Trip chain extraction using smartphone-collected trajectory data

Wang, Lei; Ma, Wanjing; Fan, Yingling; Zuo, Zhongyi

doi:10.1080/21680566.2017.1386599

Cited by 8 publications

(4 citation statements)

References 39 publications

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“…Feature selection refers to a relevant subset of explanatory variables selected to build a model, and can be used as a pivotal pre-processing step in machine learning tasks, to efficiently reduce data and find accurate models [40,41]. It can effectively reduce training time and model complexity, as well as prevent overfitting and improve the performance of most ML classifiers [21,42]. Filter, wrapper, and embedded are three mainstream feature selection methods that are based on how they combine the selection algorithm and model building [21].…”

Section: Feature Selection Algorithmmentioning

confidence: 99%

Predicting travel mode choice with a robust neural network and Shapley additive explanations analysis

Tang,

et al. 2024

IET Intelligent Trans Sys

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Predicting and understanding travellers’ mode choices is crucial to developing urban transportation systems and formulating traffic demand management strategies. Machine learning (ML) methods have been widely used as promising alternatives to traditional discrete choice models owing to their high prediction accuracy. However, a significant body of ML methods, especially the branch of neural networks, is constrained by overfitting and a lack of model interpretability. This study employs a neural network with feature selection for predicting travel mode choices and Shapley additive explanations (SHAP) analysis for model interpretation. A dataset collected in Chengdu, China was used for experimentation. The results reveal that the neural network achieves commendable prediction performance, with a 12% improvement over the traditional multinomial logit model. Also, feature selection using a combined result from two embedded methods can alleviate the overfitting tendency of the neural network, while establishing a more robust model against redundant or unnecessary variables. Additionally, the SHAP analysis identifies factors such as travel expenditure, age, driving experience, number of cars owned, individual monthly income, and trip purpose as significant features in our dataset. The heterogeneity of mode choice behaviour is significant among demographic groups, including different age, car ownership, and income levels.

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Section: Feature Selection Algorithmmentioning

confidence: 99%

Predicting travel mode choice with a robust neural network and Shapley additive explanations analysis

Tang,

et al. 2024

IET Intelligent Trans Sys

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“…It segments the long trajectories into smaller trips. Here the moving object is considered in a stop state when its speed is smaller than 1 m/s lasting for more than 120 s following [63]. Second, for fitting the input requirement of k-anonymity [10] , it uses interpolations to unify the training data such that the start and the end time points of different trajectories are the same.…”

Section: Travel Time Estimationmentioning

confidence: 99%

Experiments and Analyses of Anonymization Mechanisms for Trajectory Data Publishing

Sun

Song

et al. 2022

J. Comput. Sci. Technol.

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With the advancing of location-detection technologies and the increasing popularity of mobile phones and other location-aware devices, trajectory data is continuously growing. While large-scale trajectories provide opportunities for various applications, the locations in trajectories pose a threat to individual privacy. Recently, there has been an interesting debate on the reidentifiability of individuals in the Science magazine. The main finding of Sánchez et al. is exactly opposite to that of De Montjoye et al. , which raises the first question: “what is the true situation of the privacy preservation for trajectories in terms of reidentification?” Furthermore, it is known that anonymization typically causes a decline of data utility, and anonymization mechanisms need to consider the trade-off between privacy and utility. This raises the second question: “what is the true situation of the utility of anonymized trajectories?” To answer these two questions, we conduct a systematic experimental study, using three real-life trajectory datasets, five existing anonymization mechanisms (i.e., identifier anonymization, grid-based anonymization, dummy trajectories, k -anonymity and ε -differential privacy), and two practical applications (i.e., travel time estimation and window range queries). Our findings reveal the true situation of the privacy preservation for trajectories in terms of reidentification and the true situation of the utility of anonymized trajectories, and essentially close the debate between De Montjoye et al. and Sánchez et al. To the best of our knowledge, this study is among the first systematic evaluation and analysis of anonymized trajectories on the individual privacy in terms of unicity and on the utility in terms of practical applications. Supplementary Information The online version contains supplementary material available at 10.1007/s11390-022-2409-x.

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“…More sophisticated filters model the motion of trajectories, such as the Kalman filter [49][50][51][52][53] and particle filter [37,53,54]. They are good at utilising higher-order motion states such as velocity and acceleration.…”

Section: Trajectory Smoothingmentioning

confidence: 99%

Addressing robust travel mode identification with individual trip‐chain trajectory noise reduction

Zeng

Zhang

et al. 2022

IET Intelligent Trans Sys

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Identifying travel modes from Global Navigation Satellite System (GNSS) trajectories is helpful for traffic management. In mode identification, the motion features are extracted from trajectories to train the classifiers. However, features would be distorted by the positioning noise when migrating existing frameworks to poor‐quality tracks. This study aims to answer how to eliminate the impact of positioning error on mode identification. Specifically, six widely used Trajectory Noise Reduction (TNR) methods were tested. Representative motion features were calculated and sent to several classical classifiers to evaluate the effect of TNR. Then, the extent to which TNR restores motion features is analysed by information gain. To verify the robustness of these methods, multiple noise scenarios are designed to simulate possible positioning noise. The results show that the trajectory smoothing methods perform better than the outlier elimination methods regardless of the type and magnitude of noise. In particular, the Gaussian kernel smoothing can achieve the highest effect in almost all noise scenarios. For untested TNR methods that require a time window radius parameter, a 30‐s time window is a good candidate. Moreover, the visualisation verification cannot ensure the best TNR method for travel mode identification.

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Trip chain extraction using smartphone-collected trajectory data

Cited by 8 publications

References 39 publications

Predicting travel mode choice with a robust neural network and Shapley additive explanations analysis

Predicting travel mode choice with a robust neural network and Shapley additive explanations analysis

Experiments and Analyses of Anonymization Mechanisms for Trajectory Data Publishing

Addressing robust travel mode identification with individual trip‐chain trajectory noise reduction

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