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Now showing items 1 - 16 of 20

  • Pedestrian network generation based on crowdsourced tracking data

    Yang, Xue   Tang, Luliang   Ren, Chang   Chen, Yang   Xie, Zhong   Li, Qingquan  

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  • Detecting and Evaluating Urban Clusters with Spatiotemporal Big Data

    Tang, Luliang   Gao, Jie   Ren, Chang   Zhang, Xia   Yang, Xue   Kan, Zihan  

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  • A Data Cleaning Method for Big Trace Data Using Movement Consistency

    Yang, Xue   Tang, Luliang   Zhang, Xia   Li, Qingquan  

    Given the popularization of GPS technologies, the massive amount of spatiotemporal GPS traces collected by vehicles are becoming a new kind of big data source for urban geographic information extraction. The growing volume of the dataset, however, creates processing and management difficulties, while the low quality generates uncertainties when investigating human activities. Based on the conception of the error distribution law and position accuracy of the GPS data, we propose in this paper a data cleaning method for this kind of spatial big data using movement consistency. First, a trajectory is partitioned into a set of sub-trajectories using the movement characteristic points. In this process, GPS points indicate that the motion status of the vehicle has transformed from one state into another, and are regarded as the movement characteristic points. Then, GPS data are cleaned based on the similarities of GPS points and the movement consistency model of the sub-trajectory. The movement consistency model is built using the random sample consensus algorithm based on the high spatial consistency of high-quality GPS data. The proposed method is evaluated based on extensive experiments, using GPS trajectories generated by a sample of vehicles over a 7-day period in Wuhan city, China. The results show the effectiveness and efficiency of the proposed method.
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  • Estimating Vehicle Fuel Consumption and Emissions Using GPS Big Data

    Kan, Zihan   Tang, Luliang   Kwan, Mei-Po   Zhang, Xia  

    The energy consumption and emissions from vehicles adversely affect human health and urban sustainability. Analysis of GPS big data collected from vehicles can provide useful insights about the quantity and distribution of such energy consumption and emissions. Previous studies, which estimated fuel consumption/emissions from traffic based on GPS sampled data, have not sufficiently considered vehicle activities and may have led to erroneous estimations. By adopting the analytical construct of the space-time path in time geography, this study proposes methods that more accurately estimate and visualize vehicle energy consumption/emissions based on analysis of vehicles' mobile activities (MA) and stationary activities (SA). First, we build space-time paths of individual vehicles, extract moving parameters, and identify MA and SA from each space-time path segment (STPS). Then we present an N-Dimensional framework for estimating and visualizing fuel consumption/emissions. For each STPS, fuel consumption, hot emissions, and cold start emissions are estimated based on activity type, i.e., MA, SA with engine-on and SA with engine-off. In the case study, fuel consumption and emissions of a single vehicle and a road network are estimated and visualized with GPS data. The estimation accuracy of the proposed approach is 88.6%. We also analyze the types of activities that produced fuel consumption on each road segment to explore the patterns and mechanisms of fuel consumption in the study area. The results not only show the effectiveness of the proposed approaches in estimating fuel consumption/emissions but also indicate their advantages for uncovering the relationships between fuel consumption and vehicles' activities in road networks.
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  • A Review of GPS Trajectories Classification Based on Transportation Mode

    Yang, Xue   Stewart, Kathleen   Tang, Luliang   Xie, Zhong   Li, Qingquan  

    GPS trajectories generated by moving objects provide researchers with an excellent resource for revealing patterns of human activities. Relevant research based on GPS trajectories includes the fields of location-based services, transportation science, and urban studies among others. Research relating to how to obtain GPS data (e.g., GPS data acquisition, GPS data processing) is receiving significant attention because of the availability of GPS data collecting platforms. One such problem is the GPS data classification based on transportation mode. The challenge of classifying trajectories by transportation mode has approached detecting different modes of movement through the application of several strategies. From a GPS data acquisition point of view, this paper macroscopically classifies the transportation mode of GPS data into single-mode and mixed-mode. That means GPS trajectories collected based on one type of transportation mode are regarded as single-mode data; otherwise it is considered as mixed-mode data. The one big difference of classification strategy between single-mode and mixed-mode GPS data is whether we need to recognize the transition points or activity episodes first. Based on this, we systematically review existing classification methods for single-mode and mixed-mode GPS data and introduce the contributions of these methods as well as discuss their unresolved issues to provide directions for future studies in this field. Based on this review and the transportation application at hand, researchers can select the most appropriate method and endeavor to improve them.
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  • CLRIC: Collecting Lane-Based Road Information Via Crowdsourcing

    Tang, Luliang   Yang, Xue   Dong, Zhen   Li, Qingquan  

    Lane-based road network information, such as the number and locations of traffic lanes on a road, has played an important role in intelligent transportation systems. In this paper, we propose a Collecting Lane-based Road Information via Crowdsourcing (CLRIC) method, which can automatically extract detailed lane structure of roads by using crowdsourcing data collected by vehicles. First, CLRIC filters the high-precision GPS data from the raw trajectories based on region growing clustering with prior knowledge. Second, CLRIC mines the number and locations of traffic lanes through optimized constrained Gaussian mixture model. Experiments are conducted with taxi GPS trajectories in Wuhan, China, and the results show that CLRIC is quantified and displays detailed road networks with the number and locations of traffic lanes comparing with the satellite image and human-interpreted situation.
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  • CLRIC: Collecting Lane-Based Road Information Via Crowdsourcing

    Tang, Luliang   Yang, Xue   Dong, Zhen   Li, Qingquan  

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  • Thick Clouds Removal From Multitemporal ZY-3 Satellite Images Using Deep Learning

    Chen, Yang   Tang, Luliang   Yang, Xue   Fan, Rongshuang   Bilal, Muhammad   Li, Qingquan  

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  • Traffic congestion analysis at the turn level using Taxis' GPS trajectory data

    Kan, Zihan   Tang, Luliang   Kwan, Mei-Po   Ren, Chang   Liu, Dong   Li, Qingquan  

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  • Large-scale versioning data acquisition and updating - art. no. 64211H

    Tang, Luliang   Li, Qingquan   Hu, Li   Li, Hanwu   Zhu, Guobin   Sun, Li  

    This paper studies the methods of large-scale CAD-based data acquiring, data transforming between CAD data and GIS data, date updating in database. A file format called SIF (Spatial Information format) is designed for transforming data format, which is represented by XML. Based on the arithmetic for addition acquiring and addition updating, a large-scale data transferring and updating system is developed with ArcEngine fund on the project of Hi-tech Zone Infrastructure GIS (HiGIS) in ChangZhou, and there are 308 blocks of 1:2000 maps in ChangZhou have been done experiment on transferring and updating, which realizes the spatial data sharing between CAD and GIS.
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  • Cloud and Cloud Shadow Detection Based on Multiscale 3D-CNN for High Resolution Multispectral Imagery

    Chen, Yang   Tang, Luliang   Kan, Zihan   Latif, Aamir   Yang, Xiucheng   Bilal, Muhammad   Li, Qingquan  

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  • A network Kernel Density Estimation for linear features in space–time analysis of big trace data

    Tang, Luliang   Kan, Zihan   Zhang, Xia   Sun, Fei   Yang, Xue   Li, Qingquan  

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

    Tang, Luliang   Kan, Zihan   Zhang, Xia   Yang, Xue   Huang, Fangzhen   Li, Qingquan  

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

    Tang, Luliang   Kan, Zihan   Zhang, Xia   Yang, Xue   Huang, Fangzhen   Li, Qingquan  

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  • Object-Based Convolutional Neural Networks for Cloud and Snow Detection in High-Resolution Multispectral Imagers

    Wang, Lei   Chen, Yang   Tang, Luliang   Fan, Rongshuang   Yao, Yunlong  

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  • Object-based multi-modal convolution neural networks for building extraction using panchromatic and multispectral imagery

    Chen, Yang   Tang, Luliang   Yang, Xue   Bilal, Muhammad   Li, Qingquan  

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