Efficient Similarity Search for Travel Behavior

Tang, Lei; Zhao, Yaling; Duan, Zhenhai; Chen, Jun

doi:10.1109/access.2018.2879634

Cited by 7 publications

(7 citation statements)

References 36 publications

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“…Raw GPS positioning data are usually of high resolution and massive data volume, and subject to occasional point jumps, which make it necessary to preprocess the trajectory data before analysis, otherwise, it would result in excessive memory consumption and unacceptable running time if we calculate the distance similarity for two trajectories directly. For this reason, trajectory data preprocessing techniques including compression, filtering, segmentation, and transformation were one of the most important research directions (Zheng & Zhou, 2011 ), aiming at efficient distance similarity calculation and fast clustering while retaining adequate geographical information (Lee et al, 2007 ; Tang et al, 2018 ). With the involvement and contribution of researchers in the field of computer science, relevant algorithms and techniques have been developed and exploited deeply (Lee & Krumm, 2011 ), including the Douglas-Peucker (DP) algorithm for trajectory compression (Douglas & Peucker, 1973 ; Hershberger & Snoeyink, 1992 ) and the Kalman filter for trajectory smoothing (Gelb, 1974 ).…”

Section: Three Dimensions Of Trajectory Mining Approachesmentioning

confidence: 99%

“…The geographical information system (GIS) and relevant visualization methods also contribute to time and space behavioral studies (Ahas et al, 2008 ; Wu & Carson, 2008 ; Kwan et al, 2015 ; Xu et al, 2020 ). One of the emerging subjects in movement behavioral studies is connected with GPS trajectory mining and spatial-temporal sequence analysis (Cho & Kang, 2019 ; Li et al, 2021 ; Tang et al, 2018 ; Yuan et al, 2017 ; Brum-Bastos et al, 2018 ). GPS trajectory mining has great potential for applications in space-time behavioral studies in buildings and urban spaces, though there are various restrictions and inaccuracies when applied in indoor spaces.…”

Section: Introductionmentioning

confidence: 99%

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Towards big data behavioral analysis: rethinking GPS trajectory mining approaches from geographic, semantic, and quantitative perspectives

Huang

Wang

2022

ARIN

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The question regarding the actual usage of built environments is of immense importance in behavioral research. Yet traditional methods of collecting and analyzing data on movements and activities often lack needed accuracy and granularity. Thus, this article reviewed and summarized the applicability of emergent GPS trajectory mining approaches in the field of architecture from geographic, semantic, and quantitative perspectives, respectively. Accordingly, three experiments based on a case study using real GPS trajectory data from visitors to the Palace Museum in China were conducted to examine the usefulness and weakness of the aforementioned approaches. The findings revealed that although all three dimensions of the trajectory mining approaches had the potential to provide useful information for architectural and urban design, the higher the dimensionality in utilizing the data, the more effective the approach was in discovering generalizable knowledge of human behavioral pattern. Furthermore, the results suggested that to gain insights into the typological characteristics of human behaviors related to the built environments, the contribution of trajectory data alone was limited, hence, conventional field surveys and questionnaires which contain information on individual characteristics and spatial features should be used in conjunction. Future research and practical implications were outlined.

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Section: Three Dimensions Of Trajectory Mining Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards big data behavioral analysis: rethinking GPS trajectory mining approaches from geographic, semantic, and quantitative perspectives

Huang

Wang

2022

ARIN

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“…The existing logical-based approaches are reported to consider velocity constraints, reduction of travel distance, and human navigational system [49][50][51]. Nearly, similar problems are also considered when working with outlier-detection based approach where the consideration of driving behavior, statistical process controls, partitioning is carried [56][57][58][59][60][61][62][63]. Table 3 summarizes the research contribution of present times with respect to different parameters to exhibit that all the problems are associated with advantage as well as significant limitation too.…”

Section: Existing Research Trendsmentioning

confidence: 99%

A comprehensive insight towards Pre-processing Methodologies applied on GPS data

Prabha

Kabadi²

2020

IJECE

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Reliability in the utilization of the Global Positioning System (GPS) data demands a higher degree of accuracy with respect to time and positional information required by the user. However, various extrinsic and intrinsic parameters disrupt the data transmission phenomenon from GPS satellite to GPS receiver which always questions the trustworthiness of such data. Therefore, this manuscript offers a comprehensive insight into the data preprocessing methodologies evolved and adopted by present-day researchers. The discussion is carried out with respect to standard methods of data cleaning as well as diversified existing research-based approaches. The review finds that irrespective of a good number of work carried out to address the problem of data cleaning, there are critical loopholes in almost all the existing studies. The paper extracts open end research problems as well as it also offers an evidential insight using use-cases where it is found that still there is a critical need to investigate data cleaning methods.

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“…Given any destination set P ⋆ for R, Find_Group is proposed to cluster the riders based on a heterogeneous travel network developed in our previous work (Tang et al 2018). The ridesharing relationship can be described using a meta-path ' depart U → T ' , or short as UT shown in Table 1.…”

Section: Example 1 S U P P O S E S E Q L O C Rmentioning

confidence: 99%

An efficient ride-sharing recommendation for maximizing acceptance on geo-social data

Tang

Han

Duan

et al. 2019

CCF Trans. Pervasive Comp. Interact.

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Ride-sharing, which refers to assigning a set of riders for saving travel miles and alleviating traffic pressure, has drawn increasing attention. Existing works emphasize compatibility of potential riders on the basis of geographic proximity. They generally assume that no rejection would happen after the assignment is completed by the server. However, ignorance of psychological factors on ridesharing (e.g., trust on car mates) can lead to decrease rider acceptance. Thus, in this paper, we take the tendency of a rider to group with others into consideration and maximize riders' acceptance when sharing a trip. Specifically, we formally define the problem of maximizing riders' acceptance based on people′s interests, social links, and employ social networking to facilitate finding a ridesharing group for the rider with the largest acceptance. We propose a new ride-sharing mode to recommend groups that travel together from geo-social data streams. To optimize the recommendation, we develop a heterogenous travel network, based on a proposed destination-prediction algorithm, to mine the similar spatial movements among a set of riders. Then, we measure the willingness of riders for joining in a group using social context. Finally, we progressively select the riders with high acceptance to be in the top-k results. We present the results of applying framework on real world social media data from the Twitter. Computational results show our method is able to significantly reduce the travel time when ridesharing, while keeping a high level of acceptance on real-world datasets.

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Efficient Similarity Search for Travel Behavior

Cited by 7 publications

References 36 publications

Towards big data behavioral analysis: rethinking GPS trajectory mining approaches from geographic, semantic, and quantitative perspectives

Towards big data behavioral analysis: rethinking GPS trajectory mining approaches from geographic, semantic, and quantitative perspectives

A comprehensive insight towards Pre-processing Methodologies applied on GPS data

An efficient ride-sharing recommendation for maximizing acceptance on geo-social data

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