A Semantics-Based Trajectory Segmentation Simplification Method

AbstractWith the development of mobile positioning technology, a large amount of mobile trajectory data has been generated. Therefore, to store, process and mine trajectory data in a better way, trajectory data simplification is imperative. Current trajectory data simplification methods are either based on spatiotemporal features or semantic features, such as road network structure, but they do not consider semantic features of a trajectory stop. To overcome this limitation, this study presents a trajectory segmentation simplification method based on stop features. The proposed method first extracts the stop feature of a trajectory, then divides the trajectory into move segments and stop segments based on the stop features, and finally simplifies the obtained segments. The proposed method is verified by experiments on personal trajectory data and taxi trajectory data. Compared with the classic spatiotemporal simplification method, the proposed method has higher spatiotemporal and semantic accuracy under different simplification scales. The proposed method is especially suitable for trajectory data with more stop features.

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A Method of Extracting Road Network Structure from Trajectory Data Based on U-Net Network

10.1109/iciba50161.2020.9277039 ◽

2020 ◽

Author(s):

Shihao Feng ◽

Luo Chen ◽

Wei Xiong ◽

Yilin Deng

Keyword(s):

Network Structure ◽

Road Network ◽

Trajectory Data

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A Road Network Enhanced Gate Recurrent Unit Model for Gather Prediction in Smart Cities

Wireless Communications and Mobile Computing ◽

10.1155/2021/6030144 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Mingchao Yuan ◽

Ling Tian ◽

Ke Yan ◽

Xu Zheng

Keyword(s):

Real Time ◽

Network Structure ◽

Road Network ◽

Smart Cities ◽

Great Influence ◽

City Government ◽

Model Parameters ◽

Trajectory Data ◽

Trajectory Prediction ◽

Unit Model

Gather prediction is an indispensable part of smart city projects. The city government can respond in advance based on gather predictions and greatly reduce the loss and risks caused by vicious gatherings. Compared with other trajectory prediction tasks (i.e., the recommendation of point of interest), gather prediction pay more attention to real-time trajectory data and requests stronger spatial-temporal dependence. At the same time, gather prediction is more focused on scenes with multiple types of trajectories. And the existing methods majorly rely on the trajectory data and ignore the great influence of geographical environment (i.e., road network structure). Therefore, this paper transforms the gather prediction into the trajectory prediction task with strong real-time condition in a certain city and conducts the gathering situations by predicting users’ aggregated movements in next minutes or hours. A novel Spatiotemporal Gate Recurrent Unit (STGRU) model is proposed, where spatiotemporal gates and road network gate are introduced to capture the spatiotemporal relationships between trajectories. Compared with existing methods, we improve the performance of the model by adding road network structure and external knowledges, as well as time and distance gates to reduce model parameters. The proposed STGRU is evaluated on three real-world trajectory datasets, and the experimental results demonstrate the effectiveness of the proposed model.

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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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Forest path condition monitoring based on crowd-based trajectory data analysis

Journal of Ambient Intelligence and Smart Environments ◽

10.3233/ais-200586 ◽

2020 ◽

pp. 1-18

Author(s):

Francisco Arcas-Tunez ◽

Fernando Terroso-Saenz

Keyword(s):

Information Acquisition ◽

Road Network ◽

Road Networks ◽

Trajectory Data ◽

Country Road ◽

Wide Range ◽

Sport Activities ◽

Path Condition ◽

Spatio Temporal ◽

The City

The development of Road Information Acquisition Systems (RIASs) based on the Mobile Crowdsensing (MCS) paradigm has been widely studied for the last years. In that sense, most of the existing MCS-based RIASs focus on urban road networks and assume a car-based scenario. However, there exist a scarcity of approaches that pay attention to rural and country road networks. In that sense, forest paths are used for a wide range of recreational and sport activities by many different people and they can be also affected by different problems or obstacles blocking them. As a result, this work introduces SAMARITAN, a framework for rural-road network monitoring based on MCS. SAMARITAN analyzes the spatio-temporal trajectories from cyclists extracted from the fitness application Strava so as to uncover potential obstacles in a target road network. The framework has been evaluated in a real-world network of forest paths in the city of Cieza (Spain) showing quite promising results.

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Analyzing Spatiotemporal Daily Travel Source Carbon Emissions Based on Taxi Trajectory Data

IEEE Access ◽

10.1109/access.2021.3101409 ◽

2021 ◽

pp. 1-1

Author(s):

Maopeng Sun ◽

Chenlei Xue ◽

Yanqiu Cheng ◽

Ling Zhao ◽

Zhiyou Long

Keyword(s):

Carbon Emissions ◽

Trajectory Data ◽

Taxi Trajectory ◽

Daily Travel

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An Automatic Method for Detection and Update of Additive Changes in Road Network with GPS Trajectory Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8090411 ◽

2019 ◽

Vol 8 (9) ◽

pp. 411 ◽

Cited By ~ 3

Author(s):

Tang ◽

Deng ◽

Huang ◽

Liu ◽

Chen

Keyword(s):

Road Network ◽

Sampling Rate ◽

Quality Data ◽

Automatic Method ◽

Trajectory Data ◽

The Road ◽

High Sampling Rate ◽

Map Generation ◽

Gps Trajectory Data ◽

Gps Trajectory

Ubiquitous trajectory data provides new opportunities for production and update of the road network. A number of methods have been proposed for road network construction and update based on trajectory data. However, existing methods were mainly focused on reconstruction of the existing road network, and the update of newly added roads was not given much attention. Besides, most of existing methods were designed for high sampling rate trajectory data, while the commonly available GPS trajectory data are usually low-quality data with noise, low sampling rates, and uneven spatial distributions. In this paper, we present an automatic method for detection and update of newly added roads based on the common low-quality trajectory data. First, additive changes (i.e., newly added roads) are detected using a point-to-segment matching algorithm. Then, the geometric structures of new roads are constructed based on a newly developed decomposition-combination map generation algorithm. Finally, the detected new roads are refined and combined with the original road network. Seven trajectory data were used to test the proposed method. Experiments show that the proposed method can successfully detect the additive changes and generate a road network which updates efficiently.

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