Dynamic Update and Monitoring of AOI Entrance via Spatiotemporal Clustering of Drop-Off Points

This paper proposes a novel method for dynamically extracting and monitoring the entrances of areas of interest (AOIs). Most AOIs in China, such as buildings and communities, are enclosed by walls and are only accessible via one or more entrances. The entrances are not marked on most maps for route planning and navigation in an accurate way. In this work, the extraction scheme of the entrances is based on taxi trajectory data with a 30 s sampling time interval. After fine-grained data cleaning, the position accuracy of the drop-off points extracted from taxi trajectory data is guaranteed. Next, the location of the entrances is extracted, combining the density-based spatial clustering of applications with noise (DBSCAN) with the boundary of the AOI under the constraint of the road network. Based on the above processing, the dynamic update scheme of the entrance is designed. First, a time series analysis is conducted using the clusters of drop-off points within the adjacent AOI, and then, a relative heat index ( R H I ) is applied to detect the recent access status (closed or open) of the entrances. The results show the average accuracy of the current extraction algorithm is improved by 24.3% over the K-means algorithm, and the R H I can reduce the limitation of map symbols in describing the access status. The proposed scheme can, therefore, help optimize the dynamic visualization of the entry symbols in mobile navigation maps, and facilitate human travel behavior and way-finding, which is of great help to sustainable urban development.

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Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030161 ◽

2021 ◽

Vol 10 (3) ◽

pp. 161

Author(s):

Hao-xuan Chen ◽

Fei Tao ◽

Pei-long Ma ◽

Li-na Gao ◽

Tong Zhou

Keyword(s):

Spatial Analysis ◽

Spatial Clustering ◽

Heat Index ◽

Operating Efficiency ◽

Clustering Methods ◽

Clustering Method ◽

Trajectory Mining ◽

Density Peaks ◽

Analysis Methods ◽

Taxi Trajectory

Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods—KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estimation (KDE)—as examples, combined with the hotspot spatiotemporal mining problem of taxi trajectory, through quantitative analysis and experimental verification, it is found that DBSCAN and KDE algorithms have strong hotspot discovery capabilities, but the heat regions’ shape of DBSCAN is found to be relatively more robust. DBSCAN and CFSFDP can achieve high spatial accuracy in calculating the entrance and exit position of a Point of Interest (POI). KDE and DBSCAN are more suitable for the classification of heat index. When the dataset scale is similar, KMeans has the highest operating efficiency, while CFSFDP and KDE are inferior. This paper resolves to a certain extent the lack of scientific basis for selecting spatial analysis methods in current research. The conclusions drawn in this paper can provide technical support and act as a reference for the selection of methods to solve the taxi trajectory mining problem.

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Identify and Delimitate Urban Hotspot Areas Using a Network-Based Spatiotemporal Field Clustering Method

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8080344 ◽

2019 ◽

Vol 8 (8) ◽

pp. 344 ◽

Cited By ~ 3

Author(s):

Xia ◽

Li ◽

Chen ◽

Liao

Keyword(s):

Road Traffic ◽

Spatial Clustering ◽

Hotspot Detection ◽

Trajectory Data ◽

High Concentration ◽

Time Dimension ◽

Clustering Approach ◽

Rich Information ◽

Taxi Trajectory

Pick-up and drop-off events of taxi trajectory data contain rich information about residents’ travel activities and road traffic. Such data have been widely applied in urban hotspot detection in recent years. However, few studies have attempted to delimitate the urban hotspot scope using taxi trajectory data. On this basis, the current study firstly introduces a network-based spatiotemporal field (NSF) clustering approach to discover and identify hotspots. Our proposed method expands the notion from spatial to space–time dimension and from Euclidean to network space by comparing with traditional spatial clustering analyses. In addition, a concentration index of hotspot areas is presented to refine the surface of centredness to delimitate the hotspot scope further. This index supports the quantitative depiction of hotspot areas by generating two standard deviation isolines. In the case study, we analyze the spatiotemporal dynamic patterns of hotspots at different days and times of day using the NSF method. Meanwhile, we also validate the effectiveness of the proposed method in identifying hotspots to evaluate the delimitating results. Experimental results reveal that the proposed approach can not only help detect detailed microscale characteristics of urban hotspots but also identify high-concentration patterns of pick-up incidents in specific places.

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A prediction model of vessel trajectory based on generative adversarial network

Journal of Navigation ◽

10.1017/s0373463321000382 ◽

2021 ◽

pp. 1-11

Author(s):

Senjie Wang ◽

Zhengwei He

Keyword(s):

Safety Management ◽

Route Planning ◽

Local Area ◽

Trajectory Data ◽

Trajectory Prediction ◽

Generative Adversarial Network ◽

Ship Traffic ◽

Adversarial Network ◽

Historical Trajectory ◽

Vessel Motion

Abstract Trajectory prediction is an important support for analysing the vessel motion behaviour, judging the vessel traffic risk and collision avoidance route planning of intelligent ships. To improve the accuracy of trajectory prediction in complex situations, a Generative Adversarial Network with Attention Module and Interaction Module (GAN-AI) is proposed to predict the trajectories of multiple vessels. Firstly, GAN-AI can infer all vessels’ future trajectories simultaneously when in the same local area. Secondly, GAN-AI is based on adversarial architecture and trained by competition for better convergence. Thirdly, an interactive module is designed to extract the group motion features of the multiple vessels, to achieve better performance at the ship encounter situations. GAN-AI has been tested on the historical trajectory data of Zhoushan port in China; the experimental results show that the GAN-AI model improves the prediction accuracy by 20%, 24% and 72% compared with sequence to sequence (seq2seq), plain GAN, and the Kalman model. It is of great significance to improve the safety management level of the vessel traffic service system and judge the degree of ship traffic risk.

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Urban Function Identification Based on POI and Taxi Trajectory Data

Proceedings of the 2019 3rd International Conference on Big Data Research ◽

10.1145/3372454.3372468 ◽

2019 ◽

Cited By ~ 1

Author(s):

Xiaohui Mou ◽

Fei Cai ◽

Xin Zhang ◽

Jie Chen ◽

Rongrong Zhu

Keyword(s):

Trajectory Data ◽

Function Identification ◽

Urban Function ◽

Taxi Trajectory

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Taxi trajectory data based fast-charging facility planning for urban electric taxi systems

Applied Energy ◽

10.1016/j.apenergy.2021.116515 ◽

2021 ◽

Vol 286 ◽

pp. 116515

Author(s):

Hua Wang ◽

De Zhao ◽

Yutong Cai ◽

Qiang Meng ◽

Ghim Ping Ong

Keyword(s):

Facility Planning ◽

Trajectory Data ◽

Fast Charging ◽

Taxi Trajectory

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Measuring Accessibility Based on Improved Impedance and Attractive Functions Using Taxi Trajectory Data

Sustainability ◽

10.3390/su13010112 ◽

2020 ◽

Vol 13 (1) ◽

pp. 112

Author(s):

Helai Huang ◽

Jialing Wu ◽

Fang Liu ◽

Yiwei Wang

Keyword(s):

Waiting Time ◽

Urban Areas ◽

Transportation Systems ◽

Trajectory Data ◽

Peak Period ◽

Important Indicator ◽

Destination Attractiveness ◽

Urban Accessibility ◽

Spatio Temporal ◽

Taxi Trajectory

Accessibility has attracted wide interest from urban planners and transportation engineers. It is an important indicator to support the development of sustainable policies for transportation systems in major events, such as the COVID-19 pandemic. Taxis are a vital travel mode in urban areas that provide door-to-door services for individuals to perform urban activities. This study, with taxi trajectory data, proposes an improved method to evaluate dynamic accessibility depending on traditional location-based measures. A new impedance function is introduced by taking characteristics of the taxi system into account, such as passenger waiting time and the taxi fare rule. An improved attraction function is formulated by considering dynamic availability intensity. Besides, we generate five accessibility scenarios containing different indicators to compare the variation of accessibility. A case study is conducted with the data from Shenzhen, China. The results show that the proposed method found reduced urban accessibility, but with a higher value in southern center areas during the evening peak period due to short passenger waiting time and high destination attractiveness. Each spatio-temporal indicator has an influence on the variation in accessibility.

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