temporal patterns of FCD in the study area are investigated, and the results show that: (1) the temporal patterns show a strong daily rhythm, (2) the spatial patterns show that the number of pickup and drop-off locations gradually diminish from the downtown areas to the outer suburbs, (3) the spatiotemporal patterns exhibit large differences over time, and (4) the driving forces explored by regression models indicate that population density and transportation density are consistent with the population distribution, but per capita disposable income is not consistent with the population distribution.     

Extracting commuter-specific destination hotspots from trip destination data – comparing the boro taxi service with Citi Bike in NYC

ABSTRACT

Taxi trajectories from urban environments allow inferring various information about the transport service qualities and commuter dynamics. It is possible to associate starting and end points of taxi trips with requirements of individual groups of people and even social inequalities. Previous research shows that due to service restrictions, boro taxis have typical customer destination locations on selected Saturdays: many drop-off clusters appear near the restricted zone, where it is not allowed to pick up customers and only few drop-off clusters appear at complicated crossing. Detected crossings imply recent infrastructural modifications. We want to follow up on these results and add one additional group of commuters: Citi Bike users. For selected Saturdays in June 2015, we want to compare the destinations of boro taxi and Citi Bike users. This is challenging due to manifold differences between active mobility and motorized road users, and, due to the fact that station-based bike sharing services are restricted to stations. Start and end points of trips, as well as the volumes in between rely on specific numbers of bike sharing stations. Therefore, we introduce a novel spatiotemporal assigning procedure for areas of influence around static bike sharing stations for extending available computational methods.     

Modeling the traffic disruption caused by pluvial flash flood on intra‐urban road network

Pluvial flash flood (PFF) can cause serious traffic disruption in big cities. We conducted interdisciplinary research by integrating flood modeling and traffic analysis to reveal the spatiotemporal pattern of the interplay between these two processes. A simplified simulation tool, which is capable of building a road network model, assigning trip paths with the effect of road closures, and evaluating travel delay and vehicle volume redistribution in a given PFF scenario, was developed to capture the traffic disruption in the face of PFF events. Modeling outputs from a case study in the city center of Shanghai showed that the delay of vehicles diverted to dry links or trapped in flooded links may reach 0.5 to 8 times the travel time in no‐flood scenarios. Overall, approximately 1–7% of vehicle volumes on flooded links would be redistributed onto dry links (more likely major arterial roads). However, the vehicle volume variation during each time interval demonstrated evident disparity with the spatiotemporal change of flood inundations. Simulating and mapping the congestion can largely facilitate the identification of vulnerable links. Future research will test the method in other intra‐urban areas and try to bridge the gap between modeling outputs and smart city planning and management.     

利用CART分类树分类检测交通拥堵点

交通拥堵检测是城市交通管理工作的重点和难点之一,现有的拥堵检测以路段为单位,不利于拥堵时空演变规律信息的提取,且检测内容大多只涉及拥堵程度,缺少对拥堵类型的识别。基于CART（classification and regression tree）分类树算法,提出一种以路段点为检测单元的拥堵点分类检测方法,该方法可根据路段平均行驶速度实时检测拥堵点及其类型。首先,将路段等距离划分后映射为路段点,根据时空维路况异常规则和异常模式,以路段点为单元分析了4种拥堵类型的时空演变模式;其次,在路段路况检测的基础上,提取路段点路况时空序列,根据不同类型的拥堵模式对路况时空序列进行分类标记;然后,选取4种速度指标作为样本属性集合,按照属性集合提取各路段点在各时段的速度,以此作为决策树学习的数据集;最后,基于CART分类树算法,采用交叉验证的方式训练出最优模型,使其达到最佳的泛化能力。与支持向量机（support vector machine, SVM）分类模型进行比较,实验结果表明,该方法在分类检测交通拥堵点时具有较高的正确率和召回率,且分类检测时效性较好。     

基于GIS的青岛市中心城区道路网络可达性研究

针对青岛市中心城区交通网络建设不完善的问题,研究了该区域路网的综合可达性,并提出了相应的解决方案。基于GIS平台,建立了青岛市中心城区路网模型;选取最小阻抗(时间和距离)、交叉口服务指数、路网密度和路网连通度作为关键评价指标,分别从路网距离关系、路网结构合理性、路网密度和路网发育程度4个方面分析了该区域的路网可达性;在此基础上,建立了青岛市中心城区路网综合评价模型。研究结果表明,该区域的路网以多中心多圈层形式分布,整体可达性良好;西南区域路网密度低,南部外围区域机动性差,南部地区可达性较差,应不断加强该地区的基础设施与路网建设。     

基于点序列和要素加权法的地图匹配算法

浮动车数据(Floating Car Data,FCD)已广泛应用于城市规划、智能交通系统中,其中地图匹配一直以来都是浮动车数据应用的技术难点。本文在已有地图匹配算法的基础上,提出了基于点序列和要素加权法的地图匹配模型,不仅考虑了当前GPS点的信息,同时也考虑了GPS数据的历史信息和道路网的拓扑结构,从空间关系上分析车辆行驶轨迹和道路的相似性。作者通过上海市出租车轨迹数据对算法进行验证,结果表明:该匹配模型解决了已有地图匹配算法的一些弊端,并且提高了地图匹配的精度,具有高效、实用的特点。     

Traj2Traj: A road network constrained spatiotemporal interpolation model for traffic trajectory restoration

In transportation, the trajectory data generated by various mobile vehicles equipped with GPS modules are essential for traffic information mining. However, collecting trajectory data is susceptible to various factors, resulting in the lack and even error of the data. Missing trajectory data could not correctly reflect the actual situation and also affect the subsequent research work related to the trajectory. Although increasing efforts are paid to restore missing trajectory data, it still faces many challenges: (1) the difficulty of data restoration because traffic trajectories are unstructured spatiotemporal data and show complex patterns; and (2) the difficulty of improving trajectory restoration efficiency because traditional trajectory interpolation is computationally arduous. To address these issues, a novel road network constrained spatiotemporal interpolation model, namely Traj2Traj, is proposed in this work to restore the missing traffic trajectory data. The model is constructed with a seq2seq network and integrates a potential factor module to extend environmental factors. Significantly, the model uses a spatiotemporal attention mechanism with the road network constraint to mine the latent information in time and space dimensions from massive trajectory data. The Traj2Traj model completes the road-level restoration according to the entire trajectory information. We present the first attempt to omit the map-matching task when the trajectory is restored to solve the time-consuming problem of map matching. Extensive experiments conducted on the provincial vehicle GPS data sets from April 2018 to June 2018 provided by the Fujian Provincial Department of Transportation show that the Traj2Traj model outperforms the state-of-the-art models.