Recognition of Transportation State by Smartphone Sensors Using Deep Bi-LSTM Neural Network

Abstract

Smartphones have been used for recognizing different transportation states. However, current studies focus on the speed of the object, which only relies on the GPS sensor rather than considering other suitable sensors and actual application factors. In this study, we propose a novel method that considers these factors comprehensively to enhance transportation state recognition. The deep Bi-LSTM (bidirectional long short-term memory) neural network structure, the crowd-sourcing model, and the TensorFlow deep learning system are used to classify the transportation states. Meanwhile, the data captured by the accelerometer and gyroscope sensors of smartphone is used to test and adjust the deep Bi-LSTM neural network model, making it easy to transfer the model into smartphones and conduct real-time recognition. The experimental results show that this study achieves transportation activity classification with an accuracy of up to 92.8%. The model of the deep Bi-LSTM neural network can be used for other time-series fields such as signal recognition and action analysis.

On the requirements on spatial accuracy and sampling rate for transport mode detection in view of a shift to passive signalling data

“…As the method encourages realistic sequences of labels, it has to receive special treatment when interpreting the results. In addition, despite not yet being well established, we added a representative of the deep learning family, deep recurrent neural networks (RNN) using bi-LSTM layers, specifically the network used by Zhao et al (2019). We also implemented the idea of Simoncini et al (2018) that focusses on feature extraction by means of dense feed-forward layers before the LSTM layers, but this yielded inferior results, which is why we do not discuss this approach further.…”

WITHDRAWN: A Survey on Local Transport Mode Detection on The Edge

“…• An Accelerometer is the most commonly used sensor for TMD [11,10,12,4,13,18,23]. It is is an electromechanical device that is able to measure the force of acceleration caused by some movement or gravity on all three physical axis.…”

“…Additionally, position independency of the sensor (where the user puts his smartphone usually during data collection) is another important factor which affects the [a] 625 (validation) 2 co-authors Soares [39] GPS, WiFi, NA 18 students NA Cellular Liang [24] ACC 14h 4 users user's (about 2h preferences for 7 modes) Zhao [23] ACC, Gyr 71.4h, [b] 11 volunteers waist 18h [c] 8 (training) 3 (testing)…”

“…Zhao et al [23] used built-in accelerometer and gyroscope sensors of smartphones for data collection. The data-sets were collected using Huawei P9 and Xiaomi smartphones.…”

“…Position User Location independency independency independency Reddy [10] Yes Yes No Hemminki [12] Yes Yes Yes Sonderen [13] No No NA Martin [11] Yes No No Guvensan [4] Yes Yes Yes Marra [34] NA Yes No Soares [39] NA No No Liang [24] Yes No No Zhao [23] No NA NA…”