RNN Encoder-Decoder for the inference of regular human mobility patterns

Katranji, Mehdi; Sanmarty, Guilhem; Moalic, Laurent; Kraïem, Sami; Caminada, Alexandre; Selem, Fouad Hadj

doi:10.1109/ijcnn.2018.8489639

Cited by 2 publications

(3 citation statements)

References 17 publications

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“…Surveys have not disappeared as a means to collect passenger data [55], [56]. In fact, they remain a useful tool to provide additional information and context to, for instance, quantitative data such as sensor measurements.…”

Section: A Model Sensing Device and Type Of Datamentioning

confidence: 99%

“…The variety of the topics on pattern recognition and model generation leads to the use of a variety of machine learning methods. For instance, similar applications such as predicting the travel time can be addressed with methods like SVM, kNN [56] or a Boltzmann machine [55]. 50% of the works in Table 10 implement unsupervised learning.…”

Section: B Machine Learning Featuresmentioning

confidence: 99%

“…Cite Machine learning method Machine learning type [72] Poisson mixture model Unsupervised [69] K-means clustering, decision tree Supervised, unsupervised [34] Decision tree Supervised [56] SVM, k-NN, elasticnet, random forest Unsupervised [55] Mixed-variate restricted Boltzman machine Unsupervised [7] Ada boost Supervised [79] Multi-layered perceptron, linear regression Supervised, unsupervised [102] Growing HMM Supervised [82] Kernel density estimation Unsupervised [84] Logistic regression Unsupervised [70] Latent Dirichlet allocation Unsupervised [49] Long short-term memory, multi-layer perceptron Supervised [11] Artificial neural network Supervised [98] SVM, k-NN, ridge regression Supervised [71] SVM Supervised [15] Long short-term memory Supervised [91] CatBoost, XGBoost, light gradient boosting machine Supervised [96] Naive Bayes, logistic regression, SVM Unsupervised…”

Section: Conclusion and Open Challengesmentioning

confidence: 99%

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Survey of Machine Learning Methods Applied to Urban Mobility

Ahmed

Diaz

2022

IEEE Access

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To increase the sustainability in urban mobility, it is necessary to optimally combine public and shared vehicles throughout a passenger's trip. In this work, we present a survey on urban mobility based on passengers' data and machine learning methods. We focus on four applications for urban mobility: public datasets, passenger localization, detection of the transport mode and pattern recognition and generation of mobility models. Public datasets lack data of multimodal trips and are in need of guidelines to facilitate the data collection and documentation processes. Passenger localization is predominantly done through fingerprinting in indoor environments; and fingerprinting relies on unsupervised learning is used to survey access points. The most common mean of transport detected is the bus, followed by walking and biking, while e-scooters are not included within the detected transport modes. The existing works focus on predicting the travel time of the passenger's trajectory and no machine learning method stands out to estimate the travel time. There is still a need for works that analyze how passengers make use of the urban infrastructure, which will support municipalities and transport mode operators in resource planning and service design.

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Section: A Model Sensing Device and Type Of Datamentioning

confidence: 99%

Section: B Machine Learning Featuresmentioning

confidence: 99%