共查询到17条相似文献，搜索用时 109 毫秒
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《测绘科学》2020,(2)
为了利用轨迹数据提取城市居民出行特征,该文首先从出租车全球定位系统(GPS)轨迹数据中提取上下客事件,利用数理统计的方法对其时间分布特征进行研究;继而针对城市内数据不均匀分布的特性,提出了一种基于OPTICS聚类的改进方法,通过将原始聚类结果进行局部密度峰值的计算,实现了出租车上下客事件热点区域中心的进一步提取。最后,以成都市为实证对象,结合成都市电子地图及用地布局规划图探讨了居民出行时空特征与城市空间结构的相互关系,并对其成因做出了简要的分析,证明了该文所提出方法的有效性。实验结果表明:居民在工作日和休息日的出行特征呈现出明显的差异,且受到城市功能区域分化的影响。 相似文献
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《测绘科学》2021,(2)
为了利用轨迹数据提取城市居民出行特征,该文首先从出租车全球定位系统(GPS)轨迹数据中提取上下客事件,利用数理统计的方法对其时间分布特征进行研究;继而针对城市内数据不均匀分布的特性,提出了一种基于OPTICS聚类的改进方法,通过将原始聚类结果进行局部密度峰值的计算,实现了出租车上下客事件热点区域中心的进一步提取。最后,以成都市为实证对象,结合成都市电子地图及用地布局规划图探讨了居民出行时空特征与城市空间结构的相互关系,并对其成因做出了简要的分析,证明了本文所提出方法的有效性。实验结果表明:居民在工作日和休息日的出行特征呈现出明显的差异,且受到城市功能区域分化的影响。 相似文献
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出租车载客数据可以用于研究居民的出行特征,提取城市的交通热点区域,但对城市交通热点区域的交互关系研究相对较少。本文以纽约市的出租车行程记录数据为数据源,利用交通小区划分结合出租车载客数据提取城市交通热点区域,基于复杂网络的方法对不同日期类型和天气情况的城市交通热点区域空间交互网络进行研究并进行社区发现。结果表明,热点区域受城市核心区的影响而聚集在核心区域周围,城市内社区的形成可以克服地形和行政区域等因素的影响。研究结论有望为城市规划、城市交通管理、出租车调度、以及人们的出行等提供信息参考。 相似文献
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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。 相似文献
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本文以北京城市交通为例,选取北京城市路网数据,计算城市路网介数中心性,以反映城市各道路路段的通达情况;并利用北京出租车GPS定点数据,计算实际的交通轨迹。传统的路网介数中心性主要依据各路段最短路径的比重评价理论上的道路通达性,而本文以网络介数中心性为基础,提出动态介数的方法,从城市各路段交通量比重的角度评价各道路实际拥堵情况。利用两个介数的对比能够全面客观地反映交通拥堵状况,提高交通拥堵指数的参考价值,并为城市建设规划决策及道路改建等方案提供依据,有利于缓解城市道路交通拥堵现状。 相似文献
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针对现有出租车轨迹数据挖掘中时间序列邻近度量方法存在的问题,提出一种基于DBSCAN算法和改进的DTW距离的时间序列聚类算法提取具有相似性出行特征的时空模式,进而研究城市人群出行行为的时空差异。以南京市为例,结合电子地图对出行模式的空间分布特征进行分析,证明了本文所提出的方法的有效性。实验结果表明:在空间分布上,工作日出租车出行模式按照平均出行频次由高到低排序,从城市中心向四周扩散,呈中心环状分布,出行模式区域界限较为明显,同类出行模式分布区域对应相似的功能。提出了一种基于DBSCAN算法和改进的DTW距离的时间序列聚类算法提取具有相似性出行特征的时空模式,有效地分析城市人群出行行为的时空差异。 相似文献
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《武汉大学学报(信息科学版)》2021,(5)
出租车出行是表征空间不平等的重要指标,分析其特征对于城市规划、交通管理、智慧城市建设等具有重要意义。利用中国上海市出租车出行的车流数据与人口普查数据,对研究区域进行空间多尺度划分,运用Hadoop大数据处理、社会网络分析、地理加权回归等方法,探究中国上海市出租车出行的空间不平等状况及其与人口结构的关联性。结果表明:上海市存在出租车出行的空间不平等现象,在中心城区以及城市副中心区域,出租车出行便利性高于其他地区且平均出行距离小于其他地区;不同类型人口在出行能力上有差异,出行弱势人群(儿童、老人、外来人口、农业人口等)比例高的区域具有更低的出行便利性与更长的出行距离,这一相关性随空间位置与空间尺度的变化而发生变化。 相似文献
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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. 相似文献
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Qingquan Li Zhe Zeng Tong Zhang Jonathan Li Zhongheng Wu 《International Journal of Applied Earth Observation and Geoinformation》2011
Optimal paths computed by conventional path-planning algorithms are usually not “optimal” since realistic traffic information and local road network characteristics are not considered. We present a new experiential approach that computes optimal paths based on the experience of taxi drivers by mining a huge number of floating car trajectories. The approach consists of three steps. First, routes are recovered from original taxi trajectories. Second, an experiential road hierarchy is constructed using travel frequency and speed information for road segments. Third, experiential optimal paths are planned based on the experiential road hierarchy. Compared with conventional path-planning methods, the proposed method provides better experiential optimal path identification. Experiments demonstrate that the travel time is less for these experiential paths than for paths planned by conventional methods. Results obtained for a case study in the city of Wuhan, China, demonstrate that experiential optimal paths can be flexibly obtained in different time intervals, particularly during peak hours. 相似文献
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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. 相似文献
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随着城市化的快速发展,城市空间结构愈发复杂,城市功能区的快速有效识别对资源的有效配置和城市规划具有重要意义。传统的功能区识别缺乏对居民这一城市空间活动主体的动态表征,而长时间序列的出租车数据能动态表征居民出行行为,进而反映城市空间结构。动态时间扭曲(DTW)距离比传统的欧氏距离更能有效挖掘高维数据,泛化后的LB_Keogh距离和LB_Hust距离相继克服了DTW距离时间复杂度高和不对称的缺点。为了探究基于时间相似性度量的聚类算法在识别城市功能区方面的可行性,首先基于OpenStreetMap(OSM)路网数据获取研究单元,再通过滴滴订单数据提取上下车点、构建研究单元内的居民出行时间序列,然后利用PAM算法结合4种相似度度量方法进行聚类,最后结合兴趣点(POI)数据识别城市功能区,并对结果进行精度验证。结果表明,基于LB_Hust距离的PAM算法能有效挖掘高维时间序列数据,应用于城市功能区识别的精度高达86%,为应用时间序列数据进行城市研究提供了一种新的方法。 相似文献
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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. 相似文献
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Clustering is an important approach to identifying hotspots with broad applications, ranging from crime area analysis to transport prediction and urban planning. As an on-demand transport service, taxis play an important role in urban systems, and the pick-up and drop-off locations in taxi GPS trajectory data have been widely used to detect urban hotspots for various purposes. In this work, taxi drop-off events are represented as linear features in the context of the road network space. Based on such representation, instead of the most frequently used Euclidian distance, Jaccard distance is calculated to measure the similarity of road segments for cluster analysis, and further, a network distance and graph-partitioning-based clustering method is proposed for improving the accuracy of urban hotspot detection. A case study is conducted using taxi trajectory data collected from over 6500 taxis during one week, and the results indicate that the proposed method can identify urban hotspots more precisely. 相似文献