Random Forest Model for Trip End Identification Using Cellular Phone and Points of Interest Data

Yang, Fei; Wang, Yanchen; Jin, Peter J.; Li, Dingbang; Yao, Zhaohui

doi:10.1177/03611981211031537

Cited by 5 publications

(6 citation statements)

References 27 publications

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“…For example, if a trip is completed "walking-bus-walking," we consider this trip's corresponding mode to be the bus. Te trip end identifcation method designed with the same datasets using in this paper was described in the literature [19]. Tus, this paper focuses on the following steps: identifying the travel mode of each single trip segment.…”

Section: Methodsmentioning

confidence: 99%

Inferring Travel Modes from Cellular Signaling Data Based on the Gated Recurrent Unit Neural Network

Wang,

Yang,

et al. 2023

Journal of Advanced Transportation

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Cellular signaling data have become increasingly indispensable in analyzing residents’ travel characteristic. Especially with the enhancement of positioning quality in 4G-LTE and 5G wireless communication systems, it is expected that the identification accuracy of fine-grained travel modes will achieve an optimal level. However, due to data privacy issues, the empirical evaluation of the performance of different identification methods is not yet sufficient. This paper builds a travel mode identification model that utilizes the gated recurrent unit (GRU) neural network. With 24 features as input, this method can identify four traffic modes, including walking, bicycle, car, and bus. Moreover, in cooperation with the operator, we organized an experiment collecting cellular signaling data, as well as the corresponding GPS data. Using the collected dataset as ground-truth data, the performance of the method presented in this paper and other popular methods is verified and compared. The results indicate that the GRU-based method has a better performance, with a precision, recall, and F score of 90.5%. Taking F score as an example, the outcome of the GRU-based method is about 6% to 7% higher than methods based on other machine learning algorithms. Considering the identification accuracy and model training time comprehensively, the method suggested in this paper outperforms the other three deep learning-based methods, namely, recurrent neural network (RNN), long short-term memory network (LSTM), and bidirectional long short-term memory network (Bi-LSTM). This study may provide some insights for the application and development of cellular signaling-based travel information collection technology for residents in the future.

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Section: Methodsmentioning

confidence: 99%

Inferring Travel Modes from Cellular Signaling Data Based on the Gated Recurrent Unit Neural Network

Wang,

Yang,

et al. 2023

Journal of Advanced Transportation

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“…It is positively correlated with the positioning error of mobile phone data generated at activity locations theoretically. However, the traces generated at different activity locations differ in the spatial distribution [29]. It has been found that the fixed DT setting in the traditional clustering algorithm cannot be well applied to all activity locations at the same time [28, 39].…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, with the increase of BS density of mobile communication network and the development of Internet economy, the spatial and temporal resolution of mobile phone data gradually increases. The sampling frequency of mobile phone data has rapidly increased to minute-interval level [29]. Some clustering algorithms have been used to recognise activity locations, addressing the noisy and raw nature of mobile phone data [15].…”

Section: Related Workmentioning

confidence: 99%

“…Since the positioning error of mobile phone data depends on the BS density, the DT should be adjusted as the change of the BS density around each trace [13]. The DT settings in the existing research largely depend on subjective experience [29]. To a large extent, the main obstacle that prevents this study is that ground truth information behind the mobile phone data is unavailable in a passively generated and anonymous dataset [32].…”

Section: Related Workmentioning

confidence: 99%

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Activity location recognition from mobile phone data using improved HAC and Bi‐LSTM

Jiang

Yang

et al. 2022

IET Intelligent Trans Sys

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Existing studies on activity location recognition based on mobile phone data has made great progresses. However, current studies generally assume constant distance threshold when performing activity location clustering, and ignore the influence of base station layout on positioning accuracies of mobile phone data. Given different recognition accuracy requirements, the authors propose two methods to recognise activity locations: (1) An improved hierarchical agglomerative clustering algorithm that integrates a genetic algorithm component to search and dynamically adjust optimal distance thresholds based on base station densities; (2) The recognition method based on Bi‐directional long short‐term memory network that classifies travel statuses of mobile phone traces. Results show that, compared with existing methods, the activity location recognition accuracy of the proposed hierarchical agglomerative clustering algorithm increases by about 5%. The Bi‐directional long short‐term memory network model further outperforms the improved hierarchical agglomerative clustering, especially in the aspect of recognising non‐commuting activity locations. However, the Bi‐directional long short‐term memory network model training requires the users’ actual travel information, so there are certain obstacles in popularising Bi‐directional long short‐term memory network in practice.

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“…However, existing activity classification schemes mostly rely on hand-crafted features. These features are often complicated and need to be carefully designed and selected for identifying different activity types 10 , which consumes tremendous labors. Moreover, such calibrated models show poor generalizability and receive low inference accuracy when applied to new trajectories.…”

Section: Introductionmentioning

confidence: 99%

Graph-based representation for identifying individual travel activities with spatiotemporal trajectories and POI data

Liu

Peng

et al. 2022

Sci Rep

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Individual daily travel activities (e.g., work, eating) are identified with various machine learning models (e.g., Bayesian Network, Random Forest) for understanding people’s frequent travel purposes. However, labor-intensive engineering work is often required to extract effective features. Additionally, features and models are mostly calibrated for individual trajectories with regular daily travel routines and patterns, and therefore suffer from poor generalizability when applied to new trajectories with more irregular patterns. Meanwhile, most existing models cannot extract features to explicitly represent regular travel activity sequences. Therefore, this paper proposes a graph-based representation of spatiotemporal trajectories and point-of-interest (POI) data for travel activity type identification, defined as Gstp2Vec. Specifically, a weighted directed graph is constructed by connecting regular activity areas (i.e., zones) detected via clustering individual daily travel trajectories as graph nodes, with edges denoting trips between pairs of zones. Statistics of trajectories (e.g., visit frequency, activity duration) and POI distributions (e.g., percentage of restaurants) at each activity zone are encoded as node features. Next, trip frequency, average trip duration, and average trip distance are encoded as edge weights. Then a series of feedforward neural networks are trained to generate low-dimensional embeddings for activity nodes through sampling and aggregating spatiotemporal and POI features from their multihop neighborhoods. Activity type labels collected via travel surveys are used as ground truth for backpropagation. The experiment results with real-world GPS trajectories show that Gstp2Vec significantly reduces feature engineering efforts by automatically learning feature embeddings from raw trajectories with minimal prepossessing efforts. It not only enhances model generalizability to receive higher identification accuracy on test individual trajectories with diverse travel patterns, but also obtains better efficiency and robustness. In particular, our identification of the most common daily travel activities (e.g., Dwelling and Work) for people with diverse travel patterns outperforms state-of-the-art classification models.

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Random Forest Model for Trip End Identification Using Cellular Phone and Points of Interest Data

Cited by 5 publications

References 27 publications

Inferring Travel Modes from Cellular Signaling Data Based on the Gated Recurrent Unit Neural Network

Inferring Travel Modes from Cellular Signaling Data Based on the Gated Recurrent Unit Neural Network

Activity location recognition from mobile phone data using improved HAC and Bi‐LSTM

Graph-based representation for identifying individual travel activities with spatiotemporal trajectories and POI data

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